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Structure-Based Prediction of G-Protein-Coupled Receptor Ligand Function: A β-Adrenoceptor Case Study

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Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), Division of Medicinal Chemistry, Faculty of Science, VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands

*E-mail: [email protected]

Cite this: J. Chem. Inf. Model. 2015, 55, 5, 1045–1061

Publication Date (Web):April 7, 2015

https://doi.org/10.1021/acs.jcim.5b00066

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Abstract

The spectacular advances in G-protein-coupled receptor (GPCR) structure determination have opened up new possibilities for structure-based GPCR ligand discovery. The structure-based prediction of whether a ligand stimulates (full/partial agonist), blocks (antagonist), or reduces (inverse agonist) GPCR signaling activity is, however, still challenging. A total of 31 β₁ (β₁R) and β₂ (β₂R) adrenoceptor crystal structures, including antagonist, inverse agonist, and partial/full agonist-bound structures, allowed us to explore the possibilities and limitations of structure-based prediction of GPCR ligand function. We used all unique protein–ligand interaction fingerprints (IFPs) derived from all ligand-bound β-adrenergic crystal structure monomers to post-process the docking poses of known β₁R/β₂R partial/full agonists, antagonists/inverse agonists, and physicochemically similar decoys in each of the β₁R/β₂R structures. The systematic analysis of these 1920 unique IFP–structure combinations offered new insights into the relative impact of protein conformation and IFP scoring on selective virtual screening (VS) for ligands with a specific functional effect. Our studies show that ligands with the same function can be efficiently classified on the basis of their protein–ligand interaction profile. Small differences between the receptor conformation (used for docking) and reference IFP (used for scoring of the docking poses) determine, however, the enrichment of specific ligand types in VS hit lists. Interestingly, the selective enrichment of partial/full agonists can be achieved by using agonist IFPs to post-process docking poses in agonist-bound as well as antagonist-bound structures. We have identified optimal structure–IFP combinations for the identification and discrimination of antagonists/inverse agonist and partial/full agonists, and defined a predicted IFP for the small full agonist norepinephrine that gave the highest retrieval rate of agonists over antagonists for all structures (with an enrichment factor of 46 for agonists and 8 for antagonists on average at a 1% false-positive rate). This β-adrenoceptor case study provides new insights into the opportunities for selective structure-based discovery of GPCR ligands with a desired function and emphasizes the importance of IFPs in scoring docking poses.

Introduction

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G-protein-coupled receptors (GPCRs) are versatile membrane-bound switch nodes of cell function and are arguably one of the most important classes of proteins that is encoded in the human genome. (1, 2) GPCR ligands are able to stimulate (agonism), block (antagonism), or reduce (inverse agonism) the G-protein signaling activity of these receptor. (3-5) More recently, the fact that these GPCR ligands are able to (independently) modulate G protein-dependent but also G protein-independent pathways (via β-arrestin) has been getting more attention of the research community. (3-5) This new concept of biased signaling is an intriguing phenomenon, in which different ligands can promote or block multiple conformational states of the same receptor that are each linked to a different functional outcome. (4-8) The first human GPCR crystal structures were only solved in the past 7 years (9, 10) and have substantially increased our understanding of the molecular details of how GPCRs work. (10-12) These spectacular advances in GPCR structure determination have resulted in 115 crystal structures (13) at this moment, covering 21 class A, (9, 10) 2 class B, (14, 15) 2 class C, (16) and 1 class F (17) GPCRs. Despite these advances, less than 2% of all human GPCRs have been crystallized. Furthermore, most of these structures represent inactive-state conformations, with the exception of the adenosine A_2A receptor, (18) the neurotensin NTS₁ receptor, (19) and the β-adrenoceptors 1 (β₁R) and 2 (β₂R). (10) The 31 different β₁R/β₂R structures (while we were finalizing this manuscript, a 32nd, covalently bound, β-adrenergic structure was released (20)) cover multiple receptor activation states in combination with 19 ligands with different functional effects (Table 1 and Supporting Information, Table S1). (21-35) This GPCR structure–function data set offers a unique opportunity to assess the possibilities and limitations of the structure-based computational prediction of GPCR ligand function (agonism, antagonism, and inverse agonism) in virtual screening (VS) studies, as such a set of crystal structures has never been available for GPCRs before.

GPCR crystal structures (18, 36-41) and homology models (42, 43) have been successfully used to discover new GPCR ligands. (2) While in most VS studies new antagonists were found by docking into inverse agonist/antagonist-bound GPCR crystal structures (36-41) or GPCR models based on the inactive bovine rhodopsin (bRho) structure (44) and refined using known antagonists, (42, 43) several reports show that also agonists can be found by structure-based VS in inactive GPCR models. (45-50) In some of these studies, (48-50) the initial bRho-based inactive state homology model was refined using true agonists, but in other cases (45, 46) the inactive model was refined using antagonists. Similarly, agonist-biased models have also been successfully applied to discover new antagonists. (47) More recently, a structure-based VS for A_2Aagonists based on an active-state agonist-bound crystal structure resulted in the identification of 9 high-affinity adenosine antagonists, illustrating that the identification of novel ligands with the desired functional effect is challenging. (18) Possible reasons for this discrepancy between the (modeled) activation state of the GPCR structure used for docking and the functional activity of the VS are the limited understanding of the structural details of the receptor activation mechanism and associated ligand binding mode, inaccuracies in docking scoring functions, or a chemical bias toward ligands with a specific functional effect in the screened library. (18, 51, 52)

Previous docking-based VS studies against β₂R crystal structures nevertheless indicated that the functional activity of in silico hits is the same as the functional activity of the co-crystallized ligand bound to the structure that is used for the docking simulations. (53) While docking against the first carazolol (antagonist/inverse agonist)-bound β₂R crystal structure enabled the discovery of new β-adrenergic antagonists/inverse agonists, (40, 54) docking studies against the BI167107 (full agonist)-bound active-state crystal structure facilitated the identification of partial/full agonists. (52) Moreover, even before the determination of partial/full agonist-bound β₂R crystal structures, (28-30) several studies showed that the inverse-agonist-bound β₂R crystal structure could be used for the selective identification of agonists by introducing (small) conformational changes in the β₂R structure and using customized scoring approaches to select docking poses. (55-58) In independent modeling studies, (55-58) the position and/or orientation of the side chains of S204^5.43 and S207^5.46 (through rotation of TM5 and/or adjustment of the rotameric states of S204^5.43 and S207^5.46) was customized on the basis of ligand structure–activity relationship (SAR) and receptor mutation studies, (59-64) thereby enabling the preferential scoring of agonists over antagonists. The selective identification of partial/full agonists was significantly increased by employing an interaction fingerprint (IFP) (65) scoring method that determines ligand-binding-mode similarity instead of classical (energy-based) scoring functions. (55) These proof-of-concept studies illustrate how (small) differences in protein conformation can influence structure-based VS results, as has also been demonstrated by recent comparative VS studies against GPCR crystal structures and homology models. (11, 12, 34, 37) Systematic consideration of alternative protein conformations/flexibility in docking simulations can, for example, be done either by incorporating flexibility for (selected) binding-site residues during the docking simulations, (66, 67) by creating binding-site ensembles, (68) by using multiple crystal structures, (69) or by refining protein models with different ligands. (42, 70, 71)

GPCR crystal structure-based identification of new ligands with a different functional effect than the co-crystallized ligand has been defined as one of the challenges in the current era of GPCR structural and chemical biology. (11) The growing number of β-adrenoceptor crystal structures meanwhile covers multiple activation states and multiple co-crystallized ligands with different functional efficacies. (2, 5, 72) This unique GPCR structure–function data set allowed us, for the first time, to systematically and separately investigate the effects of both the reference ligands and the receptor conformations in combination with classical (energy-based) (53, 73) and IFP scoring (65, 74) approaches on the basis of GPCR crystal structures. In this study, we have investigated whether (and to what extent) (i) the crystal structures retain their preference for ligands with the same functional effect (agonism, antagonism, and inverse agonism) as the co-crystallized ligand, (ii) the different binding site conformations of the crystal structures have an impact on the outcome of VS studies, (iii) specific IFPs can change or amplify the preference of a crystal structure for ligands with a specific functional effect, (iv) the IFP derived from the predicted docking pose of a small agonist (norepinephrine) can be used for the selective retrieval of partial/full agonists (p/fAGO) over antagonist/inverse agonists (ANT/iAGO) and decoys, and (v) sufficient consistent pharmacological data are available to train structure-based VS models to accurately predict biased signaling of GPCR ligands.

Results and Discussion

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Subtle Ligand-Type-Dependent Structural Changes in the Receptor Binding Sites

We have analyzed 31 β-adrenergic structures: 15 human β₂R and 16 turkey β₁R structures (Table 1). (21-35, 75) While we were finalizing this manuscript, a 32nd β-adrenergic structure was released (20) with a covalently bound agonist that forms similar H-bond interactions with TM5 as epinephrine (PDB code 4LDO) (75) and has the same covalent linker as FAUC50 (PDB code 3PDS). (30) Multiple crystallization methods have been employed to obtain stable crystals for structure determination of these receptor–ligand complexes, (9) among them T4 lysozyme insertion at the intracellular loop (ICL) 3 or at the N-terminus, introduction of thermostabilizing mutations, introduction of covalently binding ligands, and addition of antibody fragments. (76) All crystal structures were obtained in combination with one of the 19 unique ligands (apart from the ligand-free structure, (35) see Tables 1 and S1): 9 (partial/full) inverse agonists or antagonists, 7 (partial/full) agonists, 1 covalently bound full agonist, and 2 unvalidated fragments (but suggested to be antagonists, (34)vide infra). The functional effect of the cocrystallized ligands (Table 1) has been assigned in line with the functional classification as indicated in the first publication describing their co-crystallization (except for bucindolol, see footnotes in Table 1). It should be noted that this classification nearly always coincides with the way these compounds modulate the G-protein signaling pathway (Table S1). Throughout this article, we abbreviate full and partial agonists as f/pAGO, antagonists as ANT, and (full/partial) inverse agonists as iAGO (see legend in Table 1).

Table 1. Overview of All β-Adrenoceptor Crystal Structures, the Co-crystallized Ligands, and Their Function

^{Table a}

Function abbreviations, based: ANT, antagonist; AGO, agonist; i, inverse; p, partial; f, full.

^{Table b}

Not validated. (34)

^{Table c}

Covalently bound ligand.

^{Table d}

Ligand and extracellular part of the receptor were not resolved due to weak electron density.

^{Table e}

This column indicates whether a ligand has a signaling preference for the β-arrestin over the G-protein pathway or not according to the following references:

^{Table f}

Casella et al., (78)

^{Table g}

Drake et al., (79)

^{Table h}

Kahsai et al., (80)

^{Table i}

Kaya et al., (81)

^{Table j}

Kim et al., (82)

^{Table k}

Liu et al., (83)

^{Table l}

Rajagopal et al., (84)

^{Table m}

Warne et al., (32) and

^{Table n}

Weiss et al. (52)

^{Table o}

Although these ligands are generally considered as antagonists/inverse agonists, they can also have a partial agonist effect depending on the activation state of the receptor. (85, 86)

^{Table p}

Color coding: red, full/partial agonist; blue, antagonist/inverse agonist; cyan, unvalidated antagonist. Ligands with a circle icon do not show a signaling preference or it is not known for these compounds; ligands with a triangle icon have been shown to have a β-arrestin signaling bias.

^{Table q}

The receptor is in its active state.

^{Table r}

The signaling bias is subtype specific according to Casella et al. (78)

^{Table s}

The observed difference between the signaling pathways was small for these ligands. More information regarding the pharmacological properties of the co-crystallized ligands is detailed in Table S1.

It should be noted that we do not focus on the differences between β₁R and β₂R in this article, as the pocket-lining residues are almost identical (only residue 7.35 differs, a phenylalanine in β₁R but tyrosine in β₂R), we assume that the ligand-binding modes in β₁R and β₂R are transferable. (87) Also, in the set of ligands that we analyzed there is only one highly selective compound (ICI 118,551, which has a ∼540-fold higher affinity for β₂R than for β₁R). (88) Moreover, it is suggested that selectivity for one of the receptors (78, 85, 88) is based on ligand kinetics (89) (i.e., binding to and dissociation from the receptor) and receptor activation (87) rather than differences in ligand binding modes. (89)

Superposition of all individual monomers of the crystal structures of β₁R and β₂R on the basis of their pocket residues (Figure 1) shows the conserved heptahelical fold (Supporting Information, Figure S1) but also indicates some structural variation, in particular in TM1 (a 60° kink), (24, 27) TM5, and TM6 (which differ between active and inactive states (28, 29) and the straight and bent inactive-state TM6 (27)). While the structural differences in the intracellular regions have been described in several reviews, (10, 12) our systematic analysis will focus on the structural variations in the ligand-binding site within the β-adrenergic GPCR family. Figure 1A,B shows that the pocket residues in all (β₁R and β₂R) crystal structures have largely conserved conformations. The outliers are the FAB complexes (PDB codes 2R4R, 2R4S, (22) and 3KJ6 (25)) in which the ECL domain, the top of the helices, and the ligand (carazolol) were not solved due to the distorted density in this region. (22, 25) When comparing the pocket residues by means of the mutual root-mean-square deviation (rmsd) values (Figure 1C and Supporting Information, Figure S2), they are generally low (average all-atom rmsd 0.9 Å). By determining the average rmsd of the binding site residues of each individual structure to all f/pAGO and all ANT/iAGO structures, it becomes apparent that all f/pAGO structures and all inactive-state ANT/iAGO structures cluster together (Figure 1C). This means that the binding site conformation within the inactive-state f/pAGO structures, but also within the ANT/iAGO structures is conserved. (8) Moreover, the active-state structures form a separate cluster compared to other f/pAGO structures, which can be ascribed to the movement of TM5 and TM6 (28, 29) (Table 1, Figures S1 and S2). (90) Outliers are the FAB complexes (due to the distorted electron density), but also the ligand-free structure (PDB code 4GPO (35)), which have a high rmsd value compared to the other structures. Interestingly, this apo structure does not cluster with any of the f/pAGO structures or with ANT/iAGO structures.

Figure 1. (A,B) Visual and (C) quantitative comparisons of the pockets from all X-ray structures. Pocket residues (carbon atoms are colored blue, salmon, white, and gray for ANT/iAGO, f/pAGO, FAB-complexed, and ligand-free structures, respectively) of all chains of all crystal structures (listed in panel C by their PDB code followed by their chain identifier) are shown superimposed with the cartoon representation of β₂R (PDB code 2RH1 (21)) and bucindolol (green carbon atoms, PDB code 4AMI (32)), as seen from the side (panel A) and from the top (panel B). Panel C gives an overview of ^aaverage all-atom rmsd using the pocket residues (Å) compared to all 16 f/pAGO and all 27 ANT/iAGO structures (full overview in Supporting Information, Figure S2); ^bthe average Z-score of the distances between G^2.61, D^3.32, N^7.39, and S^5.42 (full overview in Supporting Information, Figures S3 and S4); and ^cthe pocket volume (Å³, Supporting Information, Figure S5). ^dThe side chains of the ECL2 residues are not all fully resolved, thereby influencing the pocket volume (Figure S5) as well as rmsd values. The red and blue background coloring mark values associated with f/pAGO and ANT/iAGO properties, respectively. Icons represent f/pAGO (red), ANT/iAGO (blue), or unvalidated ANT (cyan) with no or unknown signaling preference (circles) or β-arrestin-biased ligands (triangles) (see Table 1).

To further quantify the differences between the pockets, we measured the distances between the Cα atoms of G^2.61, D^3.32, N^7.39, and S^5.42 (Figure 1C and Supporting Information, Figures S3 and S4). While the measurements show contraction of the pocket upon agonist binding (31) (mainly through movement of TM5), the highest contraction of the pocket was found for the ligand-free β₁R structure. (35) Note, however, that these differences are small and that mainly the distance between D^3.32 and N^7.39 to S^5.42 varies between the different structures (on average 0.6 and 1.0 Å, respectively). Analysis of volumes of the individual binding pockets (Figure 1C and Supporting Information, Figure S5) indicates that β₁R generally has a bigger pocket volume than β₂R (on average 415 Å³ compared to 308 Å³). This can mainly be ascribed to the residue differences at positions 7.32 (D/K in β₁R/β₂R, respectively) and 7.35 (F/Y in β₁R/β₂R, respectively) resulting in a more confined ligand-binding space in β₂R (Figures 1 and S5). Although the rmsd values between all pocket residues are low (Figure 1C and Supporting Information, Figure S2), the effect of all subtle changes combined is reflected by the large differences in the pocket volumes. For the β₁R and β₂R the pocket volumes range from 282 to 493 Å³ and from 236 to 388 Å³, respectively (only comparing structures with fully resolved pocket residues). In line with the results of the distance measurements and the rmsd calculations, the ligand-free structure has the smallest pocket volume of all β₁R structures. The active-state epinephrine-bound structure (PDB code 4LDO (75)) has the smallest pockets volume of all β₂R structures, adapted to this small endogenous agonist.

Identification of Ligand-Type-Specific Molecular Interaction Profiles

The binding modes and interaction profiles of the 19 different co-crystallized ligands (Table 1 and Figure 2) were subsequently investigated using the aforementioned IFP method. (65) Like other protein–ligand interaction fingerprinting methods (e.g., PLIF, (91) SIFt, (92) and TIFP (93)), the IFP method has been used to improve the selection and scoring of docking poses for binding mode prediction and VS purposes, and has been successfully applied in both retrospective and prospective VS studies (38, 42, 45, 94-98) (including the discovery of new histamine H₁ receptor, (38) histamine H₄ receptor, (99) and glucagon receptor (45) ligands). This technique encodes 7 different types of interactions between the ligand and each of the binding pocket residues. The following interactions with the ligand are encoded: hydrophobic contact, aromatic face-to-edge and face-to-face, H-bond acceptor–donor and donor–acceptor, and negative–positive and positive–negative ionic interactions (as shown in Figure 2). (65) The IFPs for all ligands in all monomers were calculated for the available β-adrenergic crystal structures. The 31 crystal structures contain in total 48 monomers (43 ligand-bound). Of the 14 crystallized ligand–protein complexes containing more than one monomer, only 3 structures had identical interaction patterns within different monomers of the same crystal structure (see Figure 2). Moreover, ligands present in multiple co-crystal structures (e.g., cyanopindolol, carazolol, BI-167107) did also not yield identical IFPs. This emphasizes once more that crystal structures are snapshots in time (100) of entities that are flexible by nature.

Figure 2. Overview of the unique interaction fingerprints of all cocrystallized ligands. The colors indicate the presence of an interaction (as seen from the residue) according to the colors described at the bottom of the figure. Identical IFPs for multiple monomers within a PDB entry are grouped (e.g., 2VT4_chainA-D). The last two columns describe the amount of times (as a percentage of the total comparisons) an IFP comparison results in a score ≥0.6 when compared with the ANT/iAGO reference IFPs (a blue background indicates a high percentage) and the f/pAGO reference IFPs (a red background indicates a high percentage). ^aThe IFP of the highest scoring docking pose of norepinephrine in 2Y03-chainA (see Figure 6). Names and icons represent f/pAGO (red), ANT/iAGO (blue), or unvalidated ANT (34) (cyan) with no or unknown signaling preference (circle) or β-arrestin-biased ligands (triangle) (see Table 1).

The overview of all generated reference IFPs from the ligand-bound crystal structures (Figure 2) shows that there are several conserved interactions within the pocket. Apart from dobutamine and arylpiperazines 19 and 20, all co-crystallized β₁R and β₂R ligands contain an ethanolamine moiety (which is abundant in β-adrenergic ligands (101)) that can form a tight H-bond interaction network with the side chains of D^3.32 and N^7.39 (Figure 2). (59, 102) In chain B of the arylpiperazine 19 structure the ligand does not form H-bonds with N^7.39 nor with D^3.32. We therefore redocked the co-crystallized ligand into its structure considering alternative ring conformations, resulting in an additional IFP (annotated as 3ZPQ_dock, see Figure 2, Supporting Information, Figure S6, and Materials and Methods). Most β-adrenergic ligands contain an H-bond donor with which they are able to interact with S211/S203^5.42 (74%, Figures 2 and S7). (87) More specific for full agonists (carmoterol, isoproterenol, BI-167107, and FAUC50) is a catechol or a catechol-mimicking moiety with two (or more) H-bond donors that allows for an extra H-bond with another TM5 serine, namely S215/S207^5.46. (103) Partial agonists also have two H-bond donors that can interact with TM5, but their interactions seem to be less optimal (e.g., the shortest observed distance between the H-bond donor from the catechol in dobutamine to the S^5.46 acceptor is 3.8 Å). (87) All ligands also contain at least one aromatic ring, which allows for aromatic stacking with F307/F290^6.52, F306/F289^6.51, and F201/F193^45.52 in 95%, 56%, and 54% of all structures, respectively.

We furthermore observed aromatic interactions with W117/W109^3.28 for many agonists (60%), ionic interactions with the key ionic anchor D121/D113^3.32 for all ligands, and double H-bond interactions with N329/N312^7.39 as described above (see Figure S7).

All interaction profiles (IFPs) of co-crystallized ligands (8 f/pAGO and 11 ANT/iAGO) in 33 β₁R and 15 β₂R binding sites were compared, and their Tanimoto similarity scores were determined (Supporting Information, Figure S8). Figure 2 reports for each individual IFP the percentage of similar IFPs (Tanimoto similarity score ≥0.6 (65)) derived from (other) ANT/iAGO and f/pAGO co-crystal structures complexes. This analysis shows that the IFPs of ANT/iAGO ligands are more similar to each other than to f/pAGO IFPs (75% versus 19% similar pairs, respectively). Vice versa, the pairwise similarity between f/pAGO IFPs is higher (62%) than similarity to ANT/iAGO (62% versus 21%, respectively, Figure 2). Moreover, the arylpiperazine ligands cluster together with the ANT/iAGO IFPs (on average 40% similar pairs compared to 1% similarity with f/pAGO IFPs) suggesting that that these compounds are antagonists, as previously proposed. (34) These analyses show that the functional effect of a ligand is encoded in the ligand–protein interaction patterns and suggest that these IFPs can be used to discriminate ANT/iAGO from f/pAGO ligands in structure-based VS studies.

Selective Retrieval of f/pAGO over ANT/iAGO in Structure-Based Virtual Screening

In order to quantify to what extent the differences between the individual structures and the reference IFPs have an influence on the outcome and selectivity of a VS, we docked a set of 1030 compounds comprising 25 f/pAGO (Supporting Information, Figure S9A), 25 ANT/iAGO (Figure S9B), and 980 physicochemically similar decoys (55) in all β₁R and β₂R structures. The docking studies were performed using PLANTS (104) using the ChemPLP scoring function, and the resulting binding modes were post-processed using IFP (65) and ranked according to their PLANTS and IFP scores. Figure 3 shows that overall the ANT/iAGO ligand–protein complexes are more selective (average EF_1% = 27 and 4 for ANT/iAGO and f/pAGO, respectively) than the f/pAGO complexes (average EF_1% = 13 and 46 for ANT/iAGO and f/pAGO, respectively). However, VS against f/pAGO-bound structures in many cases gives a high enrichment for ANT/iAGO and an even higher enrichment for p/fAGO ligands. For example, VS against chain A of the salbutamol-bound β₁R structure (PDB code 2Y04) results in a remarkably high enrichment factor for both f/pAGO and ANT/iAGO (EF_1% = 72 and 32, respectively).

For 36 of the 43 protein–ligand complexes, the EF_1% of ligands with the same functional effect as the co-crystallized ligand was higher than the enrichment of ligands with a different functional effect (Figure 3). VS runs against five (putative) ANT/iAGO-bound receptor structures, namely the arylpiperazine-19-bound β₁R complexes (3ZPQ), the bucindolol-β₂R complexes (4AMI), and one of the carvedilol-β₂R complexes (4AMJ), resulted in a higher enrichment for p/fAGO ligands than for ANT/iAGO ligands. For the arylpiperazine-20-bound β₁R structures (34) (PDB code 3ZPR) no enrichment was obtained for any of the known ligands (EF_1% = 0).

Figure 3. Overall enrichment at 1% false positive rate (FP-rate) for the retrieval of 25 partial/full agonists and 25 inverse agonists/antagonists over a set of 980 decoy molecules using IFP scoring (A). Full ROC curves visualizing the retrieval rate (TP-rate) of f/pAGO (red) and ANT/iAGO (blue) in the best ANT/iAGO structure (B) and best f/pAGO structure (C, legend shown in panel B). The structures are indicated by their PDB code followed by an underscore and the chain identifier (except when there was only one chain or all chains of the structure had similar performance). The 2D structures represent the co-crystallized ligand for selected structures. The ANT/iAGO axis is scaled from 0 to 60 and the f/pAGO axis from 0 to 100. *1 = 3ZPQ_A, 3ZPQ_dock, 2Y00_B; *2 = 4AMJ_B, 3ZPQ_B. Icons represent f/pAGO (red), ANT/iAGO (blue), or unvalidated ANT (34) (cyan) with no or unknown signaling preference (circle) or β-arrestin-biased ligands (triangle) (see Table 1).

In 85% of all f/pAGO and ANT/iAGO structures, IFP scoring yielded equal or higher enrichment factors (EF_1%) compared to ChemPLP-scoring (Figure 4 and Supporting Information, Figure S10). Using IFP to rank the docking poses of ligands of the same functional activity class gives a VS enrichment that is on average 2 times as high as the enrichment obtained by ChemPLP-scoring. Moreover, in 90% of the structures IFP scoring yielded an equal or higher selectivity (higher retrieval of f/pAGO than ANT/iAGO over decoys for f/pAGO structures and vice versa) than when using ChemPLP scoring (Figures 4 and S10). The resulting EF_1% values of f/pAGO and ANT/iAGO based on IFP similarity scoring (using the co-crystallized ligand reference IFP) are shown in Figure 4A.

Figure 4. Enrichment factors at a 1% false positive rate using all reference IFPs (columns) on all β-adrenergic monomers for the retrieval of for f/pAGO (A) and ANT/iAGO (B) over physicochemically similar decoys. The white to red and white to blue gradients as background color mark a low to high enrichment for f/pAGO (A) and ANT/iAGO, respectively. Icons represent f/pAGO (red), ANT/iAGO (blue), or unvalidated ANT (34) (cyan) with no or unknown signaling preference (circle) or β-arrestin-biased ligands (triangle) (see Table 1).

For 11 unique crystal structures (of the 14 structures with multiple monomers) a significant difference in the EF_1% is observed (ΔEF_1% > 5) between the individual monomers, demonstrating the impact of subtle differences in the reference IFP and/or binding site structure in different monomers of the same crystal structure. For example, the EF_1% for f/pAGO over decoys for chain A of structure 2Y02 is 84 compared to 52 for chain B, while the reference IFPs of both chains is identical. This can potentially be ascribed to the larger pocket volume of chain A (490 versus 389 Å³ for chain B, which is mainly due to the rotation of F325^7.35), therefore the pocket of chain A is better able to accommodate the larger ligands. In contrast, the small pocket of the ligand-free β₁R structure (35) (PDB code 4GPO) is not able to effectively accommodate ligands as D121^3.32, F307^6.52, and N329^7.39 are moved inward (Figure S5), thereby preventing the prediction of correct binding modes using rigid-protein docking simulations. Similarly, the small pocket of the active-state, epinephrine-bound β₂R structure (PDB code 4LDO (75)) is not able to accommodate the relatively bigger TM5/6 binding moieties of most ANT/iAGO and is therefore more selective for f/pAGO.

To evaluate the individual contribution of the reference IFPs and the protein structure, we rescored all docked compounds in all structures with all reference IFPs (i.e., not only with the IFP that was derived from the corresponding receptor–ligand complex). This furthermore allowed us to rescore the docked compounds with IFP in the structures without a ligand (PDB codes 2R4R, 2R4S, 3KJ6, and 4GPO). The resulting scoring matrices containing the EF_1% of f/pAGO and ANT/iAGO are shown in Figure 4A,B.

In Figure 4A, we see that also ANT/iAGO structures can be used to efficiently retrieve f/pAGO ligands when using the right reference IFP. This is surprising, as this contrasts the results of the previous VS studies in which the rotamers of the TM5 serines and/or whole TM5 had to be rotated for the efficient retrieval of f/pAGO ligands. (55-58) Interestingly, the isoproterenol IFP (PDB code 2Y03) allows efficient retrieval of f/pAGO ligands in all ANT/iAGO structures with an average EF_1% = 47. Ranking docking poses in f/pAGO structures with an ANT/iAGO reference IFP, on the other hand, does not yield high enrichments of ANT/iAGO ligands (with an average ANT/iAGO EF_1% = 13). The highest average EF_1% (= 21) was obtained by the cyanopindolol IFP (2YCY_B) in the f/pAGO structures.

An unanticipated finding was that when pairing specific f/pAGO IFPs with ANT/iAGO structures also high EF_1% values were obtained for the selective retrieval of ANT/iAGO. When the IFPs of salbutamol (pAGO, PDB code 2Y04) were used, an average ANT/iAGO EF_1% = 27 was obtained (Figure 4). In short, these statistics emphasize that not only the choice of structure but also the choice of reference IFP has a high impact on the resulting enrichment factors and, more interestingly, on the ligand selectivity of the results.

Selective Structure-Based Virtual Screening for Ligands with the Desired Functional Effect

On the basis of the highest EF_1% values (Figure 3), we selected one f/pAGO and one ANT/iAGO ligand–protein complex for a more detailed investigation. From all ANT/iAGO-bound complexes the cyanopindolol-bound (iAGO) β₁R structure 2VT4 (both chains A and B) obtained the highest ANT/iAGO EF_1% value (= 48) and a f/pAGO EF_1% value of 0. As both chains gave similar VS enrichments (Figure 4B), we randomly selected chain A of 2VT4 (Figure 5B/C) for further investigation. From all f/pAGO complexes, chain A of the carmoterol (fAGO)-bound structure β₁R structure 2Y02 (Figure 5A/C) obtained the highest f/pAGO EF_1% value (= 84) and an ANT/iAGO EF_1% value of 24. However, it should be noted that chain B of the 2Y02 structure and the active-state 3P0G structure also obtained high f/pAGO EF_1% values (= 52 and 36, respectively) with a much lower ANT/iAGO EF_1% value (= 4). This active-state structure was successfully used in a prospective VS study in which 26 new β₂R agonists were identified using a similar IFP scoring approach (based on 24 overlapping residues and 9 additional residues to define the binding pocket for docking and IFP scoring). (105) In Figure 3B,C the individual ROC plots are displayed for the retrieval of ANT/iAGO and f/pAGO over decoys by IFP similarity scoring of docking poses generated in the 2VT4 and 2Y02 complexes using the reference IFPs (Figures 2 and 5C) of the corresponding co-crystallized ligands. Figure 3B,C shows the high early and overall enrichment for ligands with the same functional effect of the co-crystallized ligand and much lower early and overall enrichment for ligands with a different functional effect.

Figure 5. Analysis of VS runs using the IFPs (D,E) and structures (F,G) of the most f/p AGO-selective (2Y02_A) and ANT/iAGO-selective (2VT4_A) protein–ligand complexes (see Figure 3A), showing that both the reference IFP and protein structure determine enrichment and functional selectivity of the VS study. The binding modes of the selected f/pAGO (A, 2Y02_A) and selected ANT/iAGO (B, 2VT4_A) protein–ligand complex, a docked ligand with the same efficacy class (green carbon atoms), and their corresponding IFPs (C) for the displayed residues. EF_1% results for agonists and antagonist versus the decoys when using the reference IFP of the selected f/pAGO (D) and selected ANT/iAGO (E) for scoring docked compounds in all 48 protein structures. EF_1% results for f/pAGO and ANT/iAGO versus the decoys when using the 38 unique reference IFPs of all ligand–protein complexes for rescoring the docked compounds in the 2Y02_A (F) and 2VT4_A (G) structure. Icons represent f/pAGO (red), ANT/iAGO (blue), or unvalidated ANT (34) (cyan) with no or unknown signaling preference (circle) or β-arrestin-biased ligands (triangle) (see Table 1).

By rescoring all the docking poses of the compounds that were docked in all 48 structures with the reference IFPs from the two selected structures, the impact of the reference IFP selection was investigated (Figure 5D,E). For the selected f/pAGO IFP (2Y02_A), 93% of all ligand–protein complexes (excluding the 2Y02 structures) yielded an equal or higher f/pAGO EF_1% than when using their own IFP, in 64% of all cases a lower ANT/iAGO EF_1%, and for almost all ligand–protein complexes a higher f/pAGO EF_1% than ANT/iAGO EF_1% (Figure 5D, 89% of all cases). When using the selected ANT/iAGO IFP (2VT4_A) instead of the IFP of the co-crystallized ligand to rescore all docked compounds in the ligand–protein complexes, a higher ANT/iAGO EF_1% (80% of all cases), a lower f/pAGO EF_1% (51% of all cases), and a higher ANT/iAGO EF_1% than f/pAGO EF_1% (82% of all cases) was obtained (Figure 5E). In two cases the use of the selected ANT/iAGO IFP resulted in an even higher ANT/iAGO EF_1% than with its own structure, namely in combination with the structure of cyanopindolol-bound β₁R (EF_1% = 52, 2YCY_A) and timolol-bound β₁R (EF_1% = 60, 3D4S). This is an increase of 4 and 24 in ANT/iAGO EF_1% for the 2YCY_A and 3D4S structures, respectively, compared to the enrichment obtained with their native IFP. This was not observed for the selected f/pAGO IFP, as the combination with its own structure (2Y02_A) resulted in the highest f/pAGO EF_1% (= 84). The results for one of the Fab complexes (2R4S) were somewhat surprising, as the f/pAGO EF_1% in combination with the selected ANT/iAGO IFP was higher than with the selected f/pAGO IFP (36 and 16, respectively). Combining this with the fact that this structure is missing 23 of the binding pocket residues (vide supra), it was even more surprising that these enrichment factors can be obtained when docking in a structure with only half of the binding pocket. This does, however, match the success of previously published VS studies using homology models without ECL2 (106) and low-resolution homology models. (45) Overall, it is clear that the selection of the reference IFP is essential and can influence the enrichment as well as selectivity of the outcome of a VS study, regardless of which structure was used.

In order to separately quantify the impact of the structure on the enrichment values, we studied the effect of rescoring all docking poses in the selected two structures with all 39 reference IFPs (Figure 5F,G). The results of rescoring the docking poses in the 2 selected structures with all reference IFPs show similar trends as rescoring with the reference IFP of the selected complexes (Figure 5D,E). For the selected f/pAGO structure (2Y02_A, Figure 5F), we see a shift in the results toward the f/pAGO and the opposite for when using the selected structure (2VT4_A, Figure 5G). The observed effect is, however, less pronounced. When we compare the obtained enrichment factors, they are on average 38% lower for the selected structures than when using their IFP to rescore the docking poses.

Moreover, with the right IFP, the selectivity of a crystal structure can be shifted toward ligands with another functional effect than the co-crystallized ligand (Figure 5D,E); the opposite (shifting the selectivity for ligands with another functional effect than the reference IFP ligand by using the right structure) is less frequently and less prominently observed. The selection of the crystal structure does, however, clearly influence the extent to which the IFP to able to shift this selectivity and also the final enrichment values. Combined, these results indicate that the correct IFP is more crucial for obtaining selectivity as well as high enrichments than the structure, but they also indicate that the best results are obtained by finding the right combination of IFP and structure.

Selective Structure-Based Virtual Screening for Partial/Full Agonists with a Computationally Predicted Norepinephrine Interaction Fingerprint

From the f/pAGO and ANT/iAGO retrieval rates in all structures (Figure 4A,B), we can see that in particular the IFP of epinephrine (PDB code 4LDO) is able to selectively retrieve f/pAGO ligands in all structures (with an average f/pAGO and ANT/iAGO EF_1% = 43.6 and 9.4, respectively). This small endogenous agonist makes all key interactions with D113^3.32, N312^7.39, and S203^5.42 in β₂R (Figure 2). On the basis of this observation, we hypothesized that the IFP of an even smaller agonist that could also make all these interactions should also be highly effective for the retrieval of f/pAGO over ANT/iAGO in all structures. As the crystal structure of norepinephrine bound to β₁R or β₂R has not yet been resolved, we docked norepinephrine and used the docking pose (Figure 6B) with the highest IFP score (compared to the IFP of isoproterenol) to obtain the reference IFP for norepinephrine in β₁R (Figure 2).

Figure 6. EF1% results for agonists and antagonists (A) when using the reference IFP of docked norepinephrine (Figure 2) in 2Y03_A (B). The individual ROC plots for the retrieval of agonists (red curve) and antagonists (blue curve) in the selected ANT/iAGO structure 2VT4_A (C) and f/pAGO structure 2Y02_A (D). Icons represent f/pAGO (red), ANT/iAGO (blue), or unvalidated ANT (34) (cyan) with no or unknown signaling preference (circle) or β-arrestin-biased ligands (triangle) (see Table 1).

Indeed, when using this computationally predicted IFP to rescore all docked compounds in all structures, we were able to retrieve f/pAGO ligands with a high efficiency (the average f/pAGO and ANT/iAGO EF_1% = 46 and 8, respectively, Figures 4A and 6). On average, the obtained f/pAGO EF_1% was almost twice as high compared to the 2Y02_A IFP (the IFP from the highest-scoring f/pAGO complex). It should be noted that this is mostly due to higher f/pAGO EF_1% in the ANT/iAGO structures. At the same time, a 20% lower ANT/iAGO EF_1% was obtained compared to the 2Y02_A IFP. Moreover, the ANT/iAGO structure that obtained the highest f/pAGO EF_1% with this IFP was a cyanopindolol-bound β₁R structure (2YCW_A, Figure 6A), which coincides with the slight contraction of the pocket and the low f/pAGO rmsd as previously determined (Figure 1C). Interestingly, also in combination with the selected ANT/iAGO structure (cyanopindolol-bound 2VT4_A) the norepinephrine IFP was able to obtain a high and selective retrieval rate for f/pAGO (EF_1% = 56 and 12 for f/pAGO and ANT/iAGO, respectively, Figure 6C). The norepinephrine IFP combined with the 2Y02_A structure resulted in a slightly reduced f/pAGO EF_1%, but at the same time reduced the ANT/iAGO EF_1%, thereby making the results much more selective (Figure 6D).

By using the IFP from a small compound that makes all key interactions, these key interactions are highly emphasized, yielding more selective results. Whenever a docking pose of a compound misses one or more of the key interactions present in the reference IFP, this immediately results in a relatively low IFP score.

