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Identification of Novel Adenosine A2A Receptor Antagonists by Virtual Screening

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† Heptares Therapeutics Limited, BioPark, Broadwater Road, Welwyn Garden City, Hertfordshire AL7 3AX, U.K.
‡ BioFocus, Great Chesterford Research Park, Saffron Walden, CB10 1XL, U.K.
*Phone: +44 (0)1707 358631. Fax: +44 (0)1707 358640. E-mail: [email protected]
Cite this: J. Med. Chem. 2012, 55, 5, 1904–1909
Publication Date (Web):January 17, 2012
https://doi.org/10.1021/jm201455y
Copyright © 2012 American Chemical Society
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Abstract

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Virtual screening was performed against experimentally enabled homology models of the adenosine A2A receptor, identifying a diverse range of ligand efficient antagonists (hit rate 9%). By use of ligand docking and Biophysical Mapping (BPM), hits 1 and 5 were optimized to potent and selective lead molecules (11–13 from 5, pKI = 7.5–8.5, 13- to >100-fold selective versus adenosine A1; 14–16 from 1, pKI = 7.9–9.0, 19- to 59-fold selective).

Introduction

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The adenosine A2A receptor is a member of the G-protein-coupled receptor (GPCR) superfamily that mediates effects of the purine nucleotide adenosine on cellular signaling. The receptor is expressed in the CNS and periphery and has been a long-standing drug target for the treatment of inflammatory disorders and Parkinson’s disease. The A2A receptor is expressed in midbrain regions of the CNS where it functionally opposes the actions of the dopamine D2 receptor. Given that the primary pathology in Parkinson’s disease is a loss of dopamine and hence reduced dopamine D2 receptor activation, adenosine A2A receptor antagonism represents a potential nondopaminergic therapy for this disorder. Initial work to identify antagonists focused on purine and xanthine derivatives, essentially based on adenosine and the naturally occurring antagonist caffeine. This class of compounds is exemplified by istradefylline (1) (KW-6002), which progressed to phase III clinical development; however, despite extensive efforts, no other clinical agents that selectively target the A2A receptor have emerged from this area of chemistry. Further work has focused on bicyclic and tricyclic derivatives such as triazolotriazines and triazolopyrimidines, exemplified by ZM241385 and vipadenant, respectively. (1) The most advanced of this class of compounds is preladenant, which is currently in phase III clinical trials. (1) However, despite good affinity and selectivity across other adenosine receptor subtypes, these compounds are generally high molecular weight and all contain a furan group that has proven difficult to replace by empirical medicinal chemistry. Such an electron-rich group is prone to oxidative metabolism and potential reactive metabolite formation. (2) Therefore, we sought an alternative structure-based approach to identify novel antagonist chemotypes for the adenosine A2A receptor. By use of an experimentally enabled (site-directed mutagenesis, SDM) homology model of the receptor (based on the crystal structure of the turkey β1 adrenergic receptor in complex with cyanopindolol (3)), a virtual screen was performed to attempt to identify novel hits. In silico screening of 545K compounds, filtered to focus on compounds with CNS druglike properties and without undesirable heterocycles such as the furan or xanthine moieties, resulted in 20 confirmed hits in vitro (9% hit rate). These hits included a highly potent 1,3,5-triazine derivative and a chromone scaffold, the latter a completely novel chemotype for adenosine receptors. The binding modes of these hits were refined using our Biophysical Mapping approach, allowing optimal interactions to be identified and enabling these compounds to be rapidly developed into potent antagonists suitable for further optimization. (4)

Results and Discussion

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Virtual Screening

At the time of this work, no structural data were available for the A2A receptor, and therefore homology models were constructed based on the avian β1 adrenergic GPCR crystal structure bound to cyanopindolol (PDB code 2VT4). (3) Several different computational methods were used to generate and validate two homology models (see methods section and Supporting Information) because there is relatively low percentage identity between the two proteins (25% overall, <20% around the putative ligand binding site). The validation step included an assessment of the consistency in the alignments and of the variability, including which regions of the models had higher and lower confidence associated with them. The helical bundles between the two models and the template agreed closely, with lower confidence in the loop regions, particularly the extracellular loop 2, which did not align well to the template. By use of published and in-house SDM data to validate and improve the models and docking experiments using Glide (5) of a small number of known A2A receptor ligands and similarly sized decoys, virtual screening conditions and protocols were established. A discussion of the SDM data used to improve the models has been described elsewhere and is summarized in the methods section. (4) A comparison of the homology modeling results with ligand-complexed crystal structures of the adenosine A2A receptor is detailed in ref 6.
Virtual screening was then carried out with compound data sets of commercially available compounds, filtered to focus on compounds with desired CNS druglike properties. 545 000 compounds were prepared for screening, and all or a more filtered and clustered set were docked into each of the models using the SP algorithm within the Glide software, running on a 28 CPU Linux cluster. Details of the workflows, screening compound numbers and filters used for the virtual screening, and postprocessing analyses with each homology model are detailed in the Supporting Information (Figures S2–S4). The aim of the screen was to identify novel chemotypes that would provide starting points for optimization to compounds with good druglike properties. A number of different protocols were used to analyze the output from the virtual screens to provide a set of complementary compound selections, each biased toward certain features of the binding site; in each case, up to 10–20000 ligand poses were initially selected based on the score from Glide. This data set was then sectioned in various ways including the use of consensus scoring, use of other Glide generated scores, and overlap with the docked poses of known small ligands for the receptor. It was of particular interest to assess the utility of the SDM data in guiding compound selections. Therefore, most of the compounds were selected based on balanced polar and lipophilic score components and proximity to one or more residues chosen based on the experimental (SDM) data. As part of this process, a bias was employed to focus on compounds that docked in the most buried part of the site, remote from the low confidence region bordered by the extracellular loop 2. Compound sets resulting from the various selections were combined, and a final selection was made involving 3D visualization and assessment in the binding site, including the fit to the binding site shape and key features and the ligand conformation, and final triage by medicinal chemistry. As a result of this process, a set of 372 compounds was prioritized. Of these, only 230 were logistically available commercially and were tested for in vitro binding to the adenosine A2A receptor. Twenty compounds exhibited activity (IC50 < 55 μM), giving a 9% hit rate overall. Of the top 10 hits, all have ligand efficiencies (LEs) of >0.27, with seven compounds having >0.3, three having >0.4, and one notable hit with LE > 0.5. (7) These best hits also have reasonable to good ligand lipophilicity efficiencies (LLEs) in the range 2.1–5.4, with eight having LLE ≥ 3. (8) A number of structurally distinct chemotypes were identified in the screen, providing multiple starting points for potential optimization to generate a new A2A antagonist series: Table 1 and Figure 1 indicate the top 10 hits ranked by LE. Full binding curves for these hits are shown in the Supporting Information, which includes a table of the nearest published adenosine A2A antagonist to each of the hits. The most potent and most efficient compounds were all identified by the protocols that used key interacting residues from the SDM data as part of the selection process. This study highlights the importance of using experimental data, where available, in analysis and virtual hit selection during a virtual screen.
The results from this virtual screening exercise have demonstrated that good hit rates of diverse leadlike compounds are possible using high-quality GPCR (experimentally enabled and enhanced) homology models. The utility of virtual screening is supported by two recent papers from academic labs documenting this approach using an X-ray structure (rather than a homology model) of the adenosine A2A receptor. (9, 10) One group reported that a hit rate of 41% was achieved with 23 of the 56 assayed compounds showing activity better than 10 μM. (9) Similarly a second study yielded a hit rate of 35% with 7 of 20 compounds tested having affinities from 200 nM to 10 μM. (10) Interestingly, the top ranked hit from one of these two screens contains the same chemical scaffold as observed here from virtual screening of our experimentally enhanced homology model. (9)
Table 1. Virtual Screening Hits
hitpKILELLEclogPPSAMW
18.460.525.43.184.9310.4
25.150.474.50.772.2222.3
35.750.443.91.961.7264.3
46.150.363.23.066.6327.4
55.650.333.71.985.7331.3
65.620.312.63.076.7367.9
75.910.303.22.778.4367.4
85.330.293.41.979.8340.4
95.700.293.91.880.1363.4
105.530.272.13.495.9382.4

Figure 1

Figure 1. Structures of virtual screening hits.