Biased Signaling

G-protein-coupled receptors can signal via G-proteins as well as β-arrestins. (107) So-called biased ligands can induce a preference of the receptor for one pathway over the other, this in contrast to the “classical” view in which GPCR ligands are solely able to stimulate, block, or reduce the G-protein signaling activity. (108) An in-depth literature search learned that for 7 of the 19 co-crystallized ligands there is a record of β-arrestin-biased signaling (Table 1). Among them are bucindolol and carvedilol, two β-blockers that promote signaling via the β-arrestin pathway. (32) For another 7 of the 19 co-crystallized compounds, we consistently found one or more publications reporting that ligand signaling is not biased (Table 1). It should be noted that the 7 biased ligands are all, with the exception of alprenolol, (82) relatively large compounds (Figure 1, Table 1) that stretch from the major pocket (between TM3, 4, 5, and 6) into the minor pocket (59) (between TM1, 2, 3, and 7). One should also note that the signaling bias of compounds is not always consistently observed and reported (e.g., for cyanopindolol, (78, 81-83) carvedilol, (32, 78, 79, 82) dobutamine (78, 84)) as the effect seems to be dependent on assay type and conditions, (84, 109) receptor-activation state, (86) and receptor subtype (78) (β₁ versus β₂). Moreover, even for well-known and frequently used ligands the insights regarding signaling preference are constantly changing. (109, 110)

For 29 of the 50 ligands in the docking set, there have been publications describing their signaling bias (Figure S9). (32, 52, 78-83, 107) However, for 11 of those 29 compounds the signaling bias has been inconsistently reported, for 15 there is only 1 source, and for only 3 compounds (epinephrine, fenoterol, isoetharine) the signaling bias has been consistently reported in 2 or more publications of which only one (isoetharine) has a (β-arrestin) signaling bias (Figure S9). This hampers the structural investigation of the molecular determinants for biased signaling, which calls for consistent experimental methods for the identification and characterization of the signaling bias of GPCR ligands. (108, 111, 112)

In an attempt to identify potential key interactions for β-arrestin-biased ligand, we grouped all IFPs (Figure 2) of both the reported 7 biased and the reported 7 unbiased ligands that were co-crystallized. Subsequently, we calculated the difference in abundance for each interaction with each pocket residue (Supporting Information, Figure S11). This highlighted four interactions that had a high abundance (≥50%) and were unique for the biased ligands. Three of them are hydrophobic contacts, namely with L101/H93^2.64, D200/D192^45.51, and V326/I309^7.36, and the fourth is an aromatic stacking interaction (face-to-face or face-to-edge) with W117/W109^3.28. Interestingly, this last interaction was present for all biased ligands, except for alprenolol. This could indicate that the signaling bias for these ligands is (partially) due to the aromatic stacking with this tryptophan residue. Moreover, isoproterenol is a full agonist with no signaling bias (it is even used as unbiased reference ligand (113)), but when a hydroxybenzyl moiety is attached the ligand (hydroxybenzylisoproterenol, 4LDL) makes an aromatic interaction with W^3.28 and induces a β-arrestin signaling bias. (52) This is also in line with mutation studies of the homologous W^3.28 in the muscarinic M₂ receptor (also a member of the aminergic-GPCR family (114)) that changed the signaling preference of the receptor when stimulated by specific ligands. (115) Also the contacts with L101/H93^2.64, D200/D192^45.51, and V326/I309^7.36 as well as (water-mediated) hydrogen bonds with ECL2 (as found for bucindolol and carvedilol (32)) might play a role. (87, 116) For smaller compounds like alprenolol (82) and isoetharine (79) (not co-crystallized), there might be a different mode of action. Soriano-Ursua et al. (7) extensively reviewed this topic and discussed the possibility of two binding sites (the orthosteric pocket and a secondary binding region between TM 2, 3, 6, and 7) that can either be (partially) targeted simultaneously by larger ligands or independently by smaller ligands. Liu et al. (83) proposed, based on ¹⁹F NMR, that all β-arrestin-biased ligands impact the conformational states of TM7. This theory is supported by the high abundance of hydrophobic contacts with V326/I309^7.36. The mechanism of biased signaling is, however, not yet fully understood and requires further structural, experimental, and in silico research. (7, 87, 117)

Conclusions

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Due to the outstanding progress in GPCR crystallography and the resulting diversity of β-adrenergic X-ray structures, we were able to investigate the impact of receptor conformation (including activation state) and reference ligand on the selective and effective retrieval of full/partial agonists versus antagonist/inverse agonist in this β-adrenoceptor case study. By systematically analyzing the selectivity and enrichment of 1920 combinations of unique reference IFPs and X-ray monomers, we gained new insights in structure-based prediction of GPCR ligand function. IFP rescoring was shown to be essential in order to obtain high enrichment factors and at the same time a high selectivity. The reference IFP has been shown to be more important than the receptor conformation for the VS outcome. By using the correct f/pAGO reference IFP, it was shown to be possible to selectively retrieve agonists in ANT/iAGO structures. Moreover, even the activation state of the receptor did not seem to have a great impact, as f/pAGO structures in both states gave comparable results. In the end, it is the match between receptor conformation and (predicted) reference IFP that can yield optimal results. It should be taken into account that the β₁R and β₂R represent a unique case because of the amount of diverse crystal structures which are now available. However, GPCR ligand function prediction and function-selective screenings are now also becoming possible for a few other GPCRs, for which both f/pAGO- and ANT/iAGO-bound crystal structures are available (i.e., the A_2A, (18, 118, 119) M₂, (120, 121) and P2Y₁₂ (122, 123) receptors). The use of predicted ligand binding modes (guided and/or validated by experimental data such a ligand SAR or receptor mutation studies) could, however, bridge this gap for receptors for which such structural information is not yet available. In the current study, the potential of such an approach is illustrated by the fact that the predicted IFP of the small full agonist norepinephrine gave the highest retrieval rate of agonists over antagonists for all structures.

The ultimate next step would be to predict the signaling bias of GPCR ligands. For this, the IFP analysis of ligands with a stimulus bias and the identified key interaction points (L101/H93^2.64, D200/D192^45.51, and V326/I309^7.36) can be valuable starting points. However, the observed issues with regard to the consistent experimental identification and characterization of biased ligands are currently hampering the structural investigation of the molecular determinants for biased signaling.

Taken together, this study provides insights into an effective approach for the selective identification of ligands with a specific functional effect. Ultimately, this will allow medicinal chemists to move from optimizing ligand affinity to designing ligands with the desired signaling properties.

Materials and Methods

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Crystal Structures Retrieval and Preparation

All β-adrenergic crystal structures were downloaded from the PDB (last accession date, April 4, 2014; see Table 1). (124) Subsequently, all structures were superposed using MOE (125) based on the residues surrounding the ligands (Figure 1). On the basis of the initial superposed structures, all residues within 4.5 Å of any ligand were determined as being the pocket residues (a total of 47):

V90/M82^2.53, V94/V86^2.57, G98/G90^2.61, A99/A91^2.62, L101/H93^2.64, V102/I94^2.65, G105/K97^2.68, W107/W99, C114/C106^3.25, W117/W109^3.28, T118/T110^3.29, D121/D113^3.32, V122/V114^3.33, L123/L115^3.34, V125/V117^3.36, T126/T118^3.37, V172/T164^4.56, S173/S165^4.57, C198/C190^45.49, C199/C191^45.50, D200/D192^45.51, F201/F193^45.52, V202/F194^45.53, T203/T195^45.54, N204/N196^5.35, Y207/Y199^5.38, A208/A200^5.39, I209/I201^5.40, S211/S203^5.42, S212/S204^5.43, I214/V206^5.45, S215/S207^5.46, F216/F208^5.47, W303/W286^6.48, F306/F289^6.51, F307/F290^6.52, N310/N293^6.55, N313/H296^6.58, V314/V297^6.59, D322/K305^7.32, F325/Y308^7.35, V326/I309^7.36, A327/L310^7.37, N329/N312^7.39, W330/W313^7.40, G332/G315^7.42, and Y333/Y315^7.43.

The first and second residue numbers refer to the Uniprot residue numbers of β₁R and β₂R, respectively, while the Ballesteros–Weinstein (126) and extracellular loop 2 (ECL2) (106) residue numbers are reported as superscript. An in-house protocol similar to the KLIFS protocol (127) was performed to align all β-adrenergic crystal structures based on the selected 47 pocket residues, protonate the structures, and separately extract the ligand, pocket, protein, and (if present) waters, ions, organometallics, and cofactors in MOL2 format. All structures were subsequently inspected using MOE, and when necessary the protein-hydroxyl groups were adjusted for optimal H-bonding between the co-crystallized ligand and the receptor.

Consideration of Alternative Ligand Ring Conformations in 3ZPQ

The ring conformation of the fragment-like arylpiperazine ligand 19 in chain B of the β₁R crystal structure (PDB code 3ZPQ) did not allow for the secondary amine in the piperazine moiety to interact with both N329^7.39 and D121^3.32 due to its twist conformation. We therefore redocked the co-crystallized ligand, allowing for all possible ring conformations, and selected the highest-ranking pose, which is in the more stable chair conformation (128) (named 3ZPQ_dock, see Figure S6).

Pocket and Ligand Analyses

Pocket volumes were calculated using POVME (129) (version 1.1.0) with a grid spacing of 0.5 Å and a padding of 1.09 Å. The initial inclusion volume was generated on the basis of three spheres with a radius of 7.0 Å, of which the centers were based on the superposed co-crystallized ligands (blue volume in Figure S5). Rmsd values between the pockets from the crystal structures were calculated using MOE. Distance calculations within the quadruplet (formed by G98/G90^2.61, D121/D113^3.32, N329/N312^7.39, and S211/S203^5.42) and the subsequent Z-score analyses were performed using an in-house script. The residues that form the quadruplet were selected on the basis of their location and function in the binding pocket: G^2.61 is opposite of S^5.42 (an important interaction point for polar groups, in particular for agonists), N^7.39 is opposite of D^3.32 (both are key interaction points for the ligand amine group), the N^7.39/D^3.32 pair is perpendicular to the G^2.61/S^5.42 pair (Figure S4), and all four residues are roughly positioned at the same height in the TM helices with respect to the membrane.

Compound Preparation and Docking

Twenty-six ligands (13 f/pAGO and 13 ANT/iAGO) and 980 decoy compounds were obtained from the Supporting Information accompanying the publication from de Graaf et al. (55) In total 24 additional β-adrenergic ligands (12 f/pAGO and 12 ANT/iAGO) were selected on the basis of the classification reported by Baker. (85, 88) Molecular (2D) structures of all 50 ligands are shown in Figure S9, comprising 25 f/pAGO (Figure S9A) and 25 ANT/iAGO (Figure S9B). Additional annotations (including references) regarding the experimentally observed signaling bias of these ligands have also been integrated in Figure S9.

All compounds were prepared starting from the SMILES format by first protonating all compounds at physiological pH using Chemaxon’s Calculator (version 5.1.4) (130) and were subsequently converted to MOL2 format using CORINA (version 3.49). (131, 132) All molecular docking simulations were carried out in fivefold using PLANTS (104) (version 1.2) using the (default) ChemPLP scoring function, search speed 2, flipping of free ring corners enabled, and generating 99 poses for every compound with a clustering rmsd of 1.0. The docking experiments were performed in fivefold in order to reduce the effect of the stochastic nature of PLANTS, (104) as the use of a stochastic algorithm (as used in multiple docking algorithms, e.g., PLANTS, (104) GOLD, (133) and QXP (134)) can result in different docking poses for the same compound when performing multiple docking runs.

The binding site was based on the center of carazolol in β₂R (PDB code 2RH1) with a binding-site radius of 15 Å, thereby covering all pocket residues (except for G105/K97^2.68, which is pointing away from the binding site). In total, 1030 compounds were docked in 48 β-adrenergic structures, yielding almost 24.5 million docking poses.

Interaction Fingerprint Calculation and Scoring

From the individual pockets and ligands that were extracted from the crystal structures, interactions fingerprints were calculated through application of the FingerPrintLib (version 2.2, downloaded from http://bioinfo-pharma.u-strasbg.fr, using OpenEye’s OEChem Toolkit (41)). (65, 135) All docking poses were post-processed with IFP by combining the docking poses with the consistent pocket residue selection as indicated earlier. The IFPs from all docking-poses were scored using all 40 reference IFPs, resulting in ∼979 million IFP scores representing their interaction profile similarity (as calculated by the Tanimoto similarity coefficient).

Virtual Screening Assessment

On the basis of the PLANTS and IFP scores of all docking poses, the highest scoring docking poses according to each of the scoring methods were selected. The resulting hit lists were used to rank both the f/pAGO and the ANT/iAGO ligands as well as the decoys and determine the enrichment factor (EF_x, where x is the false-positive rate) for each reference IFP (or ChemPLP score) and docking structure combination. The EF was determined by dividing the true-positive rate (TP-rate, i.e., the percentage of f/pAGO and ANT/iAGO, respectively) at a 1% false-positive rate (136) (FP-rate, i.e., 1% of the decoys; see Figures 3 and 4A,B) by the false positive rate, i.e., EF_x = TP-rate/FP-rate_x. This implementation of the enrichment factor used is not database dependent (in contrast to the more classical enrichment factor (137)) and reflects the early enrichment well. (137, 138) A possible drawback of the use of this EF implementation is that, to the best of our knowledge, there is no statistically sound way available to estimate the analytical error (e.g., the confidence interval) for the enrichment factors obtained. (137)

All EFs were calculated, resulting in the EF_1% of 1968 scoring combinations (40 reference IFP scores and 1 PLANTS score combined with 48 structures) for both the ranking of f/pAGO and ANT/iAGO ligands (see Figure 5). (137, 138)

Supporting Information

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Pharmacological details of all co-crystallized ligands, additional analyses of the pockets from the β-adrenergic X-rays (rmsd, distances, pocket contraction, binding modes, volumes), additional IFP analyses (interaction frequency per pocket residue, cross-comparison), two conformations of arylpiperazine 19, the 2D chemical structures of all docked ligands, and a scatter plot describing the overall enrichment using the ChemPLP scoring function. This material is available free of charge via the Internet at http://pubs.acs.org.

ci5b00066_si_001.pdf (8.07 MB)

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Author Information

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Corresponding Author
- Chris de Graaf - Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), Division of Medicinal Chemistry, Faculty of Science, VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands; Email: [email protected]
Authors
- Albert J. Kooistra - Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), Division of Medicinal Chemistry, Faculty of Science, VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
- Rob Leurs - Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), Division of Medicinal Chemistry, Faculty of Science, VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
- Iwan J. P. de Esch - Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), Division of Medicinal Chemistry, Faculty of Science, VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
Notes
The authors declare no competing financial interest.

Acknowledgment

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We thank Dr. Chris G. Tate (MRC Laboratory of Molecular Biology, Cambridge, UK) for kindly providing the high-resolution cyanopindolol β₁R (4BVN) structure and for his feedback on the manuscript. This research was financially supported by The Netherlands Organization for Scientific Research (NWO VENI Grant 700.59.408 to C.d.G. and TOP PUNT Grant to R.L.). A.J.K., R.L., I.J.P.d.E., and C.d.G. participate in the European Cooperation in Science and Technology Action CM1207 [GPCR-Ligand Interactions, Structures, and Transmembrane Signalling: A European Research Network (GLISTEN)].

References

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This article references 138 other publications.

1
Hopkins, A. L.; Groom, C. R. The Druggable Genome Nat. Rev. Drug Discovery 2002, 1, 727– 730
Google Scholar
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Opinion: The druggable genome
Hopkins, Andrew L.; Groom, Colin R.
Nature Reviews Drug Discovery (2002), 1 (9), 727-730CODEN: NRDDAG; ISSN:1474-1776. (Nature Publishing Group)
A review. An assessment of the no. of mol. targets that represent an opportunity for therapeutic intervention is crucial to the development of post-genomic research strategies within the pharmaceutical industry. Now that we know the size of the human genome, it is interesting to consider just how many mol. targets this opportunity represents. We start from the position that we understand the properties that are required for a good drug, and therefore must be able to understand what makes a good drug target.
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Shoichet, B. K.; Kobilka, B. K. Structure-Based Drug Screening for G-Protein-Coupled Receptors Trends Pharmacol. Sci. 2012, 33, 268– 272
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Violin, J. D.; Lefkowitz, R. J. Beta-Arrestin-Biased Ligands at Seven-Transmembrane Receptors Trends Pharmacol. Sci. 2007, 28, 416– 422
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β-Arrestin-biased ligands at seven-transmembrane receptors
Violin, Jonathan D.; Lefkowitz, Robert J.
Trends in Pharmacological Sciences (2007), 28 (8), 416-422CODEN: TPHSDY; ISSN:0165-6147. (Elsevier B.V.)
A review. Seven-transmembrane receptors (7TMRs), the most common mol. targets of modern drug therapy, are critically regulated by β-arrestins, which both inhibit classic G-protein signaling and initiate distinct β-arrestin signaling. The interplay of G-protein and β-arrestin signals largely dets. the cellular consequences of 7TMR-targeted drugs. Until recently, a drug's efficacy for β-arrestin recruitment was believed to be proportional to its efficacy for G-protein activities. This paradigm restricts 7TMR drug effects to a linear spectrum of responses, ranging from inhibition of all responses to stimulation of all responses. However, it is now clear that 'biased ligands' can selectively activate G-protein or β-arrestin functions and thus elicit novel biol. effects from even well-studied 7TMRs. Here, we discuss the current state of β-arrestin-biased ligand research and the prospects for β-arrestin bias as a therapeutic target. Consideration of ligand bias might have profound influences on the way scientists approach 7TMR-targeted drug discovery.
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Kenakin, T.; Miller, L. J. Seven Transmembrane Receptors as Shapeshifting Proteins: The Impact of Allosteric Modulation and Functional Selectivity on New Drug Discovery Pharmacol. Rev. 2010, 62, 265– 304
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Seven transmembrane receptors as shapeshifting proteins: the impact of allosteric modulation and functional selectivity on new drug discovery
Kenakin, Terry; Miller, Laurence J.
Pharmacological Reviews (2010), 62 (2), 265-304CODEN: PAREAQ; ISSN:0031-6997. (American Society for Pharmacology and Experimental Therapeutics)
A review. It is useful to consider seven transmembrane receptors (7TMRs) as disordered proteins able to allosterically respond to a no. of binding partners. Considering 7TMRs as allosteric systems, affinity and efficacy can be thought of in terms of energy flow between a modulator, conduit (the receptor protein), and a no. of guests. These guests can be other mols., receptors, membrane-bound proteins, or signaling proteins in the cytosol. These vectorial flows of energy can yield std. canonical guest allostery (allosteric modification of drug effect), effects along the plane of the cell membrane (receptor oligomerization), or effects directed into the cytosol (differential signaling as functional selectivity). This review discusses these apparently diverse pharmacol. effects in terms of mol. dynamics and protein ensemble theory, which tends to unify 7TMR behavior toward cells. Special consideration will be given to functional selectivity (biased agonism and biased antagonism) in terms of mechanism of action and potential therapeutic application. The explosion of technol. that has enabled observation of diverse 7TMR behavior has also shown how drugs can have multiple (pluridimensional) efficacies and how this can cause paradoxical drug classification and nomenclatures.
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Kobilka, B. The Structural Basis of G-Protein-Coupled Receptor Signaling (Nobel Lecture) Angew. Chem., Int. Ed. 2013, 52, 6380– 6388
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The Structural Basis of G-Protein-Coupled Receptor Signaling (Nobel Lecture)
Kobilka, Brian
Angewandte Chemie, International Edition (2013), 52 (25), 6380-6388CODEN: ACIEF5; ISSN:1433-7851. (Wiley-VCH Verlag GmbH & Co. KGaA)
A review. Given the central role played by GPCRs in nearly all physiol. processes, they represent the largest group of targets for drug discovery for a broad spectrum of diseases.
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Urban, J. D.; Clarke, W. P.; von Zastrow, M.; Nichols, D. E.; Kobilka, B.; Weinstein, H.; Javitch, J. A.; Roth, B. L.; Christopoulos, A.; Sexton, P. M.; Miller, K. J.; Spedding, M.; Mailman, R. B. Functional Selectivity and Classical Concepts of Quantitative Pharmacology J. Pharmacol. Exp. Ther. 2007, 320, 1– 13
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Functional selectivity and classical concepts of quantitative pharmacology
Urban, Jonathan D.; Clarke, William P.; von Zastrow, Mark; Nichols, David E.; Kobilka, Brian; Weinstein, Harel; Javitch, Jonathan A.; Roth, Bryan L.; Christopoulos, Arthur; Sexton, Patrick M.; Miller, Keith J.; Spedding, Michael; Mailman, Richard B.
Journal of Pharmacology and Experimental Therapeutics (2007), 320 (1), 1-13CODEN: JPETAB; ISSN:0022-3565. (American Society for Pharmacology and Experimental Therapeutics)
A review. The concept of intrinsic efficacy has been enshrined in pharmacol. for half of a century, yet recent data have revealed that many ligands can differentially activate signaling pathways mediated via a single G protein-coupled receptor in a manner that challenges the traditional definition of intrinsic efficacy. Some terms for this phenomenon include functional selectivity, agonist-directed trafficking, and biased agonism. At the extreme, functionally selective ligands may be both agonists and antagonists at different functions mediated by the same receptor. Data illustrating this phenomenon are presented from serotonin, opioid, dopamine, vasopressin, and adrenergic receptor systems. A variety of mechanisms may influence this apparently ubiquitous phenomenon. It may be initiated by differences in ligand-induced intermediate conformational states, as shown for the β2-adrenergic receptor. Subsequent mechanisms that may play a role include diversity of G proteins, scaffolding and signaling partners, and receptor oligomers. Clearly, expanded research is needed to elucidate the proximal (e.g., how functionally selective ligands cause conformational changes that initiate differential signaling), intermediate (mechanisms that translate conformation changes into differential signaling), and distal mechanisms (differential effects on target tissue or organism). Besides the heuristically interesting nature of functional selectivity, there is a clear impact on drug discovery, because this mechanism raises the possibility of selecting or designing novel ligands that differentially activate only a subset of functions of a single receptor, thereby optimizing therapeutic action. It also may be timely to revise classic concepts in quant. pharmacol. and relevant pharmacol. conventions to incorporate these new concepts.
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Soriano-Ursua, M. A.; Trujillo-Ferrara, J. G.; Correa-Basurto, J.; Vilar, S. Recent Structural Advances of Beta1 and Beta2 Adrenoceptors Yield Keys for Ligand Recognition and Drug Design J. Med. Chem. 2013, 56, 8207– 8223
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Shukla, A. K.; Singh, G.; Ghosh, E. Emerging Structural Insights into Biased Gpcr Signaling Trends Biochem. Sci. 2014, 39, 594– 602
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Jacobson, K. A.; Costanzi, S. New Insights for Drug Design from the X-Ray Crystallographic Structures of G-Protein-Coupled Receptors Mol. Pharmacol. 2012, 82, 361– 371
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New insights for drug design from the X-ray crystallographic structures of G-protein-coupled receptors
Jacobson, Kenneth A.; Costanzi, Stefano
Molecular Pharmacology (2012), 82 (3), 361-371CODEN: MOPMA3; ISSN:1521-0111. (American Society for Pharmacology and Experimental Therapeutics)
A review. Methodol. advances in x-ray crystallog. have made possible the recent soln. of x-ray structures of pharmaceutically important G protein-coupled receptors (GPCRs), including receptors for biogenic amines, peptides, a nucleoside, and a sphingolipid. These high-resoln. structures have greatly increased our understanding of ligand recognition and receptor activation. Conformational changes assocd. with activation common to several receptors entail outward movements of the intracellular side of transmembrane helix 6 (TM6) and movements of TM5 toward TM6. Movements assocd. with specific agonists or receptors were also described [e.g., extracellular loop (EL) 3 in the A2A adenosine receptor]. The binding sites of different receptors partly overlap but differ significantly in ligand orientation, depth, and breadth of contact areas in TM regions and the involvement of the ELs. A current challenge is how to use this structural information for the rational design of novel potent and selective ligands. For example, new chemotypes were discovered as antagonists of various GPCRs by subjecting chem. libraries to in silico docking in the x-ray structures. The vast majority of GPCR structures and their ligand complexes are still unsolved, and no structures are known outside of family A GPCRs. Mol. modeling, informed by supporting information from site-directed mutagenesis and structure-activity relationships, was validated as a useful tool to extend structural insights to related GPCRs and to analyze docking of other ligands in already crystd. GPCRs.
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Katritch, V.; Cherezov, V.; Stevens, R. C. Structure-Function of the G Protein-Coupled Receptor Superfamily Annu. Rev. Pharmacol. Toxicol. 2013, 53, 531– 556
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Structure-function of the G protein-coupled receptor superfamily
Katritch, Vsevolod; Cherezov, Vadim; Stevens, Raymond C.
Annual Review of Pharmacology and Toxicology (2013), 53 (), 531-556CODEN: ARPTDI; ISSN:0362-1642. (Annual Reviews Inc.)
A review. During the past few years, crystallog. of G protein-coupled receptors (GPCRs) has experienced exponential growth, resulting in the detn. of the structures of 16 distinct receptors-9 of them in 2012 alone. Including closely related subtype homol. models, this coverage amts. to approx. 12% of the human GPCR superfamily. The adrenergic, rhodopsin, and adenosine receptor systems are also described by agonist-bound active-state structures, including a structure of the receptor-G protein complex for the β2-adrenergic receptor. Biochem. and biophys. techniques, such as NMR and hydrogen-deuterium exchange coupled with mass spectrometry, are providing complementary insights into ligand-dependent dynamic equil. between different functional states. Addnl. details revealed by high-resoln. structures illustrate the receptors as allosteric machines that are controlled not only by ligands but also by ions, lipids, cholesterol, and water. This wealth of data is helping redefine the authors' knowledge of how GPCRs recognize such a diverse array of ligands and how they transmit signals 30 angstroms across the cell membrane; it also is shedding light on a structural basis of GPCR allosteric modulation and biased signaling.
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Granier, S.; Kobilka, B. A New Era of Gpcr Structural and Chemical Biology Nat. Chem. Biol. 2012, 8, 670– 673
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A new era of GPCR structural and chemical biology
Granier, Sebastien; Kobilka, Brian
Nature Chemical Biology (2012), 8 (8), 670-673CODEN: NCBABT; ISSN:1552-4450. (Nature Publishing Group)
A review. G protein-coupled receptors (GPCRs) are versatile mol. machines that regulate the majority of physiol. responses to chem. diverse hormones and neurotransmitters. Recent breakthroughs in structural studies have advanced the understanding of GPCR signaling, particularly the selectivity of ligand recognition and receptor activation of G proteins.
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Venkatakrishnan, A. J.; Deupi, X.; Lebon, G.; Tate, C. G.; Schertler, G. F.; Babu, M. M. Molecular Signatures of G-Protein-Coupled Receptors Nature 2013, 494, 185– 194
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Molecular signatures of G-protein-coupled receptors
Venkatakrishnan, A. J.; Deupi, Xavier; Lebon, Guillaume; Tate, Christopher G.; Schertler, Gebhard F.; Babu, M. Madan
Nature (London, United Kingdom) (2013), 494 (7436), 185-194CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
A review. G-protein-coupled receptors (GPCRs) are physiol. important membrane proteins that sense signaling mols. such as hormones and neurotransmitters, and are the targets of several prescribed drugs. Recent exciting developments are providing unprecedented insights into the structure and function of several medically important GPCRs. Here, through a systematic anal. of high-resoln. GPCR structures, we uncover a conserved network of non-covalent contacts that defines the GPCR fold. Furthermore, our comparative anal. reveals characteristic features of ligand binding and conformational changes during receptor activation. A holistic understanding that integrates mol. and systems biol. of GPCRs holds promise for new therapeutics and personalized medicine.
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Jianyi, Y.; Yang, Z.GPCR-EXP: A Database for Experimentally Solved Gpcr Structures, http://zhanglab.ccmb.med.umich.edu/GPCR-EXP/ (accessed March 30, 2014) .
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Hollenstein, K.; Kean, J.; Bortolato, A.; Cheng, R. K.; Dore, A. S.; Jazayeri, A.; Cooke, R. M.; Weir, M.; Marshall, F. H. Structure of Class B Gpcr Corticotropin-Releasing Factor Receptor 1 Nature 2013, 499, 438– 443
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Structure of class B GPCR corticotropin-releasing factor receptor 1
Hollenstein, Kaspar; Kean, James; Bortolato, Andrea; Cheng, Robert K. Y.; Dore, Andrew S.; Jazayeri, Ali; Cooke, Robert M.; Weir, Malcolm; Marshall, Fiona H.
Nature (London, United Kingdom) (2013), 499 (7459), 438-443CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
Structural anal. of class B G-protein-coupled receptors (GPCRs), cell-surface proteins that respond to peptide hormones, has been restricted to the amino-terminal extracellular domain, thus providing little understanding of the membrane-spanning signal transduction domain. The corticotropin-releasing factor receptor type 1 (CRF1R) is a class B receptor which mediates the response to stress and has been considered a drug target for depression and anxiety. Here we report the crystal structure of the transmembrane domain of the human corticotropin-releasing factor receptor type 1 in complex with the small-mol. antagonist CP-376395. The structure provides detailed insight into the architecture of class B receptors. Atomic details of the interactions of the receptor with the non-peptide ligand that binds deep within the receptor are described. This structure provides a model for all class B GPCRs and may aid in the design of new small-mol. drugs for diseases of brain and metab.
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Siu, F. Y.; He, M.; de Graaf, C.; Han, G. W.; Yang, D.; Zhang, Z.; Zhou, C.; Xu, Q.; Wacker, D.; Joseph, J. S.; Liu, W.; Lau, J.; Cherezov, V.; Katritch, V.; Wang, M. W.; Stevens, R. C. Structure of the Human Glucagon Class B G-Protein-Coupled Receptor Nature 2013, 499, 444– 449
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Structure of the human glucagon class B G-protein-coupled receptor
Siu, Fai Yiu; He, Min; de Graaf, Chris; Han, Gye Won; Yang, Dehua; Zhang, Zhiyun; Zhou, Caihong; Xu, Qingping; Wacker, Daniel; Joseph, Jeremiah S.; Liu, Wei; Lau, Jesper; Cherezov, Vadim; Katritch, Vsevolod; Wang, Ming-Wei; Stevens, Raymond C.
Nature (London, United Kingdom) (2013), 499 (7459), 444-449CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
Binding of the glucagon peptide to the glucagon receptor (GCGR) triggers the release of glucose from the liver during fasting; thus GCGR plays an important role in glucose homeostasis. Here we report the crystal structure of the seven transmembrane helical domain of human GCGR at 3.4 Å resoln., complemented by extensive site-specific mutagenesis, and a hybrid model of glucagon bound to GCGR to understand the mol. recognition of the receptor for its native ligand. Beyond the shared seven transmembrane fold, the GCGR transmembrane domain deviates from class A G-protein-coupled receptors with a large ligand-binding pocket and the first transmembrane helix having a 'stalk' region that extends three alpha-helical turns above the plane of the membrane. The stalk positions the extracellular domain (∼12 kilodaltons) relative to the membrane to form the glucagon-binding site that captures the peptide and facilitates the insertion of glucagon's amino terminus into the seven transmembrane domain.
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Wu, H.; Wang, C.; Gregory, K. J.; Han, G. W.; Cho, H. P.; Xia, Y.; Niswender, C. M.; Katritch, V.; Meiler, J.; Cherezov, V.; Conn, P. J.; Stevens, R. C. Structure of a Class C Gpcr Metabotropic Glutamate Receptor 1 Bound to an Allosteric Modulator Science 2014, 344, 58– 64
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Structure of a Class C GPCR Metabotropic Glutamate Receptor 1 Bound to an Allosteric Modulator
Wu, Huixian; Wang, Chong; Gregory, Karen J.; Han, Gye Won; Cho, Hyekyung P.; Xia, Yan; Niswender, Colleen M.; Katritch, Vsevolod; Meiler, Jens; Cherezov, Vadim; Conn, P. Jeffrey; Stevens, Raymond C.
Science (Washington, DC, United States) (2014), 344 (6179), 58-64CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
The excitatory neurotransmitter glutamate induces modulatory actions via the metabotropic glutamate receptors (mGlus), which are class C G protein-coupled receptors (GPCRs). The authors detd. the structure of the human mGlu1 receptor seven-transmembrane (7TM) domain bound to a neg. allosteric modulator, FITM, at a resoln. of 2.8 angstroms. The modulator binding site partially overlaps with the orthosteric binding sites of class A GPCRs but is more restricted than most other GPCRs. The authors obsd. a parallel 7TM dimer mediated by cholesterols, which suggests that signaling initiated by glutamate's interaction with the extracellular domain might be mediated via 7TM interactions within the full-length receptor dimer. A combination of crystallog., structure-activity relationships, mutagenesis, and full-length dimer modeling provides insights about the allosteric modulation and activation mechanism of class C GPCRs.
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Wang, C.; Wu, H.; Katritch, V.; Han, G. W.; Huang, X. P.; Liu, W.; Siu, F. Y.; Roth, B. L.; Cherezov, V.; Stevens, R. C. Structure of the Human Smoothened Receptor Bound to an Antitumour Agent Nature 2013, 497, 338– 343
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Structure of the human smoothened receptor bound to an antitumour agent
Wang, Chong; Wu, Huixian; Katritch, Vsevolod; Han, Gye Won; Huang, Xi-Ping; Liu, Wei; Siu, Fai Yiu; Roth, Bryan L.; Cherezov, Vadim; Stevens, Raymond C.
Nature (London, United Kingdom) (2013), 497 (7449), 338-343CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
The smoothened (SMO) receptor, a key signal transducer in the hedgehog signalling pathway, is responsible for the maintenance of normal embryonic development and is implicated in carcinogenesis. It is classified as a class frizzled (class F) G-protein-coupled receptor (GPCR), although the canonical hedgehog signalling pathway involves the GLI transcription factors and the sequence similarity with class A GPCRs is less than 10%. Here we report the crystal structure of the transmembrane domain of the human SMO receptor bound to the small-mol. antagonist LY2940680 at 2.5 Å resoln. Although the SMO receptor shares the seven-transmembrane helical (7TM) fold, most of the conserved motifs for class A GPCRs are absent, and the structure reveals an unusually complex arrangement of long extracellular loops stabilized by four disulfide bonds. The ligand binds at the extracellular end of the seven-transmembrane-helix bundle and forms extensive contacts with the loops.
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Rodriguez, D.; Gao, Z. G.; Moss, S. M.; Jacobson, K. A.; Carlsson, J. Molecular Docking Screening Using Agonist-Bound Gpcr Structures: Probing the a Adenosine Receptor J. Chem. Inf. Model. 2015, 55, 550– 563
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Molecular Docking Screening Using Agonist-Bound GPCR Structures: Probing the A2A Adenosine Receptor
Rodriguez, David; Gao, Zhang-Guo; Moss, Steven M.; Jacobson, Kenneth A.; Carlsson, Jens
Journal of Chemical Information and Modeling (2015), 55 (3), 550-563CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
Crystal structures of G protein-coupled receptors (GPCRs) have recently revealed the mol. basis of ligand binding and activation, which has provided exciting opportunities for structure-based drug design. The A2A adenosine receptor (A2AAR) is a promising therapeutic target for cardiovascular diseases, but progress in this area is limited by the lack of novel agonist scaffolds. The authors carried out docking screens of 6.7 million com. available mols. against active-like conformations of the A2AAR to investigate whether these structures could guide the discovery of agonists. Nine out of the 20 predicted agonists were confirmed to be A2AAR ligands, but none of these activated the ARs. The difficulties in discovering AR agonists using structure-based methods originated from limited at.-level understanding of the activation mechanism and a chem. bias toward antagonists in the screened library. In particular, the compn. of the screened library was found to strongly reduce the likelihood of identifying AR agonists, which reflected the high ligand complexity required for receptor activation. Extension of this anal. to other pharmaceutically relevant GPCRs suggested that library screening may not be suitable for targets requiring a complex receptor-ligand interaction network. The authors' results provide specific directions for the future development of novel A2AAR agonists and general strategies for structure-based drug discovery.
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White, J. F.; Noinaj, N.; Shibata, Y.; Love, J.; Kloss, B.; Xu, F.; Gvozdenovic-Jeremic, J.; Shah, P.; Shiloach, J.; Tate, C. G.; Grisshammer, R. Structure of the Agonist-Bound Neurotensin Receptor Nature 2012, 490, 508– 513
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Structure of the agonist-bound neurotensin receptor
White, Jim F.; Noinaj, Nicholas; Shibata, Yoko; Love, James; Kloss, Brian; Xu, Feng; Gvozdenovic-Jeremic, Jelena; Shah, Priyanka; Shiloach, Joseph; Tate, Christopher G.; Grisshammer, Reinhard
Nature (London, United Kingdom) (2012), 490 (7421), 508-513CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
Neurotensin (NTS) is a 13-amino-acid peptide that functions as both a neurotransmitter and a hormone through the activation of the neurotensin receptor NTSR1, a G-protein-coupled receptor (GPCR). In the brain, NTS modulates the activity of dopaminergic systems, opioid-independent analgesia, and the inhibition of food intake; in the gut, NTS regulates a range of digestive processes. Here we present the structure at 2.8 Å resoln. of Rattus norvegicus NTSR1 in an active-like state, bound to NTS8-13, the carboxy-terminal portion of NTS responsible for agonist-induced activation of the receptor. The peptide agonist binds to NTSR1 in an extended conformation nearly perpendicular to the membrane plane, with the C terminus oriented towards the receptor core. Our findings provide, to our knowledge, the first insight into the binding mode of a peptide agonist to a GPCR and may support the development of non-peptide ligands that could be useful in the treatment of neurol. disorders, cancer and obesity.
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Weichert, D.; Kruse, A. C.; Manglik, A.; Hiller, C.; Zhang, C.; Hubner, H.; Kobilka, B. K.; Gmeiner, P. Covalent Agonists for Studying G Protein-Coupled Receptor Activation Proc. Natl. Acad. Sci. U.S.A. 2014, 111, 10744– 10748
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Covalent agonists for studying G protein-coupled receptor activation
Weichert, Dietmar; Kruse, Andrew C.; Manglik, Aashish; Hiller, Christine; Zhang, Cheng; Huebner, Harald; Kobilka, Brian K.; Gmeiner, Peter
Proceedings of the National Academy of Sciences of the United States of America (2014), 111 (29), 10744-10748CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
Structural studies on G protein-coupled receptors (GPCRs) provide important insights into the architecture and function of these important drug targets. However, the crystn. of GPCRs in active states is particularly challenging, requiring the formation of stable and conformationally homogeneous ligand-receptor complexes. Native hormones, neurotransmitters, and synthetic agonists that bind with low affinity are ineffective at stabilizing an active state for crystallogenesis. To promote structural studies on the pharmacol. highly relevant class of aminergic GPCRs, the authors here present the development of covalently binding mol. tools activating Gs-, Gi-, and Gq-coupled receptors. The covalent agonists are derived from the monoamine neurotransmitters noradrenaline, dopamine, serotonin, and histamine, and they were accessed using a general and versatile synthetic strategy. The authors demonstrate that the tool compds. presented herein display an efficient covalent binding mode and that the resp. covalent ligand-receptor complexes activate G proteins comparable to the natural neurotransmitters. A crystal structure of the β2-adrenoreceptor in complex with a covalent noradrenaline analog and a conformationally selective antibody (nanobody) verified that these agonists can be used to facilitate crystallogenesis.
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Cherezov, V.; Rosenbaum, D. M.; Hanson, M. A.; Rasmussen, S. G.; Thian, F. S.; Kobilka, T. S.; Choi, H. J.; Kuhn, P.; Weis, W. I.; Kobilka, B. K.; Stevens, R. C. High-Resolution Crystal Structure of an Engineered Human Beta2-Adrenergic G Protein-Coupled Receptor Science 2007, 318, 1258– 1265
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High-Resolution Crystal Structure of an Engineered Human β2-Adrenergic G Protein-Coupled Receptor
Cherezov, Vadim; Rosenbaum, Daniel M.; Hanson, Michael A.; Rasmussen, Soren G. F.; Thian, Foon Sun; Kobilka, Tong Sun; Choi, Hee-Jung; Kuhn, Peter; Weis, William I.; Kobilka, Brian K.; Stevens, Raymond C.; Takeda, S.; Kadowaki, S.; Haga, T.; Takaesu, H.; Mitaku, S.; Fredriksson, R.; Lagerstrom, M. C.; Lundin, L. G.; Schioth, H. B.; Pierce, K. L.; Premont, R. T.; Lefkowitz, R. J.; Lefkowitz, R. J.; Shenoy, S. K.; Rosenbaum, D. M.
Science (Washington, DC, United States) (2007), 318 (5854), 1258-1265CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
Second extracellular loop, which in the β2-adrenergic receptor contains an unusual pair of disulfide bonds and an extra helix. This loop and the absence Heterotrimeric guanine nucleotide-binding protein (G protein)-coupled receptors constitute the largest family of eukaryotic signal transduction proteins that communicate across the membrane. We report the crystal structure of a human β2-adrenergic receptor-T4 lysozyme fusion protein bound to the partial inverse agonist carazolol at 2.4 angstrom resoln. The structure provides a high-resoln. view of a human G protein-coupled receptor bound to a diffusible ligand. Ligand-binding site accessibility is enabled by the second extracellular loop, which is held out of the binding cavity by a pair of closely spaced disulfide bridges and a short helical segment within the loop. Cholesterol, a necessary component for crystn., mediates an intriguing parallel assocn. of receptor mols. in the crystal lattice. Although the location of carazolol in the β2-adrenergic receptor is very similar to that of retinal in rhodopsin, structural differences in the ligand-binding site and other regions highlight the challenges in using rhodopsin as a template model for this large receptor family.
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Rasmussen, S. G.; Choi, H. J.; Rosenbaum, D. M.; Kobilka, T. S.; Thian, F. S.; Edwards, P. C.; Burghammer, M.; Ratnala, V. R.; Sanishvili, R.; Fischetti, R. F.; Schertler, G. F.; Weis, W. I.; Kobilka, B. K. Crystal Structure of the Human Beta2 Adrenergic G-Protein-Coupled Receptor Nature 2007, 450, 383– 387
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Crystal structure of the human β2 adrenergic G-protein-coupled receptor
Rasmussen, Soren G. F.; Choi, Hee-Jung; Rosenbaum, Daniel M.; Kobilka, Tong Sun; Thian, Foon Sun; Edwards, Patricia C.; Burghammer, Manfred; Ratnala, Venkata R. P.; Sanishvili, Ruslan; Fischetti, Robert F.; Schertler, Gebhard F. X.; Weis, William I.; Kobilka, Brian K.
Nature (London, United Kingdom) (2007), 450 (7168), 383-387CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
Structural anal. of G-protein-coupled receptors (GPCRs) for hormones and neurotransmitters has been hindered by their low natural abundance, inherent structural flexibility, and instability in detergent solns. Here we report a structure of the human β2 adrenoceptor (β2AR), which was crystd. in a lipid environment when bound to an inverse agonist and in complex with a Fab that binds to the third intracellular loop. Diffraction data were obtained by high-brilliance microcrystallog. and the structure detd. at 3.4 Å/3.7 Å resoln. The cytoplasmic ends of the β2AR transmembrane segments and the connecting loops are well resolved, whereas the extracellular regions of the β2AR are not seen. The β2AR structure differs from rhodopsin in having weaker interactions between the cytoplasmic ends of transmembrane (TM)3 and TM6, involving the conserved E/DRY sequences. These differences may be responsible for the relatively high basal activity and structural instability of the β2AR, and contribute to the challenges in obtaining diffraction-quality crystals of non-rhodopsin GPCRs.
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Hanson, M. A.; Cherezov, V.; Griffith, M. T.; Roth, C. B.; Jaakola, V. P.; Chien, E. Y.; Velasquez, J.; Kuhn, P.; Stevens, R. C. A Specific Cholesterol Binding Site Is Established by the 2.8 a Structure of the Human Beta2-Adrenergic Receptor Structure 2008, 16, 897– 905
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A Specific Cholesterol Binding Site Is Established by the 2.8 Å Structure of the Human β2-Adrenergic Receptor
Hanson, Michael A.; Cherezov, Vadim; Griffith, Mark T.; Roth, Christopher B.; Jaakola, Veli-Pekka; Chien, Ellen Y. T.; Velasquez, Jeffrey; Kuhn, Peter; Stevens, Raymond C.
Structure (Cambridge, MA, United States) (2008), 16 (6), 897-905CODEN: STRUE6; ISSN:0969-2126. (Cell Press)
The role of cholesterol in eukaryotic membrane protein function has been attributed primarily to an influence on membrane fluidity and curvature. We present the 2.8 Å resoln. crystal structure of a thermally stabilized human β2-adrenergic receptor (β2AR) bound to cholesterol and the partial inverse agonist timolol. The receptors pack as monomers in an antiparallel assocn. with two distinct cholesterol mols. bound per receptor, but not in the packing interface, thereby indicating a structurally relevant cholesterol-binding site between helixes I, II, III, and IV. Thermal stability anal. using isothermal denaturation confirms that a cholesterol analog significantly enhances the stability of the receptor. A consensus motif is defined that predicts cholesterol binding for 44% of human class A receptors, suggesting that specific sterol binding is important to the structure and stability of other G protein-coupled receptors (GPCRs), and that this site may provide a target for therapeutic discovery.
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Warne, T.; Serrano-Vega, M. J.; Baker, J. G.; Moukhametzianov, R.; Edwards, P. C.; Henderson, R.; Leslie, A. G.; Tate, C. G.; Schertler, G. F. Structure of a Beta1-Adrenergic G-Protein-Coupled Receptor Nature 2008, 454, 486– 491
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Structure of a β1-adrenergic G-protein-coupled receptor
Warne, Tony; Serrano-Vega, Maria J.; Baker, Jillian G.; Moukhametzianov, Rouslan; Edwards, Patricia C.; Henderson, Richard; Leslie, Andrew G. W.; Tate, Christopher G.; Schertler, Gebhard F. X.
Nature (London, United Kingdom) (2008), 454 (7203), 486-491CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
G-protein-coupled receptors have a major role in transmembrane signaling in most eukaryotes and many are important drug targets. Here we report the 2.7Å resoln. crystal structure of a β1-adrenergic receptor in complex with high affinity antagonist cyanopindolol. The modified turkey (Meleagris gallopavo) receptor was selected to be in its antagonist conformation and its thermostability improved by earlier limited mutagenesis. The ligand-binding pocket comprises 15 side chains from amino acid residues in 4 transmembrane α-helixes and extracellular loop 2. This loop defines the entrance of the ligand-binding pocket and is stabilized by two disulfide bonds and a sodium ion. Binding of cyanopindolol to the β1-adrenergic receptor and binding of Carazolol to the β2-adrenergic receptor involve similar interactions. A short well-defined helix in cytoplasmic loop 2, not obsd. in either rhodopsin or the β2-adrenergic receptor, directly interacts by means of a tyrosine with the highly conserved DRY motif at the end of helix 3 that is essential for receptor activation.
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Bokoch, M. P.; Zou, Y.; Rasmussen, S. G.; Liu, C. W.; Nygaard, R.; Rosenbaum, D. M.; Fung, J. J.; Choi, H. J.; Thian, F. S.; Kobilka, T. S.; Puglisi, J. D.; Weis, W. I.; Pardo, L.; Prosser, R. S.; Mueller, L.; Kobilka, B. K. Ligand-Specific Regulation of the Extracellular Surface of a G-Protein-Coupled Receptor Nature 2010, 463, 108– 112
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Ligand-specific regulation of the extracellular surface of a G-protein-coupled receptor
Bokoch, Michael P.; Zou, Yaozhong; Rasmussen, Soren G. F.; Liu, Corey W.; Nygaard, Rie; Rosenbaum, Daniel M.; Fung, Juan Jose; Choi, Hee-Jung; Thian, Foon Sun; Kobilka, Tong Sun; Puglisi, Joseph D.; Weis, William I.; Pardo, Leonardo; Prosser, R. Scott; Mueller, Luciano; Kobilka, Brian K.
Nature (London, United Kingdom) (2010), 463 (7277), 108-112CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
G protein-coupled receptors (GPCRs) are seven-transmembrane proteins that mediate most cellular responses to hormones and neurotransmitters. They are the largest group of therapeutic targets for a broad spectrum of diseases. Recent crystal structures of GPCRs have revealed structural conservation extending from the orthosteric ligand-binding site in the transmembrane core to the cytoplasmic G protein-coupling domains. In contrast, the extracellular surface (ECS) of GPCRs is remarkably diverse and is therefore an ideal target for the discovery of subtype-selective drugs. However, little is known about the functional role of the ECS in receptor activation, or about conformational coupling of this surface to the native ligand-binding pocket. Here we use NMR spectroscopy to investigate ligand-specific conformational changes around a central structural feature in the ECS of the β2 adrenergic receptor (β2AR): a salt bridge linking extracellular loops 2 and 3. Small-mol. drugs that bind within the transmembrane core and exhibit different efficacies towards G protein activation (agonist, neutral antagonist, and inverse agonist) also stabilize distinct conformations of the ECS. We thereby demonstrate conformational coupling between the ECS and the orthosteric binding site, showing that drugs targeting this diverse surface could function as allosteric modulators with high subtype selectivity. Moreover, these studies provide a new insight into the dynamic behavior of GPCRs not addressable by static, inactive-state crystal structures.
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Wacker, D.; Fenalti, G.; Brown, M. A.; Katritch, V.; Abagyan, R.; Cherezov, V.; Stevens, R. C. Conserved Binding Mode of Human Beta2 Adrenergic Receptor Inverse Agonists and Antagonist Revealed by X-Ray Crystallography J. Am. Chem. Soc. 2010, 132, 11443– 11445
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Moukhametzianov, R.; Warne, T.; Edwards, P. C.; Serrano-Vega, M. J.; Leslie, A. G.; Tate, C. G.; Schertler, G. F. Two Distinct Conformations of Helix 6 Observed in Antagonist-Bound Structures of a Beta1-Adrenergic Receptor Proc. Natl. Acad. Sci. U.S.A. 2011, 108, 8228– 8232
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Two distinct conformations of helix 6 observed in antagonist-bound structures of a β1-adrenergic receptor
Moukhametzianov, Rouslan; Warne, Tony; Edwards, Patricia C.; Serrano-Vega, Maria J.; Leslie, Andrew G. W.; Tate, Christopher G.; Schertler, Gebhard F. X.
Proceedings of the National Academy of Sciences of the United States of America (2011), 108 (20), 8228-8232, S8228/1-S8228/5CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
The β1-adrenergic receptor (β1AR) is a G-protein-coupled receptor whose inactive state structure was detd. using a thermostabilized mutant (β1AR-M23). However, it was not thought to be in a fully inactivated state because there was no salt bridge between Arg139 and Glu285 linking the cytoplasmic ends of transmembrane helixes 3 and 6 (the R3.50-D/E6.30 "ionic lock"). Here we compare eight new structures of β1AR-M23, detd. from crystallog. independent mols. in four different crystals with three different antagonists bound. These structures are all in the inactive R state and show clear electron d. for cytoplasmic loop 3 linking transmembrane helixes 5 and 6 that had not been seen previously. Despite significantly different crystal packing interactions, there are only two distinct conformations of the cytoplasmic end of helix 6, bent and straight. In the bent conformation, the Arg139-Glu285 salt bridge is present, as in the crystal structure of dark-state rhodopsin. The straight conformation, obsd. in previously solved structures of β-receptors, results in the ends of helixes 3 and 6 being too far apart for the ionic lock to form. In the bent conformation, the R3.50-E6.30 distance is significantly longer than in rhodopsin, suggesting that the interaction is also weaker, which could explain the high basal activity in β1AR compared to rhodopsin. Many mutations that increase the constitutive activity of G-protein-coupled receptors are found in the bent region at the cytoplasmic end of helix 6, supporting the idea that this region plays an important role in receptor activation.
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Rasmussen, S. G.; Choi, H. J.; Fung, J. J.; Pardon, E.; Casarosa, P.; Chae, P. S.; Devree, B. T.; Rosenbaum, D. M.; Thian, F. S.; Kobilka, T. S.; Schnapp, A.; Konetzki, I.; Sunahara, R. K.; Gellman, S. H.; Pautsch, A.; Steyaert, J.; Weis, W. I.; Kobilka, B. K. Structure of a Nanobody-Stabilized Active State of the Beta(2) Adrenoceptor Nature 2011, 469, 175– 180
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Structure of a nanobody-stabilized active state of the β2 adrenoceptor
Rasmussen, Soren G. F.; Choi, Hee-Jung; Fung, Juan Jose; Pardon, Els; Casarosa, Paola; Chae, Pil Seok; DeVree, Brian T.; Rosenbaum, Daniel M.; Thian, Foon Sun; Kobilka, Tong Sun; Schnapp, Andreas; Konetzki, Ingo; Sunahara, Roger K.; Gellman, Samuel H.; Pautsch, Alexander; Steyaert, Jan; Weis, William I.; Kobilka, Brian K.
Nature (London, United Kingdom) (2011), 469 (7329), 175-180CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
G protein coupled receptors (GPCRs) exhibit a spectrum of functional behaviors in response to natural and synthetic ligands. Recent crystal structures provide insights into inactive states of several GPCRs. Efforts to obtain an agonist-bound active-state GPCR structure have proven difficult due to the inherent instability of this state in the absence of a G protein. We generated a camelid antibody fragment (nanobody) to the human β2 adrenergic receptor (β2AR) that exhibits G protein-like behavior, and obtained an agonist-bound, active-state crystal structure of the receptor-nanobody complex. Comparison with the inactive β2AR structure reveals subtle changes in the binding pocket; however, these small changes are assocd. with an 11 Å outward movement of the cytoplasmic end of transmembrane segment 6, and rearrangements of transmembrane segments 5 and 7 that are remarkably similar to those obsd. in opsin, an active form of rhodopsin. This structure provides insights into the process of agonist binding and activation.
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Rasmussen, S. G.; DeVree, B. T.; Zou, Y.; Kruse, A. C.; Chung, K. Y.; Kobilka, T. S.; Thian, F. S.; Chae, P. S.; Pardon, E.; Calinski, D.; Mathiesen, J. M.; Shah, S. T.; Lyons, J. A.; Caffrey, M.; Gellman, S. H.; Steyaert, J.; Skiniotis, G.; Weis, W. I.; Sunahara, R. K.; Kobilka, B. K. Crystal Structure of the Beta2 Adrenergic Receptor-Gs Protein Complex Nature 2011, 477, 549– 555
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Crystal structure of the β2 adrenergic receptor-Gs protein complex
Rasmussen, Soren G. F.; DeVree, Brian T.; Zou, Yao-Zhong; Kruse, Andrew C.; Chung, Ka-Young; Kobilka, Tong-Sun; Thian, Foon-Sun; Chae, Pil-Seok; Pardon, Els; Calinski, Diane; Mathiesen, Jesper M.; Shah, Syed T. A.; Lyons, Joseph A.; Caffrey, Martin; Gellman, Samuel H.; Steyaert, Jan; Skiniotis, Georgios; Weis, William I.; Sunahara, Roger K.; Kobilka, Brian K.
Nature (London, United Kingdom) (2011), 477 (7366), 549-555CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
G protein-coupled receptors (GPCRs) are responsible for the majority of cellular responses to hormones and neurotransmitters as well as the senses of sight, olfaction and taste. The paradigm of GPCR signalling is the activation of a heterotrimeric GTP binding protein (G protein) by an agonist-occupied receptor. The β2 adrenergic receptor (β2AR) activation of Gs, the stimulatory G protein for adenylyl cyclase, has long been a model system for GPCR signalling. Here we present the crystal structure of the active state ternary complex composed of agonist-occupied monomeric β2AR and nucleotide-free Gs heterotrimer. The principal interactions between the β2AR and Gs involve the amino- and carboxy-terminal α-helixes of Gs, with conformational changes propagating to the nucleotide-binding pocket. The largest conformational changes in the β2AR include a 14 Å outward movement at the cytoplasmic end of transmembrane segment 6 (TM6) and an α-helical extension of the cytoplasmic end of TM5. The most surprising observation is a major displacement of the α-helical domain of Gαs relative to the Ras-like GTPase domain. This crystal structure represents the first high-resoln. view of transmembrane signalling by a GPCR.
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Rosenbaum, D. M.; Zhang, C.; Lyons, J. A.; Holl, R.; Aragao, D.; Arlow, D. H.; Rasmussen, S. G.; Choi, H. J.; Devree, B. T.; Sunahara, R. K.; Chae, P. S.; Gellman, S. H.; Dror, R. O.; Shaw, D. E.; Weis, W. I.; Caffrey, M.; Gmeiner, P.; Kobilka, B. K. Structure and Function of an Irreversible Agonist-Beta(2) Adrenoceptor Complex Nature 2011, 469, 236– 240
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Structure and function of an irreversible agonist-β2 adrenoceptor complex
Rosenbaum, Daniel M.; Zhang, Cheng; Lyons, Joseph A.; Holl, Ralph; Aragao, David; Arlow, Daniel H.; Rasmussen, Soren G. F.; Choi, Hee-Jung; DeVree, Brian T.; Sunahara, Roger K.; Chae, Pil Seok; Gellman, Samuel H.; Dror, Ron O.; Shaw, David E.; Weis, William I.; Caffrey, Martin; Gmeiner, Peter; Kobilka, Brian K.
Nature (London, United Kingdom) (2011), 469 (7329), 236-240CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
G-protein-coupled receptors (GPCRs) are eukaryotic integral membrane proteins that modulate biol. function by initiating cellular signaling in response to chem. diverse agonists. Despite recent progress in the structural biol. of GPCRs, the mol. basis for agonist binding and allosteric modulation of these proteins is poorly understood. Structural knowledge of agonist-bound states is essential for deciphering the mechanism of receptor activation, and for structure-guided design and optimization of ligands. However, the crystn. of agonist-bound GPCRs has been hampered by modest affinities and rapid off-rates of available agonists. Using the inactive structure of the human β2 adrenergic receptor (β2AR) as a guide, we designed a β2AR agonist that can be covalently tethered to a specific site on the receptor through a disulfide bond. The covalent β2AR-agonist complex forms efficiently, and is capable of activating a heterotrimeric G protein. We crystd. a covalent agonist-bound β2AR-T4L fusion protein in lipid bilayers through the use of the lipidic mesophase method, and detd. its structure at 3.5 Å resoln. A comparison to the inactive structure and an antibody-stabilized active structure (companion paper) shows how binding events at both the extracellular and intracellular surfaces are required to stabilize an active conformation of the receptor. The structures are in agreement with long-timescale (up to 30 μs) mol. dynamics simulations showing that an agonist-bound active conformation spontaneously relaxes to an inactive-like conformation in the absence of a G protein or stabilizing antibody.
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Warne, T.; Moukhametzianov, R.; Baker, J. G.; Nehme, R.; Edwards, P. C.; Leslie, A. G.; Schertler, G. F.; Tate, C. G. The Structural Basis for Agonist and Partial Agonist Action on a Beta(1)-Adrenergic Receptor Nature 2011, 469, 241– 244
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Warne, T.; Edwards, P. C.; Leslie, A. G.; Tate, C. G. Crystal Structures of a Stabilized Beta1-Adrenoceptor Bound to the Biased Agonists Bucindolol and Carvedilol Structure 2012, 20, 841– 849
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Zou, Y.; Weis, W. I.; Kobilka, B. K. N-Terminal T4 Lysozyme Fusion Facilitates Crystallization of a G Protein Coupled Receptor PLoS One 2012, 7e46039
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Christopher, J. A.; Brown, J.; Dore, A. S.; Errey, J. C.; Koglin, M.; Marshall, F. H.; Myszka, D. G.; Rich, R. L.; Tate, C. G.; Tehan, B.; Warne, T.; Congreve, M. Biophysical Fragment Screening of the Beta1-Adrenergic Receptor: Identification of High Affinity Arylpiperazine Leads Using Structure-Based Drug Design J. Med. Chem. 2013, 56, 3446– 3455
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Huang, J.; Chen, S.; Zhang, J. J.; Huang, X. Y. Crystal Structure of Oligomeric Beta1-Adrenergic G Protein-Coupled Receptors in Ligand-Free Basal State Nat. Struct. Mol. Biol. 2013, 20, 419– 425
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Crystal structure of oligomeric β1-adrenergic G protein-coupled receptors in ligand-free basal state
Huang, Jianyun; Chen, Shuai; Zhang, J. Jillian; Huang, Xin-Yun
Nature Structural & Molecular Biology (2013), 20 (4), 419-425CODEN: NSMBCU; ISSN:1545-9993. (Nature Publishing Group)
G protein-coupled receptors (GPCRs) mediate transmembrane signaling. Before ligand binding, GPCRs exist in a basal state. Crystal structures of several GPCRs bound with antagonists or agonists have been solved. However, the crystal structure of the ligand-free basal state of a GPCR, the starting point of GPCR activation and function, had not yet been detd. Here we report the x-ray crystal structure of the ligand-free basal state of a GPCR in a lipid membrane-like environment. Oligomeric turkey β1-adrenergic receptors display two dimer interfaces. One interface involves the transmembrane domain (TM) 1, TM2, the C-terminal H8 and extracellular loop 1. The other interface engages residues from TM4, TM5, intracellular loop 2 and extracellular loop 2. Structural comparisons show that this ligand-free state is in an inactive conformation. This provides the structural basis of GPCR dimerization and oligomerization.