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Hits to Leads

The process for the selection and optimization of the initial hits to develop leads was driven from docked ligand poses into the A2A homology models, coupled with 3D analysis of hotspots in the binding site, which were determined using small fragment probes. The calculated GRID maps (11) especially showed clearly the shape and pharmacophoric preferences/constrictions of the binding site (using a methyl group for shape and an aromatic C–H group, a carbonyl group, and an amide NH group for lipophilic, hydrogen-bond acceptor and donor hotspots, respectively). The relatively high LE and LLE of the hits indicate that the A2A receptor binding site is quite druggable, and this is reinforced by the GRID analysis suggesting regions of hydrophobic and H-bonding hotspots close together in the site available for very small ligands to bind. The key H-bonding residue Asn2536.55 sits centrally and is capable of forming high quality interactions with a diverse range of heterocyclic compounds. In particular, two hits were rapidly developed into lead series with potent activity versus the A2A receptor and good selectivity in key examples against the adenosine A1 receptor. Introducing at least moderate selectivity versus the A1 receptor subtype was thought desirable to minimize potential side effects, such as the stimulant effects seen with nonselective agents such as caffeine. The A2A binding site was also analyzed using molecular dynamics simulations with the WaterMap software (Schrödinger), (12) shown in ref 6, which demonstrates that our highly ligand efficient ligands occupy exactly the region where there is a cluster of waters that are termed “unhappy”, meaning that energetically they would prefer to be in bulk solvent; this contrasts with larger ligands such as ZM241385. (13)
The first hit to be optimized was the chromone 5 (Figure 1, Scheme 1). Selection of closely related analogues from commercial suppliers, influenced by the proposed binding mode from the virtual docking, quickly identified several more potent compounds including ester 11. In particular, relatively close analogues lacking the carboxylic acid functionality (presumed to be undesirable for brain penetration) and also not significantly higher in molecular weight or lipophilicity were selected. Biophysical Mapping analysis, published elsewhere (4) (chromone analogues are 1 in the earlier publication), distinguished between the binding mode shown in Figure 2 and a pose in which the compounds were rotated 180° and interacted with the key Asn2536.55 via the chromone carbonyl. (4) Further iterations of purchasing of close analogues of 11 identified 12 and 13 that are highly potent and, in the case of 13, highly selective A2A antagonists. In addition to the BPM data, a low resolution crystal structure of one member of the series was solved confirming the binding pose presented here (data not shown). Despite rapid progress with this series, in vitro metabolism issues and concerns that the thiazole might represent a liability in terms of possible generation of reactive metabolites led us to halt work in this series. Indeed, many previous adenosine A2A antagonists carry a furan group and we reasoned that a superior class of compounds should not contain a similar liability. (1) In addition, the potential of the work in the triazine scaffold (below and in ref 6) allowed us to deprioritize the chromone template.

Scheme 1

Scheme 1. Optimization of Chromone Hit 5
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Figure 2

Figure 2. Docking of the chromone 12, showing the BPM fingerprint color coded onto the binding site residues and in graphical form as change in pKD. Nonbinding is shown in red (N253A, H250A). Next largest effect is in dark orange (L85A), second largest in amber (N181A, Y271A, I66A), an increase in binding in green (S277A). H-bonding between the nitrogen of the thiazole and the aromatic C–H of the chromone is predicted to Asn2536.55. Selected BPM data are tabulated showing the change in pKD of each binding site mutation.

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The second series to be optimized was triazine 1 (Scheme 2), already a highly potent A2A antagonist with excellent LE and LLE. (7, 8) Simple outline SAR established the importance of the amino and phenol functionalities for high potency (data not shown) and that the olefin could be replaced by a range of groups, including simplification to alkyl-substituted 14. This fragment-sized molecule retains much of the affinity for the receptor and has moderate selectivity over the A1 receptor. A range of derivatives were synthesized in a hits to leads program on the chemotype, and one direction of the work was to design phenyl substituted 15 and 16 containing piperazine and piperidine solubilizing groups. These derivatives were found to be highly potent antagonists with moderate to good selectivity. BPM analysis of this series of compounds was again used during the optimization process, and a representative data set is shown in Figure 3 and is described in the legend. One further area of optimization of the series was to examine modifications to the triazine scaffold itself to allow more direct access to the “ribose pocket” from which we believed selectivity over the A1 receptor could be derived. This is the topic of ref 6.
Comparisons can be made of the binding modes of the chromone and triazine templates using BPM analysis. Alanine mutation of Asn2536.55 or His2506.52 abolished the binding of 12 and 15; ligand docking suggested that Asn2536.55 makes key hydrogen bonding interactions with the amino and phenol functional groups of 15 while for 12 the interaction with Asn2536.55 is made by the aromatic C–H of the chromone template and the nitrogen atom of the thiazole substituent. Alanine mutation of Ile662.64 (ΔpKD = −0.7) and Tyr2717.36 (ΔpKD = −0.7) reduced the affinity of 12, consistent with these residues forming a pocket for the alkyl chain of this compound. However, these mutations had little or no effect on the binding of 15 (ΔpKD of +0.1 and −0.1, respectively). Conversely, alanine mutation of Ser2777.42 reduced the affinity of 15 (ΔpKD = −1.0) but not 12 (ΔpKD = +0.3), consistent with the piperazine group of 15 being oriented toward the pocket of the receptor occupied by the ribose group of adenosine. The rationalization of the binding modes of these two chemotypes represents the first application of BPM analysis (4) to lead generation and has enabled the progression of this chemistry into lead optimization and beyond. (6)

Scheme 2

Scheme 2. Optimization of Triazine Hit 1
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Figure 3

Figure 3. Docking of the triazine 15, showing the BPM fingerprint color coded onto the binding site residues and in graphical form as change in pKD. Nonbinding is shown in red (N253A, H250A). Next largest effect is in dark orange (L85A, S277A). H-bonding between the nitrogen of the triazine and the phenol is predicted to Asn2536.55. The polar piperazine substituent is proposed to reach into the region of the binding site occupied by ribose in the natural agonist ligand adenosine and may be the driver of selectivity versus the A1 receptor, as this region of the binding site contains some amino acid differences comparing the two receptors. (14) Selected BPM data are tabulated showing the change in pKD of each binding site mutation.

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Conclusion

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The advent of improved homology modeling and the increasing availability of structural data for GPCRs are now enabling virtual screening for receptor targets to be used as a viable alternative to high-throughput screening. In this study we aimed to identify novel adenosine A2A antagonists through virtual screening of a library of 545K compounds using a homology model based on the turkey β1 adrenoceptor. Hits were computationally filtered and then cherry picked for screening, taking into account properties of the binding site (shape, electronic), the ligand (conformation), and desired regions for interaction (SDM data), leading to a 9% hit rate from 230 compounds tested by competition radioligand binding. Of the top 10 hits, 7 had good LE (>0.3) and LLE (>3), suggesting that they may represent suitable starting points for further optimization. Notably, one of the hits was a chromone (5), a chemotype completely novel in the field of adenosine receptors antagonists. Using ligand docking and Biophysical Mapping, latterly supported by X-ray structure determination, we have been able to identify a credible ligand binding model. (4) A series of optimal ligands that displayed greatly improved affinities compared to 5 and selectivity over the adenosine A1 receptor subtype were discovered. However, other in vitro properties made this series unsuitable for further optimization. The top ranked hit from the screen, containing a 1,3,5-triazine core, was by far the most potent, with LE > 0.5 and LLE > 5 and an affinity of <10 nM for the A2A receptor. Notably, this core group was also identified in a separate virtual screening exercise for the A2A receptor using an X-ray structure. (9) SAR in this series demonstrated the importance of the amino and phenol groups, but the olefin moiety could be readily replaced by a simple alkyl substituent with minimal loss of affinity. Such a potent, low molecular weight compound made an excellent starting point for optimization. Further analogues were designed using a binding model determined using Biophysical Mapping, to exploit the “ribose pocket” within the receptor to improve affinity and selectivity over the A1 receptor. This process resulted in several highly potent analogues with favorable selectivity profiles, suitable for further optimization. Continued exploitation of the results presented in this article is the subject of ref 6.

Experimental Protocols

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Virtual Screening Compounds

The compounds used for the virtual screening were from CAP, (15) a collection of commercial vendor catalogues, together with a subset of the BioFocus SoftFocus library collections. The hits shown were provided by Chembridge (1, 5), Interchim (2, 3, 8, 9), Asinex (4), Inter-bioscreen (7, 10), and BioFocus (6).