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Carlsson, J.; Coleman, R. G.; Setola, V.; Irwin, J. J.; Fan, H.; Schlessinger, A.; Sali, A.; Roth, B. L.; Shoichet, B. K. Ligand Discovery from a Dopamine D3 Receptor Homology Model and Crystal Structure Nat. Chem. Biol. 2011, 7, 769– 778
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Ligand discovery from a dopamine D3 receptor homology model and crystal structure
Carlsson, Jens; Coleman, Ryan G.; Setola, Vincent; Irwin, John J.; Fan, Hao; Schlessinger, Avner; Sali, Andrej; Roth, Bryan L.; Shoichet, Brian K.
Nature Chemical Biology (2011), 7 (11), 769-778CODEN: NCBABT; ISSN:1552-4450. (Nature Publishing Group)
G protein-coupled receptors (GPCRs) are intensely studied as drug targets and for their role in signaling. With the detn. of the first crystal structures, interest in structure-based ligand discovery increased. Unfortunately, for most GPCRs no exptl. structures are available. The detn. of the D3 receptor structure and the challenge to the community to predict it enabled a fully prospective comparison of ligand discovery from a modeled structure vs. that of the subsequently released crystal structure. Over 3.3 million mols. were docked against a homol. model, and 26 of the highest ranking were tested for binding. Six had affinities ranging from 0.2 to 3.1 μM. Subsequently, the crystal structure was released and the docking screen repeated. Of the 25 compds. selected, five had affinities ranging from 0.3 to 3.0 μM. One of the new ligands from the homol. model screen was optimized for affinity to 81 nM. The feasibility of docking screens against modeled GPCRs more generally is considered.
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Carlsson, J.; Yoo, L.; Gao, Z. G.; Irwin, J. J.; Shoichet, B. K.; Jacobson, K. A. Structure-Based Discovery of A2a Adenosine Receptor Ligands J. Med. Chem. 2010, 53, 3748– 3755
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Structure-Based Discovery of A2A Adenosine Receptor Ligands
Carlsson, Jens; Yoo, Lena; Gao, Zhan-Guo; Irwin, John J.; Shoichet, Brian K.; Jacobson, Kenneth A.
Journal of Medicinal Chemistry (2010), 53 (9), 3748-3755CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
The recent detn. of X-ray structures of pharmacol. relevant GPCRs has made these targets accessible to structure-based ligand discovery. Here we explore whether novel chemotypes may be discovered for the A2A adenosine receptor, based on complementarity to its recently detd. structure. The A2A adenosine receptor signals in the periphery and the CNS, with agonists explored as anti-inflammatory drugs and antagonists explored for neurodegenerative diseases. We used mol. docking to screen a 1.4 million compd. database against the X-ray structure computationally and tested 20 high-ranking, previously unknown mols. exptl. Of these 35% showed substantial activity with affinities between 200 nM and 9 μM. For the most potent of these new inhibitors, over 50-fold specificity was obsd. for the A2A vs. the related A1 and A3 subtypes. These high hit rates and affinities at least partly reflect the bias of com. libraries toward GPCR-like chemotypes, an issue that we attempt to investigate quant. Despite this bias, many of the most potent new ligands were novel, dissimilar from known ligands, providing new lead structures for modulation of this medically important target.
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de Graaf, C.; Kooistra, A. J.; Vischer, H. F.; Katritch, V.; Kuijer, M.; Shiroishi, M.; Iwata, S.; Shimamura, T.; Stevens, R. C.; de Esch, I. J.; Leurs, R. Crystal Structure-Based Virtual Screening for Fragment-Like Ligands of the Human Histamine H(1) Receptor J. Med. Chem. 2011, 54, 8195– 8206
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Katritch, V.; Jaakola, V. P.; Lane, J. R.; Lin, J.; Ijzerman, A. P.; Yeager, M.; Kufareva, I.; Stevens, R. C.; Abagyan, R. Structure-Based Discovery of Novel Chemotypes for Adenosine a(2a) Receptor Antagonists J. Med. Chem. 2010, 53, 1799– 1809
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Structure-Based Discovery of Novel Chemotypes for Adenosine A2A Receptor Antagonists
Katritch, Vsevolod; Jaakola, Veli-Pekka; Lane, J. Robert; Lin, Judy; IJzerman, Adriaan P.; Yeager, Mark; Kufareva, Irina; Stevens, Raymond C.; Abagyan, Ruben
Journal of Medicinal Chemistry (2010), 53 (4), 1799-1809CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
The recent progress in crystallog. of G-protein coupled receptors opens an unprecedented venue for structure-based GPCR drug discovery. To test efficiency of the structure-based approach, we performed mol. docking and virtual ligand screening (VLS) of more than 4 million com. available "drug-like" and "lead-like" compds. against the A2AAR 2.6 Å resoln. crystal structure. Out of 56 high ranking compds. tested in A2AAR binding assays, 23 showed affinities under 10 μM, 11 of those had sub-μM affinities and two compds. had affinities under 60 nM. The identified hits represent at least 9 different chem. scaffolds and are characterized by very high ligand efficiency (0.3-0.5 kcal/mol per heavy atom). Significant A2AAR antagonist activities were confirmed for 10 out of 13 ligands tested in functional assays. High success rate, novelty, and diversity of the chem. scaffolds and strong ligand efficiency of the A2AAR antagonists identified in this study suggest practical applicability of receptor-based VLS in GPCR drug discovery.
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Kolb, P.; Rosenbaum, D. M.; Irwin, J. J.; Fung, J. J.; Kobilka, B. K.; Shoichet, B. K. Structure-Based Discovery of Beta2-Adrenergic Receptor Ligands Proc. Natl. Acad. Sci. U.S.A. 2009, 106, 6843– 6848
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Structure-based discovery of β2-adrenergic receptor ligands
Kolb, Peter; Rosenbaum, Daniel M.; Irwin, John J.; Fung, Juan Jose; Kobilka, Brian K.; Shoichet, Brian K.
Proceedings of the National Academy of Sciences of the United States of America (2009), 106 (16), 6843-6848CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
Aminergic G protein-coupled receptors (GPCRs) have been a major focus of pharmaceutical research for many years. Due partly to the lack of reliable receptor structures, drug discovery efforts have been largely ligand-based. The recently detd. X-ray structure of the β2-adrenergic receptor offers an opportunity to investigate the advantages and limitations inherent in a structure-based approach to ligand discovery against this and related GPCR targets. Approx. 1 million com. available, "lead-like" mols. were docked against the β2-adrenergic receptor structure. On testing of 25 high-ranking mols., 6 were active with binding affinities <4 μM, with the best mol. binding with a Ki of 9 nM (95% confidence interval 7-10 nM). Five of these mols. were inverse agonists. The high hit rate, the high affinity of the most potent mol., the discovery of unprecedented chemotypes among the new inhibitors, and the apparent bias toward inverse agonists among the docking hits, have implications for structure-based approaches against GPCRs that recognize small org. mols.
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Mysinger, M. M.; Weiss, D. R.; Ziarek, J. J.; Gravel, S.; Doak, A. K.; Karpiak, J.; Heveker, N.; Shoichet, B. K.; Volkman, B. F. Structure-Based Ligand Discovery for the Protein-Protein Interface of Chemokine Receptor Cxcr4 Proc. Natl. Acad. Sci. U.S.A. 2012, 109, 5517– 5522
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de Graaf, C.; Rognan, D. Customizing G Protein-Coupled Receptor Models for Structure-Based Virtual Screening Curr. Pharm. Des. 2009, 15, 4026– 4048
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Customizing G protein-coupled receptor models for structure-based virtual screening
de Graaf, Chris; Rognan, Didier
Current Pharmaceutical Design (2009), 15 (35), 4026-4048CODEN: CPDEFP; ISSN:1381-6128. (Bentham Science Publishers Ltd.)
This review will focus on the construction, refinement, and validation of G Protein-coupled receptor models for the purpose of structure-based virtual screening. Practical tips and tricks derived from concrete modeling and virtual screening exercises to overcome the problems and pitfalls assocd. with the different steps of the receptor modeling workflow will be presented. These examples will not only include rhodopsin-like (class A), but also secretine-like (class B), and glutamate-like (class C) receptors. In addn., the review will present a careful comparative anal. of current crystal structures and their implication on homol. modeling. The following themes will be discussed: (i) the use of exptl. anchors in guiding the modeling procedure; (ii) amino acid sequence alignments; (iii) ligand binding mode accommodation and binding cavity expansion; (iv) proline-induced kinks in transmembrane helixes; (v) binding mode prediction and virtual screening by receptor-ligand interaction fingerprint scoring; (vi) extracellular loop modeling; (vii) virtual filtering schemes. Finally, an overview of several successful structure-based screening shows that receptor models, despite structural inaccuracies, can be efficiently used to find novel ligands.
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Kooistra, A. J.; Roumen, L.; Leurs, R.; de Esch, I. J.; de Graaf, C. From Heptahelical Bundle to Hits from the Haystack: Structure-Based Virtual Screening for GPCR Ligands. Methods Enzymol. 2013, 522, 279– 336.
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Palczewski, K.; Kumasaka, T.; Hori, T.; Behnke, C. A.; Motoshima, H.; Fox, B. A.; Le Trong, I.; Teller, D. C.; Okada, T.; Stenkamp, R. E.; Yamamoto, M.; Miyano, M. Crystal Structure of Rhodopsin: A G Protein-Coupled Receptor Science 2000, 289, 739– 745
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Crystal structure of rhodopsin: A G protein-coupled receptor
Palczewski, Krzysztof; Kumasaka, Takashi; Hori, Tetsuya; Behnke, Craig A.; Motoshima, Hiroyuki; Fox, Brian A.; Le Trong, Isolde; Teller, David C.; Okada, Tetsuji; Stenkamp, Ronald E.; Yamamoto, Masaki; Miyano, Masashi
Science (Washington, D. C.) (2000), 289 (5480), 739-745CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
Heterotrimeric guanine nucleotide-binding protein (G protein)-coupled receptors (GPCRs) respond to a variety of different external stimuli and activate G proteins. GPCRs share many structural features, including a bundle of seven transmembrane α-helixes connected by six loops of varying lengths. We detd. the structure of rhodopsin from diffraction data extending to 2.8 angstroms resoln. The highly organized structure in the extracellular region, including a conserved disulfide bridge, forms a basis for the arrangement of the seven-helix transmembrane motif. The ground-state chromophore, 11-cis-retinal, holds the transmembrane region of the protein in the inactive conformation. Interactions of the chromophore with a cluster of key residues det. the wavelength of the max. absorption. Changes in these interactions among rhodopsins facilitate color discrimination. Identification of a set of residues that mediate interactions between the transmembrane helixes and the cytoplasmic surface, where G-protein activation occurs, also suggests a possible structural change upon photoactivation.
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de Graaf, C.; Rein, C.; Piwnica, D.; Giordanetto, F.; Rognan, D. Structure-Based Discovery of Allosteric Modulators of Two Related Class B G-Protein-Coupled Receptors ChemMedChem 2011, 6, 2159– 2169
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Kellenberger, E.; Springael, J. Y.; Parmentier, M.; Hachet-Haas, M.; Galzi, J. L.; Rognan, D. Identification of Nonpeptide Ccr5 Receptor Agonists by Structure-Based Virtual Screening J. Med. Chem. 2007, 50, 1294– 1303
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Identification of Nonpeptide CCR5 Receptor Agonists by Structure-based Virtual Screening
Kellenberger, Esther; Springael, Jean-Yves; Parmentier, Marc; Hachet-Haas, Muriel; Galzi, Jean-Luc; Rognan, Didier
Journal of Medicinal Chemistry (2007), 50 (6), 1294-1303CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
A three-dimensional model of the chemokine receptor CCR5 has been built to fulfill structural peculiarities of its α-helix bundle and to distinguish known CCR5 antagonists from randomly chosen drug-like decoys. In silico screening of a library of 1.6 million com. available compds. against the CCR5 model by sequential filters (drug-likeness, 2-D pharmacophore, 3-D docking, scaffold clustering) yielded a hit list of 59 compds., out of which 10 exhibited a detectable binding affinity to the CCR5 receptor. Unexpectedly, most binders tested in a functional assay were shown to be agonists of the CCR5 receptor. A follow-up database query based on similarity to the most potent binders identified three new CCR5 agonists. Despite a moderate affinity of all nonpeptide ligands for the CCR5 receptor, one of the agonists was shown to promote efficient receptor internalization, which is a process therapeutically favorable for protection against HIV-1 infection.
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Kiss, R.; Kiss, B.; Konczol, A.; Szalai, F.; Jelinek, I.; Laszlo, V.; Noszal, B.; Falus, A.; Keseru, G. M. Discovery of Novel Human Histamine H4 Receptor Ligands by Large-Scale Structure-Based Virtual Screening J. Med. Chem. 2008, 51, 3145– 3153
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Discovery of Novel Human Histamine H4 Receptor Ligands by Large-Scale Structure-Based Virtual Screening
Kiss, Robert; Kiss, Bela; Konczol, Arpad; Szalai, Ferenc; Jelinek, Ivett; Laszlo, Valeria; Noszal, Bela; Falus, Andras; Keseru, Gyorgy M.
Journal of Medicinal Chemistry (2008), 51 (11), 3145-3153CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
A structure-based virtual screening (SBVS) was conducted on a ligand-supported homol. model of the human histamine H4 receptor (hH4R). More than 8.7 million 3D structures derived from different vendor databases were investigated by docking to the hH4R binding site using FlexX. A total of 255 selected compds. were tested by radioligand binding assay and 16 of them possessed significant [3H]histamine displacement. Several novel scaffolds were identified that can be used to develop selective H4 ligands in the future. As far as we know, this is the first SBVS reported on H4R, representing one of the largest virtual screens validated by the biol. evaluation of the virtual hits.
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Salo, O. M.; Raitio, K. H.; Savinainen, J. R.; Nevalainen, T.; Lahtela-Kakkonen, M.; Laitinen, J. T.; Jarvinen, T.; Poso, A. Virtual Screening of Novel Cb2 Ligands Using a Comparative Model of the Human Cannabinoid Cb2 Receptor J. Med. Chem. 2005, 48, 7166– 7171
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Virtual Screening of Novel CB2 Ligands Using a Comparative Model of the Human Cannabinoid CB2 Receptor
Salo, Outi M. H.; Raitio, Katri H.; Savinainen, Juha R.; Nevalainen, Tapio; Lahtela-Kakkonen, Maija; Laitinen, Jarmo T.; Jaervinen, Tomi; Poso, Antti
Journal of Medicinal Chemistry (2005), 48 (23), 7166-7171CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
To identify novel selective CB2 lead compds., a comparative model of the CB2 receptor was constructed using the high-resoln. bovine rhodopsin X-ray structure as a template. The CB2 model was utilized both in building the database queries and in filtering the hit compds. by a docking and scoring method. In G-protein activation assays, 1-isoquinolyl[3-(trifluoromethyl)phenyl]methanone (40, NRB 04079) was found to act as a selective agonist at the human CB2 receptor.
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Tikhonova, I. G.; Sum, C. S.; Neumann, S.; Engel, S.; Raaka, B. M.; Costanzi, S.; Gershengorn, M. C. Discovery of Novel Agonists and Antagonists of the Free Fatty Acid Receptor 1 (Ffar1) Using Virtual Screening J. Med. Chem. 2008, 51, 625– 633
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Discovery of novel agonists and antagonists of the free fatty acid receptor 1 (FFAR1) using virtual screening
Tikhonova, Irina G.; Sum, Chi Shing; Neumann, Susanne; Engel, Stanislav; Raaka, Bruce M.; Costanzi, Stefano; Gershengorn, Marvin C.
Journal of Medicinal Chemistry (2008), 51 (3), 625-633CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
The G-protein-coupled receptor free fatty acid receptor 1 (FFAR1), previously named GPR40, is a possible novel target for the treatment of type 2 diabetes. In an attempt to identify new ligands for this receptor, we performed virtual screening (VS) based on 2-dimensional (2D) similarity, 3-dimensional (3D) pharmacophore searches, and docking studies by the structure of known agonists and our model of the ligand binding site, which was validated by mutagenesis. VS of a database of 2.6 million compds. followed by extn. of structural neighbors of functionally confirmed hits resulted in identification of 15 compds. active at FFAR1 either as full agonists, partial agonists, or pure antagonists. Site-directed mutagenesis and docking studies revealed different patterns of ligand-receptor interactions and provided important information on the role of specific amino acids in binding and activation of FFAR1.
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Varady, J.; Wu, X.; Fang, X.; Min, J.; Hu, Z.; Levant, B.; Wang, S. Molecular Modeling of the Three-Dimensional Structure of Dopamine 3 (D3) Subtype Receptor: Discovery of Novel and Potent D3 Ligands through a Hybrid Pharmacophore- and Structure-Based Database Searching Approach J. Med. Chem. 2003, 46, 4377– 4392
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Molecular Modeling of the Three-Dimensional Structure of Dopamine 3 (D3) Subtype Receptor: Discovery of Novel and Potent D3 Ligands through a Hybrid Pharmacophore- and Structure-Based Database Searching Approach
Varady, Judith; Wu, Xihan; Fang, Xueliang; Min, Ji; Hu, Zengjian; Levant, Beth; Wang, Shaomeng
Journal of Medicinal Chemistry (2003), 46 (21), 4377-4392CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
The dopamine 3 (D3) subtype receptor has been implicated in several neurol. conditions, and potent and selective D3 ligands may have therapeutic potential for the treatment of drug addiction, Parkinson's disease, and schizophrenia. In this paper, we report computational homol. modeling of the D3 receptor based upon the high-resoln. x-ray structure of rhodopsin, extensive structural refinement in the presence of explicit lipid bilayer and water environment, and validation of the refined D3 structural models using exptl. data. We further describe the development, validation, and application of a hybrid computational screening approach for the discovery of several classes of novel and potent D3 ligands. This computational approach employs stepwise pharmacophore and structure-based searching of a large three-dimensional chem. database for the identification of potential D3 ligands. The obtained hits are then subjected to structural novelty screening, and the most promising compds. are tested in a D3 binding assay. Using this approach we identified four compds. with Ki values better than 100 nM and eight compds. with Ki values better than 1 μM out of 20 compds. selected for testing in the D3 receptor binding assay. Our results suggest that the D3 structural models obtained from this study may be useful for the discovery and design of novel and potent D3 ligands. Furthermore, the employed hybrid approach may be more effective for lead discovery from a large chem. database than either pharmacophore-based or structure-based database screening alone.
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Kooistra, A. J.; Leurs, R.; de Esch, I. J.; de Graaf, C. From Three-Dimensional GPCR Structure to Rational Ligand Discovery. Adv. Exp. Med. Biol. 2014, 796, 129– 157
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Weiss, D. R.; Ahn, S.; Sassano, M. F.; Kleist, A.; Zhu, X.; Strachan, R.; Roth, B. L.; Lefkowitz, R. J.; Shoichet, B. K. Conformation Guides Molecular Efficacy in Docking Screens of Activated Beta-2 Adrenergic G Protein Coupled Receptor ACS Chem. Biol. 2013, 8, 1018– 1026
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Moitessier, N.; Englebienne, P.; Lee, D.; Lawandi, J.; Corbeil, C. R. Towards the Development of Universal, Fast and Highly Accurate Docking/Scoring Methods: A Long Way to Go Br. J. Pharmacol. 2008, 153 (Suppl 1) S7– 26
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Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go
Moitessier, N.; Englebienne, P.; Lee, D.; Lawandi, J.; Corbeil, C. R.
British Journal of Pharmacology (2008), 153 (Suppl. 1), S7-S26CODEN: BJPCBM; ISSN:0007-1188. (Nature Publishing Group)
A review. Accelerating the drug discovery process requires predictive computational protocols capable of reducing or simplifying the synthetic and/or combinatorial challenge. Docking-based virtual screening methods have been developed and successfully applied to a no. of pharmaceutical targets. In this review, we first present the current status of docking and scoring methods, with exhaustive lists of these. We next discuss reported comparative studies, outlining criteria for their interpretation. In the final section, we describe some of the remaining developments that would potentially lead to a universally applicable docking/scoring method. Published online 26 Nov. 2007.
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Sabio, M.; Jones, K.; Topiol, S. Use of the X-Ray Structure of the Beta2-Adrenergic Receptor for Drug Discovery. Part 2: Identification of Active Compounds Bioorg. Med. Chem. Lett. 2008, 18, 5391– 5395
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Use of the X-ray structure of the β2-adrenergic receptor for drug discovery. Part 2: Identification of active compounds
Sabio, Michael; Jones, Kenneth; Topiol, Sid
Bioorganic & Medicinal Chemistry Letters (2008), 18 (20), 5391-5395CODEN: BMCLE8; ISSN:0960-894X. (Elsevier Ltd.)
The recently published X-ray structures of the β2-adrenergic receptor are the first examples of ligand-mediated GPCR crystal structures. We have previously performed computational studies that examine the potential viability of these structures for use in drug design, exploiting known ligand activities. Our previous study and a newly reported β2/Timolol X-ray complex provide validation of the computational approaches. In the present work, we use the X-ray structures to ext., via in silico high-throughput docking, compds. from proprietary and com. databases and demonstrate the successful identification of active compds. by radioligand binding.
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de Graaf, C.; Rognan, D. Selective Structure-Based Virtual Screening for Full and Partial Agonists of the Beta2 Adrenergic Receptor J. Med. Chem. 2008, 51, 4978– 4985
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Selective Structure-Based Virtual Screening for Full and Partial Agonists of the β2 Adrenergic Receptor
de Graaf, Chris; Rognan, Didier
Journal of Medicinal Chemistry (2008), 51 (16), 4978-4985CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
The recently solved high-resoln. X-ray structure of the β2 adrenergic receptor has been challenged for its ability to discriminate inverse agonists/antagonists from partial/full agonists. Whereas the X-ray structure of the ground state receptor was unsuitable to distinguish true ligands with different functional effects, modifying this structure to reflect early conformational events in receptor activation led to a receptor model able to selectively retrieve full and partial agonists by structure-based virtual screening. The use of a topol. scoring function based on mol. interaction fingerprints was shown to be mandatory to properly rank docking poses and achieve acceptable enrichments for partial and full agonists only.
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Katritch, V.; Reynolds, K. A.; Cherezov, V.; Hanson, M. A.; Roth, C. B.; Yeager, M.; Abagyan, R. Analysis of Full and Partial Agonists Binding to Beta2-Adrenergic Receptor Suggests a Role of Transmembrane Helix V in Agonist-Specific Conformational Changes J. Mol. Recognit. 2009, 22, 307– 318
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Analysis of full and partial agonists binding to β2-adrenergic receptor suggests a role of transmembrane helix V in agonist-specific conformational changes
Katritch, Vsevolod; Reynolds, Kimberly A.; Cherezov, Vadim; Hanson, Michael A.; Roth, Christopher B.; Yeager, Mark; Abagyan, Ruben
Journal of Molecular Recognition (2009), 22 (4), 307-318CODEN: JMORE4; ISSN:0952-3499. (John Wiley & Sons Ltd.)
The 2.4 Å crystal structure of the β2-adrenergic receptor (β2AR) in complex with the high-affinity inverse agonist (-)-carazolol provides a detailed structural framework for the anal. of ligand recognition by adrenergic receptors. Insights into agonist binding and the corresponding conformational changes triggering G-protein coupled receptor (GPCR) activation mechanism are of special interest. While the carazolol pocket captured in the β2AR crystal structure accommodates (-)-isoproterenol and other agonists without steric clashes, a finite movement of the flexible extracellular part of TM-V helix (TM-Ve) obtained by receptor optimization in the presence of docked ligand can further improve the calcd. binding affinities for agonist compds. Tilting of TM-Ve towards the receptor axis provides a more complete description of polar receptor-ligand interactions for full and partial agonists, by enabling optimal engagement of agonists with two exptl. identified anchor sites, formed by Asp 113/Asn 312 and Ser 203/Ser 204/Ser 207 side chains. Further, receptor models incorporating a flexible TM-V backbone allow reliable prediction of binding affinities for a set of diverse ligands, suggesting potential utility of this approach to design of effective and subtype-specific agonists for adrenergic receptors. Systematic differences in capacity of partial, full and inverse agonists to induce TM-V helix tilt in the β2AR model suggest potential role of TM-V as a conformational "rheostat" involved in the whole spectrum of β2AR responses to small mol. signals.
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Reynolds, K. A.; Katritch, V.; Abagyan, R. Identifying Conformational Changes of the Beta(2) Adrenoceptor That Enable Accurate Prediction of Ligand/Receptor Interactions and Screening for Gpcr Modulators J. Comput. Aided Mol. Des. 2009, 23, 273– 288
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Identifying conformational changes of the β2 adrenoceptor that enable accurate prediction of ligand/receptor interactions and screening for GPCR modulators
Reynolds, Kimberly A.; Katritch, Vsevolod; Abagyan, Ruben
Journal of Computer-Aided Molecular Design (2009), 23 (5), 273-288CODEN: JCADEQ; ISSN:0920-654X. (Springer)
The new β2 Adrenoceptor (β2AR) crystal structures provide a high-resoln. snapshot of receptor interactions with two particular partial inverse agonists, (-)-carazolol and timolol. However, both exptl. and computational studies of GPCR structure are significantly complicated by the existence of multiple conformational states coupled to ligand type and receptor activity. Agonists and antagonists induce or stabilize distinct changes in receptor structure that mediate a range of pharmacol. activities. In this work, the authors (1) established that the existing β2AR crystallog. conformers can be extended to describe ligand/receptor interactions for addnl. antagonist types, (2) generated agonist-bound receptor conformations, and (3) validated these models for agonist and antagonist virtual ligand screening (VLS). Using a ligand directed refinement protocol, the authors derived a single agonist-bound receptor conformation that selectively retrieved a diverse set of full and partial β2AR agonists in VLS trials. Addnl., the impact of extracellular loop two conformation on VLS was assessed by docking studies with rhodopsin-based β2AR homol. models, and loop-deleted receptor models. A general strategy for constructing and selecting agonist-bound receptor pocket conformations is presented, which may prove broadly useful in creating agonist and antagonist bound models for other GPCRs.
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Vilar, S.; Karpiak, J.; Berk, B.; Costanzi, S. In Silico Analysis of the Binding of Agonists and Blockers to the Beta2-Adrenergic Receptor J. Mol. Graph. Model. 2011, 29, 809– 817
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In silico analysis of the binding of agonists and blockers to the β2-adrenergic receptor
Vilar, Santiago; Karpiak, Joel; Berk, Barkin; Costanzi, Stefano
Journal of Molecular Graphics & Modelling (2011), 29 (6), 809-817CODEN: JMGMFI; ISSN:1093-3263. (Elsevier Ltd.)
Activation of G protein-coupled receptors (GPCRs) is a complex phenomenon. Here, we applied Induced Fit Docking (IFD) in tandem with linear discriminant anal. (LDA) to generate hypotheses on the conformational changes induced to the β2-adrenergic receptor by agonist binding, preliminary to the sequence of events that characterize activation of the receptor. This anal., corroborated by a follow-up mol. dynamics study, suggested that agonists induce subtle movements to the fifth transmembrane domain (TM5) of the receptor. Furthermore, mol. dynamics also highlighted a correlation between movements of TM5 and the second extracellular loop (EL2), suggesting that freedom of motion of EL2 is required for the agonist-induced TM5 displacement. Importantly, we also showed that the IFD/LDA procedure can be used as a computational means to distinguish agonists from blockers on the basis of the differential conformational changes induced to the receptor. In particular, the two most predictive models obtained are based on the RMSD induced to Ser207 and on the counterclockwise rotation induced to TM5.
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Kooistra, A. J.; Kuhne, S.; de Esch, I. J.; Leurs, R.; de Graaf, C. A Structural Chemogenomics Analysis of Aminergic Gpcrs: Lessons for Histamine Receptor Ligand Design Br. J. Pharmacol. 2013, 170, 101– 126
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A structural chemogenomics analysis of aminergic GPCRs: lessons for histamine receptor ligand design
Kooistra, A. J.; Kuhne, S.; de Esch, I. J. P.; Leurs, R.; de Graaf, C.
British Journal of Pharmacology (2013), 170 (1), 101-126CODEN: BJPCBM; ISSN:1476-5381. (Wiley-Blackwell)
Background and Purpose Chemogenomics focuses on the discovery of new connections between chem. and biol. space leading to the discovery of new protein targets and biol. active mols. G-protein coupled receptors (GPCRs) are a particularly interesting protein family for chemogenomics studies because there is an overwhelming amt. of ligand binding affinity data available. The increasing no. of aminergic GPCR crystal structures now for the first time allows the integration of chemogenomics studies with high-resoln. structural analyses of GPCR-ligand complexes. Exptl. Approach In this study, we have combined ligand affinity data, receptor mutagenesis studies, and amino acid sequence analyses to high-resoln. structural analyses of (hist)aminergic GPCR-ligand interactions. This integrated structural chemogenomics anal. is used to more accurately describe the mol. and structural determinants of ligand affinity and selectivity in different key binding regions of the crystd. aminergic GPCRs, and histamine receptors in particular. Key Results Our investigations highlight interesting correlations and differences between ligand similarity and ligand binding site similarity of different aminergic receptors. Apparent discrepancies can be explained by combining detailed anal. of crystd. or predicted protein-ligand binding modes, receptor mutation studies, and ligand structure-selectivity relationships that identify local differences in essential pharmacophore features in the ligand binding sites of different receptors. Conclusions and Implications We have performed structural chemogenomics studies that identify links between (hist)aminergic receptor ligands and their binding sites and binding modes. This knowledge can be used to identify structure-selectivity relationships that increase our understanding of ligand binding to (hist)aminergic receptors and hence can be used in future GPCR ligand discovery and design.
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Strader, C. D.; Candelore, M. R.; Hill, W. S.; Sigal, I. S.; Dixon, R. A. Identification of Two Serine Residues Involved in Agonist Activation of the Beta-Adrenergic Receptor J. Biol. Chem. 1989, 264, 13572– 13578
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Identification of two serine residues involved in agonist activation of the β-adrenergic receptor
Strader, Catherine D.; Candelore, Mari Rios; Hill, Wendy S.; Sigal, Irving S.; Dixon, Richard A. F.
Journal of Biological Chemistry (1989), 264 (23), 13572-8CODEN: JBCHA3; ISSN:0021-9258.
Pharmacophore mapping of the ligand-binding domain of the β-adrenergic receptor has revealed specific mol. interactions which are important for agonist and antagonist binding to the receptor. Previous site-directed mutagenesis expts. have demonstrated that the binding of amine agonists and antagonists to the receptor involves an interaction between the amine group of the ligand and the carboxylate side chain of Asp113 in the 3rd hydrophobic domain of the receptor. Two serine residues, at positions 204 and 207 in the 5th hydrophobic domain of the β-adrenergic receptor, which are crit. for agonist binding and activation of the receptor have now been identified. These serine residues are conserved with G-protein-coupled receptors which bind catecholamine agonists, but not with receptors whose endogenous ligands do not have the catechol moiety. Removal of the OH side chain from either Ser204 or Ser207 by substitution of the serine residue with an alanine attenuates the activity of catecholamine agonists at the receptor. The effects of these mutations on agonist activity are mimicked selectively by the removal of the catechol OH moieties from the arom. ring of the agonist. The data suggest that the interaction of catecholamine agonists with the β-adrenergic receptor involves 2 H bonds, one between the OH side chain of Ser204 and the meta-OH group of the ligand and a second between the OH side chain of Ser207 and the para-OH group of the ligand.
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Kikkawa, H.; Kurose, H.; Isogaya, M.; Sato, Y.; Nagao, T. Differential Contribution of Two Serine Residues of Wild Type and Constitutively Active Beta2-Adrenoceptors to the Interaction with Beta2-Selective Agonists Br. J. Pharmacol. 1997, 121, 1059– 1064
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Differential contribution of two serine residues of wild type and constitutively active β2-adrenoceptors to the interaction with β2-selective agonists
Kikkawa, Hideo; Kurose, Hitoshi; Isogaya, Masafumi; Sato, Yoji; Nagao, Taku
British Journal of Pharmacology (1997), 121 (6), 1059-1064CODEN: BJPCBM; ISSN:0007-1188. (Stockton)
The authors have studied the difference in receptor binding activity between partial and full β2-adrenoceptor agonists and the abilities of the agonists to interact with Ser204 and Ser207 in the fifth transmembrane region of the β2-adrenoceptor, amino acid residues that are important for activation of the β2-adrenoceptor. In the binding study with [125I]-iodocyanopindolol, the Ki values of (±)-salbutamol, (±)-salmeterol, TA-2005 and (-)-isoprenaline for the β2-adrenoceptor expressed in COS-7 cell membranes were 3340, 21.0, 12.0 and 904 nM, resp. The β1/β2 selectively of these agonists was in the order of (±)-salmeterol (332-fold) > A-2005 (52.8) > (±)-salbutamol (6.8) > (-)-isoprenaline (1.1), and the β3-/β2-adrenoceptor selectivity of these agonists was in the order of TA-2005 (150-fold) > (±)-salmeterol (88.6) > (±)-salbutamol (10.4) > (-)-isoprenaline (3.2). The maximal activation of adenylyl cyclase by stimulation of the β1-, β2- and β3-adrenoceptors by TA-2005 was 32, 100 and 100% of that by (-)-isoprenaline, resp., indicating that TA-2005 is a full agonist at the β2- and β3-adrenoceptors and a partial agonist at the β1-adrenoceptor. (±)-Salbutamol and (±)-salmeterol were partial agonists at both β1- (8%) and 9% of (-)-isoprenaline and β2- (83% and 74% of (-)-isoprenaline) adrenoceptors. The affinities of full agonists, TA-2005 and (-)-isoprenaline, were markedly decreased by substitution of Ala for Ser204 (S204A) of the β2-adrenoceptor, whereas this substitution slightly reduced the affinities of partial agonists, (±)-salbutamol and (±)-salmeterol. Although the affinities of full agonists for the S207A-β2-adrenoceptor were decrease, those of partial agonists for the S207A-β2-adrenoceptor were essentially the same as for the wild type receptor. The constitutively active mutant (L266S, L272A) of the β2-adrenoceptor had an increased affinity for all four agonists. The affinities of full agonists were decreased by substitution of Ser204 of the constitutively active mutant, whereas the degree of decrease was smaller than that caused by the substitution of the wild type receptor. Although the affinities of (±)-salbutamol and (±)-salmeterol for the S207A-β2-adrenoceptor were essentially the same as those for the wild type β2-adrenoceptor, the affinities of (±)-salbutamol and (±)-salmeterol for the constitutively active β2-adrenoceptor were decreased by substitution of Ser207. These results suggest that Ser204 and Ser207 of the wild type and constitutively active β2-adrenoceptors differentially interacted with β2-selective agonists.
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Liapakis, G.; Ballesteros, J. A.; Papachristou, S.; Chan, W. C.; Chen, X.; Javitch, J. A. The Forgotten Serine. A Critical Role for Ser-2035.42 in Ligand Binding to and Activation of the Beta 2-Adrenergic Receptor J. Biol. Chem. 2000, 275, 37779– 37788
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The forgotten serine. A critical role for Ser-2035.42 in ligand binding to and activation of the β2-adrenergic receptor
Liapakis, George; Ballesteros, Juan A.; Papachristou, Stavros; Chan, Wai Chi; Chen, Xun; Javitch, Jonathan A.
Journal of Biological Chemistry (2000), 275 (48), 37779-37788CODEN: JBCHA3; ISSN:0021-9258. (American Society for Biochemistry and Molecular Biology)
Previous work in the β2-adrenergic receptor demonstrated crit. interactions between Ser-204 and Ser-207 in the fifth membrane-spanning segment and the meta-OH and para-OH, resp., of catecholamine agonists. Using the substituted cysteine accessibility method in the β2-adrenergic receptor, we have found that in addn. to Ser-204 and Ser-207, Ser-203 is also accessible on the surface of the binding-site crevice and is occluded by bound agonist. Mutation of Ser-203 to Ala, Val, or Cys reduced the binding affinity and adenylyl cyclase-activating potency of agonists contg. a meta-OH, whereas their affinities and potencies were largely preserved by mutation of Ser-203 to Thr, which maintained an OH at this position. Thus both Ser-203 and Ser-204 appear to interact with the meta-OH of catecholamines, perhaps through a bifurcated H bond. Furthermore, the removal of the OH at position 203 led to a significant loss of affinity of antagonists with nitrogen in their heterocyclic ring structure. The greatest effect was seen with pindolol, a partial agonist, suggesting that a H bond between the heterocyclic ring and Ser-203 may play a role in partial agonism. In contrast, the affinities of antagonists such as propranolol or alprenolol, which have cyclic structures without H-bonding capability, were unaltered after mutation of Ser-203.
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Sato, T.; Kobayashi, H.; Nagao, T.; Kurose, H. Ser203 as Well as Ser204 and Ser207 in Fifth Transmembrane Domain of the Human Beta2-Adrenoceptor Contributes to Agonist Binding and Receptor Activation Br. J. Pharmacol. 1999, 128, 272– 274
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Ser203 as well as Ser204 and Ser207 in fifth transmembrane domain of the human β2-adrenoceptor contributes to agonist binding and receptor activation
Sato, Takayuki; Kobayashi, Hiroyuki; Nagao, Taku; Kurose, Hitoshi
British Journal of Pharmacology (1999), 128 (2), 272-274CODEN: BJPCBM; ISSN:0007-1188. (Stockton Press)
We examd. the contribution of Ser203 of the human β2-adrenoceptor (β2-AR) to the interaction with isoprenaline. The affinity of (-)-isoprenaline was reduced by substitution of an alanine for Ser203, as well as for Ser204 and Ser207. An (-)-isoprenaline deriv. with only one hydroxyl group, at the meta-position, showed reduced affinity for wild-type β2-AR and S207A-β2-AR and even lower affinities for S203A-β2-AR and S204A-β2-AR. By contrast, an (-)-isoprenaline deriv. with only a para-hydroxyl group showed reduced affinity for wild-type β2-AR but the serine to alanine mutations did not cause further decreases. The EC50 value for cAMP generation in response to (-)-isoprenaline was increased, by about 120 fold, for each alanine-substituted β2-AR mutant. These results suggest that Ser203 of the human β2-AR is important for both ligand binding and receptor activation.
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Ambrosio, C.; Molinari, P.; Cotecchia, S.; Costa, T. Catechol-Binding Serines of Beta(2)-Adrenergic Receptors Control the Equilibrium between Active and Inactive Receptor States Mol. Pharmacol. 2000, 57, 198– 210
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Catechol-binding serines of β2-adrenergic receptors control the equilibrium between active and inactive receptor states
Ambrosio, Caterina; Molinari, Paola; Cotecchia, Susanna; Costa, Tommaso
Molecular Pharmacology (2000), 57 (1), 198-210CODEN: MOPMA3; ISSN:0026-895X. (American Society for Pharmacology and Experimental Therapeutics)
The binding free energy for the interaction between serines 204 and 207 of the fifth transmembrane helix of the β2-adrenergic receptor (β2-AR) and catecholic hydroxyl (OH) groups of adrenergic agonists was analyzed using double mutant cycles. Binding affinities for catecholic and noncatecholic agonists were measured in wild-type and mutant receptors, carrying alanine replacement of the two serines (S204A, S207A β2-AR), a constitutive activating mutation, or both. The free energy coupling between the losses of binding energy attributable to OH deletion from the ligand and from the receptor indicates a strong interaction (nonadditivity) as expected for a direct binding between the two sets of groups. However, the authors also measured a significant interaction between the deletion of OH groups from the receptor and the constitutive activating mutation. This suggests that a fraction of the decrease in agonist affinity caused by serine mutagenesis may involve a shift in the conformational equil. of the receptor toward the inactive state. Direct measurements using a transient transfection assay confirm this prediction. The constitutive activity of the (S204A, S207A) β2-AR mutant is 50 to 60% lower than that of the wild-type β2-AR. The authors conclude that S204 and S207 do not only provide a docking site for the agonist, but also control the equil. of the receptor between active (R*) and inactive (R) forms.
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Marcou, G.; Rognan, D. Optimizing Fragment and Scaffold Docking by Use of Molecular Interaction Fingerprints J. Chem. Inf. Model. 2007, 47, 195– 207
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Optimizing Fragment and Scaffold Docking by Use of Molecular Interaction Fingerprints
Marcou, Gilles; Rognan, Didier
Journal of Chemical Information and Modeling (2007), 47 (1), 195-207CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
Protein-ligand interaction fingerprints have been used to postprocess docking poses of three ligand data sets: a set of 40 low-mol.-wt. compds. from the Protein Data Bank, a collection of 40 scaffolds from pharmaceutically relevant protein ligands, and a database of 19 scaffolds extd. from true cdk2 inhibitors seeded in 2230 scaffold decoys. Four popular docking tools (FlexX, Glide, Gold, and Surflex) were used to generate poses for ligands of the three data sets. In all cases, scoring by the similarity of interaction fingerprints to a given ref. was statistically superior to conventional scoring functions in posing low-mol.-wt. fragments, predicting protein-bound scaffold coordinates according to the known binding mode of related ligands, and screening a scaffold library to enrich a hit list in true cdk2-targeted scaffolds.
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Verlinde, C. L.; Hol, W. G. Structure-Based Drug Design: Progress, Results and Challenges Structure 1994, 2, 577– 587
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Structure-based drug design: progress, results and challenges
Verlinde, Christophe L. M. J.; Hol, Wim G. J.
Structure (Cambridge, MA, United States) (1994), 2 (7), 577-87CODEN: STRUE6; ISSN:0969-2126.
A review with 85 refs. Protein structure-based drug design is rapidly gaining momentum. The new opportunities, developments and results in this field are almost unbelievable compared with the situation less than a decade ago.
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Jones, G.; Willett, P.; Glen, R. C.; Leach, A. R.; Taylor, R. Development and Validation of a Genetic Algorithm for Flexible Docking J. Mol. Biol. 1997, 267, 727– 748
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Development and validation of a genetic algorithm for flexible docking
Jones, Gareth; Willett, Peter; Glen, Robert C.; Leach, Andrew R.; Taylor, Robin
Journal of Molecular Biology (1997), 267 (3), 727-748CODEN: JMOBAK; ISSN:0022-2836. (Academic)
Prediction of small mol. binding modes to macromols. of known three-dimensional structure is a problem of paramount importance in rational drug design (the "docking" problem). We report the development and validation of the program GOLD (Genetic Optimization for Ligand Docking). GOLD is an automated ligand docking program that uses a genetic algorithm to explore the full range of ligand conformational flexibility with partial flexibility of the protein and satisfies the fundamental requirement that the ligand must displace loosely bound water on binding. Numerous enhancements and modifications have been applied to the original technique resulting in a substantial increase in the reliability and the applicability of the algorithm. The advanced algorithm has been tested on a dataset of 100 complexes extd. from the Brookhaven Protein Data Bank. When used to dock the ligand back into the binding site, GOLD achieved a 71% success rate in identifying the exptl. binding mode.
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Evers, A.; Klebe, G. Successful Virtual Screening for a Submicromolar Antagonist of the Neurokinin-1 Receptor Based on a Ligand-Supported Homology Model J. Med. Chem. 2004, 47, 5381– 5392
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Successful virtual screening for a submicromolar antagonist of the neurokinin-1 receptor based on a ligand-supported homology model
Evers, Andreas; Klebe, Gerhard
Journal of Medicinal Chemistry (2004), 47 (22), 5381-5392CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
The neurokinin-1 (NK1) receptor belongs to the family of G-protein-coupled receptors (GPCRs), which represents one of the most relevant target families in small-mol. drug design. In this paper, we describe a homol. modeling of the NK1 receptor based on the high-resoln. X-ray structure of rhodopsin and the successful virtual screening based on this protein model. The NK1 receptor model has been generated using our new MOBILE (modeling binding sites including ligand information explicitly) approach. Starting with preliminary homol. models, it generates improved models of the protein binding pocket together with bound ligands. Ligand information is used as an integral part in the homol. modeling process. For the construction of the NK1 receptor, antagonist CP-96345 was used to restrain the modeling. The quality of the obtained model was validated by probing its ability to accommodate addnl. known NK1 antagonists from structurally diverse classes. On the basis of the generated model and on the anal. of known NK1 antagonists, a pharmacophore model was deduced, which subsequently guided the 2D and 3D database search with UNITY. As a following step, the remaining hits were docked into the modeled binding pocket of the NK1 receptor. Finally, seven compds. were selected for biochem. testing, from which one showed affinity in the submicromolar range. Our results suggest that ligand-supported homol. models of GPCRs may be used as effective platforms for structure-based drug design.
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Barril, X.; Morley, S. D. Unveiling the Full Potential of Flexible Receptor Docking Using Multiple Crystallographic Structures J. Med. Chem. 2005, 48, 4432– 4443
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Unveiling the Full Potential of Flexible Receptor Docking Using Multiple Crystallographic Structures
Barril, Xavier; Morley, S. David
Journal of Medicinal Chemistry (2005), 48 (13), 4432-4443CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
One of the current challenges in docking studies is the inclusion of receptor flexibility. This is crucial because the binding sites of many therapeutic targets sample a wide range of conformational states, which has major consequences on mol. recognition. In this paper, we make use of very large sets of x-ray structures of cyclin dependent kinase 2 (CDK2) and heat shock protein 90 (HSP90) to assess the performance of flexible receptor docking in binding-mode prediction and virtual screening expts. Flexible receptor docking performs much better than rigid receptor docking in the former application. Regarding the latter, we observe a significant improvement in the prediction of binding affinities, but owing to an increase in the no. of false positives, this is not translated into better hit rates. A simple scoring scheme to correct this limitation is presented. More importantly, pitfalls inherent to flexible receptor docking have been identified and guidelines are presented to avoid them.
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Bissantz, C.; Bernard, P.; Hibert, M.; Rognan, D. Protein-Based Virtual Screening of Chemical Databases. Ii. Are Homology Models of G-Protein Coupled Receptors Suitable Targets? Proteins 2003, 50, 5– 25
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Katritch, V.; Rueda, M.; Abagyan, R. Ligand-Guided Receptor Optimization Methods Mol. Biol. 2012, 857, 189– 205
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Ligand-guided receptor optimization
Katritch Vsevolod; Rueda Manuel; Abagyan Ruben
Methods in molecular biology (Clifton, N.J.) (2012), 857 (), 189-205 ISSN:.
Receptor models generated by homology or even obtained by crystallography often have their binding pockets suboptimal for ligand docking and virtual screening applications due to insufficient accuracy or induced fit bias. Knowledge of previously discovered receptor ligands provides key information that can be used for improving docking and screening performance of the receptor. Here, we present a comprehensive ligand-guided receptor optimization (LiBERO) algorithm that exploits ligand information for selecting the best performing protein models from an ensemble. The energetically feasible protein conformers are generated through normal mode analysis and Monte Carlo conformational sampling. The algorithm allows iteration of the conformer generation and selection steps until convergence of a specially developed fitness function which quantifies the conformer's ability to select known ligands from decoys in a small-scale virtual screening test. Because of the requirement for a large number of computationally intensive docking calculations, the automated algorithm has been implemented to use Linux clusters allowing easy parallel scaling. Here, we will discuss the setup of LiBERO calculations, selection of parameters, and a range of possible uses of the algorithm which has already proven itself in several practical applications to binding pocket optimization and prospective virtual ligand screening.
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Tehan, B. G.; Bortolato, A.; Blaney, F. E.; Weir, M. P.; Mason, J. S. Unifying Family a Gpcr Theories of Activation Pharmacol. Ther. 2014, 143, 51– 60
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Unifying Family A GPCR Theories of Activation
Tehan, Benjamin G.; Bortolato, Andrea; Blaney, Frank E.; Weir, Malcolm P.; Mason, Jonathan S.
Pharmacology & Therapeutics (2014), 143 (1), 51-60CODEN: PHTHDT; ISSN:0163-7258. (Elsevier)
A review. Several new pairs of active and inactive GPCR structures have recently been solved enabling detailed structural insight into the activation process, not only of rhodopsin but now also of the β2 adrenergic, M2 muscarinic and adenosine A2A receptors. Combined with structural analyses they have enabled us to examine the different recent theories proposed for GPCR activation and show that they are all indeed parts of the same process, and are intrinsically related through their effect on the central hydrophobic core of GPCRs. This new unifying general process of activation is consistent with the identification of known constitutively active mutants and an in-depth conservational anal. of significant residues implicated in the process.
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Meng, X. Y.; Zhang, H. X.; Mezei, M.; Cui, M. Molecular Docking: A Powerful Approach for Structure-Based Drug Discovery Curr. Comput. Aided Drug Des. 2011, 7, 146– 157
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Molecular docking: A powerful approach for structure-based drug discovery
Meng, Xuan-Yu; Zhang, Hong-Xing; Mezei, Mihaly; Cui, Meng
Current Computer-Aided Drug Design (2011), 7 (2), 146-157CODEN: CCDDAS; ISSN:1573-4099. (Bentham Science Publishers Ltd.)
A review. Mol. docking has become an increasingly important tool for drug discovery. In this review, we present a brief introduction of the available mol. docking methods, and their development and applications in drug discovery. The relevant basic theories, including sampling algorithms and scoring functions, are summarized. The differences in and performance of available docking software are also discussed. Flexible receptor mol. docking approaches, esp. those including backbone flexibility in receptors, are a challenge for available docking methods. A recently developed Local Move Monte Carlo (LMMC) based approach is introduced as a potential soln. to flexible receptor docking problems. Three application examples of mol. docking approaches for drug discovery are provided.
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Rognan, D.; Desaphy, J. Molecular Interaction Fingerprints. Scaffold Hopping in Medicinal Chemistry; Wiley-VCH Verlag GmbH & Co. KGaA: Weinheim, 2013; pp 215– 230.
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Ring, A. M.; Manglik, A.; Kruse, A. C.; Enos, M. D.; Weis, W. I.; Garcia, K. C.; Kobilka, B. K. Adrenaline-Activated Structure of Beta2-Adrenoceptor Stabilized by an Engineered Nanobody Nature 2013, 502, 575– 579
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Andrews, S. P.; Brown, G. A.; Christopher, J. A. Structure-Based and Fragment-Based Gpcr Drug Discovery ChemMedChem 2014, 9, 256– 275
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Miller-Gallacher, J. L.; Nehme, R.; Warne, T.; Edwards, P. C.; Schertler, G. F.; Leslie, A. G.; Tate, C. G. The 2.1 a Resolution Structure of Cyanopindolol-Bound Beta1-Adrenoceptor Identifies an Intramembrane Na+ Ion That Stabilises the Ligand-Free Receptor PLoS One 2014, 9e92727
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Casella, I.; Ambrosio, C.; Gro, M. C.; Molinari, P.; Costa, T. Divergent Agonist Selectivity in Activating Beta1- and Beta2-Adrenoceptors for G-Protein and Arrestin Coupling Biochem. J. 2011, 438, 191– 202
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Drake, M. T.; Violin, J. D.; Whalen, E. J.; Wisler, J. W.; Shenoy, S. K.; Lefkowitz, R. J. Beta-Arrestin-Biased Agonism at the Beta2-Adrenergic Receptor J. Biol. Chem. 2008, 283, 5669– 5676
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Kahsai, A. W.; Xiao, K.; Rajagopal, S.; Ahn, S.; Shukla, A. K.; Sun, J.; Oas, T. G.; Lefkowitz, R. J. Multiple Ligand-Specific Conformations of the Beta2-Adrenergic Receptor Nat. Chem. Biol. 2011, 7, 692– 700
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Multiple ligand-specific conformations of the β2-adrenergic receptor
Kahsai, Alem W.; Xiao, Kunhong; Rajagopal, Sudarshan; Ahn, Seungkirl; Shukla, Arun K.; Sun, Jinpeng; Oas, Terrence G.; Lefkowitz, Robert J.
Nature Chemical Biology (2011), 7 (10), 692-700CODEN: NCBABT; ISSN:1552-4450. (Nature Publishing Group)
Seven-transmembrane receptors (7TMRs), also called G protein-coupled receptors (GPCRs), represent the largest class of drug targets, and they can signal through several distinct mechanisms including those mediated by G proteins and the multifunctional adaptor proteins β-arrestins. Moreover, several receptor ligands with differential efficacies toward these distinct signaling pathways have been identified. However, the structural basis and mechanism underlying this 'biased agonism' remains largely unknown. Here, we develop a quant. mass spectrometry strategy that measures specific reactivities of individual side chains to investigate dynamic conformational changes in the β2-adrenergic receptor occupied by nine functionally distinct ligands. Unexpectedly, only a minority of residues showed reactivity patterns consistent with classical agonism, whereas the majority showed distinct patterns of reactivity even between functionally similar ligands. These findings demonstrate, contrary to two-state models for receptor activity, that there is significant variability in receptor conformations induced by different ligands, which has significant implications for the design of new therapeutic agents.
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Kaya, A. I.; Onaran, H. O.; Ozcan, G.; Ambrosio, C.; Costa, T.; Balli, S.; Ugur, O. Cell Contact-Dependent Functional Selectivity of Beta2-Adrenergic Receptor Ligands in Stimulating Camp Accumulation and Extracellular Signal-Regulated Kinase Phosphorylation J. Biol. Chem. 2012, 287, 6362– 6374
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Kim, I. M.; Tilley, D. G.; Chen, J.; Salazar, N. C.; Whalen, E. J.; Violin, J. D.; Rockman, H. A. Beta-Blockers Alprenolol and Carvedilol Stimulate Beta-Arrestin-Mediated Egfr Transactivation Proc. Natl. Acad. Sci. U.S.A. 2008, 105, 14555– 14560
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Liu, J. J.; Horst, R.; Katritch, V.; Stevens, R. C.; Wuthrich, K. Biased Signaling Pathways in Beta2-Adrenergic Receptor Characterized by 19f-Nmr Science 2012, 335, 1106– 1110
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Rajagopal, S.; Ahn, S.; Rominger, D. H.; Gowen-MacDonald, W.; Lam, C. M.; Dewire, S. M.; Violin, J. D.; Lefkowitz, R. J. Quantifying Ligand Bias at Seven-Transmembrane Receptors Mol. Pharmacol. 2011, 80, 367– 377
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Quantifying ligand bias at seven-transmembrane receptors
Rajagopal, Sudarshan; Ahn, Seungkirl; Rominger, David H.; Gowen-MacDonald, William; Lam, Christopher M.; DeWire, Scott M.; Violin, Jonathan D.; Lefkowitz, Robert J.
Molecular Pharmacology (2011), 80 (3), 367-377CODEN: MOPMA3; ISSN:0026-895X. (American Society for Pharmacology and Experimental Therapeutics)
Seven transmembrane receptors (7TMRs), commonly referred to as G protein-coupled receptors, form a large part of the "druggable" genome. 7TMRs can signal through parallel pathways simultaneously, such as through heterotrimeric G proteins from different families, or, as more recently appreciated, through the multifunctional adapters, β-arrestins. Biased agonists, which signal with different efficacies to a receptor's multiple downstream pathways, are useful tools for deconvoluting this signaling complexity. These compds. may also be of therapeutic use because they have distinct functional and therapeutic profiles from "balanced agonists.". Although some methods have been proposed to identify biased ligands, no comparison of these methods applied to the same set of data has been performed. Therefore, at this time, there are no generally accepted methods to quantify the relative bias of different ligands, making studies of biased signaling difficult. Here, we use complementary computational approaches for the quantification of ligand bias and demonstrate their application to two well known drug targets, the β2 adrenergic and angiotensin II type 1A receptors. The strategy outlined here allows a quantification of ligand bias and the identification of weakly biased compds. This general method should aid in deciphering complex signaling pathways and may be useful for the development of novel biased therapeutic ligands as drugs.
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Baker, J. G. The Selectivity of Beta-Adrenoceptor Agonists at Human Beta1-, Beta2- and Beta3-Adrenoceptors Br. J. Pharmacol. 2010, 160, 1048– 1061
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The selectivity of β-adrenoceptor agonists at human β1-, β2- and β3-adrenoceptors
Baker, Jillian G.
British Journal of Pharmacology (2010), 160 (5), 1048-1061CODEN: BJPCBM; ISSN:1476-5381. (Wiley-Blackwell)
There are two important properties of receptor-ligand interactions: affinity (the ability of the ligand to bind to the receptor) and efficacy (the ability of the receptor-ligand complex to induce a response). Ligands are classified as agonists or antagonists depending on whether or not they have efficacy. In theory, it is possible to develop selective agonists based on selective affinity, selective intrinsic efficacy or both. This study examd. the affinity and intrinsic efficacy of β-adrenoceptor agonists at the three human β-adrenoceptors to det. whether the current agonists are subtype selective because of affinity or intrinsic efficacy. Stable clonal CHO-K1 cell lines, transfected with either the human β1, β2 or β3-adrenoceptor, were used, and whole-cell [3H]-CGP 12177 radioligand binding and [3H]-cAMP accumulation were measured. Several agonists were highly subtype selective because of selective affinity (e.g. salmeterol and formoterol, for the β2-adrenoceptor over the β1 or β3), while others (e.g. isoprenaline) had little affinity-selectivity. However, the intrinsic efficacy of salmeterol, formoterol and isoprenaline was similar across all three receptor subtypes. Other ligands (e.g. denopamine for β1; clenbuterol, AZ 40140d, salbutamol for β2) were found to have subtype-selective intrinsic efficacy. Several ligands appeared to activate two agonist conformations of the β1- and β3-adrenoceptors. There are agonists with subtype selectivity based upon both selective affinity and selective intrinsic efficacy. Therefore, there is scope to develop better selective agonists based upon both selective affinity and selective intrinsic efficacy.
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Maack, C.; Bohm, M.; Vlaskin, L.; Dabew, E.; Lorenz, K.; Schafers, H. J.; Lohse, M. J.; Engelhardt, S. Partial Agonist Activity of Bucindolol Is Dependent on the Activation State of the Human Beta1-Adrenergic Receptor Circulation 2003, 108, 348– 353
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Warne, T.; Tate, C. G. The Importance of Interactions with Helix 5 in Determining the Efficacy of Beta-Adrenoceptor Ligands Biochem. Soc. Trans. 2013, 41, 159– 165
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The importance of interactions with helix 5 in determining the efficacy of β-adrenoceptor ligands
Warne, Tony; Tate, Christopher G.
Biochemical Society Transactions (2013), 41 (1), 159-165CODEN: BCSTB5; ISSN:0300-5127. (Portland Press Ltd.)
A review. Structures of the inactive state of the thermostabilized β1-adrenoceptor have been detd. bound to eight different ligands, including full agonists, partial agonists, inverse agonists and biased agonists. Comparison of the structures shows distinct differences within the binding pocket that correlate with the pharmacol. properties of the ligands. These data suggest that full agonists stabilize a structure with a contracted binding pocket and a rotamer change of serine (5.46) compared with when antagonists are bound. Inverse agonists may prevent both of these occurrences, whereas partial agonists stabilize a contraction of the binding pocket but not the rotamer change of serine (5.46). It is likely that subtle changes in the interactions between transmembrane helix 5 (H5) and H3/H4 on agonist binding promote the formation of the activated state.
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Baker, J. G. The Selectivity of Beta-Adrenoceptor Antagonists at the Human Beta1, Beta2 and Beta3 Adrenoceptors Br. J. Pharmacol. 2005, 144, 317– 322
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The selectivity of β-adrenoceptor antagonists at the human β1, β2 and β3 adrenoceptors
Baker, Jillian G.
British Journal of Pharmacology (2005), 144 (3), 317-322CODEN: BJPCBM; ISSN:0007-1188. (Nature Publishing Group)
β-Adrenoceptor antagonists (β-blockers) are one of the most widely used classes of drugs in cardiovascular medicine (hypertension, ischemic heart disease and increasingly in heart failure) as well as in the management of anxiety, migraine and glaucoma. Where known, the mode of action in cardiovascular disease is from antagonism of endogenous catecholamine responses in the heart (mainly at β1-adrenoceptors), while the worrisome side effects of bronchospasm result from airway β2-adrenoceptor blockade. The aim of this study was to det. the selectivity of β-antagonists for the human β-adrenoceptor subtypes. 3H-CGP 12177 whole cell-binding studies were undertaken in CHO cell lines stably expressing either the human β1-, β2- or the β3-adrenoceptor to det. the affinity of ligands for each receptor subtype in the same cell background. In this study, the selectivity of well-known subtype-selective ligands was clearly demonstrated: thus, the selective β1 antagonist CGP 20712A was 501-fold selective over β2 and 4169-fold selective over β3; the β2-selective antagonist ICI 118551 was 550- and 661-fold selective over β1 and β3, resp., and the selective β3 compd. CL 316243 was 10-fold selective over β2 and more than 129-fold selective over β1. Those β2-adrenoceptor agonists used clin. for the treatment of asthma and COPD were β2 selective: 29-, 61- and 2818-fold for salbutamol, terbutaline and salmeterol over β1, resp. There was little difference in the affinity of these ligands between β1 and β3 adrenoceptors. The clin. used β-antagonists studied ranged from bisoprolol (14-fold β1-selective) to timolol (26-fold β2-selective). However, the majority showed little selectivity for the β1- over the β2-adrenoceptor, with many actually being more β2-selective. This study shows that the β1/β2 selectivity of most clin. used β-blockers is poor in intact cells, and that some compds. that are traditionally classed as β1-selective' actually have higher affinity for the β2-adrenoceptor. There is therefore considerable potential for developing more selective β-antagonists for clin. use and thereby reducing the side-effect profile of β-blockers.
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Selvam, B.; Wereszczynski, J.; Tikhonova, I. G. Comparison of Dynamics of Extracellular Accesses to the Beta(1) and Beta(2) Adrenoceptors Binding Sites Uncovers the Potential of Kinetic Basis of Antagonist Selectivity Chem. Biol. Drug Des. 2012, 80, 215– 226
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Comparison of dynamics of extracellular accesses to the β1 and β2 adrenoceptors binding sites uncovers the potential of kinetic basis of antagonist selectivity
Selvam, Balaji; Wereszczynski, Jeff; Tikhonova, Irina G.
Chemical Biology & Drug Design (2012), 80 (2), 215-226CODEN: CBDDAL; ISSN:1747-0277. (Wiley-Blackwell)
From the mol. mechanism of antagonist unbinding in the β1 and β2 adrenoceptors investigated by steered mol. dynamics, we attempt to provide further possibilities of ligand subtype and subspecies selectivity. We have simulated unbinding of β1-selective Esmolol and β2-selective ICI-118551 from both receptors to the extracellular environment and found distinct mol. features of unbinding. By calcg. work profiles, we show different preference in antagonist unbinding pathways between the receptors, in particular, perpendicular to the membrane pathway is favorable in the β1 adrenoceptor, whereas the lateral pathway involving helixes 5, 6 and 7 is preferable in the β2 adrenoceptor. The estd. free energy change of unbinding based on the preferable pathway correlates with the exptl. ligand selectivity. We then show that the non-conserved K347 (6.58) appears to facilitate in guiding Esmolol to the extracellular surface via hydrogen bonds in the β1 adrenoceptor. In contrast, hydrophobic and arom. interactions dominate in driving ICI-118551 through the easiest pathway in the β2 adrenoceptor. We show how our study can stimulate design of selective antagonists and discuss other possible mol. reasons of ligand selectivity, involving sequential binding of agonists and glycosylation of the receptor extracellular surface.
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Sheftel, S.; Muratore, K. E.; Black, M.; Costanzi, S. Graph Analysis of β₂ Adrenergic Receptor Structures: A “Social Network” of GPCR Residues In Silico Pharmacol. 2013, DOI: 10.1186/2193-9616-1-16
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91
Sato, M.; Hirokawa, T. Extended Template-Based Modeling and Evaluation Method Using Consensus of Binding Mode of Gpcrs for Virtual Screening J. Chem. Inf. Model. 2014, 54, 3153– 3161
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Extended Template-Based Modeling and Evaluation Method Using Consensus of Binding Mode of GPCRs for Virtual Screening
Sato, Miwa; Hirokawa, Takatsugu
Journal of Chemical Information and Modeling (2014), 54 (11), 3153-3161CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
G-protein-coupled receptors (GPCRs) are a pharmaceutically important protein family because they mediate numerous physiol. functions. The crystal structures of several GPCR subtypes have been detd. recently, encouraging efforts to apply structure-based virtual screening (SBVS) along with ligand-based virtual screening (LBVS) to improve the hit rate of active ligands from large chem. libraries. Three-dimensional models are also necessary for GPCR targets whose structures are unknown. Current challenges include the selection of structural templates from available structurally known GPCRs to use for accurate modeling and understanding the diversity of sites recognizing distinct ligands. We have developed and validated an extended template-based modeling and evaluation method for SBVS. Models were generated using a fragmental template procedure in addn. to typical template-based modeling methods. The reliability of the models was evaluated using a virtual screening test with known active ligands and decoys and the consensus of the binding mode using the protein-ligand interaction fingerprint (PLIF) derived from the results of docking simulations. This novel workflow was applied to three targets with known structures (human dopamine receptor 3, human histamine H1 receptor, and human delta opioid receptor) and to a target with an unknown structure (human serotonin 2A receptor). In each case, model structures having high ligand selectivity with consensus binding mode were generated.
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Deng, Z.; Chuaqui, C.; Singh, J. Structural Interaction Fingerprint (Sift): A Novel Method for Analyzing Three-Dimensional Protein-Ligand Binding Interactions J. Med. Chem. 2004, 47, 337– 344
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Structural Interaction Fingerprint (SIFt): A Novel Method for Analyzing Three-Dimensional Protein-Ligand Binding Interactions
Deng, Zhan; Chuaqui, Claudio; Singh, Juswinder
Journal of Medicinal Chemistry (2004), 47 (2), 337-344CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
Representing and understanding the three-dimensional (3D) structural information of protein-ligand complexes is a crit. step in the rational drug discovery process. Traditional anal. methods are proving inadequate and inefficient in dealing with the massive amt. of structural information being generated from x-ray crystallog., NMR, and in silico approaches such as structure-based docking expts. Here, we present SIFt (structural interaction fingerprint), a novel method for representing and analyzing 3D protein-ligand binding interactions. Key to this approach is the generation of an interaction fingerprint that translates 3D structural binding information from a protein-ligand complex into a one-dimensional binary string. Each fingerprint represents the "structural interaction profile" of the complex that can be used to organize, analyze, and visualize the rich amt. of information encoded in ligand-receptor complexes and also to assist database mining. We have applied SIFt to tackle three common tasks in structure-based drug design. The first involved the anal. and organization of a typical set of results generated from a docking study. Using SIFt, docking poses with similar binding modes were identified, clustered, and subsequently compared with conventional scoring function information. A second application of SIFt was to analyze ∼90 known x-ray crystal structures of protein kinase-inhibitor complexes obtained from the Protein Databank. Using SIFt, we were able to organize the structures and reveal striking similarities and diversity between their small mol. binding interactions. Finally, we have shown how SIFt can be used as an effective mol. filter during the virtual chem. library screening process to select mols. with desirable binding mode(s) and/or desirable interaction patterns with the protein target. In summary, SIFt shows promise to fully leverage the wealth of information being generated in rational drug design.
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Desaphy, J.; Raimbaud, E.; Ducrot, P.; Rognan, D. Encoding Protein-Ligand Interaction Patterns in Fingerprints and Graphs J. Chem. Inf. Model. 2013, 53, 623– 637
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Encoding Protein-Ligand Interaction Patterns in Fingerprints and Graphs
Desaphy, Jeremy; Raimbaud, Eric; Ducrot, Pierre; Rognan, Didier
Journal of Chemical Information and Modeling (2013), 53 (3), 623-637CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
We herewith present a novel and universal method to convert protein-ligand coordinates into a simple fingerprint of 210 integers registering the corresponding mol. interaction pattern. Each interaction (hydrophobic, arom., hydrogen bond, ionic bond, metal complexation) is detected on the fly and phys. described by a pseudoatom centered either on the interacting ligand atom, the interacting protein atom, or the geometric center of both interacting atoms. Counting all possible triplets of interaction pseudoatoms within six distance ranges, and pruning the full integer vector to keep the most frequent triplets enables the definition of a simple (210 integers) and coordinate frame-invariant interaction pattern descriptor (TIFP) that can be applied to compare any pair of protein-ligand complexes. TIFP fingerprints have been calcd. for ca. 10 000 druggable protein-ligand complexes therefore enabling a wide comparison of relationships between interaction pattern similarity and ligand or binding site pairwise similarity. We notably show that interaction pattern similarity strongly depends on binding site similarity. In addn. to the TIFP fingerprint which registers intermol. interactions between a ligand and its target protein, we developed two tools (Ishape, Grim) to align protein-ligand complexes from their interaction patterns. Ishape is based on the overlap of interaction pseudoatoms using a smooth Gaussian function, whereas Grim utilizes a std. clique detection algorithm to match interaction pattern graphs. Both tools are complementary and enable protein-ligand complex alignments capitalizing on both global and local pattern similarities. The new fingerprint and companion alignment tools have been successfully used in three scenarios: (i) interaction-biased alignment of protein-ligand complexes, (ii) postprocessing docking poses according to known interaction patterns for a particular target, and (iii) virtual screening for bioisosteric scaffolds sharing similar interaction patterns.
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Venhorst, J.; Nunez, S.; Terpstra, J. W.; Kruse, C. G. Assessment of Scaffold Hopping Efficiency by Use of Molecular Interaction Fingerprints J. Med. Chem. 2008, 51, 3222– 3229
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Assessment of scaffold hopping efficiency by use of molecular interaction fingerprints
Venhorst, Jennifer; Nunez, Sara; Terpstra, Jan Willem; Kruse, Chris G.
Journal of Medicinal Chemistry (2008), 51 (11), 3222-3229CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
A novel scoring algorithm based on mol. interaction fingerprints (IFPs) was comparatively evaluated in its scaffold hopping efficiency against four virtual screening stds. (GlideXP, Gold, ROCS, and a Bayesian classifier). Decoy databases for the two targets under examn., adenosine deaminase and retinoid X receptor alpha, were obtained from the Directory of Useful Decoys and were further enriched with approx. 5% of active ligands. Structure and ligand-based methods were used to generate the ligand poses, and a Tanimoto metric was chosen for the calcn. of the similarity interaction fingerprint between the ref. ligand and the screening database. Database enrichments were found to strongly depend on the pose generator algorithm. In spite of these dependencies, enrichments using mol. IFPs were comparable to those obtained with GlideXP, Gold, ROCS, and the Bayesian classifier. More interestingly, the mol. IFP scoring algorithm outperformed these methods at scaffold hopping enrichment, regardless of the pose generator algorithm.
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Jansen, C.; Wang, H.; Kooistra, A. J.; de Graaf, C.; Orrling, K. M.; Tenor, H.; Seebeck, T.; Bailey, D.; de Esch, I. J.; Ke, H.; Leurs, R. Discovery of Novel Trypanosoma Brucei Phosphodiesterase B1 Inhibitors by Virtual Screening against the Unliganded Tbrpdeb1 Crystal Structure J. Med. Chem. 2013, 56, 2087– 2096
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Discovery of Novel Trypanosoma brucei Phosphodiesterase B1 Inhibitors by Virtual Screening against the Unliganded TbrPDEB1 Crystal Structure
Jansen, Chimed; Wang, Huanchen; Kooistra, Albert J.; de Graaf, Chris; Orrling, Kristina M.; Tenor, Hermann; Seebeck, Thomas; Bailey, David; de Esch, Iwan J. P.; Ke, Hengming; Leurs, Rob
Journal of Medicinal Chemistry (2013), 56 (5), 2087-2096CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
Trypanosoma brucei cyclic nucleotide phosphodiesterase B1 (TbrPDEB1) and TbrPDEB2 have recently been validated as new therapeutic targets for human African trypanosomiasis by both genetic and pharmacol. means. In this study the crystal structure of the catalytic domain of the unliganded TbrPDEB1 and its use for the in silico screening for new TbrPDEB1 inhibitors with novel scaffolds are reported. The TbrPDEB1 crystal structure shows the characteristic folds of human PDE enzymes but also contains the parasite-specific P-pocket found in the structures of Leishmania major PDEB1 and Trypanosoma cruzi PDEC. The unliganded TbrPDEB1 X-ray structure was subjected to a structure-based in silico screening approach that combines mol. docking simulations with a protein-ligand interaction fingerprint (IFP) scoring method. This approach identified six novel TbrPDEB1 inhibitors with IC50 values of 10-80 μM, which may be further optimized as potential selective TbrPDEB inhibitors.
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Mpamhanga, C. P.; Spinks, D.; Tulloch, L. B.; Shanks, E. J.; Robinson, D. A.; Collie, I. T.; Fairlamb, A. H.; Wyatt, P. G.; Frearson, J. A.; Hunter, W. N.; Gilbert, I. H.; Brenk, R. One Scaffold, Three Binding Modes: Novel and Selective Pteridine Reductase 1 Inhibitors Derived from Fragment Hits Discovered by Virtual Screening J. Med. Chem. 2009, 52, 4454– 4465
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One Scaffold, Three Binding Modes: Novel and Selective Pteridine Reductase 1 Inhibitors Derived from Fragment Hits Discovered by Virtual Screening
Mpamhanga, Chidochangu P.; Spinks, Daniel; Tulloch, Lindsay B.; Shanks, Emma J.; Robinson, David A.; Collie, Iain T.; Fairlamb, Alan H.; Wyatt, Paul G.; Frearson, Julie A.; Hunter, William N.; Gilbert, Ian H.; Brenk, Ruth
Journal of Medicinal Chemistry (2009), 52 (14), 4454-4465CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
The enzyme pteridine reductase 1 (PTR1) is a potential target for new compds. to treat human African trypanosomiasis. A virtual screening campaign for fragments inhibiting PTR1 was carried out. Two novel chem. series were identified contg. aminobenzothiazole and aminobenzimidazole scaffolds, resp. One of the hits (2-amino-5-chlorobenzimidazole) was subjected to crystal structure anal. and a high resoln. crystal structure in complex with PTR1 was obtained, confirming the predicted binding mode. However, the crystal structures of two analogs (2-aminobenzimidazole and 1-(3,4-dichlorobenzyl)-2-aminobenzimidazole) in complex with PTR1 revealed two alternative binding modes. In these complexes, previously unobserved protein movements and water-mediated protein-ligand contacts occurred, which prohibited a correct prediction of the binding modes. On the basis of the alternative binding mode of 1-(3,4-dichlorobenzyl)-2-aminobenzimidazole, derivs. were designed and selective PTR1 inhibitors with low nanomolar potency and favorable physicochem. properties were obtained.
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Richter, L.; de Graaf, C.; Sieghart, W.; Varagic, Z.; Morzinger, M.; de Esch, I. J.; Ecker, G. F.; Ernst, M. Diazepam-Bound Gabaa Receptor Models Identify New Benzodiazepine Binding-Site Ligands Nat. Chem. Biol. 2012, 8, 455– 464
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Diazepam-bound GABAA receptor models identify new benzodiazepine binding-site ligands
Richter, Lars; de Graaf, Chris; Sieghart, Werner; Varagic, Zdravko; Moerzinger, Martina; de Esch, Iwan J. P.; Ecker, Gerhard F.; Ernst, Margot
Nature Chemical Biology (2012), 8 (5), 455-464CODEN: NCBABT; ISSN:1552-4450. (Nature Publishing Group)
Benzodiazepines exert their anxiolytic, anticonvulsant, muscle-relaxant and sedative-hypnotic properties by allosterically enhancing the action of GABA at GABAA receptors via their benzodiazepine-binding site. Although these drugs have been used clin. since 1960, the mol. basis of this interaction is still not known. By using multiple homol. models and an unbiased docking protocol, we identified a binding hypothesis for the diazepam-bound structure of the benzodiazepine site, which was confirmed by exptl. evidence. Moreover, two independent virtual screening approaches based on this structure identified known benzodiazepine-site ligands from different structural classes and predicted potential new ligands for this site. Receptor-binding assays and electrophysiol. studies on recombinant receptors confirmed these predictions and thus identified new chemotypes for the benzodiazepine-binding site. Our results support the validity of the diazepam-bound structure of the benzodiazepine-binding pocket, demonstrate its suitability for drug discovery and pave the way for structure-based drug design.
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Daval, S. B.; Kellenberger, E.; Bonnet, D.; Utard, V.; Galzi, J. L.; Ilien, B. Exploration of the Orthosteric/Allosteric Interface in Human M1Muscarinic Receptors by Bitopic Fluorescent Ligands Mol. Pharmacol. 2013, 84, 71– 85
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Istyastono, E. P.; Kooistra, A. J.; Vischer, H. F.; Kuijer, M.; Roumen, L.; Nijmeijer, S.; Smits, R. A.; de Esch, I. J.; Leurs, R.; de Graaf, C. Structure-based virtual screening for fragment- like ligands of the G protein-coupled histamine H₄ receptor Med. Chem Commun. 2015, DOI: 10.1039/C5MD00022J
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100
Weis, W. I.; Kobilka, B. K. Structural Insights into G-Protein-Coupled Receptor Activation Curr. Opin. Struct. Biol. 2008, 18, 734– 740
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Structural insights into G-protein-coupled receptor activation
Weis, William I.; Kobilka, Brian K.
Current Opinion in Structural Biology (2008), 18 (6), 734-740CODEN: COSBEF; ISSN:0959-440X. (Elsevier B.V.)
A review. G-protein-coupled receptors (GPCRs) are the largest family of eukaryotic plasma membrane receptors, and are responsible for the majority of cellular responses to external signals. GPCRs share a common architecture comprising seven transmembrane (TM) helixes. The binding of an activating ligand enables the receptor to catalyze the exchange of GTP for GDP in a heterotrimeric G protein. GPCRs are in conformational equil. between inactive and activating states. Crystallog. and spectroscopic studies of the visual pigment, rhodopsin, and 2 β-adrenergic receptors have defined some of the conformational changes assocd. with activation.
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Petrongolo, C.; Macchia, B.; Macchia, F.; Martinelli, A. Molecular Orbital Studies on the Mechanism of Drug-Receptor Interaction. 2. Beta-Adrenergic Drugs. An Approach to Explain the Role of the Aromatic Moiety J. Med. Chem. 1977, 20, 1645– 1653
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102
Swaminath, G.; Xiang, Y.; Lee, T. W.; Steenhuis, J.; Parnot, C.; Kobilka, B. K. Sequential Binding of Agonists to the Beta2 Adrenoceptor. Kinetic Evidence for Intermediate Conformational States J. Biol. Chem. 2004, 279, 686– 691
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There is no corresponding record for this reference.
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Strader, C. D.; Candelore, M. R.; Hill, W. S.; Dixon, R. A.; Sigal, I. S. A Single Amino Acid Substitution in the Beta-Adrenergic Receptor Promotes Partial Agonist Activity from Antagonists J. Biol. Chem. 1989, 264, 16470– 16477
Google Scholar
There is no corresponding record for this reference.
104
Korb, O.; Stutzle, T.; Exner, T. E. Empirical Scoring Functions for Advanced Protein-Ligand Docking with Plants J. Chem. Inf. Model. 2009, 49, 84– 96
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Empirical Scoring Functions for Advanced Protein-Ligand Docking with PLANTS
Korb, Oliver; Stuetzle, Thomas; Exner, Thomas E.
Journal of Chemical Information and Modeling (2009), 49 (1), 84-96CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
In this paper we present two empirical scoring functions, PLANTSCHEMPLP and PLANTSPLP, designed for our docking algorithm PLANTS (Protein-Ligand ANT System), which is based on ant colony optimization (ACO). They are related, regarding their functional form, to parts of already published scoring functions and force fields. The parametrization procedure described here was able to identify several parameter settings showing an excellent performance for the task of pose prediction on two test sets comprising 298 complexes in total. Up to 87% of the complexes of the Astex diverse set and 77% of the CCDC/Astex clean listnc (noncovalently bound complexes of the clean list) could be reproduced with root-mean-square deviations of less than 2 Å with respect to the exptl. detd. structures. A comparison with the state-of-the-art docking tool GOLD clearly shows that this is, esp. for the druglike Astex diverse set, an improvement in pose prediction performance. Addnl., optimized parameter settings for the search algorithm were identified, which can be used to balance pose prediction reliability and search speed.
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Kooistra, A. J.; Vischer, H. F.; McNaught-Flores, D. A.; De Esch, I. J. P.; Leurs, R.; de Graaf, C. Structure-Based Virtual Screening for Gpcr Ligands with a Specific Functional Effect, unpublished, 2014.
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de Graaf, C.; Foata, N.; Engkvist, O.; Rognan, D. Molecular Modeling of the Second Extracellular Loop of G-Protein Coupled Receptors and Its Implication on Structure-Based Virtual Screening Proteins 2008, 71, 599– 620
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Rajagopal, S.; Rajagopal, K.; Lefkowitz, R. J. Teaching Old Receptors New Tricks: Biasing Seven-Transmembrane Receptors Nat. Rev. Drug Discovery 2010, 9, 373– 386
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Teaching old receptors new tricks: biasing seven-transmembrane receptors
Rajagopal, Sudarshan; Rajagopal, Keshava; Lefkowitz, Robert J.
Nature Reviews Drug Discovery (2010), 9 (5), 373-386CODEN: NRDDAG; ISSN:1474-1776. (Nature Publishing Group)
A review. Seven-transmembrane receptors (7TMRs; also known as G protein-coupled receptors) are the largest class of receptors in the human genome and are common targets for therapeutics. Originally identified as mediators of 7TMR desensitization, β-arrestins (arrestin 2 and arrestin 3) are now recognized as true adaptor proteins that transduce signals to multiple effector pathways. Signaling that is mediated by β-arrestins has distinct biochem. and functional consequences from those mediated by G proteins, and several biased ligands and receptors have been identified that preferentially signal through either G protein- or β-arrestin-mediated pathways. These ligands are not only useful tools for investigating the biochem. of 7TMR signaling, but they also have the potential to be developed into new classes of therapeutics.
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van der Westhuizen, E. T.; Breton, B.; Christopoulos, A.; Bouvier, M. Quantification of Ligand Bias for Clinically Relevant Beta2-Adrenergic Receptor Ligands: Implications for Drug Taxonomy Mol. Pharmacol. 2014, 85, 492– 509
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Reiner, S.; Ambrosio, M.; Hoffmann, C.; Lohse, M. J. Differential Signaling of the Endogenous Agonists at the Beta2-Adrenergic Receptor J. Biol. Chem. 2010, 285, 36188– 36198
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Copik, A. J.; Baldys, A.; Nguyen, K.; Sahdeo, S.; Ho, H.; Kosaka, A.; Dietrich, P. J.; Fitch, B.; Raymond, J. R.; Ford, A. P.; Button, D.; Milla, M. E. Isoproterenol Acts as a Biased Agonist of the Alpha-1a-Adrenoceptor That Selectively Activates the Mapk/Erk Pathway PLoS One 2015, 10e0115701
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Nijmeijer, S.; Vischer, H. F.; Sirci, F.; Schultes, S.; Engelhardt, H.; de Graaf, C.; Rosethorne, E. M.; Charlton, S. J.; Leurs, R. Detailed Analysis of Biased Histamine H(4) Receptor Signalling by Jnj 7777120 Analogues Br. J. Pharmacol. 2013, 170, 78– 88
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Detailed analysis of biased histamine H4 receptor signalling by JNJ 7777120 analogues
Nijmeijer, S.; Vischer, H. F.; Sirci, F.; Schultes, S.; Engelhardt, H.; de Graaf, C.; Rosethorne, E. M.; Charlton, S. J.; Leurs, R.
British Journal of Pharmacology (2013), 170 (1), 78-88CODEN: BJPCBM; ISSN:1476-5381. (Wiley-Blackwell)
Background and Purpose The histamine H4 receptor, originally thought to signal merely through Gαi proteins, has recently been shown to also recruit and signal via β-arrestin2. Following the discovery that the ref. antagonist indolecarboxamide JNJ 7777120 appears to be a partial agonist in β-arrestin2 recruitment, we have identified addnl. biased hH4R ligands that preferentially couple to Gαi or β-arrestin2 proteins. In this study, we explored ligand and receptor regions that are important for biased hH4R signalling. Exptl. Approach We evaluated a series of 48 indolecarboxamides with subtle structural differences for their ability to induce hH4R-mediated Gαi protein signalling or β-arrestin2 recruitment. Subsequently, a Fingerprints for Ligands and Proteins three-dimensional quant. structure-activity relationship anal. correlated intrinsic activity values with structural ligand requirements. Moreover, a hH4R homol. model was used to identify receptor regions important for biased hH4R signalling. Key Results One indolecarboxamide (75) with a nitro substituent on position R7 of the arom. ring displayed an equal preference for the Gαi and β-arrestin2 pathway and was classified as unbiased hH4R ligand. The other 47 indolecarboxamides were β-arrestin2-biased agonists. Intrinsic activities of the unbiased as well as β-arrestin2-biased indolecarboxamides to induce β-arrestin2 recruitment could be correlated with different ligand features and hH4R regions. Conclusion and Implications Small structural modifications resulted in diverse intrinsic activities for unbiased (75) and β-arrestin2-biased indolecarboxamides. Anal. of ligand and receptor features revealed efficacy hotspots responsible for biased-β-arrestin2 recruitment. This knowledge is useful for the design of hH4R ligands with biased intrinsic activities and aids our understanding of the mechanism of H4R activation.
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Nijmeijer, S.; Vischer, H. F.; Rosethorne, E. M.; Charlton, S. J.; Leurs, R. Analysis of Multiple Histamine H(4) Receptor Compound Classes Uncovers Galphai Protein- and Beta-Arrestin2-Biased Ligands Mol. Pharmacol. 2012, 82, 1174– 1182
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Galandrin, S.; Bouvier, M. Distinct Signaling Profiles of Beta1 and Beta2 Adrenergic Receptor Ligands toward Adenylyl Cyclase and Mitogen-Activated Protein Kinase Reveals the Pluridimensionality of Efficacy Mol. Pharmacol. 2006, 70, 1575– 1584
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Surgand, J. S.; Rodrigo, J.; Kellenberger, E.; Rognan, D. A Chemogenomic Analysis of the Transmembrane Binding Cavity of Human G-Protein-Coupled Receptors Proteins 2006, 62, 509– 538
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A chemogenomic analysis of the transmembrane binding cavity of human G-protein-coupled receptors
Surgand Jean-Sebastien; Rodrigo Jordi; Kellenberger Esther; Rognan Didier
Proteins (2006), 62 (2), 509-38 ISSN:.
The amino acid sequences of 369 human nonolfactory G-protein-coupled receptors (GPCRs) have been aligned at the seven transmembrane domain (TM) and used to extract the nature of 30 critical residues supposed--from the X-ray structure of bovine rhodopsin bound to retinal--to line the TM binding cavity of ground-state receptors. Interestingly, the clustering of human GPCRs from these 30 residues mirrors the recently described phylogenetic tree of full-sequence human GPCRs (Fredriksson et al., Mol Pharmacol 2003;63:1256-1272) with few exceptions. A TM cavity could be found for all investigated GPCRs with physicochemical properties matching that of their cognate ligands. The current approach allows a very fast comparison of most human GPCRs from the focused perspective of the predicted TM cavity and permits to easily detect key residues that drive ligand selectivity or promiscuity.
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Gregory, K. J.; Sexton, P. M.; Tobin, A. B.; Christopoulos, A. Stimulus Bias Provides Evidence for Conformational Constraints in the Structure of a G Protein-Coupled Receptor J. Biol. Chem. 2012, 287, 37066– 37077
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Audet, M.; Bouvier, M. Insights into Signaling from the Beta2-Adrenergic Receptor Structure Nat. Chem. Biol. 2008, 4, 397– 403
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Insights into signaling from the β2-adrenergic receptor structure
Audet, Martin; Bouvier, Michel
Nature Chemical Biology (2008), 4 (7), 397-403CODEN: NCBABT; ISSN:1552-4450. (Nature Publishing Group)
A review. With more than 800 members, the G protein-coupled receptor family constitutes the largest group of membrane proteins involved in signal transduction. Until the end of last year, high-resoln. three-dimensional structures were available for only one of them-the light receptor rhodopsin. Recently the structure of the β2-adrenergic receptor has been obtained, and it revealed interesting differences with the structure of rhodopsin. Analyses of these differences raise important questions about the binding modes of diffusible ligands in the receptor and allow formulation of testable hypotheses about the structural determinants linking drug binding to specific signaling responses. The three-dimensional structure derived from the β2-adrenergic receptor crystal has been used to virtually dock ligands with distinct activities. The different binding modes of these ligands, which correlated with their reported efficacy profiles, suggest that it could be possible to predict the structural determinants of drug signaling efficacies.
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Costanzi, S. Modeling G Protein-Coupled Receptors in Complex with Biased Agonists Trends Pharmacol. Sci. 2014, 35, 277– 283
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Jaakola, V. P.; Griffith, M. T.; Hanson, M. A.; Cherezov, V.; Chien, E. Y.; Lane, J. R.; Ijzerman, A. P.; Stevens, R. C. The 2.6 Angstrom Crystal Structure of a Human A2a Adenosine Receptor Bound to an Antagonist Science 2008, 322, 1211– 1217