Computational Chemistry

Homology models were constructed from the avian β1 adrenergic GPCR crystal structure bound to cyanopindolol (PDB code 2VT4). (3, 16) Owing to the relatively low percentage identity between the two proteins (25% overall, <20% around the ligand binding site), two initial homology models of the adenosine A2A receptor were generated, using different methods. This provided a means to assess consistency in the alignments, the variability within the built structures, and which regions of the models had higher and lower confidence associated with them. One model was constructed using MODELLER, (17, 18) while the other was constructed using MOE (19) with manual readjustment of the ClustalW alignment where necessary. (20) The alignment in each case was checked to ensure consistency with known GPCR conserved motifs (21) and particularly the conserved disulfide bond, common to family A GPCRs, which is located between the top of helix 3 and the extracellular loop 2. Apart from the extracellular loop 2, the rest of the modeled structures showed good agreement and in the MOE model this loop was not modeled beyond the first few residues up to and including Phe168 because of the very poor alignment in this region. The two homology models were then further evaluated using two different approaches. First, SDM data, both from the literature (22) and in-house, (4) were mapped onto the modeled protein structures. The majority of these residues lined the anticipated ligand binding site in each of the models. The mutation sites showed good consistency in the locations of the residues when comparing the two structures. Second, both models were used to dock a small number of known A2A antagonists, including ZM241385, into each of the structures using Glide as the docking engine. (5, 23) This was done to explore the potential docking modes that could be achieved and to assist in the development of conditions and protocols for use in analysis of the virtual screen, with similarly sized decoys also being used in the studies. It was decided to use the two models in parallel in the virtual screen.
Ligand data sets were drawn from CAP, (15) a collection of vendor catalogues giving details of screening samples for purchase. A subset of the BioFocus SoftFocus library collections were also screened after excluding compounds designed to target GPCRs. The compounds from CAP were prefiltered to remove those molecules containing unwanted chemical functionality. Physicochemical profiles for the data set were biased toward a CNS-like profile based on the recommendations in Pajouhesh et al. (24) and the properties of a set of literature A2A antagonists. 545K compounds were prepared for screening, and all or a subset from more stringent prefiltering and clustering docked into each of the models using the SP algorithm within the Schrödinger Glide software, running on a 28 CPU Linux cluster. Details of the workflows, screening compound numbers, and filters used for the virtual screening and postprocessing analyses with each homology model are detailed in the Supporting Information (Figures S2–S4). The protein preparation and docking experiments were done within the Schrödinger Maestro package. The grid generation necessary for docking was done within Glide. The residues highlighted in SDM experiments (in-house and external) were used to further define the cavity of the grid. However, no constraints were added in the grid generation to ensure that subsequent dockings were not biased in any way. As standard, up to 3 poses per molecular structure were stored for analysis. For some compound subsets, Glide XP docking was carried out on the ligands with 10 poses per ligand being stored. A selection of 372 virtual hits was finally prioritized for purchasing, following manual inspection and subsequent triaging by medicinal chemistry of the most promising docking solutions.
Subsequent docking experiments on the hits from the radioligand binding assay and also on analogues of the two hit chemotypes derived from 1 and 5 were carried out. They were guided by ligand SAR, an iterative process of assessing SDM data, and also by designing our own BPM mutants to confirm or rule out possible binding modes, as previously described. (4) As part of this, more detailed modeling work was carried out, including the use of induced fit docking and restrained minimization work. For the more active compounds, the MOE derived model gave more plausible results, and therefore, this was used as the basis for further improvement and validation work. In particular, validation and improvement of the homology models for docking were conducted, focused on ZM241385, because of the wealth of SAR for this series and the amount of SDM data available for the ligand at the adenosine A2A receptor. (22, 25) The induced fit docking (IFD) protocol (23) was used within Maestro with an autogenerated box size around the residues highlighted by SDM as having a large effect on antagonist binding, namely, Ile662.64, Val843.32, Leu853.33, Glu151ECL2, Leu167ECL2, Glu169ECL2, Asn1815.42, Phe1825.43, His2506.52, Asn2536.55, Phe2576.59, Tyr2717.36, Ile2747.39, and His2787.43.

Adenosine Receptor Assays

Inhibition binding assays were performed using 2.5 μg of membranes prepared from HEK293 cells transiently transfected with human adenosine A2A receptor or 10 μg of membranes prepared from CHO cells stably transfected with human adenosine A1 receptor. Membranes were incubated in 50 mM Tris-HCl (HEK293-hA2A, pH 7.4) or 20 mM HEPES, 100 mM NaCl, 10 mM MgCl2 (CHO-hA1, pH 7.4) in the presence of 5–10 concentrations of test compound and 1 nM [3H]ZM241385 (HEK293-hA2A) or [3H]DPCPX (CHO-hA1) at 25 °C for 1 h. The DMSO concentration was 0.1% (final). The assay was then terminated by rapid filtration onto GF/B grade Unifilter plates using a TomTec cell harvester, followed by 5 × 0.5 mL washes with doubly distilled H2O. Total binding was defined in the presence of 0.1% DMSO; nonspecific binding was defined in the presence of 1 μM CGS15943 (HEK293-hA2A) or 1 μM DPCPX (CHO-hA1). Bound radioactivity was determined by liquid scintillation counting, and inhibition curves were analyzed using a four-parameter logistic equation. IC50 values were converted to KI values with the Cheng–Prusoff equation using a KD derived from saturation binding studies. Compounds were tested to at least n = 2; concentration response curves displayed Hill slopes not significantly different from unity, consistent with a competitive mode of action.

Chemical Synthesis

Hit compounds 1–10 and follow-up compounds 11–14 were provided by Chembridge, Interchim, Asinex, Interbioscreen, or BioFocus. The compounds were supplied with LCMS purities of >95%, as determined by the vendors. Quality control data are provided in the Supporting Information. Chemical synthesis and analysis of 15 and 16 were carried out at Oxygen Healthcare, India, according to Scheme 3. Full experimental details can be found in the Supporting Information.

Scheme 3

Scheme 3. Synthesis of 15 and 16a
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Scheme aReagents and conditions: (a) THF, iPr2EtN, NH3. (b) For 16: (i) 3-(4-methoxypiperidin-1-yl)phenylboronic acid, Na2CO3, 1,4-dioxane/H2O, Pd(PPh3)4, 90 °C, then (ii) 2-hydroxylphenylboronic acid, Na2CO3, 1,4-dioxane/H2O, Pd(PPh3)4, 90 °C. (c) For 17: (i) 2-benzyloxyphenylboronic acid, Na2CO3, 1,4-dioxane/H2O, Pd(PPh3)4, 70 °C, then (ii) 3-(4-methylpiperazine-1-carbonyl)phenylboronic acid hydrochloride, Na2CO3, 1,4-dioxane/H2O, Pd(PPh3)4, 90 °C; (d) 17, EtOAc, Pd(OH)2/C, 1,4-cyclohexadiene, 140 °C (microwave).

Supporting Information

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Chemical synthesis protocols, QC data and binding curves for the top 10 hits, more detailed computational methods including virtual screening workflows, and a table of calculated blood–brain barrier prediction and the closest published adenosine A2A antagonist to each of the hits. This material is available free of charge via the Internet at http://pubs.acs.org.

Terms & Conditions

Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

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  • Corresponding Author
    • Christopher J. Langmead - Heptares Therapeutics Limited, BioPark, Broadwater Road, Welwyn Garden City, Hertfordshire AL7 3AX, U.K.;  Email: [email protected]
  • Authors
    • Stephen P. Andrews - Heptares Therapeutics Limited, BioPark, Broadwater Road, Welwyn Garden City, Hertfordshire AL7 3AX, U.K.
    • Miles Congreve - Heptares Therapeutics Limited, BioPark, Broadwater Road, Welwyn Garden City, Hertfordshire AL7 3AX, U.K.
    • James C. Errey - Heptares Therapeutics Limited, BioPark, Broadwater Road, Welwyn Garden City, Hertfordshire AL7 3AX, U.K.
    • Edward Hurrell - Heptares Therapeutics Limited, BioPark, Broadwater Road, Welwyn Garden City, Hertfordshire AL7 3AX, U.K.
    • Fiona H. Marshall - Heptares Therapeutics Limited, BioPark, Broadwater Road, Welwyn Garden City, Hertfordshire AL7 3AX, U.K.
    • Jonathan S. Mason - Heptares Therapeutics Limited, BioPark, Broadwater Road, Welwyn Garden City, Hertfordshire AL7 3AX, U.K.
    • Christine M. Richardson - BioFocus, Great Chesterford Research Park, Saffron Walden, CB10 1XL, U.K.
    • Nathan Robertson - Heptares Therapeutics Limited, BioPark, Broadwater Road, Welwyn Garden City, Hertfordshire AL7 3AX, U.K.
    • Andrei Zhukov - Heptares Therapeutics Limited, BioPark, Broadwater Road, Welwyn Garden City, Hertfordshire AL7 3AX, U.K.
    • Malcolm Weir - Heptares Therapeutics Limited, BioPark, Broadwater Road, Welwyn Garden City, Hertfordshire AL7 3AX, U.K.

Acknowledgment

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The authors thank Bissan Al-Lazikani for help with constructing the first generation of homology models, Benjamin Tehan for assistance with computational chemistry, and Nat Monck for assisting with the triaging of screening hits.

Abbreviations Used

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BPM

Biophysical Mapping

PDB

Protein Data Bank

LE

ligand efficiency

LLE

ligand lipophilicity efficiency

SDM

site directed mutagenesis

References

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This article references 25 other publications.