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The 2.6 Angstrom Crystal Structure of a Human A2A Adenosine Receptor Bound to an Antagonist
Jaakola, Veli-Pekka; Griffith, Mark T.; Hanson, Michael A.; Cherezov, Vadim; Chien, Ellen Y. T.; Lane, J. Robert; IJzerman, Adriaan P.; Stevens, Raymond C.
Science (Washington, DC, United States) (2008), 322 (5905), 1211-1217CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
The adenosine class of heterotrimeric guanine nucleotide-binding protein (G protein)-coupled receptors (GPCRs) mediates the important role of extracellular adenosine in many physiol. processes and is antagonized by caffeine. The authors have detd. the crystal structure of the human A2A adenosine receptor, in complex with a high-affinity subtype-selective antagonist, ZM241385, to 2.6 angstrom resoln. Four disulfide bridges in the extracellular domain, combined with a subtle repacking of the transmembrane helixes relative to the adrenergic and rhodopsin receptor structures, define a pocket distinct from that of other structurally detd. GPCRs. The arrangement allows for the binding of the antagonist in an extended conformation, perpendicular to the membrane plane. The binding site highlights an integral role for the extracellular loops, together with the helical core, in ligand recognition by this class of GPCRs and suggests a role for ZM241385 in restricting the movement of a tryptophan residue important in the activation mechanism of the class A receptors.
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Xu, F.; Wu, H.; Katritch, V.; Han, G. W.; Jacobson, K. A.; Gao, Z. G.; Cherezov, V.; Stevens, R. C. Structure of an Agonist-Bound Human A2a Adenosine Receptor Science 2011, 332, 322– 327
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Structure of an Agonist-Bound Human A2A Adenosine Receptor
Xu, Fei; Wu, Huixian; Katritch, Vsevolod; Han, Gye Won; Jacobson, Kenneth A.; Gao, Zhan-Guo; Cherezov, Vadim; Stevens, Raymond C.
Science (Washington, DC, United States) (2011), 332 (6027), 322-327CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
Activation of G protein-coupled receptors upon agonist binding is a crit. step in the signaling cascade for this family of cell surface proteins. We report the crystal structure of the A2A adenosine receptor (A2AAR) bound to an agonist UK-432097 at 2.7 angstrom resoln. Relative to inactive, antagonist-bound A2AAR, the agonist-bound structure displays an outward tilt and rotation of the cytoplasmic half of helix VI, a movement of helix V, and an axial shift of helix III, resembling the changes assocd. with the active-state opsin structure. Addnl., a seesaw movement of helix VII and a shift of extracellular loop 3 are likely specific to A2AAR and its ligand. The results define the mol. UK-432097 as a "conformationally selective agonist" capable of receptor stabilization in a specific active-state configuration.
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Haga, K.; Kruse, A. C.; Asada, H.; Yurugi-Kobayashi, T.; Shiroishi, M.; Zhang, C.; Weis, W. I.; Okada, T.; Kobilka, B. K.; Haga, T.; Kobayashi, T. Structure of the Human M2Muscarinic Acetylcholine Receptor Bound to an Antagonist Nature 2012, 482, 547– 551
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Structure of the human M2 muscarinic acetylcholine receptor bound to an antagonist
Haga, Kazuko; Kruse, Andrew C.; Asada, Hidetsugu; Yurugi-Kobayashi, Takami; Shiroishi, Mitsunori; Zhang, Cheng; Weis, William I.; Okada, Tetsuji; Kobilka, Brian K.; Haga, Tatsuya; Kobayashi, Takuya
Nature (London, United Kingdom) (2012), 482 (7386), 547-551CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
The X-ray crystal structure of the M2 muscarinic acetylcholine receptor, which is essential for the physiol. control of cardiovascular function, is reported.
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Kruse, A. C.; Ring, A. M.; Manglik, A.; Hu, J.; Hu, K.; Eitel, K.; Hubner, H.; Pardon, E.; Valant, C.; Sexton, P. M.; Christopoulos, A.; Felder, C. C.; Gmeiner, P.; Steyaert, J.; Weis, W. I.; Garcia, K. C.; Wess, J.; Kobilka, B. K. Activation and Allosteric Modulation of a Muscarinic Acetylcholine Receptor Nature 2013, 504, 101– 106
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Activation and allosteric modulation of a muscarinic acetylcholine receptor
Kruse, Andrew C.; Ring, Aaron M.; Manglik, Aashish; Hu, Jianxin; Hu, Kelly; Eitel, Katrin; Huebner, Harald; Pardon, Els; Valant, Celine; Sexton, Patrick M.; Christopoulos, Arthur; Felder, Christian C.; Gmeiner, Peter; Steyaert, Jan; Weis, William I.; Garcia, K. Christopher; Wess, Juergen; Kobilka, Brian K.
Nature (London, United Kingdom) (2013), 504 (7478), 101-106CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
Despite recent advances in crystallog. and the availability of G-protein-coupled receptor (GPCR) structures, little is known about the mechanism of their activation process, as only the β2 adrenergic receptor (β2AR) and rhodopsin have been crystd. in fully active conformations. Here we report the structure of an agonist-bound, active state of the human M2 muscarinic acetylcholine receptor stabilized by a G-protein mimetic camelid antibody fragment isolated by conformational selection using yeast surface display. In addn. to the expected changes in the intracellular surface, the structure reveals larger conformational changes in the extracellular region and orthosteric binding site than obsd. in the active states of the β2AR and rhodopsin. We also report the structure of the M2 receptor simultaneously bound to the orthosteric agonist iperoxo and the pos. allosteric modulator LY2119620. This structure reveals that LY2119620 recognizes a largely pre-formed binding site in the extracellular vestibule of the iperoxo-bound receptor, inducing a slight contraction of this outer binding pocket. These structures offer important insights into the activation mechanism and allosteric modulation of muscarinic receptors.
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Zhang, J.; Zhang, K.; Gao, Z. G.; Paoletta, S.; Zhang, D.; Han, G. W.; Li, T.; Ma, L.; Zhang, W.; Muller, C. E.; Yang, H.; Jiang, H.; Cherezov, V.; Katritch, V.; Jacobson, K. A.; Stevens, R. C.; Wu, B.; Zhao, Q. Agonist-Bound Structure of the Human P2y12 Receptor Nature 2014, 509, 119– 122
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Agonist-bound structure of the human P2Y12 receptor
Zhang, Jin; Zhang, Kaihua; Gao, Zhan-Guo; Paoletta, Silvia; Zhang, Dandan; Han, Gye Won; Li, Tingting; Ma, Limin; Zhang, Wenru; Mueller, Christa E.; Yang, Huaiyu; Jiang, Hualiang; Cherezov, Vadim; Katritch, Vsevolod; Jacobson, Kenneth A.; Stevens, Raymond C.; Wu, Beili; Zhao, Qiang
Nature (London, United Kingdom) (2014), 509 (7498), 119-122CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
The P2Y12 receptor (P2Y12R), one of eight members of the P2YR family expressed in humans, is one of the most prominent clin. drug targets for inhibition of platelet aggregation. Although mutagenesis and modeling studies of the P2Y12R provided useful insights into ligand binding, the agonist and antagonist recognition and function at the P2Y12R remain poorly understood at the mol. level. Here we report the structures of the human P2Y12R in complex with the full agonist 2-methylthio-adenosine-5'-diphosphate (2MeSADP, a close analog of endogenous agonist ADP) at 2.5 Å resoln., and the corresponding ATP deriv. 2-methylthio-adenosine-5'-triphosphate (2MeSATP) at 3.1 Å resoln. These structures, together with the structure of the P2Y12R with antagonist Et 6-(4-((benzylsulfonyl)carbamoyl)piperidin-1-yl)-5-cyano-2-methylnicotinate (AZD1283), reveal striking conformational changes between nucleotide and non-nucleotide ligand complexes in the extracellular regions. Further anal. of these changes provides insight into a distinct ligand binding landscape in the δ-group of class A G-protein-coupled receptors (GPCRs). Agonist and non-nucleotide antagonist adopt different orientations in the P2Y12R, with only partially overlapped binding pockets. The agonist-bound P2Y12R structure answers long-standing questions surrounding P2Y12R-agonist recognition, and reveals interactions with several residues that had not been reported to be involved in agonist binding. As a first example, to our knowledge, of a GPCR in which agonist access to the binding pocket requires large-scale rearrangements in the highly malleable extracellular region, the structural and docking studies will therefore provide invaluable insight into the pharmacol. and mechanisms of action of agonists and different classes of antagonists for the P2Y12R and potentially for other closely related P2YRs.
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Zhang, K.; Zhang, J.; Gao, Z. G.; Zhang, D.; Zhu, L.; Han, G. W.; Moss, S. M.; Paoletta, S.; Kiselev, E.; Lu, W.; Fenalti, G.; Zhang, W.; Muller, C. E.; Yang, H.; Jiang, H.; Cherezov, V.; Katritch, V.; Jacobson, K. A.; Stevens, R. C.; Wu, B.; Zhao, Q. Structure of the Human P2y12 Receptor in Complex with an Antithrombotic Drug Nature 2014, 509, 115– 118
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Structure of the human P2Y12 receptor in complex with an antithrombotic drug
Zhang, Kaihua; Zhang, Jin; Gao, Zhan-Guo; Zhang, Dandan; Zhu, Lan; Han, Gye Won; Moss, Steven M.; Paoletta, Silvia; Kiselev, Evgeny; Lu, Weizhen; Fenalti, Gustavo; Zhang, Wenru; Mueller, Christa E.; Yang, Huaiyu; Jiang, Hualiang; Cherezov, Vadim; Katritch, Vsevolod; Jacobson, Kenneth A.; Stevens, Raymond C.; Wu, Beili; Zhao, Qiang
Nature (London, United Kingdom) (2014), 509 (7498), 115-118CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
P2Y receptors (P2YRs), a family of purinergic G-protein-coupled receptors (GPCRs), are activated by extracellular nucleotides. There are a total of eight distinct functional P2YRs expressed in human, which are subdivided into P2Y1-like receptors and P2Y12-like receptors. Their ligands are generally charged mols. with relatively low bioavailability and stability in vivo, which limits the authors' understanding of this receptor family. P2Y12R regulates platelet activation and thrombus formation, and several antithrombotic drugs targeting P2Y12R-including the prodrugs clopidogrel (Plavix) and prasugrel (Effient) that are metabolized and bind covalently, and the nucleoside analog ticagrelor (Brilinta) that acts directly on the receptor-have been approved for the prevention of stroke and myocardial infarction. However, limitations of these drugs (for example, a very long half-life of clopidogrel action and a characteristic adverse effect profile of ticagrelor) suggest that there is an unfulfilled medical need for developing a new generation of P2Y12R inhibitors. Here the authors report the 2.6 Å resoln. crystal structure of human P2Y12R in complex with a non-nucleotide reversible antagonist, AZD1283. The structure reveals a distinct straight conformation of helix V, which sets P2Y12R apart from all other known class A GPCR structures. With AZD1283 bound, the highly conserved disulfide bridge in GPCRs between helix III and extracellular loop 2 is not obsd. and appears to be dynamic. Along with the details of the AZD1283-binding site, anal. of the extracellular interface reveals an adjacent ligand-binding region and suggests that both pockets could be required for dinucleotide binding. The structure provides essential insights for the development of improved P2Y12R ligands and allosteric modulators as drug candidates.
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Bernstein, F. C.; Koetzle, T. F.; Williams, G. J.; Meyer, E. F., Jr.; Brice, M. D.; Rodgers, J. R.; Kennard, O.; Shimanouchi, T.; Tasumi, M. The Protein Data Bank: A Computer-Based Archival File for Macromolecular Structures J. Mol. Biol. 1977, 112, 535– 542
Google Scholar
124
The Protein Data Bank: a computer-based archival file for macromolecular structures
Bernstein, Frances C.; Koetzle, Thomas F.; Williams, Graheme J. B.; Meyer, Edgar F., Jr.; Brice, Michael D.; Rodgers, John R.; Kennard, Olga; Shimanouchi, Takehiko; Tasumi, Mitsuo
Journal of Molecular Biology (1977), 112 (3), 535-42CODEN: JMOBAK; ISSN:0022-2836.
The Protein Data Bank is a computer-based archival file for macromol. structures. The Bank stores in a uniform format at. coordinates and partial bond connectivities, as derived from crystallog. studies. Text included in each data entry gives pertinent information for the structure at hand (e.g., species from which the mol. was obtained, resoln. of diffraction data, literature citations, and specifications of secondary structure). In addn., the Protein Data Bank stores structure factors and phases. Input of data and general maintenance functions are at Brookhaven National Lab. All data are available on magnetic tape for public distribution.
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Molecular Operating Environment (Moe), 2012.10; Chemical Computing Group Inc.: Montreal, Canada, 2012.
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There is no corresponding record for this reference.
126
Ballesteros, J. A.; Weinstein, H. Integrated Methods for the Construction of Three-Dimensional Models and Computational Probing of Structure-Function Relations of G Protein-Coupled Receptors Methods Neurosci. 1995, 25, 366– 428
Google Scholar
126
Integrated methods for the construction of three-dimensional models and computational probing of structure-function relations in G protein-coupled receptors
Ballesteros, Juan A.; Weinstein, Harel
Methods in Neurosciences (1995), 25 (), 366-428CODEN: MENEE5; ISSN:1043-9471.
A review, with 135 refs., on approaches that can be used to resolve the apparent ambiguities that burden the pharmacol. testing of G protein-coupled receptor (GPCR) models, based on the integration of structural information about the receptor, about mutants, and about the changes induced by ligand binding.
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van Linden, O. P.; Kooistra, A. J.; Leurs, R.; de Esch, I. J.; de Graaf, C. Klifs: A Knowledge-Based Structural Database to Navigate Kinase-Ligand Interaction Space J. Med. Chem. 2014, 57, 249– 277
Google Scholar
There is no corresponding record for this reference.
128
Carey, F. A.; Sundberg, R. J. Advanced Organic Chemistry: Part A: Structure and Mechanisms. Springer: Berlin, 2007.
Google Scholar
There is no corresponding record for this reference.
129
Durrant, J. D.; de Oliveira, C. A.; McCammon, J. A. Povme: An Algorithm for Measuring Binding-Pocket Volumes J. Mol. Graph. Model. 2011, 29, 773– 776
Google Scholar
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POVME: An algorithm for measuring binding-pocket volumes
Durrant, Jacob D.; de Oliveira, Cesar Augusto F.; McCammon, J. Andrew
Journal of Molecular Graphics & Modelling (2011), 29 (5), 773-776CODEN: JMGMFI; ISSN:1093-3263. (Elsevier Ltd.)
Researchers engaged in computer-aided drug design often wish to measure the vol. of a ligand-binding pocket in order to predict pharmacol. We have recently developed a simple algorithm, called POVME (POcket Vol. MEasurer), for this purpose. POVME is Python implemented, fast, and freely available. To demonstrate its utility, we use the new algorithm to study three members of the matrix-metalloproteinase family of proteins. Despite the structural similarity of these proteins, differences in binding-pocket dynamics are easily identified.
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Calculator, 5.1.4; ChemAxon Kft.: Budapest, Hungary.
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There is no corresponding record for this reference.
131
Corina, 3.4.9; Molecular Networks GmbH: Erlangen, Germany.
Google Scholar
There is no corresponding record for this reference.
132
Sadowski, J.; Gasteiger, J.; Klebe, G. Comparison of Automatic Three-Dimensional Model Builders Using 639 X-Ray Structures J. Chem. Inf. Comput. Sci. 1994, 34, 1000– 1008
Google Scholar
132
Comparison of Automatic Three-Dimensional Model Builders Using 639 X-ray Structures
Sadowski, Jens; Gasteiger, Johann; Klebe, Gerhard
Journal of Chemical Information and Computer Sciences (1994), 34 (4), 1000-8CODEN: JCISD8; ISSN:0095-2338.
Several criteria were defined to select a dataset of high-quality x-ray structures from the Cambridge file resulting in 639 mols. Six currently available programs for automatic 3D structure generation were compared by converting the connectivity tables, including appropriate stereodescriptors from this dataset of 639 mol. structures into 3D geometries: CONCORD, ALCOGEN, Chem-X, MOLGEO, COBRA, and CORINA. The geometries produced by the different programs were evaluated in terms of several quality criteria and discussed in detail. These criteria measure how well the different programs reproduce the x-ray geometries of the 639 input structures. Accordingly, the major strengths and weaknesses of the programs are indicated.
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133
Verdonk, M. L.; Cole, J. C.; Hartshorn, M. J.; Murray, C. W.; Taylor, R. D. Improved Protein-Ligand Docking Using Gold Proteins 2003, 52, 609– 623
Google Scholar
133
Improved protein-ligand docking using GOLD
Verdonk, Marcel L.; Cole, Jason C.; Hartshorn, Michael J.; Murray, Christopher W.; Taylor, Richard D.
Proteins: Structure, Function, and Genetics (2003), 52 (4), 609-623CODEN: PSFGEY; ISSN:0887-3585. (Wiley-Liss, Inc.)
The Chemscore function was implemented as a scoring function for the protein-ligand docking program GOLD, and its performance compared to the original Goldscore function and two consensus docking protocols, "Goldscore-CS" and "Chemscore-GS," in terms of docking accuracy, prediction of binding affinities, and speed. In the "Goldscore-CS" protocol, dockings produced with the Goldscore function are scored and ranked with the Chemscore function; in the "Chemscore-GS" protocol, dockings produced with the Chemscore function are scored and ranked with the Goldscore function. Comparisons were made for a "clean" set of 224 protein-ligand complexes, and for two subsets of this set, one for which the ligands are "drug-like," the other for which they are "fragment-like.". For "drug-like" and "fragment-like" ligands, the docking accuracies obtained with Chemscore and Goldscore functions are similar. For larger ligands, Goldscore gives superior results. Docking with the Chemscore function is up to three times faster than docking with the Goldscore function. Both combined docking protocols give significant improvements in docking accuracy over the use of the Goldscore or Chemscore function alone. "Goldscore-CS" gives success rates of up to 81% (top-ranked GOLD soln. within 2.0 Å of the exptl. binding mode) for the "clean list," but at the cost of long search times. For most virtual screening applications, "Chemscore-GS" seems optimal; search settings that give docking speeds of around 0.25-1.3 min/compd. have success rates of about 78% for "drug-like" compds. and 85% for "fragment-like" compds. In terms of producing binding energy ests., the Goldscore function appears to perform better than the Chemscore function and the two consensus protocols, particularly for faster search settings. Even at docking speeds of around 1-2 min/compd., the Goldscore function predicts binding energies with a std. deviation of ∼10.5 kJ/mol.
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134
McMartin, C.; Bohacek, R. S. Qxp: Powerful, Rapid Computer Algorithms for Structure-Based Drug Design J. Comput. Aided Mol. Des. 1997, 11, 333– 344
Google Scholar
134
QXP: powerful, rapid computer algorithms for structure-based drug design
Mcmartin, Colin; Bohacek, Regine S.
Journal of Computer-Aided Molecular Design (1997), 11 (4), 333-344CODEN: JCADEQ; ISSN:0920-654X. (Kluwer)
New methods for docking, template fitting and building pseudo-receptors are described. Full conformational searches are carried out for flexible cyclic and acyclic mols. QXP (quick explore) search algorithms are derived from the method of Monte Carlo perturbation with energy minimization in Cartesian space. An addnl. fast search step is introduced between the initial perturbation and energy minimization. The fast search produces approx. low-energy structures, which are likely to minimize to a low energy. For template fitting, QXP uses a superposition force field which automatically assigns short-range attractive forces to similar atoms in different mols. The docking algorithms were evaluated using x-ray data for 12 protein-ligand complexes. The ligands had ≤24 rotatable bonds and ranged from highly polar to mostly nonpolar. Docking searches of the randomly disordered ligands gave rms differences between the lowest energy docked structure and the energy-minimized x-ray structure, of less than 0.76 Å for 10 of the ligands. For all the ligands, the rms difference between the energy-minimized x-ray structure and the closest docked structure was less than 0.4 Å, when parts of one of the mols. which are in the solvent were excluded from the rms calcn. Template fitting was tested using four ACE inhibitors. Three ACE templates have been previously published. A single run using QXP generated a series of templates which contained examples of each of the three. A pseudo-receptor, complementary to an ACE template, was built out of small mols., such as pyrrole, cyclo-pentanone and propane. When individually energy minimized in the pseudo-receptor, each of the four ACE inhibitors moved with an rms of less than 0.25 Å. After random perturbation, the inhibitors were docked into the pseudo-receptor. Each lowest energy docked structure matched the energy-minimized geometry with an rms of less than 0.08 Å. Thus, the pseudo-receptor shows steric and chem. complementarity to all four mols. The QXP program is reliable, easy to use and sufficiently rapid for routine application in structure-based drug design.
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135
Oechem Tk, 1.7.2.4; OpenEye Scientific Software Inc.: Santa Fe, NM.
Google Scholar
There is no corresponding record for this reference.
136
Jain, A. N.; Nicholls, A. Recommendations for Evaluation of Computational Methods J. Comput. Aided Mol. Des. 2008, 22, 133– 139
Google Scholar
136
Recommendations for evaluation of computational methods
Jain, Ajay N.; Nicholls, Anthony
Journal of Computer-Aided Molecular Design (2008), 22 (3-4), 133-139CODEN: JCADEQ; ISSN:0920-654X. (Springer)
A review. The field of computational chem., particularly as applied to drug design, has become increasingly important in terms of the practical application of predictive modeling to pharmaceutical research and development. Tools for exploiting protein structures or sets of ligands known to bind particular targets can be used for binding-mode prediction, virtual screening, and prediction of activity. A serious weakness within the field is a lack of stds. with respect to quant. evaluation of methods, data set prepn., and data set sharing. Our goal should be to report new methods or comparative evaluations of methods in a manner that supports decision making for practical applications. Here we propose a modest beginning, with recommendations for requirements on statistical reporting, requirements for data sharing, and best practices for benchmark prepn. and usage.
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137
Hawkins, P. C.; Warren, G. L.; Skillman, A. G.; Nicholls, A. How to Do an Evaluation: Pitfalls and Traps J. Comput. Aided Mol. Des. 2008, 22, 179– 190
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137
How to do an evaluation: pitfalls and traps
Hawkins, Paul C. D.; Warren, Gregory L.; Skillman, A. Geoffrey; Nicholls, Anthony
Journal of Computer-Aided Molecular Design (2008), 22 (3-4), 179-190CODEN: JCADEQ; ISSN:0920-654X. (Springer)
The recent literature is replete with papers evaluating computational tools (often those operating on 3D structures) for their performance in a certain set of tasks. Most commonly these papers compare a no. of docking tools for their performance in cognate re-docking (pose prediction) and/or virtual screening. Related papers have been published on ligand-based tools: pose prediction by conformer generators and virtual screening using a variety of ligand-based approaches. The reliability of these comparisons is critically affected by a no. of factors usually ignored by the authors, including bias in the datasets used in virtual screening, the metrics used to assess performance in virtual screening and pose prediction and errors in crystal structures used.
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Scior, T.; Bender, A.; Tresadern, G.; Medina-Franco, J. L.; Martinez-Mayorga, K.; Langer, T.; Cuanalo-Contreras, K.; Agrafiotis, D. K. Recognizing Pitfalls in Virtual Screening: A Critical Review J. Chem. Inf. Model. 2012, 52, 867– 881
Google Scholar
138
Recognizing Pitfalls in Virtual Screening: A Critical Review
Scior, Thomas; Bender, Andreas; Tresadern, Gary; Medina-Franco, Jose L.; Martinez-Mayorga, Karina; Langer, Thierry; Cuanalo-Contreras, Karina; Agrafiotis, Dimitris K.
Journal of Chemical Information and Modeling (2012), 52 (4), 867-881CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
A review. The aim of virtual screening (VS) is to identify bioactive compds. through computational means, by employing knowledge about the protein target (structure-based VS) or known bioactive ligands (ligand-based VS). In VS, a large no. of mols. are ranked according to their likelihood to be bioactive compds., with the aim to enrich the top fraction of the resulting list (which can be tested in bioassays afterward). At its core, VS attempts to improve the odds of identifying bioactive mols. by maximizing the true pos. rate, i.e., by ranking the truly active mols. as high as possible (and, correspondingly, the truly inactive ones as low as possible). In choosing the right approach, the researcher is faced with many questions: where does the optimal balance between efficiency and accuracy lie when evaluating a particular algorithm; do some methods perform better than others and in what particular situations; and what do retrospective results tell us about the prospective utility of a particular method. Given the multitude of settings, parameters, and data sets the practitioner can choose from, there are many pitfalls that lurk along the way which might render VS less efficient or downright useless. This review attempts to catalog published and unpublished problems, shortcomings, failures, and tech. traps of VS methods with the aim to avoid pitfalls by making the user aware of them in the first place.
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Abstract
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Figure 1
Figure 1. (A,B) Visual and (C) quantitative comparisons of the pockets from all X-ray structures. Pocket residues (carbon atoms are colored blue, salmon, white, and gray for ANT/iAGO, f/pAGO, FAB-complexed, and ligand-free structures, respectively) of all chains of all crystal structures (listed in panel C by their PDB code followed by their chain identifier) are shown superimposed with the cartoon representation of β₂R (PDB code 2RH1 (21)) and bucindolol (green carbon atoms, PDB code 4AMI (32)), as seen from the side (panel A) and from the top (panel B). Panel C gives an overview of ^aaverage all-atom rmsd using the pocket residues (Å) compared to all 16 f/pAGO and all 27 ANT/iAGO structures (full overview in Supporting Information, Figure S2); ^bthe average Z-score of the distances between G^2.61, D^3.32, N^7.39, and S^5.42 (full overview in Supporting Information, Figures S3 and S4); and ^cthe pocket volume (Å³, Supporting Information, Figure S5). ^dThe side chains of the ECL2 residues are not all fully resolved, thereby influencing the pocket volume (Figure S5) as well as rmsd values. The red and blue background coloring mark values associated with f/pAGO and ANT/iAGO properties, respectively. Icons represent f/pAGO (red), ANT/iAGO (blue), or unvalidated ANT (cyan) with no or unknown signaling preference (circles) or β-arrestin-biased ligands (triangles) (see Table 1).
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Figure 2
Figure 2. Overview of the unique interaction fingerprints of all cocrystallized ligands. The colors indicate the presence of an interaction (as seen from the residue) according to the colors described at the bottom of the figure. Identical IFPs for multiple monomers within a PDB entry are grouped (e.g., 2VT4_chainA-D). The last two columns describe the amount of times (as a percentage of the total comparisons) an IFP comparison results in a score ≥0.6 when compared with the ANT/iAGO reference IFPs (a blue background indicates a high percentage) and the f/pAGO reference IFPs (a red background indicates a high percentage). ^aThe IFP of the highest scoring docking pose of norepinephrine in 2Y03-chainA (see Figure 6). Names and icons represent f/pAGO (red), ANT/iAGO (blue), or unvalidated ANT (34) (cyan) with no or unknown signaling preference (circle) or β-arrestin-biased ligands (triangle) (see Table 1).
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Figure 3
Figure 3. Overall enrichment at 1% false positive rate (FP-rate) for the retrieval of 25 partial/full agonists and 25 inverse agonists/antagonists over a set of 980 decoy molecules using IFP scoring (A). Full ROC curves visualizing the retrieval rate (TP-rate) of f/pAGO (red) and ANT/iAGO (blue) in the best ANT/iAGO structure (B) and best f/pAGO structure (C, legend shown in panel B). The structures are indicated by their PDB code followed by an underscore and the chain identifier (except when there was only one chain or all chains of the structure had similar performance). The 2D structures represent the co-crystallized ligand for selected structures. The ANT/iAGO axis is scaled from 0 to 60 and the f/pAGO axis from 0 to 100. *1 = 3ZPQ_A, 3ZPQ_dock, 2Y00_B; *2 = 4AMJ_B, 3ZPQ_B. Icons represent f/pAGO (red), ANT/iAGO (blue), or unvalidated ANT (34) (cyan) with no or unknown signaling preference (circle) or β-arrestin-biased ligands (triangle) (see Table 1).
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Figure 4
Figure 4. Enrichment factors at a 1% false positive rate using all reference IFPs (columns) on all β-adrenergic monomers for the retrieval of for f/pAGO (A) and ANT/iAGO (B) over physicochemically similar decoys. The white to red and white to blue gradients as background color mark a low to high enrichment for f/pAGO (A) and ANT/iAGO, respectively. Icons represent f/pAGO (red), ANT/iAGO (blue), or unvalidated ANT (34) (cyan) with no or unknown signaling preference (circle) or β-arrestin-biased ligands (triangle) (see Table 1).
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Figure 5
Figure 5. Analysis of VS runs using the IFPs (D,E) and structures (F,G) of the most f/p AGO-selective (2Y02_A) and ANT/iAGO-selective (2VT4_A) protein–ligand complexes (see Figure 3A), showing that both the reference IFP and protein structure determine enrichment and functional selectivity of the VS study. The binding modes of the selected f/pAGO (A, 2Y02_A) and selected ANT/iAGO (B, 2VT4_A) protein–ligand complex, a docked ligand with the same efficacy class (green carbon atoms), and their corresponding IFPs (C) for the displayed residues. EF_1% results for agonists and antagonist versus the decoys when using the reference IFP of the selected f/pAGO (D) and selected ANT/iAGO (E) for scoring docked compounds in all 48 protein structures. EF_1% results for f/pAGO and ANT/iAGO versus the decoys when using the 38 unique reference IFPs of all ligand–protein complexes for rescoring the docked compounds in the 2Y02_A (F) and 2VT4_A (G) structure. Icons represent f/pAGO (red), ANT/iAGO (blue), or unvalidated ANT (34) (cyan) with no or unknown signaling preference (circle) or β-arrestin-biased ligands (triangle) (see Table 1).
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Figure 6
Figure 6. EF1% results for agonists and antagonists (A) when using the reference IFP of docked norepinephrine (Figure 2) in 2Y03_A (B). The individual ROC plots for the retrieval of agonists (red curve) and antagonists (blue curve) in the selected ANT/iAGO structure 2VT4_A (C) and f/pAGO structure 2Y02_A (D). Icons represent f/pAGO (red), ANT/iAGO (blue), or unvalidated ANT (34) (cyan) with no or unknown signaling preference (circle) or β-arrestin-biased ligands (triangle) (see Table 1).
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This article references 138 other publications.
1. 1
  Hopkins, A. L.; Groom, C. R. The Druggable Genome Nat. Rev. Drug Discovery 2002, 1, 727– 730
  Google Scholar
  1
  Opinion: The druggable genome
  Hopkins, Andrew L.; Groom, Colin R.
  Nature Reviews Drug Discovery (2002), 1 (9), 727-730CODEN: NRDDAG; ISSN:1474-1776. (Nature Publishing Group)
  A review. An assessment of the no. of mol. targets that represent an opportunity for therapeutic intervention is crucial to the development of post-genomic research strategies within the pharmaceutical industry. Now that we know the size of the human genome, it is interesting to consider just how many mol. targets this opportunity represents. We start from the position that we understand the properties that are required for a good drug, and therefore must be able to understand what makes a good drug target.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD38XmslamtrY%253D&md5=d6dc7c67822661fe03e920695def1329
2. 2
  Shoichet, B. K.; Kobilka, B. K. Structure-Based Drug Screening for G-Protein-Coupled Receptors Trends Pharmacol. Sci. 2012, 33, 268– 272
  Google Scholar
  There is no corresponding record for this reference.
3. 3
  Violin, J. D.; Lefkowitz, R. J. Beta-Arrestin-Biased Ligands at Seven-Transmembrane Receptors Trends Pharmacol. Sci. 2007, 28, 416– 422
  Google Scholar
  3
  β-Arrestin-biased ligands at seven-transmembrane receptors
  Violin, Jonathan D.; Lefkowitz, Robert J.
  Trends in Pharmacological Sciences (2007), 28 (8), 416-422CODEN: TPHSDY; ISSN:0165-6147. (Elsevier B.V.)
  A review. Seven-transmembrane receptors (7TMRs), the most common mol. targets of modern drug therapy, are critically regulated by β-arrestins, which both inhibit classic G-protein signaling and initiate distinct β-arrestin signaling. The interplay of G-protein and β-arrestin signals largely dets. the cellular consequences of 7TMR-targeted drugs. Until recently, a drug's efficacy for β-arrestin recruitment was believed to be proportional to its efficacy for G-protein activities. This paradigm restricts 7TMR drug effects to a linear spectrum of responses, ranging from inhibition of all responses to stimulation of all responses. However, it is now clear that 'biased ligands' can selectively activate G-protein or β-arrestin functions and thus elicit novel biol. effects from even well-studied 7TMRs. Here, we discuss the current state of β-arrestin-biased ligand research and the prospects for β-arrestin bias as a therapeutic target. Consideration of ligand bias might have profound influences on the way scientists approach 7TMR-targeted drug discovery.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXotlWltLc%253D&md5=3bd811aa19edfdc583e7e3c1266b4730
4. 4
  Kenakin, T.; Miller, L. J. Seven Transmembrane Receptors as Shapeshifting Proteins: The Impact of Allosteric Modulation and Functional Selectivity on New Drug Discovery Pharmacol. Rev. 2010, 62, 265– 304
  Google Scholar
  4
  Seven transmembrane receptors as shapeshifting proteins: the impact of allosteric modulation and functional selectivity on new drug discovery
  Kenakin, Terry; Miller, Laurence J.
  Pharmacological Reviews (2010), 62 (2), 265-304CODEN: PAREAQ; ISSN:0031-6997. (American Society for Pharmacology and Experimental Therapeutics)
  A review. It is useful to consider seven transmembrane receptors (7TMRs) as disordered proteins able to allosterically respond to a no. of binding partners. Considering 7TMRs as allosteric systems, affinity and efficacy can be thought of in terms of energy flow between a modulator, conduit (the receptor protein), and a no. of guests. These guests can be other mols., receptors, membrane-bound proteins, or signaling proteins in the cytosol. These vectorial flows of energy can yield std. canonical guest allostery (allosteric modification of drug effect), effects along the plane of the cell membrane (receptor oligomerization), or effects directed into the cytosol (differential signaling as functional selectivity). This review discusses these apparently diverse pharmacol. effects in terms of mol. dynamics and protein ensemble theory, which tends to unify 7TMR behavior toward cells. Special consideration will be given to functional selectivity (biased agonism and biased antagonism) in terms of mechanism of action and potential therapeutic application. The explosion of technol. that has enabled observation of diverse 7TMR behavior has also shown how drugs can have multiple (pluridimensional) efficacies and how this can cause paradoxical drug classification and nomenclatures.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXhsVSltbbM&md5=fa2394cfb3ae2ecdde09775b50b45b8b
5. 5
  Kobilka, B. The Structural Basis of G-Protein-Coupled Receptor Signaling (Nobel Lecture) Angew. Chem., Int. Ed. 2013, 52, 6380– 6388
  Google Scholar
  5
  The Structural Basis of G-Protein-Coupled Receptor Signaling (Nobel Lecture)
  Kobilka, Brian
  Angewandte Chemie, International Edition (2013), 52 (25), 6380-6388CODEN: ACIEF5; ISSN:1433-7851. (Wiley-VCH Verlag GmbH & Co. KGaA)
  A review. Given the central role played by GPCRs in nearly all physiol. processes, they represent the largest group of targets for drug discovery for a broad spectrum of diseases.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXntF2ksbY%253D&md5=51d9e269076b0f248ebafcaa28d7f956
6. 6
  Urban, J. D.; Clarke, W. P.; von Zastrow, M.; Nichols, D. E.; Kobilka, B.; Weinstein, H.; Javitch, J. A.; Roth, B. L.; Christopoulos, A.; Sexton, P. M.; Miller, K. J.; Spedding, M.; Mailman, R. B. Functional Selectivity and Classical Concepts of Quantitative Pharmacology J. Pharmacol. Exp. Ther. 2007, 320, 1– 13
  Google Scholar
  6
  Functional selectivity and classical concepts of quantitative pharmacology
  Urban, Jonathan D.; Clarke, William P.; von Zastrow, Mark; Nichols, David E.; Kobilka, Brian; Weinstein, Harel; Javitch, Jonathan A.; Roth, Bryan L.; Christopoulos, Arthur; Sexton, Patrick M.; Miller, Keith J.; Spedding, Michael; Mailman, Richard B.
  Journal of Pharmacology and Experimental Therapeutics (2007), 320 (1), 1-13CODEN: JPETAB; ISSN:0022-3565. (American Society for Pharmacology and Experimental Therapeutics)
  A review. The concept of intrinsic efficacy has been enshrined in pharmacol. for half of a century, yet recent data have revealed that many ligands can differentially activate signaling pathways mediated via a single G protein-coupled receptor in a manner that challenges the traditional definition of intrinsic efficacy. Some terms for this phenomenon include functional selectivity, agonist-directed trafficking, and biased agonism. At the extreme, functionally selective ligands may be both agonists and antagonists at different functions mediated by the same receptor. Data illustrating this phenomenon are presented from serotonin, opioid, dopamine, vasopressin, and adrenergic receptor systems. A variety of mechanisms may influence this apparently ubiquitous phenomenon. It may be initiated by differences in ligand-induced intermediate conformational states, as shown for the β2-adrenergic receptor. Subsequent mechanisms that may play a role include diversity of G proteins, scaffolding and signaling partners, and receptor oligomers. Clearly, expanded research is needed to elucidate the proximal (e.g., how functionally selective ligands cause conformational changes that initiate differential signaling), intermediate (mechanisms that translate conformation changes into differential signaling), and distal mechanisms (differential effects on target tissue or organism). Besides the heuristically interesting nature of functional selectivity, there is a clear impact on drug discovery, because this mechanism raises the possibility of selecting or designing novel ligands that differentially activate only a subset of functions of a single receptor, thereby optimizing therapeutic action. It also may be timely to revise classic concepts in quant. pharmacol. and relevant pharmacol. conventions to incorporate these new concepts.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXjsFersw%253D%253D&md5=54465473b5996d0755baeee89eeb0ba9
7. 7
  Soriano-Ursua, M. A.; Trujillo-Ferrara, J. G.; Correa-Basurto, J.; Vilar, S. Recent Structural Advances of Beta1 and Beta2 Adrenoceptors Yield Keys for Ligand Recognition and Drug Design J. Med. Chem. 2013, 56, 8207– 8223
  Google Scholar
  There is no corresponding record for this reference.
8. 8
  Shukla, A. K.; Singh, G.; Ghosh, E. Emerging Structural Insights into Biased Gpcr Signaling Trends Biochem. Sci. 2014, 39, 594– 602
  Google Scholar
  There is no corresponding record for this reference.
9. 9
  Jacobson, K. A.; Costanzi, S. New Insights for Drug Design from the X-Ray Crystallographic Structures of G-Protein-Coupled Receptors Mol. Pharmacol. 2012, 82, 361– 371
  Google Scholar
  9
  New insights for drug design from the X-ray crystallographic structures of G-protein-coupled receptors
  Jacobson, Kenneth A.; Costanzi, Stefano
  Molecular Pharmacology (2012), 82 (3), 361-371CODEN: MOPMA3; ISSN:1521-0111. (American Society for Pharmacology and Experimental Therapeutics)
  A review. Methodol. advances in x-ray crystallog. have made possible the recent soln. of x-ray structures of pharmaceutically important G protein-coupled receptors (GPCRs), including receptors for biogenic amines, peptides, a nucleoside, and a sphingolipid. These high-resoln. structures have greatly increased our understanding of ligand recognition and receptor activation. Conformational changes assocd. with activation common to several receptors entail outward movements of the intracellular side of transmembrane helix 6 (TM6) and movements of TM5 toward TM6. Movements assocd. with specific agonists or receptors were also described [e.g., extracellular loop (EL) 3 in the A2A adenosine receptor]. The binding sites of different receptors partly overlap but differ significantly in ligand orientation, depth, and breadth of contact areas in TM regions and the involvement of the ELs. A current challenge is how to use this structural information for the rational design of novel potent and selective ligands. For example, new chemotypes were discovered as antagonists of various GPCRs by subjecting chem. libraries to in silico docking in the x-ray structures. The vast majority of GPCR structures and their ligand complexes are still unsolved, and no structures are known outside of family A GPCRs. Mol. modeling, informed by supporting information from site-directed mutagenesis and structure-activity relationships, was validated as a useful tool to extend structural insights to related GPCRs and to analyze docking of other ligands in already crystd. GPCRs.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhtlShu7nE&md5=8e4646b652271c9129971a34d01e4eb7
10. 10
  Katritch, V.; Cherezov, V.; Stevens, R. C. Structure-Function of the G Protein-Coupled Receptor Superfamily Annu. Rev. Pharmacol. Toxicol. 2013, 53, 531– 556
  Google Scholar
  10
  Structure-function of the G protein-coupled receptor superfamily
  Katritch, Vsevolod; Cherezov, Vadim; Stevens, Raymond C.
  Annual Review of Pharmacology and Toxicology (2013), 53 (), 531-556CODEN: ARPTDI; ISSN:0362-1642. (Annual Reviews Inc.)
  A review. During the past few years, crystallog. of G protein-coupled receptors (GPCRs) has experienced exponential growth, resulting in the detn. of the structures of 16 distinct receptors-9 of them in 2012 alone. Including closely related subtype homol. models, this coverage amts. to approx. 12% of the human GPCR superfamily. The adrenergic, rhodopsin, and adenosine receptor systems are also described by agonist-bound active-state structures, including a structure of the receptor-G protein complex for the β2-adrenergic receptor. Biochem. and biophys. techniques, such as NMR and hydrogen-deuterium exchange coupled with mass spectrometry, are providing complementary insights into ligand-dependent dynamic equil. between different functional states. Addnl. details revealed by high-resoln. structures illustrate the receptors as allosteric machines that are controlled not only by ligands but also by ions, lipids, cholesterol, and water. This wealth of data is helping redefine the authors' knowledge of how GPCRs recognize such a diverse array of ligands and how they transmit signals 30 angstroms across the cell membrane; it also is shedding light on a structural basis of GPCR allosteric modulation and biased signaling.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXjt1Wgurc%253D&md5=5e8c26100a9eb33a32755af09199138d
11. 11
  Granier, S.; Kobilka, B. A New Era of Gpcr Structural and Chemical Biology Nat. Chem. Biol. 2012, 8, 670– 673
  Google Scholar
  11
  A new era of GPCR structural and chemical biology
  Granier, Sebastien; Kobilka, Brian
  Nature Chemical Biology (2012), 8 (8), 670-673CODEN: NCBABT; ISSN:1552-4450. (Nature Publishing Group)
  A review. G protein-coupled receptors (GPCRs) are versatile mol. machines that regulate the majority of physiol. responses to chem. diverse hormones and neurotransmitters. Recent breakthroughs in structural studies have advanced the understanding of GPCR signaling, particularly the selectivity of ligand recognition and receptor activation of G proteins.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhtVCltLvK&md5=43f50b2b5f0d6fae2bf77ffe369fa899
12. 12
  Venkatakrishnan, A. J.; Deupi, X.; Lebon, G.; Tate, C. G.; Schertler, G. F.; Babu, M. M. Molecular Signatures of G-Protein-Coupled Receptors Nature 2013, 494, 185– 194
  Google Scholar
  12
  Molecular signatures of G-protein-coupled receptors
  Venkatakrishnan, A. J.; Deupi, Xavier; Lebon, Guillaume; Tate, Christopher G.; Schertler, Gebhard F.; Babu, M. Madan
  Nature (London, United Kingdom) (2013), 494 (7436), 185-194CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
  A review. G-protein-coupled receptors (GPCRs) are physiol. important membrane proteins that sense signaling mols. such as hormones and neurotransmitters, and are the targets of several prescribed drugs. Recent exciting developments are providing unprecedented insights into the structure and function of several medically important GPCRs. Here, through a systematic anal. of high-resoln. GPCR structures, we uncover a conserved network of non-covalent contacts that defines the GPCR fold. Furthermore, our comparative anal. reveals characteristic features of ligand binding and conformational changes during receptor activation. A holistic understanding that integrates mol. and systems biol. of GPCRs holds promise for new therapeutics and personalized medicine.
  >> More from SciFinder ^®
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13. 13
  Jianyi, Y.; Yang, Z.GPCR-EXP: A Database for Experimentally Solved Gpcr Structures, http://zhanglab.ccmb.med.umich.edu/GPCR-EXP/ (accessed March 30, 2014) .
  Google Scholar
  There is no corresponding record for this reference.
14. 14
  Hollenstein, K.; Kean, J.; Bortolato, A.; Cheng, R. K.; Dore, A. S.; Jazayeri, A.; Cooke, R. M.; Weir, M.; Marshall, F. H. Structure of Class B Gpcr Corticotropin-Releasing Factor Receptor 1 Nature 2013, 499, 438– 443
  Google Scholar
  14
  Structure of class B GPCR corticotropin-releasing factor receptor 1
  Hollenstein, Kaspar; Kean, James; Bortolato, Andrea; Cheng, Robert K. Y.; Dore, Andrew S.; Jazayeri, Ali; Cooke, Robert M.; Weir, Malcolm; Marshall, Fiona H.
  Nature (London, United Kingdom) (2013), 499 (7459), 438-443CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
  Structural anal. of class B G-protein-coupled receptors (GPCRs), cell-surface proteins that respond to peptide hormones, has been restricted to the amino-terminal extracellular domain, thus providing little understanding of the membrane-spanning signal transduction domain. The corticotropin-releasing factor receptor type 1 (CRF1R) is a class B receptor which mediates the response to stress and has been considered a drug target for depression and anxiety. Here we report the crystal structure of the transmembrane domain of the human corticotropin-releasing factor receptor type 1 in complex with the small-mol. antagonist CP-376395. The structure provides detailed insight into the architecture of class B receptors. Atomic details of the interactions of the receptor with the non-peptide ligand that binds deep within the receptor are described. This structure provides a model for all class B GPCRs and may aid in the design of new small-mol. drugs for diseases of brain and metab.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhtFWku7nN&md5=429ae98fb6d5bf65c5ef3b6a612ea3a0
15. 15