  1. 1
    Pinna, A. Novel investigational adenosine A2A receptor antagonists for Parkinson’s disease Expert Opin. Invest. Drugs 2009, 18, 1619– 1631
    Google Scholar
    There is no corresponding record for this reference.
  2. 2
    Blagg, J. Structural Alerts for Toxicity. In Burger’s Medicinal Chemistry, Drug Discovery and Development, 7th ed.; Abraham, D. J.; Rotella, D. P., Eds.; Wiley: Hoboken, NJ, 2010; pp 301– 344.
    Google Scholar
    There is no corresponding record for this reference.
  3. 3
    Warne, A.; Serrano-Vega, M. J.; Baker, J. G.; Moukhametzianov, R.; Edwards, P. C.; Henderson, R.; Leslie, A. G. W.; Tate, C. G.; Schertler, G. F. X. Structure of the beta1-adrenergic G protein-coupled receptor Nature 2008, 454, 486– 491

    (PDB code 2VT4)

    Google Scholar
    There is no corresponding record for this reference.
  4. 4
    Zhukov, A.; Andrews, S. P.; Errey, J. C.; Robertson, N.; Tehan, B.; Mason, J. S.; Marshall, F. H.; Weir, M.; Congreve, M. Biophysical Mapping of the adenosine A2A receptor J. Med. Chem. 2011, 54, 4312– 4323
    Google Scholar
    4
    Biophysical Mapping of the Adenosine A2A Receptor
    Zhukov, Andrei; Andrews, Stephen P.; Errey, James C.; Robertson, Nathan; Tehan, Benjamin; Mason, Jonathan S.; Marshall, Fiona H.; Weir, Malcolm; Congreve, Miles
    Journal of Medicinal Chemistry (2011), 54 (13), 4312-4323CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
    A new approach to generating information on ligand receptor interactions within the binding pocket of G protein-coupled receptors has been developed, called Biophys. Mapping (BPM). Starting from a stabilized receptor (StaR), minimally engineered for thermostability, addnl. single mutations are then added at positions that could be involved in small mol. interactions. The StaR and a panel of binding site mutants are captured onto Biacore chips to enable characterization of the binding of small mol. ligands using surface plasmon resonance (SPR) measurement. A matrix of binding data for a set of ligands vs. each active site mutation is then generated, providing specific affinity and kinetic information (KD, kon, and koff) of receptor-ligand interactions. This data set, in combination with mol. modeling and docking, is used to map the small mol. binding site for each class of compds. Taken together, the many constraints provided by these data identify key protein-ligand interactions and allow the shape of the site to be refined to produce a high quality three-dimensional picture of ligand binding, thereby facilitating structure based drug design. Results of biophys. mapping of the adenosine A2A receptor are presented.
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    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXnt12nsb0%253D&md5=d7f5c6bf24f9093d823bb41db9867c09
  5. 5
    Halgren, T. A.; Murphy, R. B.; Friesner, R. A.; Beard, H. S.; Frye, L. L.; Pollard, W. T.; Banks, J. L. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening J. Med. Chem. 2004, 47, 1750– 1759
    Google Scholar
    5
    Glide: A new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening
    Halgren, Thomas A.; Murphy, Robert B.; Friesner, Richard A.; Beard, Hege S.; Frye, Leah L.; Pollard, W. Thomas; Banks, Jay L.
    Journal of Medicinal Chemistry (2004), 47 (7), 1750-1759CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
    A review. Glide's ability to identify active compds. in a database screen is characterized by applying Glide to a diverse set of nine protein receptors. In many cases, two, or even three, protein sites are employed to probe the sensitivity of the results to the site geometry. To make the database screens as realistic as possible, the screens use sets of "druglike" decoy ligands that have been selected to be representative of what we believe is likely to be found in the compd. collection of a pharmaceutical or biotechnol. company. Results are presented for releases 1.8, 2.0, and 2.5 of Glide. The comparisons show that av. measures for both "early" and "global" enrichment for Glide 2.5 are 3 times higher than for Glide 1.8 and more than 2 times higher than for Glide 2.0 because of better results for the least well-handled screens. This improvement in enrichment stems largely from the better balance of the more widely parametrized GlideScore 2.5 function and the inclusion of terms that penalize ligand-protein interactions that violate established principles of phys. chem., particularly as it concerns the exposure to solvent of charged protein and ligand groups. Comparisons to results for the thymidine kinase and estrogen receptors published by Rognan and co-workers (J. Med. Chem. 2000, 43, 4759-4767) show that Glide 2.5 performs better than GOLD 1.1, FlexX 1.8, or DOCK 4.01.
    >> More from SciFinder ®
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  6. 6
    Congreve, M.; Andrews, S. P.; Dore, A. S.; Hollenstein, K.; Hurrell, E.; Langmead, C. J.; Mason, J. S.; Ng, I. W.; Tehan, B.; Zhukov, A.; Weir, M.; Marshall, F. H. Discovery of 1,2,4-triazine derivatives as adenosine A2A antagonists using structure based drug design. J. Med. Chem. [Online early access]. DOI:  DOI: 10.1021/jm201376w . Published Online: Jan 5, 2012.
    Google Scholar
    There is no corresponding record for this reference.
  7. 7
    Hopkins, A. L.; Groom, C. R.; Alex, A. Ligand efficiency: a useful metric for lead selection Drug Discovery Today 2004, 9, 430– 431
    Google Scholar
    7
    Ligand efficiency: a useful metric for lead selection
    Hopkins Andrew L; Groom Colin R; Alex Alexander
    Drug discovery today (2004), 9 (10), 430-1 ISSN:1359-6446.
    There is no expanded citation for this reference.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD2c3ht1ygtw%253D%253D&md5=eb19a1bca53247d0bd9adcb99d5817e6
  8. 8
    Leeson, P. D.; Springthorpe, B. The influence of drug-like concepts on decision-making in medicinal chemistry Nat. Rev. Drug Discovery 2007, 6, 881– 890
    Google Scholar
    8
    The influence of drug-like concepts on decision-making in medicinal chemistry
    Leeson, Paul D.; Springthorpe, Brian
    Nature Reviews Drug Discovery (2007), 6 (11), 881-890CODEN: NRDDAG; ISSN:1474-1776. (Nature Publishing Group)
    A review. Despite the wide acceptance of drug-like principles such as the 'rule of five', this anal. of mols. currently being synthesized in leading pharmaceutical companies reveals that their phys. properties differ significantly from those of recently discovered oral drugs. The marked increase in lipophilicity in particular could increase the likelihood of attrition in drug development. The application of guidelines linked to the concept of drug-likeness, such as the 'rule of five', has gained wide acceptance as an approach to reduce attrition in drug discovery and development. However, despite this acceptance, anal. of recent trends reveals that the phys. properties of mols. that are currently being synthesized in leading drug discovery companies differ significantly from those of recently discovered oral drugs and compds. in clin. development. The consequences of the marked increase in lipophilicity - the most important drug-like phys. property - include a greater likelihood of lack of selectivity and attrition in drug development. Tackling the threat of compd.-related toxicol. attrition needs to move to the mainstream of medicinal chem. decision-making.
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  9. 9
    Katritch, V.; Jaakola, V.-P.; Lane, J. R.; Lin, J.; Izerman, A. P.; Yaeger, M.; Kufarena, I.; Stevens, R. C.; Abagyan, R. Structure-based discovery of novel chemotypes for adenosine A2a receptor antagonists J. Med. Chem. 2010, 53, 1799– 1809
    Google Scholar
    9
    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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  10. 10
    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
    Google Scholar
    10
    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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  11. 11
    Goodford, P. J. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules J. Med. Chem. 1985, 28 (7) 849– 857
    Google Scholar
    11
    A computational procedure for determining energetically favorable binding sites on biologically important macromolecules
    Goodford, P. J.
    Journal of Medicinal Chemistry (1985), 28 (7), 849-57CODEN: JMCMAR; ISSN:0022-2623.
    The interaction of a probe group with a protein of known structure is computed at sample positions throughout and around the macromol., giving an array of energy values. The probe include H2O, the Me group, amine-N, carboxy-O, and OH. Contour surfaces at appropriate energy levels are calcd. for each probe and displayed by computer graphics together with the protein structure. Contours at neg. energy levels delineate regions of attraction between probe and protein and are found at known ligand binding clefts in particular. The contours also enable other regions of attraction to be identified and facilitate the interpretation of protein-ligand energetics. They may, therefore, be of value for drug design.
    >> More from SciFinder ®
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  12. 12
    Higgs, C.; Beuming, T.; Sherman, W. Hydration site thermodynamics explain SARs for triazolylpurines analogues binding to the A2A receptor ACS Med. Chem. Lett. 2010, 1, 160– 164
    Google Scholar
    There is no corresponding record for this reference.
  13. 13
    Congreve, M.; Langmead, C. J.; Mason, J. S.; Marshall, F. H. Progress in structure based drug design for G protein-coupled receptors J. Med. Chem. 2011, 54, 4283– 4311
    Google Scholar
    13
    Progress in Structure Based Drug Design for G Protein-Coupled Receptors
    Congreve, Miles; Langmead, Christopher J.; Mason, Jonathan S.; Marshall, Fiona H.
    Journal of Medicinal Chemistry (2011), 54 (13), 4283-4311CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
    A review. The field of GPCR drug discovery is set to have a new lease of life, invigorated by a quantum leap of progress in structural biol. over the past 3 years. The new era of SBDD for GPCRS should now allow even some of the most challenging targets to yield to the efforts of medicinal chemists. This very well established drug family can expect to see many more first in class agents and better mols. with improved physicochem. properties and selectivity profiles, derived from the careful, rational optimization of hits using structure based and fragment based methods. In particular, a disciplined emphasis on maintaining high ligand efficiency and ligand lipophilicity efficiency should allow the next generation of research on this target family to profit from improved clin. success rates and lead to safe and efficacious medicines over the next decade.
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  14. 14
    Lebon, G.; Warne, T.; Edwards, P. C.; Bennett, K.; Langmead, C. J.; Leslie, A. G. W.; Tate, C. G. Agonist-bound adenosine A2A receptor structures reveal common features of GPCR activation Nature 2011, 474, 521– 523
    Google Scholar
    14
    Agonist-bound adenosine A2A receptor structures reveal common features of GPCR activation
    Lebon, Guillaume; Warne, Tony; Edwards, Patricia C.; Bennett, Kirstie; Langmead, Christopher J.; Leslie, Andrew G. W.; Tate, Christopher G.
    Nature (London, United Kingdom) (2011), 474 (7352), 521-525CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
    Adenosine receptors and β-adrenoceptors are G-protein-coupled receptors (GPCRs) that activate intracellular G proteins on binding the agonists adenosine or noradrenaline, resp. GPCRs have similar structures consisting of seven transmembrane helixes that contain well-conserved sequence motifs, indicating that they are probably activated by a common mechanism. Recent structures of β-adrenoceptors highlight residues in transmembrane region 5 that initially bind specifically to agonists rather than to antagonists, indicating that these residues have an important role in agonist-induced activation of receptors. Here we present two crystal structures of the thermostabilized human adenosine A2A receptor (A2AR-GL31) bound to its endogenous agonist adenosine and the synthetic agonist NECA. The structures represent an intermediate conformation between the inactive and active states, because they share all the features of GPCRs that are thought to be in a fully activated state, except that the cytoplasmic end of transmembrane helix 6 partially occludes the G-protein-binding site. The adenine substituent of the agonists binds in a similar fashion to the chem. related region of the inverse agonist ZM241385 (ref. 8). Both agonists contain a ribose group, not found in ZM241385, which extends deep into the ligand-binding pocket where it makes polar interactions with conserved residues in H7 (Ser 2777.42 and His 2787.43; superscripts refer to Ballesteros-Weinstein numbering) and non-polar interactions with residues in H3. In contrast, the inverse agonist ZM241385 does not interact with any of these residues and comparison with the agonist-bound structures indicates that ZM241385 sterically prevents the conformational change in H5 and therefore it acts as an inverse agonist. Comparison of the agonist-bound structures of A2AR with the agonist-bound structures of β-adrenoceptors indicates that the contraction of the ligand-binding pocket caused by the inward motion of helixes 3, 5 and 7 may be a common feature in the activation of all GPCRs.
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  15. 15
    “Chemicals Available for Purchase” database. Available from http://www.accelrys.com.
    Google Scholar
    There is no corresponding record for this reference.
  16. 16
    Berman, H. M; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T. N.; Weissig, H.; Shindyalov, I. N.; Bourne, P. E. The Protein Data Bank Nucleic Acids Res. 2000, 28, 235– 242