  Siu, F. Y.; He, M.; de Graaf, C.; Han, G. W.; Yang, D.; Zhang, Z.; Zhou, C.; Xu, Q.; Wacker, D.; Joseph, J. S.; Liu, W.; Lau, J.; Cherezov, V.; Katritch, V.; Wang, M. W.; Stevens, R. C. Structure of the Human Glucagon Class B G-Protein-Coupled Receptor Nature 2013, 499, 444– 449
  Google Scholar
  15
  Structure of the human glucagon class B G-protein-coupled receptor
  Siu, Fai Yiu; He, Min; de Graaf, Chris; Han, Gye Won; Yang, Dehua; Zhang, Zhiyun; Zhou, Caihong; Xu, Qingping; Wacker, Daniel; Joseph, Jeremiah S.; Liu, Wei; Lau, Jesper; Cherezov, Vadim; Katritch, Vsevolod; Wang, Ming-Wei; Stevens, Raymond C.
  Nature (London, United Kingdom) (2013), 499 (7459), 444-449CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
  Binding of the glucagon peptide to the glucagon receptor (GCGR) triggers the release of glucose from the liver during fasting; thus GCGR plays an important role in glucose homeostasis. Here we report the crystal structure of the seven transmembrane helical domain of human GCGR at 3.4 Å resoln., complemented by extensive site-specific mutagenesis, and a hybrid model of glucagon bound to GCGR to understand the mol. recognition of the receptor for its native ligand. Beyond the shared seven transmembrane fold, the GCGR transmembrane domain deviates from class A G-protein-coupled receptors with a large ligand-binding pocket and the first transmembrane helix having a 'stalk' region that extends three alpha-helical turns above the plane of the membrane. The stalk positions the extracellular domain (∼12 kilodaltons) relative to the membrane to form the glucagon-binding site that captures the peptide and facilitates the insertion of glucagon's amino terminus into the seven transmembrane domain.
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16. 16
  Wu, H.; Wang, C.; Gregory, K. J.; Han, G. W.; Cho, H. P.; Xia, Y.; Niswender, C. M.; Katritch, V.; Meiler, J.; Cherezov, V.; Conn, P. J.; Stevens, R. C. Structure of a Class C Gpcr Metabotropic Glutamate Receptor 1 Bound to an Allosteric Modulator Science 2014, 344, 58– 64
  Google Scholar
  16
  Structure of a Class C GPCR Metabotropic Glutamate Receptor 1 Bound to an Allosteric Modulator
  Wu, Huixian; Wang, Chong; Gregory, Karen J.; Han, Gye Won; Cho, Hyekyung P.; Xia, Yan; Niswender, Colleen M.; Katritch, Vsevolod; Meiler, Jens; Cherezov, Vadim; Conn, P. Jeffrey; Stevens, Raymond C.
  Science (Washington, DC, United States) (2014), 344 (6179), 58-64CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
  The excitatory neurotransmitter glutamate induces modulatory actions via the metabotropic glutamate receptors (mGlus), which are class C G protein-coupled receptors (GPCRs). The authors detd. the structure of the human mGlu1 receptor seven-transmembrane (7TM) domain bound to a neg. allosteric modulator, FITM, at a resoln. of 2.8 angstroms. The modulator binding site partially overlaps with the orthosteric binding sites of class A GPCRs but is more restricted than most other GPCRs. The authors obsd. a parallel 7TM dimer mediated by cholesterols, which suggests that signaling initiated by glutamate's interaction with the extracellular domain might be mediated via 7TM interactions within the full-length receptor dimer. A combination of crystallog., structure-activity relationships, mutagenesis, and full-length dimer modeling provides insights about the allosteric modulation and activation mechanism of class C GPCRs.
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17. 17
  Wang, C.; Wu, H.; Katritch, V.; Han, G. W.; Huang, X. P.; Liu, W.; Siu, F. Y.; Roth, B. L.; Cherezov, V.; Stevens, R. C. Structure of the Human Smoothened Receptor Bound to an Antitumour Agent Nature 2013, 497, 338– 343
  Google Scholar
  17
  Structure of the human smoothened receptor bound to an antitumour agent
  Wang, Chong; Wu, Huixian; Katritch, Vsevolod; Han, Gye Won; Huang, Xi-Ping; Liu, Wei; Siu, Fai Yiu; Roth, Bryan L.; Cherezov, Vadim; Stevens, Raymond C.
  Nature (London, United Kingdom) (2013), 497 (7449), 338-343CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
  The smoothened (SMO) receptor, a key signal transducer in the hedgehog signalling pathway, is responsible for the maintenance of normal embryonic development and is implicated in carcinogenesis. It is classified as a class frizzled (class F) G-protein-coupled receptor (GPCR), although the canonical hedgehog signalling pathway involves the GLI transcription factors and the sequence similarity with class A GPCRs is less than 10%. Here we report the crystal structure of the transmembrane domain of the human SMO receptor bound to the small-mol. antagonist LY2940680 at 2.5 Å resoln. Although the SMO receptor shares the seven-transmembrane helical (7TM) fold, most of the conserved motifs for class A GPCRs are absent, and the structure reveals an unusually complex arrangement of long extracellular loops stabilized by four disulfide bonds. The ligand binds at the extracellular end of the seven-transmembrane-helix bundle and forms extensive contacts with the loops.
  >> More from SciFinder ^®
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18. 18
  Rodriguez, D.; Gao, Z. G.; Moss, S. M.; Jacobson, K. A.; Carlsson, J. Molecular Docking Screening Using Agonist-Bound Gpcr Structures: Probing the a Adenosine Receptor J. Chem. Inf. Model. 2015, 55, 550– 563
  Google Scholar
  18
  Molecular Docking Screening Using Agonist-Bound GPCR Structures: Probing the A2A Adenosine Receptor
  Rodriguez, David; Gao, Zhang-Guo; Moss, Steven M.; Jacobson, Kenneth A.; Carlsson, Jens
  Journal of Chemical Information and Modeling (2015), 55 (3), 550-563CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
  Crystal structures of G protein-coupled receptors (GPCRs) have recently revealed the mol. basis of ligand binding and activation, which has provided exciting opportunities for structure-based drug design. The A2A adenosine receptor (A2AAR) is a promising therapeutic target for cardiovascular diseases, but progress in this area is limited by the lack of novel agonist scaffolds. The authors carried out docking screens of 6.7 million com. available mols. against active-like conformations of the A2AAR to investigate whether these structures could guide the discovery of agonists. Nine out of the 20 predicted agonists were confirmed to be A2AAR ligands, but none of these activated the ARs. The difficulties in discovering AR agonists using structure-based methods originated from limited at.-level understanding of the activation mechanism and a chem. bias toward antagonists in the screened library. In particular, the compn. of the screened library was found to strongly reduce the likelihood of identifying AR agonists, which reflected the high ligand complexity required for receptor activation. Extension of this anal. to other pharmaceutically relevant GPCRs suggested that library screening may not be suitable for targets requiring a complex receptor-ligand interaction network. The authors' results provide specific directions for the future development of novel A2AAR agonists and general strategies for structure-based drug discovery.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhsVWgtbs%253D&md5=0faec5c3d810a9ef32e5f374b6f9b447
19. 19
  White, J. F.; Noinaj, N.; Shibata, Y.; Love, J.; Kloss, B.; Xu, F.; Gvozdenovic-Jeremic, J.; Shah, P.; Shiloach, J.; Tate, C. G.; Grisshammer, R. Structure of the Agonist-Bound Neurotensin Receptor Nature 2012, 490, 508– 513
  Google Scholar
  19
  Structure of the agonist-bound neurotensin receptor
  White, Jim F.; Noinaj, Nicholas; Shibata, Yoko; Love, James; Kloss, Brian; Xu, Feng; Gvozdenovic-Jeremic, Jelena; Shah, Priyanka; Shiloach, Joseph; Tate, Christopher G.; Grisshammer, Reinhard
  Nature (London, United Kingdom) (2012), 490 (7421), 508-513CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
  Neurotensin (NTS) is a 13-amino-acid peptide that functions as both a neurotransmitter and a hormone through the activation of the neurotensin receptor NTSR1, a G-protein-coupled receptor (GPCR). In the brain, NTS modulates the activity of dopaminergic systems, opioid-independent analgesia, and the inhibition of food intake; in the gut, NTS regulates a range of digestive processes. Here we present the structure at 2.8 Å resoln. of Rattus norvegicus NTSR1 in an active-like state, bound to NTS8-13, the carboxy-terminal portion of NTS responsible for agonist-induced activation of the receptor. The peptide agonist binds to NTSR1 in an extended conformation nearly perpendicular to the membrane plane, with the C terminus oriented towards the receptor core. Our findings provide, to our knowledge, the first insight into the binding mode of a peptide agonist to a GPCR and may support the development of non-peptide ligands that could be useful in the treatment of neurol. disorders, cancer and obesity.
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20. 20
  Weichert, D.; Kruse, A. C.; Manglik, A.; Hiller, C.; Zhang, C.; Hubner, H.; Kobilka, B. K.; Gmeiner, P. Covalent Agonists for Studying G Protein-Coupled Receptor Activation Proc. Natl. Acad. Sci. U.S.A. 2014, 111, 10744– 10748
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  20
  Covalent agonists for studying G protein-coupled receptor activation
  Weichert, Dietmar; Kruse, Andrew C.; Manglik, Aashish; Hiller, Christine; Zhang, Cheng; Huebner, Harald; Kobilka, Brian K.; Gmeiner, Peter
  Proceedings of the National Academy of Sciences of the United States of America (2014), 111 (29), 10744-10748CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
  Structural studies on G protein-coupled receptors (GPCRs) provide important insights into the architecture and function of these important drug targets. However, the crystn. of GPCRs in active states is particularly challenging, requiring the formation of stable and conformationally homogeneous ligand-receptor complexes. Native hormones, neurotransmitters, and synthetic agonists that bind with low affinity are ineffective at stabilizing an active state for crystallogenesis. To promote structural studies on the pharmacol. highly relevant class of aminergic GPCRs, the authors here present the development of covalently binding mol. tools activating Gs-, Gi-, and Gq-coupled receptors. The covalent agonists are derived from the monoamine neurotransmitters noradrenaline, dopamine, serotonin, and histamine, and they were accessed using a general and versatile synthetic strategy. The authors demonstrate that the tool compds. presented herein display an efficient covalent binding mode and that the resp. covalent ligand-receptor complexes activate G proteins comparable to the natural neurotransmitters. A crystal structure of the β2-adrenoreceptor in complex with a covalent noradrenaline analog and a conformationally selective antibody (nanobody) verified that these agonists can be used to facilitate crystallogenesis.
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21. 21
  Cherezov, V.; Rosenbaum, D. M.; Hanson, M. A.; Rasmussen, S. G.; Thian, F. S.; Kobilka, T. S.; Choi, H. J.; Kuhn, P.; Weis, W. I.; Kobilka, B. K.; Stevens, R. C. High-Resolution Crystal Structure of an Engineered Human Beta2-Adrenergic G Protein-Coupled Receptor Science 2007, 318, 1258– 1265
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  21
  High-Resolution Crystal Structure of an Engineered Human β2-Adrenergic G Protein-Coupled Receptor
  Cherezov, Vadim; Rosenbaum, Daniel M.; Hanson, Michael A.; Rasmussen, Soren G. F.; Thian, Foon Sun; Kobilka, Tong Sun; Choi, Hee-Jung; Kuhn, Peter; Weis, William I.; Kobilka, Brian K.; Stevens, Raymond C.; Takeda, S.; Kadowaki, S.; Haga, T.; Takaesu, H.; Mitaku, S.; Fredriksson, R.; Lagerstrom, M. C.; Lundin, L. G.; Schioth, H. B.; Pierce, K. L.; Premont, R. T.; Lefkowitz, R. J.; Lefkowitz, R. J.; Shenoy, S. K.; Rosenbaum, D. M.
  Science (Washington, DC, United States) (2007), 318 (5854), 1258-1265CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
  Second extracellular loop, which in the β2-adrenergic receptor contains an unusual pair of disulfide bonds and an extra helix. This loop and the absence Heterotrimeric guanine nucleotide-binding protein (G protein)-coupled receptors constitute the largest family of eukaryotic signal transduction proteins that communicate across the membrane. We report the crystal structure of a human β2-adrenergic receptor-T4 lysozyme fusion protein bound to the partial inverse agonist carazolol at 2.4 angstrom resoln. The structure provides a high-resoln. view of a human G protein-coupled receptor bound to a diffusible ligand. Ligand-binding site accessibility is enabled by the second extracellular loop, which is held out of the binding cavity by a pair of closely spaced disulfide bridges and a short helical segment within the loop. Cholesterol, a necessary component for crystn., mediates an intriguing parallel assocn. of receptor mols. in the crystal lattice. Although the location of carazolol in the β2-adrenergic receptor is very similar to that of retinal in rhodopsin, structural differences in the ligand-binding site and other regions highlight the challenges in using rhodopsin as a template model for this large receptor family.
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22. 22
  Rasmussen, S. G.; Choi, H. J.; Rosenbaum, D. M.; Kobilka, T. S.; Thian, F. S.; Edwards, P. C.; Burghammer, M.; Ratnala, V. R.; Sanishvili, R.; Fischetti, R. F.; Schertler, G. F.; Weis, W. I.; Kobilka, B. K. Crystal Structure of the Human Beta2 Adrenergic G-Protein-Coupled Receptor Nature 2007, 450, 383– 387
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  22
  Crystal structure of the human β2 adrenergic G-protein-coupled receptor
  Rasmussen, Soren G. F.; Choi, Hee-Jung; Rosenbaum, Daniel M.; Kobilka, Tong Sun; Thian, Foon Sun; Edwards, Patricia C.; Burghammer, Manfred; Ratnala, Venkata R. P.; Sanishvili, Ruslan; Fischetti, Robert F.; Schertler, Gebhard F. X.; Weis, William I.; Kobilka, Brian K.
  Nature (London, United Kingdom) (2007), 450 (7168), 383-387CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
  Structural anal. of G-protein-coupled receptors (GPCRs) for hormones and neurotransmitters has been hindered by their low natural abundance, inherent structural flexibility, and instability in detergent solns. Here we report a structure of the human β2 adrenoceptor (β2AR), which was crystd. in a lipid environment when bound to an inverse agonist and in complex with a Fab that binds to the third intracellular loop. Diffraction data were obtained by high-brilliance microcrystallog. and the structure detd. at 3.4 Å/3.7 Å resoln. The cytoplasmic ends of the β2AR transmembrane segments and the connecting loops are well resolved, whereas the extracellular regions of the β2AR are not seen. The β2AR structure differs from rhodopsin in having weaker interactions between the cytoplasmic ends of transmembrane (TM)3 and TM6, involving the conserved E/DRY sequences. These differences may be responsible for the relatively high basal activity and structural instability of the β2AR, and contribute to the challenges in obtaining diffraction-quality crystals of non-rhodopsin GPCRs.
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23. 23
  Hanson, M. A.; Cherezov, V.; Griffith, M. T.; Roth, C. B.; Jaakola, V. P.; Chien, E. Y.; Velasquez, J.; Kuhn, P.; Stevens, R. C. A Specific Cholesterol Binding Site Is Established by the 2.8 a Structure of the Human Beta2-Adrenergic Receptor Structure 2008, 16, 897– 905
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  23
  A Specific Cholesterol Binding Site Is Established by the 2.8 Å Structure of the Human β2-Adrenergic Receptor
  Hanson, Michael A.; Cherezov, Vadim; Griffith, Mark T.; Roth, Christopher B.; Jaakola, Veli-Pekka; Chien, Ellen Y. T.; Velasquez, Jeffrey; Kuhn, Peter; Stevens, Raymond C.
  Structure (Cambridge, MA, United States) (2008), 16 (6), 897-905CODEN: STRUE6; ISSN:0969-2126. (Cell Press)
  The role of cholesterol in eukaryotic membrane protein function has been attributed primarily to an influence on membrane fluidity and curvature. We present the 2.8 Å resoln. crystal structure of a thermally stabilized human β2-adrenergic receptor (β2AR) bound to cholesterol and the partial inverse agonist timolol. The receptors pack as monomers in an antiparallel assocn. with two distinct cholesterol mols. bound per receptor, but not in the packing interface, thereby indicating a structurally relevant cholesterol-binding site between helixes I, II, III, and IV. Thermal stability anal. using isothermal denaturation confirms that a cholesterol analog significantly enhances the stability of the receptor. A consensus motif is defined that predicts cholesterol binding for 44% of human class A receptors, suggesting that specific sterol binding is important to the structure and stability of other G protein-coupled receptors (GPCRs), and that this site may provide a target for therapeutic discovery.
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24. 24
  Warne, T.; Serrano-Vega, M. J.; Baker, J. G.; Moukhametzianov, R.; Edwards, P. C.; Henderson, R.; Leslie, A. G.; Tate, C. G.; Schertler, G. F. Structure of a Beta1-Adrenergic G-Protein-Coupled Receptor Nature 2008, 454, 486– 491
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  24
  Structure of a β1-adrenergic G-protein-coupled receptor
  Warne, Tony; Serrano-Vega, Maria J.; Baker, Jillian G.; Moukhametzianov, Rouslan; Edwards, Patricia C.; Henderson, Richard; Leslie, Andrew G. W.; Tate, Christopher G.; Schertler, Gebhard F. X.
  Nature (London, United Kingdom) (2008), 454 (7203), 486-491CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
  G-protein-coupled receptors have a major role in transmembrane signaling in most eukaryotes and many are important drug targets. Here we report the 2.7Å resoln. crystal structure of a β1-adrenergic receptor in complex with high affinity antagonist cyanopindolol. The modified turkey (Meleagris gallopavo) receptor was selected to be in its antagonist conformation and its thermostability improved by earlier limited mutagenesis. The ligand-binding pocket comprises 15 side chains from amino acid residues in 4 transmembrane α-helixes and extracellular loop 2. This loop defines the entrance of the ligand-binding pocket and is stabilized by two disulfide bonds and a sodium ion. Binding of cyanopindolol to the β1-adrenergic receptor and binding of Carazolol to the β2-adrenergic receptor involve similar interactions. A short well-defined helix in cytoplasmic loop 2, not obsd. in either rhodopsin or the β2-adrenergic receptor, directly interacts by means of a tyrosine with the highly conserved DRY motif at the end of helix 3 that is essential for receptor activation.
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25. 25
  Bokoch, M. P.; Zou, Y.; Rasmussen, S. G.; Liu, C. W.; Nygaard, R.; Rosenbaum, D. M.; Fung, J. J.; Choi, H. J.; Thian, F. S.; Kobilka, T. S.; Puglisi, J. D.; Weis, W. I.; Pardo, L.; Prosser, R. S.; Mueller, L.; Kobilka, B. K. Ligand-Specific Regulation of the Extracellular Surface of a G-Protein-Coupled Receptor Nature 2010, 463, 108– 112
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  25
  Ligand-specific regulation of the extracellular surface of a G-protein-coupled receptor
  Bokoch, Michael P.; Zou, Yaozhong; Rasmussen, Soren G. F.; Liu, Corey W.; Nygaard, Rie; Rosenbaum, Daniel M.; Fung, Juan Jose; Choi, Hee-Jung; Thian, Foon Sun; Kobilka, Tong Sun; Puglisi, Joseph D.; Weis, William I.; Pardo, Leonardo; Prosser, R. Scott; Mueller, Luciano; Kobilka, Brian K.
  Nature (London, United Kingdom) (2010), 463 (7277), 108-112CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
  G protein-coupled receptors (GPCRs) are seven-transmembrane proteins that mediate most cellular responses to hormones and neurotransmitters. They are the largest group of therapeutic targets for a broad spectrum of diseases. Recent crystal structures of GPCRs have revealed structural conservation extending from the orthosteric ligand-binding site in the transmembrane core to the cytoplasmic G protein-coupling domains. In contrast, the extracellular surface (ECS) of GPCRs is remarkably diverse and is therefore an ideal target for the discovery of subtype-selective drugs. However, little is known about the functional role of the ECS in receptor activation, or about conformational coupling of this surface to the native ligand-binding pocket. Here we use NMR spectroscopy to investigate ligand-specific conformational changes around a central structural feature in the ECS of the β2 adrenergic receptor (β2AR): a salt bridge linking extracellular loops 2 and 3. Small-mol. drugs that bind within the transmembrane core and exhibit different efficacies towards G protein activation (agonist, neutral antagonist, and inverse agonist) also stabilize distinct conformations of the ECS. We thereby demonstrate conformational coupling between the ECS and the orthosteric binding site, showing that drugs targeting this diverse surface could function as allosteric modulators with high subtype selectivity. Moreover, these studies provide a new insight into the dynamic behavior of GPCRs not addressable by static, inactive-state crystal structures.
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26. 26
  Wacker, D.; Fenalti, G.; Brown, M. A.; Katritch, V.; Abagyan, R.; Cherezov, V.; Stevens, R. C. Conserved Binding Mode of Human Beta2 Adrenergic Receptor Inverse Agonists and Antagonist Revealed by X-Ray Crystallography J. Am. Chem. Soc. 2010, 132, 11443– 11445
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  There is no corresponding record for this reference.
27. 27
  Moukhametzianov, R.; Warne, T.; Edwards, P. C.; Serrano-Vega, M. J.; Leslie, A. G.; Tate, C. G.; Schertler, G. F. Two Distinct Conformations of Helix 6 Observed in Antagonist-Bound Structures of a Beta1-Adrenergic Receptor Proc. Natl. Acad. Sci. U.S.A. 2011, 108, 8228– 8232
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  27
  Two distinct conformations of helix 6 observed in antagonist-bound structures of a β1-adrenergic receptor
  Moukhametzianov, Rouslan; Warne, Tony; Edwards, Patricia C.; Serrano-Vega, Maria J.; Leslie, Andrew G. W.; Tate, Christopher G.; Schertler, Gebhard F. X.
  Proceedings of the National Academy of Sciences of the United States of America (2011), 108 (20), 8228-8232, S8228/1-S8228/5CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
  The β1-adrenergic receptor (β1AR) is a G-protein-coupled receptor whose inactive state structure was detd. using a thermostabilized mutant (β1AR-M23). However, it was not thought to be in a fully inactivated state because there was no salt bridge between Arg139 and Glu285 linking the cytoplasmic ends of transmembrane helixes 3 and 6 (the R3.50-D/E6.30 "ionic lock"). Here we compare eight new structures of β1AR-M23, detd. from crystallog. independent mols. in four different crystals with three different antagonists bound. These structures are all in the inactive R state and show clear electron d. for cytoplasmic loop 3 linking transmembrane helixes 5 and 6 that had not been seen previously. Despite significantly different crystal packing interactions, there are only two distinct conformations of the cytoplasmic end of helix 6, bent and straight. In the bent conformation, the Arg139-Glu285 salt bridge is present, as in the crystal structure of dark-state rhodopsin. The straight conformation, obsd. in previously solved structures of β-receptors, results in the ends of helixes 3 and 6 being too far apart for the ionic lock to form. In the bent conformation, the R3.50-E6.30 distance is significantly longer than in rhodopsin, suggesting that the interaction is also weaker, which could explain the high basal activity in β1AR compared to rhodopsin. Many mutations that increase the constitutive activity of G-protein-coupled receptors are found in the bent region at the cytoplasmic end of helix 6, supporting the idea that this region plays an important role in receptor activation.
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28. 28
  Rasmussen, S. G.; Choi, H. J.; Fung, J. J.; Pardon, E.; Casarosa, P.; Chae, P. S.; Devree, B. T.; Rosenbaum, D. M.; Thian, F. S.; Kobilka, T. S.; Schnapp, A.; Konetzki, I.; Sunahara, R. K.; Gellman, S. H.; Pautsch, A.; Steyaert, J.; Weis, W. I.; Kobilka, B. K. Structure of a Nanobody-Stabilized Active State of the Beta(2) Adrenoceptor Nature 2011, 469, 175– 180
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  28
  Structure of a nanobody-stabilized active state of the β2 adrenoceptor
  Rasmussen, Soren G. F.; Choi, Hee-Jung; Fung, Juan Jose; Pardon, Els; Casarosa, Paola; Chae, Pil Seok; DeVree, Brian T.; Rosenbaum, Daniel M.; Thian, Foon Sun; Kobilka, Tong Sun; Schnapp, Andreas; Konetzki, Ingo; Sunahara, Roger K.; Gellman, Samuel H.; Pautsch, Alexander; Steyaert, Jan; Weis, William I.; Kobilka, Brian K.
  Nature (London, United Kingdom) (2011), 469 (7329), 175-180CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
  G protein coupled receptors (GPCRs) exhibit a spectrum of functional behaviors in response to natural and synthetic ligands. Recent crystal structures provide insights into inactive states of several GPCRs. Efforts to obtain an agonist-bound active-state GPCR structure have proven difficult due to the inherent instability of this state in the absence of a G protein. We generated a camelid antibody fragment (nanobody) to the human β2 adrenergic receptor (β2AR) that exhibits G protein-like behavior, and obtained an agonist-bound, active-state crystal structure of the receptor-nanobody complex. Comparison with the inactive β2AR structure reveals subtle changes in the binding pocket; however, these small changes are assocd. with an 11 Å outward movement of the cytoplasmic end of transmembrane segment 6, and rearrangements of transmembrane segments 5 and 7 that are remarkably similar to those obsd. in opsin, an active form of rhodopsin. This structure provides insights into the process of agonist binding and activation.
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29. 29
  Rasmussen, S. G.; DeVree, B. T.; Zou, Y.; Kruse, A. C.; Chung, K. Y.; Kobilka, T. S.; Thian, F. S.; Chae, P. S.; Pardon, E.; Calinski, D.; Mathiesen, J. M.; Shah, S. T.; Lyons, J. A.; Caffrey, M.; Gellman, S. H.; Steyaert, J.; Skiniotis, G.; Weis, W. I.; Sunahara, R. K.; Kobilka, B. K. Crystal Structure of the Beta2 Adrenergic Receptor-Gs Protein Complex Nature 2011, 477, 549– 555
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  29
  Crystal structure of the β2 adrenergic receptor-Gs protein complex
  Rasmussen, Soren G. F.; DeVree, Brian T.; Zou, Yao-Zhong; Kruse, Andrew C.; Chung, Ka-Young; Kobilka, Tong-Sun; Thian, Foon-Sun; Chae, Pil-Seok; Pardon, Els; Calinski, Diane; Mathiesen, Jesper M.; Shah, Syed T. A.; Lyons, Joseph A.; Caffrey, Martin; Gellman, Samuel H.; Steyaert, Jan; Skiniotis, Georgios; Weis, William I.; Sunahara, Roger K.; Kobilka, Brian K.
  Nature (London, United Kingdom) (2011), 477 (7366), 549-555CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
  G protein-coupled receptors (GPCRs) are responsible for the majority of cellular responses to hormones and neurotransmitters as well as the senses of sight, olfaction and taste. The paradigm of GPCR signalling is the activation of a heterotrimeric GTP binding protein (G protein) by an agonist-occupied receptor. The β2 adrenergic receptor (β2AR) activation of Gs, the stimulatory G protein for adenylyl cyclase, has long been a model system for GPCR signalling. Here we present the crystal structure of the active state ternary complex composed of agonist-occupied monomeric β2AR and nucleotide-free Gs heterotrimer. The principal interactions between the β2AR and Gs involve the amino- and carboxy-terminal α-helixes of Gs, with conformational changes propagating to the nucleotide-binding pocket. The largest conformational changes in the β2AR include a 14 Å outward movement at the cytoplasmic end of transmembrane segment 6 (TM6) and an α-helical extension of the cytoplasmic end of TM5. The most surprising observation is a major displacement of the α-helical domain of Gαs relative to the Ras-like GTPase domain. This crystal structure represents the first high-resoln. view of transmembrane signalling by a GPCR.
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30. 30
  Rosenbaum, D. M.; Zhang, C.; Lyons, J. A.; Holl, R.; Aragao, D.; Arlow, D. H.; Rasmussen, S. G.; Choi, H. J.; Devree, B. T.; Sunahara, R. K.; Chae, P. S.; Gellman, S. H.; Dror, R. O.; Shaw, D. E.; Weis, W. I.; Caffrey, M.; Gmeiner, P.; Kobilka, B. K. Structure and Function of an Irreversible Agonist-Beta(2) Adrenoceptor Complex Nature 2011, 469, 236– 240
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  30
  Structure and function of an irreversible agonist-β2 adrenoceptor complex
  Rosenbaum, Daniel M.; Zhang, Cheng; Lyons, Joseph A.; Holl, Ralph; Aragao, David; Arlow, Daniel H.; Rasmussen, Soren G. F.; Choi, Hee-Jung; DeVree, Brian T.; Sunahara, Roger K.; Chae, Pil Seok; Gellman, Samuel H.; Dror, Ron O.; Shaw, David E.; Weis, William I.; Caffrey, Martin; Gmeiner, Peter; Kobilka, Brian K.
  Nature (London, United Kingdom) (2011), 469 (7329), 236-240CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
  G-protein-coupled receptors (GPCRs) are eukaryotic integral membrane proteins that modulate biol. function by initiating cellular signaling in response to chem. diverse agonists. Despite recent progress in the structural biol. of GPCRs, the mol. basis for agonist binding and allosteric modulation of these proteins is poorly understood. Structural knowledge of agonist-bound states is essential for deciphering the mechanism of receptor activation, and for structure-guided design and optimization of ligands. However, the crystn. of agonist-bound GPCRs has been hampered by modest affinities and rapid off-rates of available agonists. Using the inactive structure of the human β2 adrenergic receptor (β2AR) as a guide, we designed a β2AR agonist that can be covalently tethered to a specific site on the receptor through a disulfide bond. The covalent β2AR-agonist complex forms efficiently, and is capable of activating a heterotrimeric G protein. We crystd. a covalent agonist-bound β2AR-T4L fusion protein in lipid bilayers through the use of the lipidic mesophase method, and detd. its structure at 3.5 Å resoln. A comparison to the inactive structure and an antibody-stabilized active structure (companion paper) shows how binding events at both the extracellular and intracellular surfaces are required to stabilize an active conformation of the receptor. The structures are in agreement with long-timescale (up to 30 μs) mol. dynamics simulations showing that an agonist-bound active conformation spontaneously relaxes to an inactive-like conformation in the absence of a G protein or stabilizing antibody.
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31. 31
  Warne, T.; Moukhametzianov, R.; Baker, J. G.; Nehme, R.; Edwards, P. C.; Leslie, A. G.; Schertler, G. F.; Tate, C. G. The Structural Basis for Agonist and Partial Agonist Action on a Beta(1)-Adrenergic Receptor Nature 2011, 469, 241– 244
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  There is no corresponding record for this reference.
32. 32
  Warne, T.; Edwards, P. C.; Leslie, A. G.; Tate, C. G. Crystal Structures of a Stabilized Beta1-Adrenoceptor Bound to the Biased Agonists Bucindolol and Carvedilol Structure 2012, 20, 841– 849
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  There is no corresponding record for this reference.
33. 33
  Zou, Y.; Weis, W. I.; Kobilka, B. K. N-Terminal T4 Lysozyme Fusion Facilitates Crystallization of a G Protein Coupled Receptor PLoS One 2012, 7e46039
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  There is no corresponding record for this reference.
34. 34
  Christopher, J. A.; Brown, J.; Dore, A. S.; Errey, J. C.; Koglin, M.; Marshall, F. H.; Myszka, D. G.; Rich, R. L.; Tate, C. G.; Tehan, B.; Warne, T.; Congreve, M. Biophysical Fragment Screening of the Beta1-Adrenergic Receptor: Identification of High Affinity Arylpiperazine Leads Using Structure-Based Drug Design J. Med. Chem. 2013, 56, 3446– 3455
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  There is no corresponding record for this reference.
35. 35
  Huang, J.; Chen, S.; Zhang, J. J.; Huang, X. Y. Crystal Structure of Oligomeric Beta1-Adrenergic G Protein-Coupled Receptors in Ligand-Free Basal State Nat. Struct. Mol. Biol. 2013, 20, 419– 425
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  35
  Crystal structure of oligomeric β1-adrenergic G protein-coupled receptors in ligand-free basal state
  Huang, Jianyun; Chen, Shuai; Zhang, J. Jillian; Huang, Xin-Yun
  Nature Structural & Molecular Biology (2013), 20 (4), 419-425CODEN: NSMBCU; ISSN:1545-9993. (Nature Publishing Group)
  G protein-coupled receptors (GPCRs) mediate transmembrane signaling. Before ligand binding, GPCRs exist in a basal state. Crystal structures of several GPCRs bound with antagonists or agonists have been solved. However, the crystal structure of the ligand-free basal state of a GPCR, the starting point of GPCR activation and function, had not yet been detd. Here we report the x-ray crystal structure of the ligand-free basal state of a GPCR in a lipid membrane-like environment. Oligomeric turkey β1-adrenergic receptors display two dimer interfaces. One interface involves the transmembrane domain (TM) 1, TM2, the C-terminal H8 and extracellular loop 1. The other interface engages residues from TM4, TM5, intracellular loop 2 and extracellular loop 2. Structural comparisons show that this ligand-free state is in an inactive conformation. This provides the structural basis of GPCR dimerization and oligomerization.
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36. 36
  Carlsson, J.; Coleman, R. G.; Setola, V.; Irwin, J. J.; Fan, H.; Schlessinger, A.; Sali, A.; Roth, B. L.; Shoichet, B. K. Ligand Discovery from a Dopamine D3 Receptor Homology Model and Crystal Structure Nat. Chem. Biol. 2011, 7, 769– 778
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  36
  Ligand discovery from a dopamine D3 receptor homology model and crystal structure
  Carlsson, Jens; Coleman, Ryan G.; Setola, Vincent; Irwin, John J.; Fan, Hao; Schlessinger, Avner; Sali, Andrej; Roth, Bryan L.; Shoichet, Brian K.
  Nature Chemical Biology (2011), 7 (11), 769-778CODEN: NCBABT; ISSN:1552-4450. (Nature Publishing Group)
  G protein-coupled receptors (GPCRs) are intensely studied as drug targets and for their role in signaling. With the detn. of the first crystal structures, interest in structure-based ligand discovery increased. Unfortunately, for most GPCRs no exptl. structures are available. The detn. of the D3 receptor structure and the challenge to the community to predict it enabled a fully prospective comparison of ligand discovery from a modeled structure vs. that of the subsequently released crystal structure. Over 3.3 million mols. were docked against a homol. model, and 26 of the highest ranking were tested for binding. Six had affinities ranging from 0.2 to 3.1 μM. Subsequently, the crystal structure was released and the docking screen repeated. Of the 25 compds. selected, five had affinities ranging from 0.3 to 3.0 μM. One of the new ligands from the homol. model screen was optimized for affinity to 81 nM. The feasibility of docking screens against modeled GPCRs more generally is considered.
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37. 37
  Carlsson, J.; Yoo, L.; Gao, Z. G.; Irwin, J. J.; Shoichet, B. K.; Jacobson, K. A. Structure-Based Discovery of A2a Adenosine Receptor Ligands J. Med. Chem. 2010, 53, 3748– 3755
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  37
  Structure-Based Discovery of A2A Adenosine Receptor Ligands
  Carlsson, Jens; Yoo, Lena; Gao, Zhan-Guo; Irwin, John J.; Shoichet, Brian K.; Jacobson, Kenneth A.
  Journal of Medicinal Chemistry (2010), 53 (9), 3748-3755CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
  The recent detn. of X-ray structures of pharmacol. relevant GPCRs has made these targets accessible to structure-based ligand discovery. Here we explore whether novel chemotypes may be discovered for the A2A adenosine receptor, based on complementarity to its recently detd. structure. The A2A adenosine receptor signals in the periphery and the CNS, with agonists explored as anti-inflammatory drugs and antagonists explored for neurodegenerative diseases. We used mol. docking to screen a 1.4 million compd. database against the X-ray structure computationally and tested 20 high-ranking, previously unknown mols. exptl. Of these 35% showed substantial activity with affinities between 200 nM and 9 μM. For the most potent of these new inhibitors, over 50-fold specificity was obsd. for the A2A vs. the related A1 and A3 subtypes. These high hit rates and affinities at least partly reflect the bias of com. libraries toward GPCR-like chemotypes, an issue that we attempt to investigate quant. Despite this bias, many of the most potent new ligands were novel, dissimilar from known ligands, providing new lead structures for modulation of this medically important target.
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38. 38
  de Graaf, C.; Kooistra, A. J.; Vischer, H. F.; Katritch, V.; Kuijer, M.; Shiroishi, M.; Iwata, S.; Shimamura, T.; Stevens, R. C.; de Esch, I. J.; Leurs, R. Crystal Structure-Based Virtual Screening for Fragment-Like Ligands of the Human Histamine H(1) Receptor J. Med. Chem. 2011, 54, 8195– 8206
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  There is no corresponding record for this reference.
39. 39
  Katritch, V.; Jaakola, V. P.; Lane, J. R.; Lin, J.; Ijzerman, A. P.; Yeager, M.; Kufareva, I.; Stevens, R. C.; Abagyan, R. Structure-Based Discovery of Novel Chemotypes for Adenosine a(2a) Receptor Antagonists J. Med. Chem. 2010, 53, 1799– 1809
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  39
  Structure-Based Discovery of Novel Chemotypes for Adenosine A2A Receptor Antagonists
  Katritch, Vsevolod; Jaakola, Veli-Pekka; Lane, J. Robert; Lin, Judy; IJzerman, Adriaan P.; Yeager, Mark; Kufareva, Irina; Stevens, Raymond C.; Abagyan, Ruben
  Journal of Medicinal Chemistry (2010), 53 (4), 1799-1809CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
  The recent progress in crystallog. of G-protein coupled receptors opens an unprecedented venue for structure-based GPCR drug discovery. To test efficiency of the structure-based approach, we performed mol. docking and virtual ligand screening (VLS) of more than 4 million com. available "drug-like" and "lead-like" compds. against the A2AAR 2.6 Å resoln. crystal structure. Out of 56 high ranking compds. tested in A2AAR binding assays, 23 showed affinities under 10 μM, 11 of those had sub-μM affinities and two compds. had affinities under 60 nM. The identified hits represent at least 9 different chem. scaffolds and are characterized by very high ligand efficiency (0.3-0.5 kcal/mol per heavy atom). Significant A2AAR antagonist activities were confirmed for 10 out of 13 ligands tested in functional assays. High success rate, novelty, and diversity of the chem. scaffolds and strong ligand efficiency of the A2AAR antagonists identified in this study suggest practical applicability of receptor-based VLS in GPCR drug discovery.
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40. 40
  Kolb, P.; Rosenbaum, D. M.; Irwin, J. J.; Fung, J. J.; Kobilka, B. K.; Shoichet, B. K. Structure-Based Discovery of Beta2-Adrenergic Receptor Ligands Proc. Natl. Acad. Sci. U.S.A. 2009, 106, 6843– 6848
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  40
  Structure-based discovery of β2-adrenergic receptor ligands
  Kolb, Peter; Rosenbaum, Daniel M.; Irwin, John J.; Fung, Juan Jose; Kobilka, Brian K.; Shoichet, Brian K.
  Proceedings of the National Academy of Sciences of the United States of America (2009), 106 (16), 6843-6848CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
  Aminergic G protein-coupled receptors (GPCRs) have been a major focus of pharmaceutical research for many years. Due partly to the lack of reliable receptor structures, drug discovery efforts have been largely ligand-based. The recently detd. X-ray structure of the β2-adrenergic receptor offers an opportunity to investigate the advantages and limitations inherent in a structure-based approach to ligand discovery against this and related GPCR targets. Approx. 1 million com. available, "lead-like" mols. were docked against the β2-adrenergic receptor structure. On testing of 25 high-ranking mols., 6 were active with binding affinities <4 μM, with the best mol. binding with a Ki of 9 nM (95% confidence interval 7-10 nM). Five of these mols. were inverse agonists. The high hit rate, the high affinity of the most potent mol., the discovery of unprecedented chemotypes among the new inhibitors, and the apparent bias toward inverse agonists among the docking hits, have implications for structure-based approaches against GPCRs that recognize small org. mols.
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41. 41
  Mysinger, M. M.; Weiss, D. R.; Ziarek, J. J.; Gravel, S.; Doak, A. K.; Karpiak, J.; Heveker, N.; Shoichet, B. K.; Volkman, B. F. Structure-Based Ligand Discovery for the Protein-Protein Interface of Chemokine Receptor Cxcr4 Proc. Natl. Acad. Sci. U.S.A. 2012, 109, 5517– 5522
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  There is no corresponding record for this reference.
42. 42
  de Graaf, C.; Rognan, D. Customizing G Protein-Coupled Receptor Models for Structure-Based Virtual Screening Curr. Pharm. Des. 2009, 15, 4026– 4048
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  42
  Customizing G protein-coupled receptor models for structure-based virtual screening
  de Graaf, Chris; Rognan, Didier
  Current Pharmaceutical Design (2009), 15 (35), 4026-4048CODEN: CPDEFP; ISSN:1381-6128. (Bentham Science Publishers Ltd.)
  This review will focus on the construction, refinement, and validation of G Protein-coupled receptor models for the purpose of structure-based virtual screening. Practical tips and tricks derived from concrete modeling and virtual screening exercises to overcome the problems and pitfalls assocd. with the different steps of the receptor modeling workflow will be presented. These examples will not only include rhodopsin-like (class A), but also secretine-like (class B), and glutamate-like (class C) receptors. In addn., the review will present a careful comparative anal. of current crystal structures and their implication on homol. modeling. The following themes will be discussed: (i) the use of exptl. anchors in guiding the modeling procedure; (ii) amino acid sequence alignments; (iii) ligand binding mode accommodation and binding cavity expansion; (iv) proline-induced kinks in transmembrane helixes; (v) binding mode prediction and virtual screening by receptor-ligand interaction fingerprint scoring; (vi) extracellular loop modeling; (vii) virtual filtering schemes. Finally, an overview of several successful structure-based screening shows that receptor models, despite structural inaccuracies, can be efficiently used to find novel ligands.
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43. 43
  Kooistra, A. J.; Roumen, L.; Leurs, R.; de Esch, I. J.; de Graaf, C. From Heptahelical Bundle to Hits from the Haystack: Structure-Based Virtual Screening for GPCR Ligands. Methods Enzymol. 2013, 522, 279– 336.
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  There is no corresponding record for this reference.
44. 44
  Palczewski, K.; Kumasaka, T.; Hori, T.; Behnke, C. A.; Motoshima, H.; Fox, B. A.; Le Trong, I.; Teller, D. C.; Okada, T.; Stenkamp, R. E.; Yamamoto, M.; Miyano, M. Crystal Structure of Rhodopsin: A G Protein-Coupled Receptor Science 2000, 289, 739– 745
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  44
  Crystal structure of rhodopsin: A G protein-coupled receptor
  Palczewski, Krzysztof; Kumasaka, Takashi; Hori, Tetsuya; Behnke, Craig A.; Motoshima, Hiroyuki; Fox, Brian A.; Le Trong, Isolde; Teller, David C.; Okada, Tetsuji; Stenkamp, Ronald E.; Yamamoto, Masaki; Miyano, Masashi
  Science (Washington, D. C.) (2000), 289 (5480), 739-745CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
  Heterotrimeric guanine nucleotide-binding protein (G protein)-coupled receptors (GPCRs) respond to a variety of different external stimuli and activate G proteins. GPCRs share many structural features, including a bundle of seven transmembrane α-helixes connected by six loops of varying lengths. We detd. the structure of rhodopsin from diffraction data extending to 2.8 angstroms resoln. The highly organized structure in the extracellular region, including a conserved disulfide bridge, forms a basis for the arrangement of the seven-helix transmembrane motif. The ground-state chromophore, 11-cis-retinal, holds the transmembrane region of the protein in the inactive conformation. Interactions of the chromophore with a cluster of key residues det. the wavelength of the max. absorption. Changes in these interactions among rhodopsins facilitate color discrimination. Identification of a set of residues that mediate interactions between the transmembrane helixes and the cytoplasmic surface, where G-protein activation occurs, also suggests a possible structural change upon photoactivation.
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45. 45
  de Graaf, C.; Rein, C.; Piwnica, D.; Giordanetto, F.; Rognan, D. Structure-Based Discovery of Allosteric Modulators of Two Related Class B G-Protein-Coupled Receptors ChemMedChem 2011, 6, 2159– 2169
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  There is no corresponding record for this reference.
46. 46
  Kellenberger, E.; Springael, J. Y.; Parmentier, M.; Hachet-Haas, M.; Galzi, J. L.; Rognan, D. Identification of Nonpeptide Ccr5 Receptor Agonists by Structure-Based Virtual Screening J. Med. Chem. 2007, 50, 1294– 1303
  Google Scholar
  46
  Identification of Nonpeptide CCR5 Receptor Agonists by Structure-based Virtual Screening
  Kellenberger, Esther; Springael, Jean-Yves; Parmentier, Marc; Hachet-Haas, Muriel; Galzi, Jean-Luc; Rognan, Didier
  Journal of Medicinal Chemistry (2007), 50 (6), 1294-1303CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
  A three-dimensional model of the chemokine receptor CCR5 has been built to fulfill structural peculiarities of its α-helix bundle and to distinguish known CCR5 antagonists from randomly chosen drug-like decoys. In silico screening of a library of 1.6 million com. available compds. against the CCR5 model by sequential filters (drug-likeness, 2-D pharmacophore, 3-D docking, scaffold clustering) yielded a hit list of 59 compds., out of which 10 exhibited a detectable binding affinity to the CCR5 receptor. Unexpectedly, most binders tested in a functional assay were shown to be agonists of the CCR5 receptor. A follow-up database query based on similarity to the most potent binders identified three new CCR5 agonists. Despite a moderate affinity of all nonpeptide ligands for the CCR5 receptor, one of the agonists was shown to promote efficient receptor internalization, which is a process therapeutically favorable for protection against HIV-1 infection.
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47. 47
  Kiss, R.; Kiss, B.; Konczol, A.; Szalai, F.; Jelinek, I.; Laszlo, V.; Noszal, B.; Falus, A.; Keseru, G. M. Discovery of Novel Human Histamine H4 Receptor Ligands by Large-Scale Structure-Based Virtual Screening J. Med. Chem. 2008, 51, 3145– 3153
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  47
  Discovery of Novel Human Histamine H4 Receptor Ligands by Large-Scale Structure-Based Virtual Screening
  Kiss, Robert; Kiss, Bela; Konczol, Arpad; Szalai, Ferenc; Jelinek, Ivett; Laszlo, Valeria; Noszal, Bela; Falus, Andras; Keseru, Gyorgy M.
  Journal of Medicinal Chemistry (2008), 51 (11), 3145-3153CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
  A structure-based virtual screening (SBVS) was conducted on a ligand-supported homol. model of the human histamine H4 receptor (hH4R). More than 8.7 million 3D structures derived from different vendor databases were investigated by docking to the hH4R binding site using FlexX. A total of 255 selected compds. were tested by radioligand binding assay and 16 of them possessed significant [3H]histamine displacement. Several novel scaffolds were identified that can be used to develop selective H4 ligands in the future. As far as we know, this is the first SBVS reported on H4R, representing one of the largest virtual screens validated by the biol. evaluation of the virtual hits.
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48. 48
  Salo, O. M.; Raitio, K. H.; Savinainen, J. R.; Nevalainen, T.; Lahtela-Kakkonen, M.; Laitinen, J. T.; Jarvinen, T.; Poso, A. Virtual Screening of Novel Cb2 Ligands Using a Comparative Model of the Human Cannabinoid Cb2 Receptor J. Med. Chem. 2005, 48, 7166– 7171
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  48
  Virtual Screening of Novel CB2 Ligands Using a Comparative Model of the Human Cannabinoid CB2 Receptor