    (http://www.rcsb.org/pdb/static.do?p=general_information/about_pdb/policies_references.html)

    Google Scholar
    16
    The Protein Data Bank
    Berman, Helen M.; Westbrook, John; Feng, Zukang; Gilliland, Gary; Bhat, T. N.; Weissig, Helge; Shindyalov, Ilya N.; Bourne, Philip E.
    Nucleic Acids Research (2000), 28 (1), 235-242CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)
    The Protein Data Bank (PDB; http://www.rcsb.org/pdb/)is the single worldwide archive of structural data of biol. macromols. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.
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  17. 17
    Sali, A.; Blundell, T. L. Comparative protein modeling by satisfaction of spatial restraints J. Mol. Biol. 1993, 234, 779– 815
    Google Scholar
    17
    Comparative protein modeling by satisfaction of spatial restraints
    Sali, Andrej; Blundell, Tom L.
    Journal of Molecular Biology (1993), 234 (3), 779-815CODEN: JMOBAK; ISSN:0022-2836.
    The authors describe a comparative protein modeling method designed to find the most probable structure for a sequence given its alignment with related structures. The three-dimensional (3D) model is obtained by optimally satisfying spatial restraints derived from the alignment and expressed as probability d. functions (pdfs) for the features restrained. For example, the probabilities for main-chain conformations of a modelled residue may be restrained by its residue type, main-chain conformation of an equiv. residue in a related protein, and the local similarity between the two sequences. Several such pdfs are obtained from the correlations between structural features in 17 families of homologous proteins which have been aligned on the basis of their 3D structures. The pdfs restrain Cα-Cα distances, main-chain N-O distances, main-chain and side-chain dihedral angles. A smoothing procedure is used in the derivation of these relationships to minimize the problem of a sparse database. The 3D model of a protein is obtained by optimization of the mol. pdf such that the model violates the input restraints as little as possible. The mol. pdf is derived as a combination of pdfs restraining individual spatial features of the whole mol. The optimization procedure is a variable target function method that applies the conjugate gradients algorithm to positions of all non-hydrogen atoms. The method is automated and is illustrated by the modeling of trypsin from two other serine proteinases.
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  18. 18
    Eswar, N.; Marti-Renom, M. A.; Webb, B.; Madhusudhan, M. S.; Eramian, D.; Shen, M.; Pieper, U.; Sali, A. Comparative Protein Structure Modeling with MODELLER. Current Protocols in Bioinformatics; Wiley: New York, 2006; Suppl. 15, pp 5.6.1– 5.6.30.
    Google Scholar
    There is no corresponding record for this reference.
  19. 19
    Molecular Operating Environment (MOE), version 2008.10; Chemical Computing Group, Inc.: Montreal, Quebec, Canada, 2008; www.chemcomp.com.
    Google Scholar
    There is no corresponding record for this reference.
  20. 20
    Thompson, J. D.; Higgins, D. G.; Gibson, T. J. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice Nucleic Acids Res. 1994, 22, 4673– 4680
    Google Scholar
    20
    CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice
    Thompson, Julie D.; Higgins, Desmond G.; Gibson, Toby J.
    Nucleic Acids Research (1994), 22 (22), 4673-80CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)
    The sensitivity of the commonly used progressive multiple sequence alignment method has been greatly improved for the alignment of divergent protein sequences. Firstly, individual wts. are assigned to each sequence in a partial alignment in order to down-wt. near-duplicate sequences and up-wt. the most divergent ones. Secondly, amino acid substitution matrixes are varied at different alignment stages according to the divergence of the sequences to be aligned. Thirdly, residue-specific gap penalties and locally reduced gap penalties in hydrophilic regions encourage new gaps in potential loop regions rather than regular secondary structure. Fourthly, positions in early alignments where gaps have been opened receive locally reduced gap penalties to encourage the opening of new gaps at these positions. These modifications are incorporated into a new program, CLUSTAL W which is freely available.
    >> More from SciFinder ®
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  21. 21
    Mirzadegan, T.; Benko, G. Sequence analyses of G-protein coupled receptor: similarities to rhodopsin Biochemistry 2003, 42 (10) 2759– 2767
    Google Scholar
    21
    Sequence Analyses of G-Protein-Coupled Receptors: Similarities to Rhodopsin
    Mirzadegan, Tara; Benko, Gil; Filipek, Slawomir; Palczewski, Krzysztof
    Biochemistry (2003), 42 (10), 2759-2767CODEN: BICHAW; ISSN:0006-2960. (American Chemical Society)
    The authors performed a comprehensive anal. of the primary sequences of G-protein-coupled receptors (GPCRs) from family A. They demonstrate that the extracellular domain is the least conserved, while GPCRs display considerable conservation toward the cytoplasmic side. Interestingly, the lengths of connecting loops also suggest an importance of these elements in retaining a similar general structure of these receptors. These structural similarities may suggest a common mechanism by which GPCRs activate G-proteins and how these receptors may self-organize within native membranes.
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  22. 22
    Kim, J.; Wess, J.; van Rhee, M.; Schoneberg, T.; Jacobson, K. A. Site-directed mutagenesis identifies residues involved in ligand recognition in the human A2A adenosine receptor J. Biol. Chem. 1995, 270 (23) 13987– 13997
    Google Scholar
    There is no corresponding record for this reference.
  23. 23
    Available from Schrödinger, LLC, New York (http://www.schrodinger.com).
    Google Scholar
    There is no corresponding record for this reference.
  24. 24
    Pajouhesh, H.; Lenz, G. R. Medicinal chemical properties of successful central nervous system drugs NeuroRx 2005, 2 (4) 541– 553
    Google Scholar
    24
    Medicinal chemical properties of successful central nervous system drugs
    Pajouhesh Hassan; Lenz George R
    NeuroRx : the journal of the American Society for Experimental NeuroTherapeutics (2005), 2 (4), 541-53 ISSN:1545-5343.
    Fundamental physiochemical features of CNS drugs are related to their ability to penetrate the blood-brain barrier affinity and exhibit CNS activity. Factors relevant to the success of CNS drugs are reviewed. CNS drugs show values of molecular weight, lipophilicity, and hydrogen bond donor and acceptor that in general have a smaller range than general therapeutics. Pharmacokinetic properties can be manipulated by the medicinal chemist to a significant extent. The solubility, permeability, metabolic stability, protein binding, and human ether-ago-go-related gene inhibition of CNS compounds need to be optimized simultaneously with potency, selectivity, and other biological parameters. The balance between optimizing the physiochemical and pharmacokinetic properties to make the best compromises in properties is critical for designing new drugs likely to penetrate the blood brain barrier and affect relevant biological systems. This review is intended as a guide to designing CNS therapeutic agents with better drug-like properties.
    >> More from SciFinder ®
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  25. 25
    Dal Ben, D.; Lambertucci, C.; Marucci, G.; Volpini, R.; Cristalli, G. Adenosine Receptor modeling: What does the A2A crystal structure tell us? Curr. Top. Med. Chem. 2010, 93, 993– 1018
    Google Scholar
    There is no corresponding record for this reference.

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  12. Woong-Hee Shin, Charles W. Christoffer, Jibo Wang, and Daisuke Kihara . PL-PatchSurfer2: Improved Local Surface Matching-Based Virtual Screening Method That Is Tolerant to Target and Ligand Structure Variation. Journal of Chemical Information and Modeling 2016, 56 (9) , 1676-1691. https://doi.org/10.1021/acs.jcim.6b00163
  13. Dahlia R. Weiss, Andrea Bortolato, Benjamin Tehan, and Jonathan S. Mason . GPCR-Bench: A Benchmarking Set and Practitioners’ Guide for G Protein-Coupled Receptor Docking. Journal of Chemical Information and Modeling 2016, 56 (4) , 642-651. https://doi.org/10.1021/acs.jcim.5b00660
  14. Samuel R. Perry, Weijun Xu, Anna Wirija, Junxian Lim, Mei-Kwan Yau, Martin J. Stoermer, Andrew J. Lucke, and David P. Fairlie . Three Homology Models of PAR2 Derived from Different Templates: Application to Antagonist Discovery. Journal of Chemical Information and Modeling 2015, 55 (6) , 1181-1191. https://doi.org/10.1021/acs.jcim.5b00087
  15. Manuel de Lera Ruiz, Yeon-Hee Lim, and Junying Zheng . Adenosine A2A Receptor as a Drug Discovery Target. Journal of Medicinal Chemistry 2014, 57 (9) , 3623-3650. https://doi.org/10.1021/jm4011669
  16. Trayder Thomas, Kimberley C. McLean, Fiona M. McRobb, David T. Manallack, David K. Chalmers, and Elizabeth Yuriev . Homology Modeling of Human Muscarinic Acetylcholine Receptors. Journal of Chemical Information and Modeling 2014, 54 (1) , 243-253. https://doi.org/10.1021/ci400502u
  17. Dan Chen, Anirudh Ranganathan, Adriaan P. IJzerman, Gregg Siegal, and Jens Carlsson . Complementarity between in Silico and Biophysical Screening Approaches in Fragment-Based Lead Discovery against the A2A Adenosine Receptor. Journal of Chemical Information and Modeling 2013, 53 (10) , 2701-2714. https://doi.org/10.1021/ci4003156
  18. Andrea Bortolato, Ben G. Tehan, Michael S. Bodnarchuk, Jonathan W. Essex, and Jonathan S. Mason . Water Network Perturbation in Ligand Binding: Adenosine A2A Antagonists as a Case Study. Journal of Chemical Information and Modeling 2013, 53 (7) , 1700-1713. https://doi.org/10.1021/ci4001458
  19. Jianing Li, Amanda L. Jonsson, Thijs Beuming, John C. Shelley, and Gregory A. Voth . Ligand-Dependent Activation and Deactivation of the Human Adenosine A2A Receptor. Journal of the American Chemical Society 2013, 135 (23) , 8749-8759. https://doi.org/10.1021/ja404391q
  20. Kenneth A. Jacobson . Structure-Based Approaches to Ligands for G-Protein-Coupled Adenosine and P2Y Receptors, from Small Molecules to Nanoconjugates. Journal of Medicinal Chemistry 2013, 56 (10) , 3749-3767. https://doi.org/10.1021/jm400422s
  21. Gerard J. P. van Westen, Olaf O. van den Hoven, Rianne van der Pijl, Thea Mulder-Krieger, Henk de Vries, Jörg K. Wegner, Adriaan P. IJzerman, Herman W. T. van Vlijmen, and Andreas Bender . Identifying Novel Adenosine Receptor Ligands by Simultaneous Proteochemometric Modeling of Rat and Human Bioactivity Data. Journal of Medicinal Chemistry 2012, 55 (16) , 7010-7020. https://doi.org/10.1021/jm3003069
  22. Miles Congreve, Stephen P. Andrews, Andrew S. Doré, Kaspar Hollenstein, Edward Hurrell, Christopher J. Langmead, Jonathan S. Mason, Irene W. Ng, Benjamin Tehan, Andrei Zhukov, Malcolm Weir, and Fiona H. Marshall . Discovery of 1,2,4-Triazine Derivatives as Adenosine A2A Antagonists using Structure Based Drug Design. Journal of Medicinal Chemistry 2012, 55 (5) , 1898-1903. https://doi.org/10.1021/jm201376w
  23. Miru Tang, Chang Wen, Jie Lin, Hongming Chen, Ting Ran. Discovery of novel A2AR antagonists through deep learning-based virtual screening. Artificial Intelligence in the Life Sciences 2023, 3 , 100058. https://doi.org/10.1016/j.ailsci.2023.100058
  24. Pierre Matricon, Anh TN. Nguyen, Duc Duy Vo, Jo-Anne Baltos, Mariama Jaiteh, Andreas Luttens, Stefanie Kampen, Arthur Christopoulos, Jan Kihlberg, Lauren Therese May, Jens Carlsson. Structure-based virtual screening discovers potent and selective adenosine A1 receptor antagonists. European Journal of Medicinal Chemistry 2023, 257 , 115419. https://doi.org/10.1016/j.ejmech.2023.115419
  25. Prakashsingh M. Chauhan, Mayur I. Morja, Bhavik S. Makwana, Kishor H. Chikhalia. Facile synthesis of chromeno fused imidazolidinone derivatives via copper‐catalyzed tandem O‐arylation/oxidative acylation protocol. ChemistrySelect 2023, 8 (31) https://doi.org/10.1002/slct.202301470
  26. Jonas Goßen, Rui Pedro Ribeiro, Dirk Bier, Bernd Neumaier, Paolo Carloni, Alejandro Giorgetti, Giulia Rossetti. AI-based identification of therapeutic agents targeting GPCRs: introducing ligand type classifiers and systems biology. Chemical Science 2023, 14 (32) , 8651-8661. https://doi.org/10.1039/D3SC02352D
  27. Andrea Spinaci, Michela Buccioni, Cui Chang, Diego Dal Ben, Beatrice Francucci, Catia Lambertucci, Rosaria Volpini, Gabriella Marucci. Adenosine A2A Receptor Antagonists: Chemistry, SARs, and Therapeutic Potential. 2023, 101-141. https://doi.org/10.1007/7355_2023_162
  28. Wallace Chan, Jiansheng Wu, Eric Bell, Yang Zhang. Virtual Screening and Bioactivity Modeling for G Protein‐Coupled Receptors. 2022, 388-423. https://doi.org/10.1002/9781119564782.ch12
  29. Theresa Noonan, Katrin Denzinger, Valerij Talagayev, Yu Chen, Kristina Puls, Clemens Alexander Wolf, Sijie Liu, Trung Ngoc Nguyen, Gerhard Wolber. Mind the Gap—Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence. Pharmaceuticals 2022, 15 (11) , 1304. https://doi.org/10.3390/ph15111304
  30. Elissa A. Fink, Jun Xu, Harald Hübner, Joao M. Braz, Philipp Seemann, Charlotte Avet, Veronica Craik, Dorothee Weikert, Maximilian F. Schmidt, Chase M. Webb, Nataliya A. Tolmachova, Yurii S. Moroz, Xi-Ping Huang, Chakrapani Kalyanaraman, Stefan Gahbauer, Geng Chen, Zheng Liu, Matthew P. Jacobson, John J. Irwin, Michel Bouvier, Yang Du, Brian K. Shoichet, Allan I. Basbaum, Peter Gmeiner. Structure-based discovery of nonopioid analgesics acting through the α 2A -adrenergic receptor. Science 2022, 377 (6614) https://doi.org/10.1126/science.abn7065
  31. Zhichao Zhong, Xingrui He, Jiamin Ge, Junlong Zhu, Chuansheng Yao, Hong Cai, Xiang-Yang Ye, Tian Xie, Renren Bai. Discovery of small-molecule compounds and natural products against Parkinson's disease: Pathological mechanism and structural modification. European Journal of Medicinal Chemistry 2022, 237 , 114378. https://doi.org/10.1016/j.ejmech.2022.114378