  Salo, Outi M. H.; Raitio, Katri H.; Savinainen, Juha R.; Nevalainen, Tapio; Lahtela-Kakkonen, Maija; Laitinen, Jarmo T.; Jaervinen, Tomi; Poso, Antti
  Journal of Medicinal Chemistry (2005), 48 (23), 7166-7171CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
  To identify novel selective CB2 lead compds., a comparative model of the CB2 receptor was constructed using the high-resoln. bovine rhodopsin X-ray structure as a template. The CB2 model was utilized both in building the database queries and in filtering the hit compds. by a docking and scoring method. In G-protein activation assays, 1-isoquinolyl[3-(trifluoromethyl)phenyl]methanone (40, NRB 04079) was found to act as a selective agonist at the human CB2 receptor.
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49. 49
  Tikhonova, I. G.; Sum, C. S.; Neumann, S.; Engel, S.; Raaka, B. M.; Costanzi, S.; Gershengorn, M. C. Discovery of Novel Agonists and Antagonists of the Free Fatty Acid Receptor 1 (Ffar1) Using Virtual Screening J. Med. Chem. 2008, 51, 625– 633
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  49
  Discovery of novel agonists and antagonists of the free fatty acid receptor 1 (FFAR1) using virtual screening
  Tikhonova, Irina G.; Sum, Chi Shing; Neumann, Susanne; Engel, Stanislav; Raaka, Bruce M.; Costanzi, Stefano; Gershengorn, Marvin C.
  Journal of Medicinal Chemistry (2008), 51 (3), 625-633CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
  The G-protein-coupled receptor free fatty acid receptor 1 (FFAR1), previously named GPR40, is a possible novel target for the treatment of type 2 diabetes. In an attempt to identify new ligands for this receptor, we performed virtual screening (VS) based on 2-dimensional (2D) similarity, 3-dimensional (3D) pharmacophore searches, and docking studies by the structure of known agonists and our model of the ligand binding site, which was validated by mutagenesis. VS of a database of 2.6 million compds. followed by extn. of structural neighbors of functionally confirmed hits resulted in identification of 15 compds. active at FFAR1 either as full agonists, partial agonists, or pure antagonists. Site-directed mutagenesis and docking studies revealed different patterns of ligand-receptor interactions and provided important information on the role of specific amino acids in binding and activation of FFAR1.
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50. 50
  Varady, J.; Wu, X.; Fang, X.; Min, J.; Hu, Z.; Levant, B.; Wang, S. Molecular Modeling of the Three-Dimensional Structure of Dopamine 3 (D3) Subtype Receptor: Discovery of Novel and Potent D3 Ligands through a Hybrid Pharmacophore- and Structure-Based Database Searching Approach J. Med. Chem. 2003, 46, 4377– 4392
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  50
  Molecular Modeling of the Three-Dimensional Structure of Dopamine 3 (D3) Subtype Receptor: Discovery of Novel and Potent D3 Ligands through a Hybrid Pharmacophore- and Structure-Based Database Searching Approach
  Varady, Judith; Wu, Xihan; Fang, Xueliang; Min, Ji; Hu, Zengjian; Levant, Beth; Wang, Shaomeng
  Journal of Medicinal Chemistry (2003), 46 (21), 4377-4392CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
  The dopamine 3 (D3) subtype receptor has been implicated in several neurol. conditions, and potent and selective D3 ligands may have therapeutic potential for the treatment of drug addiction, Parkinson's disease, and schizophrenia. In this paper, we report computational homol. modeling of the D3 receptor based upon the high-resoln. x-ray structure of rhodopsin, extensive structural refinement in the presence of explicit lipid bilayer and water environment, and validation of the refined D3 structural models using exptl. data. We further describe the development, validation, and application of a hybrid computational screening approach for the discovery of several classes of novel and potent D3 ligands. This computational approach employs stepwise pharmacophore and structure-based searching of a large three-dimensional chem. database for the identification of potential D3 ligands. The obtained hits are then subjected to structural novelty screening, and the most promising compds. are tested in a D3 binding assay. Using this approach we identified four compds. with Ki values better than 100 nM and eight compds. with Ki values better than 1 μM out of 20 compds. selected for testing in the D3 receptor binding assay. Our results suggest that the D3 structural models obtained from this study may be useful for the discovery and design of novel and potent D3 ligands. Furthermore, the employed hybrid approach may be more effective for lead discovery from a large chem. database than either pharmacophore-based or structure-based database screening alone.
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51. 51
  Kooistra, A. J.; Leurs, R.; de Esch, I. J.; de Graaf, C. From Three-Dimensional GPCR Structure to Rational Ligand Discovery. Adv. Exp. Med. Biol. 2014, 796, 129– 157
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  There is no corresponding record for this reference.
52. 52
  Weiss, D. R.; Ahn, S.; Sassano, M. F.; Kleist, A.; Zhu, X.; Strachan, R.; Roth, B. L.; Lefkowitz, R. J.; Shoichet, B. K. Conformation Guides Molecular Efficacy in Docking Screens of Activated Beta-2 Adrenergic G Protein Coupled Receptor ACS Chem. Biol. 2013, 8, 1018– 1026
  Google Scholar
  There is no corresponding record for this reference.
53. 53
  Moitessier, N.; Englebienne, P.; Lee, D.; Lawandi, J.; Corbeil, C. R. Towards the Development of Universal, Fast and Highly Accurate Docking/Scoring Methods: A Long Way to Go Br. J. Pharmacol. 2008, 153 (Suppl 1) S7– 26
  Google Scholar
  53
  Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go
  Moitessier, N.; Englebienne, P.; Lee, D.; Lawandi, J.; Corbeil, C. R.
  British Journal of Pharmacology (2008), 153 (Suppl. 1), S7-S26CODEN: BJPCBM; ISSN:0007-1188. (Nature Publishing Group)
  A review. Accelerating the drug discovery process requires predictive computational protocols capable of reducing or simplifying the synthetic and/or combinatorial challenge. Docking-based virtual screening methods have been developed and successfully applied to a no. of pharmaceutical targets. In this review, we first present the current status of docking and scoring methods, with exhaustive lists of these. We next discuss reported comparative studies, outlining criteria for their interpretation. In the final section, we describe some of the remaining developments that would potentially lead to a universally applicable docking/scoring method. Published online 26 Nov. 2007.
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54. 54
  Sabio, M.; Jones, K.; Topiol, S. Use of the X-Ray Structure of the Beta2-Adrenergic Receptor for Drug Discovery. Part 2: Identification of Active Compounds Bioorg. Med. Chem. Lett. 2008, 18, 5391– 5395
  Google Scholar
  54
  Use of the X-ray structure of the β2-adrenergic receptor for drug discovery. Part 2: Identification of active compounds
  Sabio, Michael; Jones, Kenneth; Topiol, Sid
  Bioorganic & Medicinal Chemistry Letters (2008), 18 (20), 5391-5395CODEN: BMCLE8; ISSN:0960-894X. (Elsevier Ltd.)
  The recently published X-ray structures of the β2-adrenergic receptor are the first examples of ligand-mediated GPCR crystal structures. We have previously performed computational studies that examine the potential viability of these structures for use in drug design, exploiting known ligand activities. Our previous study and a newly reported β2/Timolol X-ray complex provide validation of the computational approaches. In the present work, we use the X-ray structures to ext., via in silico high-throughput docking, compds. from proprietary and com. databases and demonstrate the successful identification of active compds. by radioligand binding.
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55. 55
  de Graaf, C.; Rognan, D. Selective Structure-Based Virtual Screening for Full and Partial Agonists of the Beta2 Adrenergic Receptor J. Med. Chem. 2008, 51, 4978– 4985
  Google Scholar
  55
  Selective Structure-Based Virtual Screening for Full and Partial Agonists of the β2 Adrenergic Receptor
  de Graaf, Chris; Rognan, Didier
  Journal of Medicinal Chemistry (2008), 51 (16), 4978-4985CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
  The recently solved high-resoln. X-ray structure of the β2 adrenergic receptor has been challenged for its ability to discriminate inverse agonists/antagonists from partial/full agonists. Whereas the X-ray structure of the ground state receptor was unsuitable to distinguish true ligands with different functional effects, modifying this structure to reflect early conformational events in receptor activation led to a receptor model able to selectively retrieve full and partial agonists by structure-based virtual screening. The use of a topol. scoring function based on mol. interaction fingerprints was shown to be mandatory to properly rank docking poses and achieve acceptable enrichments for partial and full agonists only.
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  Katritch, V.; Reynolds, K. A.; Cherezov, V.; Hanson, M. A.; Roth, C. B.; Yeager, M.; Abagyan, R. Analysis of Full and Partial Agonists Binding to Beta2-Adrenergic Receptor Suggests a Role of Transmembrane Helix V in Agonist-Specific Conformational Changes J. Mol. Recognit. 2009, 22, 307– 318
  Google Scholar
  56
  Analysis of full and partial agonists binding to β2-adrenergic receptor suggests a role of transmembrane helix V in agonist-specific conformational changes
  Katritch, Vsevolod; Reynolds, Kimberly A.; Cherezov, Vadim; Hanson, Michael A.; Roth, Christopher B.; Yeager, Mark; Abagyan, Ruben
  Journal of Molecular Recognition (2009), 22 (4), 307-318CODEN: JMORE4; ISSN:0952-3499. (John Wiley & Sons Ltd.)
  The 2.4 Å crystal structure of the β2-adrenergic receptor (β2AR) in complex with the high-affinity inverse agonist (-)-carazolol provides a detailed structural framework for the anal. of ligand recognition by adrenergic receptors. Insights into agonist binding and the corresponding conformational changes triggering G-protein coupled receptor (GPCR) activation mechanism are of special interest. While the carazolol pocket captured in the β2AR crystal structure accommodates (-)-isoproterenol and other agonists without steric clashes, a finite movement of the flexible extracellular part of TM-V helix (TM-Ve) obtained by receptor optimization in the presence of docked ligand can further improve the calcd. binding affinities for agonist compds. Tilting of TM-Ve towards the receptor axis provides a more complete description of polar receptor-ligand interactions for full and partial agonists, by enabling optimal engagement of agonists with two exptl. identified anchor sites, formed by Asp 113/Asn 312 and Ser 203/Ser 204/Ser 207 side chains. Further, receptor models incorporating a flexible TM-V backbone allow reliable prediction of binding affinities for a set of diverse ligands, suggesting potential utility of this approach to design of effective and subtype-specific agonists for adrenergic receptors. Systematic differences in capacity of partial, full and inverse agonists to induce TM-V helix tilt in the β2AR model suggest potential role of TM-V as a conformational "rheostat" involved in the whole spectrum of β2AR responses to small mol. signals.
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  Reynolds, K. A.; Katritch, V.; Abagyan, R. Identifying Conformational Changes of the Beta(2) Adrenoceptor That Enable Accurate Prediction of Ligand/Receptor Interactions and Screening for Gpcr Modulators J. Comput. Aided Mol. Des. 2009, 23, 273– 288
  Google Scholar
  57
  Identifying conformational changes of the β2 adrenoceptor that enable accurate prediction of ligand/receptor interactions and screening for GPCR modulators
  Reynolds, Kimberly A.; Katritch, Vsevolod; Abagyan, Ruben
  Journal of Computer-Aided Molecular Design (2009), 23 (5), 273-288CODEN: JCADEQ; ISSN:0920-654X. (Springer)
  The new β2 Adrenoceptor (β2AR) crystal structures provide a high-resoln. snapshot of receptor interactions with two particular partial inverse agonists, (-)-carazolol and timolol. However, both exptl. and computational studies of GPCR structure are significantly complicated by the existence of multiple conformational states coupled to ligand type and receptor activity. Agonists and antagonists induce or stabilize distinct changes in receptor structure that mediate a range of pharmacol. activities. In this work, the authors (1) established that the existing β2AR crystallog. conformers can be extended to describe ligand/receptor interactions for addnl. antagonist types, (2) generated agonist-bound receptor conformations, and (3) validated these models for agonist and antagonist virtual ligand screening (VLS). Using a ligand directed refinement protocol, the authors derived a single agonist-bound receptor conformation that selectively retrieved a diverse set of full and partial β2AR agonists in VLS trials. Addnl., the impact of extracellular loop two conformation on VLS was assessed by docking studies with rhodopsin-based β2AR homol. models, and loop-deleted receptor models. A general strategy for constructing and selecting agonist-bound receptor pocket conformations is presented, which may prove broadly useful in creating agonist and antagonist bound models for other GPCRs.
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58. 58
  Vilar, S.; Karpiak, J.; Berk, B.; Costanzi, S. In Silico Analysis of the Binding of Agonists and Blockers to the Beta2-Adrenergic Receptor J. Mol. Graph. Model. 2011, 29, 809– 817
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  58
  In silico analysis of the binding of agonists and blockers to the β2-adrenergic receptor
  Vilar, Santiago; Karpiak, Joel; Berk, Barkin; Costanzi, Stefano
  Journal of Molecular Graphics & Modelling (2011), 29 (6), 809-817CODEN: JMGMFI; ISSN:1093-3263. (Elsevier Ltd.)
  Activation of G protein-coupled receptors (GPCRs) is a complex phenomenon. Here, we applied Induced Fit Docking (IFD) in tandem with linear discriminant anal. (LDA) to generate hypotheses on the conformational changes induced to the β2-adrenergic receptor by agonist binding, preliminary to the sequence of events that characterize activation of the receptor. This anal., corroborated by a follow-up mol. dynamics study, suggested that agonists induce subtle movements to the fifth transmembrane domain (TM5) of the receptor. Furthermore, mol. dynamics also highlighted a correlation between movements of TM5 and the second extracellular loop (EL2), suggesting that freedom of motion of EL2 is required for the agonist-induced TM5 displacement. Importantly, we also showed that the IFD/LDA procedure can be used as a computational means to distinguish agonists from blockers on the basis of the differential conformational changes induced to the receptor. In particular, the two most predictive models obtained are based on the RMSD induced to Ser207 and on the counterclockwise rotation induced to TM5.
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59. 59
  Kooistra, A. J.; Kuhne, S.; de Esch, I. J.; Leurs, R.; de Graaf, C. A Structural Chemogenomics Analysis of Aminergic Gpcrs: Lessons for Histamine Receptor Ligand Design Br. J. Pharmacol. 2013, 170, 101– 126
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  59
  A structural chemogenomics analysis of aminergic GPCRs: lessons for histamine receptor ligand design
  Kooistra, A. J.; Kuhne, S.; de Esch, I. J. P.; Leurs, R.; de Graaf, C.
  British Journal of Pharmacology (2013), 170 (1), 101-126CODEN: BJPCBM; ISSN:1476-5381. (Wiley-Blackwell)
  Background and Purpose Chemogenomics focuses on the discovery of new connections between chem. and biol. space leading to the discovery of new protein targets and biol. active mols. G-protein coupled receptors (GPCRs) are a particularly interesting protein family for chemogenomics studies because there is an overwhelming amt. of ligand binding affinity data available. The increasing no. of aminergic GPCR crystal structures now for the first time allows the integration of chemogenomics studies with high-resoln. structural analyses of GPCR-ligand complexes. Exptl. Approach In this study, we have combined ligand affinity data, receptor mutagenesis studies, and amino acid sequence analyses to high-resoln. structural analyses of (hist)aminergic GPCR-ligand interactions. This integrated structural chemogenomics anal. is used to more accurately describe the mol. and structural determinants of ligand affinity and selectivity in different key binding regions of the crystd. aminergic GPCRs, and histamine receptors in particular. Key Results Our investigations highlight interesting correlations and differences between ligand similarity and ligand binding site similarity of different aminergic receptors. Apparent discrepancies can be explained by combining detailed anal. of crystd. or predicted protein-ligand binding modes, receptor mutation studies, and ligand structure-selectivity relationships that identify local differences in essential pharmacophore features in the ligand binding sites of different receptors. Conclusions and Implications We have performed structural chemogenomics studies that identify links between (hist)aminergic receptor ligands and their binding sites and binding modes. This knowledge can be used to identify structure-selectivity relationships that increase our understanding of ligand binding to (hist)aminergic receptors and hence can be used in future GPCR ligand discovery and design.
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60. 60
  Strader, C. D.; Candelore, M. R.; Hill, W. S.; Sigal, I. S.; Dixon, R. A. Identification of Two Serine Residues Involved in Agonist Activation of the Beta-Adrenergic Receptor J. Biol. Chem. 1989, 264, 13572– 13578
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  60
  Identification of two serine residues involved in agonist activation of the β-adrenergic receptor
  Strader, Catherine D.; Candelore, Mari Rios; Hill, Wendy S.; Sigal, Irving S.; Dixon, Richard A. F.
  Journal of Biological Chemistry (1989), 264 (23), 13572-8CODEN: JBCHA3; ISSN:0021-9258.
  Pharmacophore mapping of the ligand-binding domain of the β-adrenergic receptor has revealed specific mol. interactions which are important for agonist and antagonist binding to the receptor. Previous site-directed mutagenesis expts. have demonstrated that the binding of amine agonists and antagonists to the receptor involves an interaction between the amine group of the ligand and the carboxylate side chain of Asp113 in the 3rd hydrophobic domain of the receptor. Two serine residues, at positions 204 and 207 in the 5th hydrophobic domain of the β-adrenergic receptor, which are crit. for agonist binding and activation of the receptor have now been identified. These serine residues are conserved with G-protein-coupled receptors which bind catecholamine agonists, but not with receptors whose endogenous ligands do not have the catechol moiety. Removal of the OH side chain from either Ser204 or Ser207 by substitution of the serine residue with an alanine attenuates the activity of catecholamine agonists at the receptor. The effects of these mutations on agonist activity are mimicked selectively by the removal of the catechol OH moieties from the arom. ring of the agonist. The data suggest that the interaction of catecholamine agonists with the β-adrenergic receptor involves 2 H bonds, one between the OH side chain of Ser204 and the meta-OH group of the ligand and a second between the OH side chain of Ser207 and the para-OH group of the ligand.
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61. 61
  Kikkawa, H.; Kurose, H.; Isogaya, M.; Sato, Y.; Nagao, T. Differential Contribution of Two Serine Residues of Wild Type and Constitutively Active Beta2-Adrenoceptors to the Interaction with Beta2-Selective Agonists Br. J. Pharmacol. 1997, 121, 1059– 1064
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  61
  Differential contribution of two serine residues of wild type and constitutively active β2-adrenoceptors to the interaction with β2-selective agonists
  Kikkawa, Hideo; Kurose, Hitoshi; Isogaya, Masafumi; Sato, Yoji; Nagao, Taku
  British Journal of Pharmacology (1997), 121 (6), 1059-1064CODEN: BJPCBM; ISSN:0007-1188. (Stockton)
  The authors have studied the difference in receptor binding activity between partial and full β2-adrenoceptor agonists and the abilities of the agonists to interact with Ser204 and Ser207 in the fifth transmembrane region of the β2-adrenoceptor, amino acid residues that are important for activation of the β2-adrenoceptor. In the binding study with [125I]-iodocyanopindolol, the Ki values of (±)-salbutamol, (±)-salmeterol, TA-2005 and (-)-isoprenaline for the β2-adrenoceptor expressed in COS-7 cell membranes were 3340, 21.0, 12.0 and 904 nM, resp. The β1/β2 selectively of these agonists was in the order of (±)-salmeterol (332-fold) > A-2005 (52.8) > (±)-salbutamol (6.8) > (-)-isoprenaline (1.1), and the β3-/β2-adrenoceptor selectivity of these agonists was in the order of TA-2005 (150-fold) > (±)-salmeterol (88.6) > (±)-salbutamol (10.4) > (-)-isoprenaline (3.2). The maximal activation of adenylyl cyclase by stimulation of the β1-, β2- and β3-adrenoceptors by TA-2005 was 32, 100 and 100% of that by (-)-isoprenaline, resp., indicating that TA-2005 is a full agonist at the β2- and β3-adrenoceptors and a partial agonist at the β1-adrenoceptor. (±)-Salbutamol and (±)-salmeterol were partial agonists at both β1- (8%) and 9% of (-)-isoprenaline and β2- (83% and 74% of (-)-isoprenaline) adrenoceptors. The affinities of full agonists, TA-2005 and (-)-isoprenaline, were markedly decreased by substitution of Ala for Ser204 (S204A) of the β2-adrenoceptor, whereas this substitution slightly reduced the affinities of partial agonists, (±)-salbutamol and (±)-salmeterol. Although the affinities of full agonists for the S207A-β2-adrenoceptor were decrease, those of partial agonists for the S207A-β2-adrenoceptor were essentially the same as for the wild type receptor. The constitutively active mutant (L266S, L272A) of the β2-adrenoceptor had an increased affinity for all four agonists. The affinities of full agonists were decreased by substitution of Ser204 of the constitutively active mutant, whereas the degree of decrease was smaller than that caused by the substitution of the wild type receptor. Although the affinities of (±)-salbutamol and (±)-salmeterol for the S207A-β2-adrenoceptor were essentially the same as those for the wild type β2-adrenoceptor, the affinities of (±)-salbutamol and (±)-salmeterol for the constitutively active β2-adrenoceptor were decreased by substitution of Ser207. These results suggest that Ser204 and Ser207 of the wild type and constitutively active β2-adrenoceptors differentially interacted with β2-selective agonists.
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62. 62
  Liapakis, G.; Ballesteros, J. A.; Papachristou, S.; Chan, W. C.; Chen, X.; Javitch, J. A. The Forgotten Serine. A Critical Role for Ser-2035.42 in Ligand Binding to and Activation of the Beta 2-Adrenergic Receptor J. Biol. Chem. 2000, 275, 37779– 37788
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  62
  The forgotten serine. A critical role for Ser-2035.42 in ligand binding to and activation of the β2-adrenergic receptor
  Liapakis, George; Ballesteros, Juan A.; Papachristou, Stavros; Chan, Wai Chi; Chen, Xun; Javitch, Jonathan A.
  Journal of Biological Chemistry (2000), 275 (48), 37779-37788CODEN: JBCHA3; ISSN:0021-9258. (American Society for Biochemistry and Molecular Biology)
  Previous work in the β2-adrenergic receptor demonstrated crit. interactions between Ser-204 and Ser-207 in the fifth membrane-spanning segment and the meta-OH and para-OH, resp., of catecholamine agonists. Using the substituted cysteine accessibility method in the β2-adrenergic receptor, we have found that in addn. to Ser-204 and Ser-207, Ser-203 is also accessible on the surface of the binding-site crevice and is occluded by bound agonist. Mutation of Ser-203 to Ala, Val, or Cys reduced the binding affinity and adenylyl cyclase-activating potency of agonists contg. a meta-OH, whereas their affinities and potencies were largely preserved by mutation of Ser-203 to Thr, which maintained an OH at this position. Thus both Ser-203 and Ser-204 appear to interact with the meta-OH of catecholamines, perhaps through a bifurcated H bond. Furthermore, the removal of the OH at position 203 led to a significant loss of affinity of antagonists with nitrogen in their heterocyclic ring structure. The greatest effect was seen with pindolol, a partial agonist, suggesting that a H bond between the heterocyclic ring and Ser-203 may play a role in partial agonism. In contrast, the affinities of antagonists such as propranolol or alprenolol, which have cyclic structures without H-bonding capability, were unaltered after mutation of Ser-203.
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63. 63
  Sato, T.; Kobayashi, H.; Nagao, T.; Kurose, H. Ser203 as Well as Ser204 and Ser207 in Fifth Transmembrane Domain of the Human Beta2-Adrenoceptor Contributes to Agonist Binding and Receptor Activation Br. J. Pharmacol. 1999, 128, 272– 274
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  63
  Ser203 as well as Ser204 and Ser207 in fifth transmembrane domain of the human β2-adrenoceptor contributes to agonist binding and receptor activation
  Sato, Takayuki; Kobayashi, Hiroyuki; Nagao, Taku; Kurose, Hitoshi
  British Journal of Pharmacology (1999), 128 (2), 272-274CODEN: BJPCBM; ISSN:0007-1188. (Stockton Press)
  We examd. the contribution of Ser203 of the human β2-adrenoceptor (β2-AR) to the interaction with isoprenaline. The affinity of (-)-isoprenaline was reduced by substitution of an alanine for Ser203, as well as for Ser204 and Ser207. An (-)-isoprenaline deriv. with only one hydroxyl group, at the meta-position, showed reduced affinity for wild-type β2-AR and S207A-β2-AR and even lower affinities for S203A-β2-AR and S204A-β2-AR. By contrast, an (-)-isoprenaline deriv. with only a para-hydroxyl group showed reduced affinity for wild-type β2-AR but the serine to alanine mutations did not cause further decreases. The EC50 value for cAMP generation in response to (-)-isoprenaline was increased, by about 120 fold, for each alanine-substituted β2-AR mutant. These results suggest that Ser203 of the human β2-AR is important for both ligand binding and receptor activation.
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64. 64
  Ambrosio, C.; Molinari, P.; Cotecchia, S.; Costa, T. Catechol-Binding Serines of Beta(2)-Adrenergic Receptors Control the Equilibrium between Active and Inactive Receptor States Mol. Pharmacol. 2000, 57, 198– 210
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  64
  Catechol-binding serines of β2-adrenergic receptors control the equilibrium between active and inactive receptor states
  Ambrosio, Caterina; Molinari, Paola; Cotecchia, Susanna; Costa, Tommaso
  Molecular Pharmacology (2000), 57 (1), 198-210CODEN: MOPMA3; ISSN:0026-895X. (American Society for Pharmacology and Experimental Therapeutics)
  The binding free energy for the interaction between serines 204 and 207 of the fifth transmembrane helix of the β2-adrenergic receptor (β2-AR) and catecholic hydroxyl (OH) groups of adrenergic agonists was analyzed using double mutant cycles. Binding affinities for catecholic and noncatecholic agonists were measured in wild-type and mutant receptors, carrying alanine replacement of the two serines (S204A, S207A β2-AR), a constitutive activating mutation, or both. The free energy coupling between the losses of binding energy attributable to OH deletion from the ligand and from the receptor indicates a strong interaction (nonadditivity) as expected for a direct binding between the two sets of groups. However, the authors also measured a significant interaction between the deletion of OH groups from the receptor and the constitutive activating mutation. This suggests that a fraction of the decrease in agonist affinity caused by serine mutagenesis may involve a shift in the conformational equil. of the receptor toward the inactive state. Direct measurements using a transient transfection assay confirm this prediction. The constitutive activity of the (S204A, S207A) β2-AR mutant is 50 to 60% lower than that of the wild-type β2-AR. The authors conclude that S204 and S207 do not only provide a docking site for the agonist, but also control the equil. of the receptor between active (R*) and inactive (R) forms.
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65. 65
  Marcou, G.; Rognan, D. Optimizing Fragment and Scaffold Docking by Use of Molecular Interaction Fingerprints J. Chem. Inf. Model. 2007, 47, 195– 207
  Google Scholar
  65
  Optimizing Fragment and Scaffold Docking by Use of Molecular Interaction Fingerprints
  Marcou, Gilles; Rognan, Didier
  Journal of Chemical Information and Modeling (2007), 47 (1), 195-207CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
  Protein-ligand interaction fingerprints have been used to postprocess docking poses of three ligand data sets: a set of 40 low-mol.-wt. compds. from the Protein Data Bank, a collection of 40 scaffolds from pharmaceutically relevant protein ligands, and a database of 19 scaffolds extd. from true cdk2 inhibitors seeded in 2230 scaffold decoys. Four popular docking tools (FlexX, Glide, Gold, and Surflex) were used to generate poses for ligands of the three data sets. In all cases, scoring by the similarity of interaction fingerprints to a given ref. was statistically superior to conventional scoring functions in posing low-mol.-wt. fragments, predicting protein-bound scaffold coordinates according to the known binding mode of related ligands, and screening a scaffold library to enrich a hit list in true cdk2-targeted scaffolds.
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66. 66
  Verlinde, C. L.; Hol, W. G. Structure-Based Drug Design: Progress, Results and Challenges Structure 1994, 2, 577– 587
  Google Scholar
  66
  Structure-based drug design: progress, results and challenges
  Verlinde, Christophe L. M. J.; Hol, Wim G. J.
  Structure (Cambridge, MA, United States) (1994), 2 (7), 577-87CODEN: STRUE6; ISSN:0969-2126.
  A review with 85 refs. Protein structure-based drug design is rapidly gaining momentum. The new opportunities, developments and results in this field are almost unbelievable compared with the situation less than a decade ago.
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67. 67
  Jones, G.; Willett, P.; Glen, R. C.; Leach, A. R.; Taylor, R. Development and Validation of a Genetic Algorithm for Flexible Docking J. Mol. Biol. 1997, 267, 727– 748
  Google Scholar
  67
  Development and validation of a genetic algorithm for flexible docking
  Jones, Gareth; Willett, Peter; Glen, Robert C.; Leach, Andrew R.; Taylor, Robin
  Journal of Molecular Biology (1997), 267 (3), 727-748CODEN: JMOBAK; ISSN:0022-2836. (Academic)
  Prediction of small mol. binding modes to macromols. of known three-dimensional structure is a problem of paramount importance in rational drug design (the "docking" problem). We report the development and validation of the program GOLD (Genetic Optimization for Ligand Docking). GOLD is an automated ligand docking program that uses a genetic algorithm to explore the full range of ligand conformational flexibility with partial flexibility of the protein and satisfies the fundamental requirement that the ligand must displace loosely bound water on binding. Numerous enhancements and modifications have been applied to the original technique resulting in a substantial increase in the reliability and the applicability of the algorithm. The advanced algorithm has been tested on a dataset of 100 complexes extd. from the Brookhaven Protein Data Bank. When used to dock the ligand back into the binding site, GOLD achieved a 71% success rate in identifying the exptl. binding mode.
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68. 68
  Evers, A.; Klebe, G. Successful Virtual Screening for a Submicromolar Antagonist of the Neurokinin-1 Receptor Based on a Ligand-Supported Homology Model J. Med. Chem. 2004, 47, 5381– 5392
  Google Scholar
  68
  Successful virtual screening for a submicromolar antagonist of the neurokinin-1 receptor based on a ligand-supported homology model
  Evers, Andreas; Klebe, Gerhard
  Journal of Medicinal Chemistry (2004), 47 (22), 5381-5392CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
  The neurokinin-1 (NK1) receptor belongs to the family of G-protein-coupled receptors (GPCRs), which represents one of the most relevant target families in small-mol. drug design. In this paper, we describe a homol. modeling of the NK1 receptor based on the high-resoln. X-ray structure of rhodopsin and the successful virtual screening based on this protein model. The NK1 receptor model has been generated using our new MOBILE (modeling binding sites including ligand information explicitly) approach. Starting with preliminary homol. models, it generates improved models of the protein binding pocket together with bound ligands. Ligand information is used as an integral part in the homol. modeling process. For the construction of the NK1 receptor, antagonist CP-96345 was used to restrain the modeling. The quality of the obtained model was validated by probing its ability to accommodate addnl. known NK1 antagonists from structurally diverse classes. On the basis of the generated model and on the anal. of known NK1 antagonists, a pharmacophore model was deduced, which subsequently guided the 2D and 3D database search with UNITY. As a following step, the remaining hits were docked into the modeled binding pocket of the NK1 receptor. Finally, seven compds. were selected for biochem. testing, from which one showed affinity in the submicromolar range. Our results suggest that ligand-supported homol. models of GPCRs may be used as effective platforms for structure-based drug design.
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  Barril, X.; Morley, S. D. Unveiling the Full Potential of Flexible Receptor Docking Using Multiple Crystallographic Structures J. Med. Chem. 2005, 48, 4432– 4443
  Google Scholar
  69
  Unveiling the Full Potential of Flexible Receptor Docking Using Multiple Crystallographic Structures
  Barril, Xavier; Morley, S. David
  Journal of Medicinal Chemistry (2005), 48 (13), 4432-4443CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
  One of the current challenges in docking studies is the inclusion of receptor flexibility. This is crucial because the binding sites of many therapeutic targets sample a wide range of conformational states, which has major consequences on mol. recognition. In this paper, we make use of very large sets of x-ray structures of cyclin dependent kinase 2 (CDK2) and heat shock protein 90 (HSP90) to assess the performance of flexible receptor docking in binding-mode prediction and virtual screening expts. Flexible receptor docking performs much better than rigid receptor docking in the former application. Regarding the latter, we observe a significant improvement in the prediction of binding affinities, but owing to an increase in the no. of false positives, this is not translated into better hit rates. A simple scoring scheme to correct this limitation is presented. More importantly, pitfalls inherent to flexible receptor docking have been identified and guidelines are presented to avoid them.
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70. 70
  Bissantz, C.; Bernard, P.; Hibert, M.; Rognan, D. Protein-Based Virtual Screening of Chemical Databases. Ii. Are Homology Models of G-Protein Coupled Receptors Suitable Targets? Proteins 2003, 50, 5– 25
  Google Scholar
  There is no corresponding record for this reference.
71. 71
  Katritch, V.; Rueda, M.; Abagyan, R. Ligand-Guided Receptor Optimization Methods Mol. Biol. 2012, 857, 189– 205
  Google Scholar
  71
  Ligand-guided receptor optimization
  Katritch Vsevolod; Rueda Manuel; Abagyan Ruben
  Methods in molecular biology (Clifton, N.J.) (2012), 857 (), 189-205 ISSN:.
  Receptor models generated by homology or even obtained by crystallography often have their binding pockets suboptimal for ligand docking and virtual screening applications due to insufficient accuracy or induced fit bias. Knowledge of previously discovered receptor ligands provides key information that can be used for improving docking and screening performance of the receptor. Here, we present a comprehensive ligand-guided receptor optimization (LiBERO) algorithm that exploits ligand information for selecting the best performing protein models from an ensemble. The energetically feasible protein conformers are generated through normal mode analysis and Monte Carlo conformational sampling. The algorithm allows iteration of the conformer generation and selection steps until convergence of a specially developed fitness function which quantifies the conformer's ability to select known ligands from decoys in a small-scale virtual screening test. Because of the requirement for a large number of computationally intensive docking calculations, the automated algorithm has been implemented to use Linux clusters allowing easy parallel scaling. Here, we will discuss the setup of LiBERO calculations, selection of parameters, and a range of possible uses of the algorithm which has already proven itself in several practical applications to binding pocket optimization and prospective virtual ligand screening.
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72. 72
  Tehan, B. G.; Bortolato, A.; Blaney, F. E.; Weir, M. P.; Mason, J. S. Unifying Family a Gpcr Theories of Activation Pharmacol. Ther. 2014, 143, 51– 60
  Google Scholar
  72
  Unifying Family A GPCR Theories of Activation
  Tehan, Benjamin G.; Bortolato, Andrea; Blaney, Frank E.; Weir, Malcolm P.; Mason, Jonathan S.
  Pharmacology & Therapeutics (2014), 143 (1), 51-60CODEN: PHTHDT; ISSN:0163-7258. (Elsevier)
  A review. Several new pairs of active and inactive GPCR structures have recently been solved enabling detailed structural insight into the activation process, not only of rhodopsin but now also of the β2 adrenergic, M2 muscarinic and adenosine A2A receptors. Combined with structural analyses they have enabled us to examine the different recent theories proposed for GPCR activation and show that they are all indeed parts of the same process, and are intrinsically related through their effect on the central hydrophobic core of GPCRs. This new unifying general process of activation is consistent with the identification of known constitutively active mutants and an in-depth conservational anal. of significant residues implicated in the process.
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  Meng, X. Y.; Zhang, H. X.; Mezei, M.; Cui, M. Molecular Docking: A Powerful Approach for Structure-Based Drug Discovery Curr. Comput. Aided Drug Des. 2011, 7, 146– 157
  Google Scholar
  73
  Molecular docking: A powerful approach for structure-based drug discovery
  Meng, Xuan-Yu; Zhang, Hong-Xing; Mezei, Mihaly; Cui, Meng
  Current Computer-Aided Drug Design (2011), 7 (2), 146-157CODEN: CCDDAS; ISSN:1573-4099. (Bentham Science Publishers Ltd.)
  A review. Mol. docking has become an increasingly important tool for drug discovery. In this review, we present a brief introduction of the available mol. docking methods, and their development and applications in drug discovery. The relevant basic theories, including sampling algorithms and scoring functions, are summarized. The differences in and performance of available docking software are also discussed. Flexible receptor mol. docking approaches, esp. those including backbone flexibility in receptors, are a challenge for available docking methods. A recently developed Local Move Monte Carlo (LMMC) based approach is introduced as a potential soln. to flexible receptor docking problems. Three application examples of mol. docking approaches for drug discovery are provided.
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74. 74
  Rognan, D.; Desaphy, J. Molecular Interaction Fingerprints. Scaffold Hopping in Medicinal Chemistry; Wiley-VCH Verlag GmbH & Co. KGaA: Weinheim, 2013; pp 215– 230.
  Google Scholar
  There is no corresponding record for this reference.
75. 75
  Ring, A. M.; Manglik, A.; Kruse, A. C.; Enos, M. D.; Weis, W. I.; Garcia, K. C.; Kobilka, B. K. Adrenaline-Activated Structure of Beta2-Adrenoceptor Stabilized by an Engineered Nanobody Nature 2013, 502, 575– 579
  Google Scholar
  There is no corresponding record for this reference.
76. 76
  Andrews, S. P.; Brown, G. A.; Christopher, J. A. Structure-Based and Fragment-Based Gpcr Drug Discovery ChemMedChem 2014, 9, 256– 275
  Google Scholar
  There is no corresponding record for this reference.
77. 77
  Miller-Gallacher, J. L.; Nehme, R.; Warne, T.; Edwards, P. C.; Schertler, G. F.; Leslie, A. G.; Tate, C. G. The 2.1 a Resolution Structure of Cyanopindolol-Bound Beta1-Adrenoceptor Identifies an Intramembrane Na+ Ion That Stabilises the Ligand-Free Receptor PLoS One 2014, 9e92727
  Google Scholar
  There is no corresponding record for this reference.
78. 78
  Casella, I.; Ambrosio, C.; Gro, M. C.; Molinari, P.; Costa, T. Divergent Agonist Selectivity in Activating Beta1- and Beta2-Adrenoceptors for G-Protein and Arrestin Coupling Biochem. J. 2011, 438, 191– 202
  Google Scholar
  There is no corresponding record for this reference.
79. 79
  Drake, M. T.; Violin, J. D.; Whalen, E. J.; Wisler, J. W.; Shenoy, S. K.; Lefkowitz, R. J. Beta-Arrestin-Biased Agonism at the Beta2-Adrenergic Receptor J. Biol. Chem. 2008, 283, 5669– 5676
  Google Scholar
  There is no corresponding record for this reference.
80. 80
  Kahsai, A. W.; Xiao, K.; Rajagopal, S.; Ahn, S.; Shukla, A. K.; Sun, J.; Oas, T. G.; Lefkowitz, R. J. Multiple Ligand-Specific Conformations of the Beta2-Adrenergic Receptor Nat. Chem. Biol. 2011, 7, 692– 700
  Google Scholar
  80
  Multiple ligand-specific conformations of the β2-adrenergic receptor
  Kahsai, Alem W.; Xiao, Kunhong; Rajagopal, Sudarshan; Ahn, Seungkirl; Shukla, Arun K.; Sun, Jinpeng; Oas, Terrence G.; Lefkowitz, Robert J.
  Nature Chemical Biology (2011), 7 (10), 692-700CODEN: NCBABT; ISSN:1552-4450. (Nature Publishing Group)
  Seven-transmembrane receptors (7TMRs), also called G protein-coupled receptors (GPCRs), represent the largest class of drug targets, and they can signal through several distinct mechanisms including those mediated by G proteins and the multifunctional adaptor proteins β-arrestins. Moreover, several receptor ligands with differential efficacies toward these distinct signaling pathways have been identified. However, the structural basis and mechanism underlying this 'biased agonism' remains largely unknown. Here, we develop a quant. mass spectrometry strategy that measures specific reactivities of individual side chains to investigate dynamic conformational changes in the β2-adrenergic receptor occupied by nine functionally distinct ligands. Unexpectedly, only a minority of residues showed reactivity patterns consistent with classical agonism, whereas the majority showed distinct patterns of reactivity even between functionally similar ligands. These findings demonstrate, contrary to two-state models for receptor activity, that there is significant variability in receptor conformations induced by different ligands, which has significant implications for the design of new therapeutic agents.
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81. 81
  Kaya, A. I.; Onaran, H. O.; Ozcan, G.; Ambrosio, C.; Costa, T.; Balli, S.; Ugur, O. Cell Contact-Dependent Functional Selectivity of Beta2-Adrenergic Receptor Ligands in Stimulating Camp Accumulation and Extracellular Signal-Regulated Kinase Phosphorylation J. Biol. Chem. 2012, 287, 6362– 6374
  Google Scholar
  There is no corresponding record for this reference.
82. 82
  Kim, I. M.; Tilley, D. G.; Chen, J.; Salazar, N. C.; Whalen, E. J.; Violin, J. D.; Rockman, H. A. Beta-Blockers Alprenolol and Carvedilol Stimulate Beta-Arrestin-Mediated Egfr Transactivation Proc. Natl. Acad. Sci. U.S.A. 2008, 105, 14555– 14560
  Google Scholar
  There is no corresponding record for this reference.
83. 83
  Liu, J. J.; Horst, R.; Katritch, V.; Stevens, R. C.; Wuthrich, K. Biased Signaling Pathways in Beta2-Adrenergic Receptor Characterized by 19f-Nmr Science 2012, 335, 1106– 1110
  Google Scholar
  There is no corresponding record for this reference.
84. 84
  Rajagopal, S.; Ahn, S.; Rominger, D. H.; Gowen-MacDonald, W.; Lam, C. M.; Dewire, S. M.; Violin, J. D.; Lefkowitz, R. J. Quantifying Ligand Bias at Seven-Transmembrane Receptors Mol. Pharmacol. 2011, 80, 367– 377
  Google Scholar
  84
  Quantifying ligand bias at seven-transmembrane receptors
  Rajagopal, Sudarshan; Ahn, Seungkirl; Rominger, David H.; Gowen-MacDonald, William; Lam, Christopher M.; DeWire, Scott M.; Violin, Jonathan D.; Lefkowitz, Robert J.
  Molecular Pharmacology (2011), 80 (3), 367-377CODEN: MOPMA3; ISSN:0026-895X. (American Society for Pharmacology and Experimental Therapeutics)
  Seven transmembrane receptors (7TMRs), commonly referred to as G protein-coupled receptors, form a large part of the "druggable" genome. 7TMRs can signal through parallel pathways simultaneously, such as through heterotrimeric G proteins from different families, or, as more recently appreciated, through the multifunctional adapters, β-arrestins. Biased agonists, which signal with different efficacies to a receptor's multiple downstream pathways, are useful tools for deconvoluting this signaling complexity. These compds. may also be of therapeutic use because they have distinct functional and therapeutic profiles from "balanced agonists.". Although some methods have been proposed to identify biased ligands, no comparison of these methods applied to the same set of data has been performed. Therefore, at this time, there are no generally accepted methods to quantify the relative bias of different ligands, making studies of biased signaling difficult. Here, we use complementary computational approaches for the quantification of ligand bias and demonstrate their application to two well known drug targets, the β2 adrenergic and angiotensin II type 1A receptors. The strategy outlined here allows a quantification of ligand bias and the identification of weakly biased compds. This general method should aid in deciphering complex signaling pathways and may be useful for the development of novel biased therapeutic ligands as drugs.
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85. 85
  Baker, J. G. The Selectivity of Beta-Adrenoceptor Agonists at Human Beta1-, Beta2- and Beta3-Adrenoceptors Br. J. Pharmacol. 2010, 160, 1048– 1061
  Google Scholar
  85
  The selectivity of β-adrenoceptor agonists at human β1-, β2- and β3-adrenoceptors
  Baker, Jillian G.
  British Journal of Pharmacology (2010), 160 (5), 1048-1061CODEN: BJPCBM; ISSN:1476-5381. (Wiley-Blackwell)
  There are two important properties of receptor-ligand interactions: affinity (the ability of the ligand to bind to the receptor) and efficacy (the ability of the receptor-ligand complex to induce a response). Ligands are classified as agonists or antagonists depending on whether or not they have efficacy. In theory, it is possible to develop selective agonists based on selective affinity, selective intrinsic efficacy or both. This study examd. the affinity and intrinsic efficacy of β-adrenoceptor agonists at the three human β-adrenoceptors to det. whether the current agonists are subtype selective because of affinity or intrinsic efficacy. Stable clonal CHO-K1 cell lines, transfected with either the human β1, β2 or β3-adrenoceptor, were used

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Structure-Based Prediction of G-Protein-Coupled Receptor Ligand Function: A β-Adrenoceptor Case Study

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Abstract

Introduction

Results and Discussion

Subtle Ligand-Type-Dependent Structural Changes in the Receptor Binding Sites

Figure 1

Identification of Ligand-Type-Specific Molecular Interaction Profiles

Figure 2

Selective Retrieval of f/pAGO over ANT/iAGO in Structure-Based Virtual Screening

Figure 3

Figure 4

Selective Structure-Based Virtual Screening for Ligands with the Desired Functional Effect

Figure 5

Selective Structure-Based Virtual Screening for Partial/Full Agonists with a Computationally Predicted Norepinephrine Interaction Fingerprint

Figure 6

Biased Signaling

Conclusions

Materials and Methods

Crystal Structures Retrieval and Preparation

Consideration of Alternative Ligand Ring Conformations in 3ZPQ

Pocket and Ligand Analyses

Compound Preparation and Docking

Interaction Fingerprint Calculation and Scoring

Virtual Screening Assessment

Supporting Information

Terms & Conditions

Author Information

Acknowledgment

References

Cited By

Abstract

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

References