  32. Kenneth A. Jacobson, Zhan‐Guo Gao, Pierre Matricon, Matthew T. Eddy, Jens Carlsson. Adenosine A 2A receptor antagonists: from caffeine to selective non‐xanthines. British Journal of Pharmacology 2022, 179 (14) , 3496-3511. https://doi.org/10.1111/bph.15103
  33. Sujin Park, Yujin Ahn, Yongchan Kim, Eun Joo Roh, Yoonji Lee, Chaebin Han, Hee Min Yoo, Jinha Yu. Design, Synthesis and Biological Evaluation of 1,3,5-Triazine Derivatives Targeting hA1 and hA3 Adenosine Receptor. Molecules 2022, 27 (13) , 4016. https://doi.org/10.3390/molecules27134016
  34. Chrisna Matthee, Gisella Terre’Blanche, Lesetja J. Legoabe, Helena D. Janse van Rensburg. Exploration of chalcones and related heterocycle compounds as ligands of adenosine receptors: therapeutics development. Molecular Diversity 2022, 26 (3) , 1779-1821. https://doi.org/10.1007/s11030-021-10257-9
  35. Tobias Claff, Tim A. Klapschinski, Udaya K. Tiruttani Subhramanyam, Victoria J. Vaaßen, Jonathan G. Schlegel, Christin Vielmuth, Jan H. Voß, Jörg Labahn, Christa E. Müller. Eine einzige stabilisierende Punktmutation ermöglicht hochaufgelöste Co‐Kristallstrukturen des Adenosin‐A 2A ‐Rezeptors mit Preladenant‐Konjugaten. Angewandte Chemie 2022, 134 (22) https://doi.org/10.1002/ange.202115545
  36. Tobias Claff, Tim A. Klapschinski, Udaya K. Tiruttani Subhramanyam, Victoria J. Vaaßen, Jonathan G. Schlegel, Christin Vielmuth, Jan H. Voß, Jörg Labahn, Christa E. Müller. Single Stabilizing Point Mutation Enables High‐Resolution Co‐Crystal Structures of the Adenosine A 2A Receptor with Preladenant Conjugates. Angewandte Chemie International Edition 2022, 61 (22) https://doi.org/10.1002/anie.202115545
  37. Miru Tang, Chang Wen, Lin Jie, Hongming Chen, Ting Ran. Discovery of Novel A 2AR Antagonists Through Deep Learning-Based Virtual Screening. SSRN Electronic Journal 2022, 31 https://doi.org/10.2139/ssrn.4188435
  38. Brian J. Bender, Stefan Gahbauer, Andreas Luttens, Jiankun Lyu, Chase M. Webb, Reed M. Stein, Elissa A. Fink, Trent E. Balius, Jens Carlsson, John J. Irwin, Brian K. Shoichet. A practical guide to large-scale docking. Nature Protocols 2021, 16 (10) , 4799-4832. https://doi.org/10.1038/s41596-021-00597-z
  39. Flavio Ballante, Albert J Kooistra, Stefanie Kampen, Chris de Graaf, Jens Carlsson, . Structure-Based Virtual Screening for Ligands of G Protein–Coupled Receptors: What Can Molecular Docking Do for You?. Pharmacological Reviews 2021, 73 (4) , 1698-1736. https://doi.org/10.1124/pharmrev.120.000246
  40. João Paulo L Velloso, David B Ascher, Douglas E V Pires, . pdCSM-GPCR: predicting potent GPCR ligands with graph-based signatures. Bioinformatics Advances 2021, 1 (1) https://doi.org/10.1093/bioadv/vbab031
  41. Miles Congreve, John A. Christopher, Chris de Graaf. Structure‐Based Drug Design for G Protein‐Coupled Receptors. 2021, 1-59. https://doi.org/10.1002/0471266949.bmc269
  42. Helena D. Janse van Rensburg, Lesetja J. Legoabe, Gisella Terre’Blanche. C3 amino-substituted chalcone derivative with selective adenosine rA1 receptor affinity in the micromolar range. Chemical Papers 2021, 75 (4) , 1581-1605. https://doi.org/10.1007/s11696-020-01414-9
  43. Veronica Salmaso, Kenneth A. Jacobson. Purinergic Signaling: Impact of GPCR Structures on Rational Drug Design. ChemMedChem 2020, 15 (21) , 1958-1973. https://doi.org/10.1002/cmdc.202000465
  44. Willem Jespers, Grégory Verdon, Jhonny Azuaje, Maria Majellaro, Henrik Keränen, Xerardo García‐Mera, Miles Congreve, Francesca Deflorian, Chris de Graaf, Andrei Zhukov, Andrew S. Doré, Jonathan S. Mason, Johan Åqvist, Robert M. Cooke, Eddy Sotelo, Hugo Gutiérrez‐de‐Terán. X‐Ray Crystallography and Free Energy Calculations Reveal the Binding Mechanism of A 2A Adenosine Receptor Antagonists. Angewandte Chemie 2020, 132 (38) , 16679-16686. https://doi.org/10.1002/ange.202003788
  45. Willem Jespers, Grégory Verdon, Jhonny Azuaje, Maria Majellaro, Henrik Keränen, Xerardo García‐Mera, Miles Congreve, Francesca Deflorian, Chris de Graaf, Andrei Zhukov, Andrew S. Doré, Jonathan S. Mason, Johan Åqvist, Robert M. Cooke, Eddy Sotelo, Hugo Gutiérrez‐de‐Terán. X‐Ray Crystallography and Free Energy Calculations Reveal the Binding Mechanism of A 2A Adenosine Receptor Antagonists. Angewandte Chemie International Edition 2020, 59 (38) , 16536-16543. https://doi.org/10.1002/anie.202003788
  46. Saleta Vazquez-Rodriguez, Santiago Vilar, Sonja Kachler, Karl-Norbert Klotz, Eugenio Uriarte, Fernanda Borges, Maria João Matos. Adenosine Receptor Ligands: Coumarin–Chalcone Hybrids as Modulating Agents on the Activity of hARs. Molecules 2020, 25 (18) , 4306. https://doi.org/10.3390/molecules25184306
  47. Miles Congreve, Chris de Graaf, Nigel A. Swain, Christopher G. Tate. Impact of GPCR Structures on Drug Discovery. Cell 2020, 181 (1) , 81-91. https://doi.org/10.1016/j.cell.2020.03.003
  48. Reed M. Stein, Hye Jin Kang, John D. McCorvy, Grant C. Glatfelter, Anthony J. Jones, Tao Che, Samuel Slocum, Xi-Ping Huang, Olena Savych, Yurii S. Moroz, Benjamin Stauch, Linda C. Johansson, Vadim Cherezov, Terry Kenakin, John J. Irwin, Brian K. Shoichet, Bryan L. Roth, Margarita L. Dubocovich. Virtual discovery of melatonin receptor ligands to modulate circadian rhythms. Nature 2020, 579 (7800) , 609-614. https://doi.org/10.1038/s41586-020-2027-0
  49. Yu Wei, Mukuo Wang, Yang Li, Zhangyong Hong, Dongmei Li, Jianping Lin. Identification of new potent A1 adenosine receptor antagonists using a multistage virtual screening approach. European Journal of Medicinal Chemistry 2020, 187 , 111936. https://doi.org/10.1016/j.ejmech.2019.111936
  50. Francesca Deflorian, Jonathan S. Mason, Andrea Bortolato, Benjamin G. Tehan. Impact of Recently Determined Crystallographic Structures of GPCRs on Drug Discovery. 2020, 449-477. https://doi.org/10.1002/9781118681121.ch19
  51. Jinan Wang, Apurba Bhattarai, Waseem Imtiaz Ahmad, Treyton S. Farnan, Karen Priyadarshini John, Yinglong Miao. Computer-aided GPCR drug discovery. 2020, 283-293. https://doi.org/10.1016/B978-0-12-816228-6.00015-5
  52. Stacy Gelhaus Wendell, Hao Fan, Cheng Zhang, . G Protein–Coupled Receptors in Asthma Therapy: Pharmacology and Drug Action. Pharmacological Reviews 2020, 72 (1) , 1-49. https://doi.org/10.1124/pr.118.016899
  53. Pabitra Narayan Samanta, Supratik Kar, Jerzy Leszczynski. Recent Advances of In-Silico Modeling of Potent Antagonists for the Adenosine Receptors. Current Pharmaceutical Design 2019, 25 (7) , 750-773. https://doi.org/10.2174/1381612825666190304123545
  54. Nikhil Agrawal, Balakumar Chandrasekaran, Amal Al-Aboudi. Recent Advances in the In-silico Structure-based and Ligand-based Approaches for the Design and Discovery of Agonists and Antagonists of A2A Adenosine Receptor. Current Pharmaceutical Design 2019, 25 (7) , 774-782. https://doi.org/10.2174/1381612825666190306162006
  55. Rita C. Acúrcio, Anna Scomparin, Ronit Satchi‐Fainaro, Helena F. Florindo, Rita C. Guedes. Computer‐aided drug design in new druggable targets for the next generation of immune‐oncology therapies. WIREs Computational Molecular Science 2019, 9 (3) https://doi.org/10.1002/wcms.1397
  56. Jiankun Lyu, Sheng Wang, Trent E. Balius, Isha Singh, Anat Levit, Yurii S. Moroz, Matthew J. O’Meara, Tao Che, Enkhjargal Algaa, Kateryna Tolmachova, Andrey A. Tolmachev, Brian K. Shoichet, Bryan L. Roth, John J. Irwin. Ultra-large library docking for discovering new chemotypes. Nature 2019, 566 (7743) , 224-229. https://doi.org/10.1038/s41586-019-0917-9
  57. Darinka Gjorgieva Ackova, Jelena Kotur-Stevuljevic, Chandra Bhushan Mishra, Pratibha Mehta Luthra, Luciano Saso. Antioxidant Properties of Synthesized Bicyclic Thiazolopyrimidine Derivatives as Possible Therapeutic Agents. Applied Sciences 2019, 9 (1) , 113. https://doi.org/10.3390/app9010113
  58. Miles Congreve, Giles A. Brown, Alexandra Borodovsky, Michelle L. Lamb. Targeting adenosine A 2A receptor antagonism for treatment of cancer. Expert Opinion on Drug Discovery 2018, 13 (11) , 997-1003. https://doi.org/10.1080/17460441.2018.1534825
  59. Victor Jun Yu Lim, Weina Du, Yu Zong Chen, Hao Fan. A benchmarking study on virtual ligand screening against homology models of human GPCRs. Proteins: Structure, Function, and Bioinformatics 2018, 86 (9) , 978-989. https://doi.org/10.1002/prot.25533
  60. Petr Popov, Yao Peng, Ling Shen, Raymond C Stevens, Vadim Cherezov, Zhi-Jie Liu, Vsevolod Katritch. Computational design of thermostabilizing point mutations for G protein-coupled receptors. eLife 2018, 7 https://doi.org/10.7554/eLife.34729
  61. Shaherin Basith, Minghua Cui, Stephani J. Y. Macalino, Jongmi Park, Nina A. B. Clavio, Soosung Kang, Sun Choi. Exploring G Protein-Coupled Receptors (GPCRs) Ligand Space via Cheminformatics Approaches: Impact on Rational Drug Design. Frontiers in Pharmacology 2018, 9 https://doi.org/10.3389/fphar.2018.00128
  62. Magdalena Korczynska, Mary J. Clark, Celine Valant, Jun Xu, Ee Von Moo, Sabine Albold, Dahlia R. Weiss, Hayarpi Torosyan, Weijiao Huang, Andrew C. Kruse, Brent R. Lyda, Lauren T. May, Jo-Anne Baltos, Patrick M. Sexton, Brian K. Kobilka, Arthur Christopoulos, Brian K. Shoichet, Roger K. Sunahara. Structure-based discovery of selective positive allosteric modulators of antagonists for the M 2 muscarinic acetylcholine receptor. Proceedings of the National Academy of Sciences 2018, 115 (10) https://doi.org/10.1073/pnas.1718037115
  63. Agostinho Lemos, Rita Melo, Irina S. Moreira, M. Natália D. S. Cordeiro. Computer-Aided Drug Design Approaches to Study Key Therapeutic Targets in Alzheimer’s Disease. 2018, 61-106. https://doi.org/10.1007/978-1-4939-7404-7_3
  64. Jason B. Cross. Methods for Virtual Screening of GPCR Targets: Approaches and Challenges. 2018, 233-264. https://doi.org/10.1007/978-1-4939-7465-8_11
  65. Antonella Ciancetta, Kenneth A. Jacobson. Breakthrough in GPCR Crystallography and Its Impact on Computer-Aided Drug Design. 2018, 45-72. https://doi.org/10.1007/978-1-4939-7465-8_3
  66. Stefania Baraldi, Pier Giovanni Baraldi, Paola Oliva, Kiran S. Toti, Antonella Ciancetta, Kenneth A. Jacobson. A2A Adenosine Receptor: Structures, Modeling, and Medicinal Chemistry. 2018, 91-136. https://doi.org/10.1007/978-3-319-90808-3_5
  67. Willem Jespers, Anke C. Schiedel, Laura H. Heitman, Robert M. Cooke, Lisa Kleene, Gerard J.P. van Westen, David E. Gloriam, Christa E. Müller, Eddy Sotelo, Hugo Gutiérrez-de-Terán. Structural Mapping of Adenosine Receptor Mutations: Ligand Binding and Signaling Mechanisms. Trends in Pharmacological Sciences 2018, 39 (1) , 75-89. https://doi.org/10.1016/j.tips.2017.11.001
  68. Chen Wang, Jiehui Zhang, Jie Tang, Gang Zou. A Sequential Suzuki Coupling Approach to Unsymmetrical Aryl s ‐Triazines from Cyanuric Chloride. Advanced Synthesis & Catalysis 2017, 359 (14) , 2514-2519. https://doi.org/10.1002/adsc.201700260
  69. Anirudh Ranganathan, David Rodríguez, Jens Carlsson. Structure-Based Discovery of GPCR Ligands from Crystal Structures and Homology Models. 2017, 65-99. https://doi.org/10.1007/7355_2016_25
  70. M. Congreve, A. Bortolato, G. Brown, R.M. Cooke. Modeling and Design for Membrane Protein Targets. 2017, 145-188. https://doi.org/10.1016/B978-0-12-409547-2.12358-3
  71. Romain Duroux, Antonella Ciancetta, Philip Mannes, Jinha Yu, Shireesha Boyapati, Elizabeth Gizewski, Said Yous, Francisco Ciruela, John A. Auchampach, Zhan-Guo Gao, Kenneth A. Jacobson. Bitopic fluorescent antagonists of the A 2A adenosine receptor based on pyrazolo[4,3-e][1,2,4]triazolo[1,5-c]pyrimidin-5-amine functionalized congeners. MedChemComm 2017, 8 (8) , 1659-1667. https://doi.org/10.1039/C7MD00247E
  72. Eelke B. Lenselink, Thijs Beuming, Corine van Veen, Arnault Massink, Woody Sherman, Herman W. T. van Vlijmen, Adriaan P. IJzerman. In search of novel ligands using a structure-based approach: a case study on the adenosine A2A receptor. Journal of Computer-Aided Molecular Design 2016, 30 (10) , 863-874. https://doi.org/10.1007/s10822-016-9963-7
  73. Tony Ngo, Irina Kufareva, James LJ Coleman, Robert M Graham, Ruben Abagyan, Nicola J Smith. Identifying ligands at orphan GPCRs: current status using structure‐based approaches. British Journal of Pharmacology 2016, 173 (20) , 2934-2951. https://doi.org/10.1111/bph.13452
  74. Aashish Manglik, Henry Lin, Dipendra K. Aryal, John D. McCorvy, Daniela Dengler, Gregory Corder, Anat Levit, Ralf C. Kling, Viachaslau Bernat, Harald Hübner, Xi-Ping Huang, Maria F. Sassano, Patrick M. Giguère, Stefan Löber, Da Duan, Grégory Scherrer, Brian K. Kobilka, Peter Gmeiner, Bryan L. Roth, Brian K. Shoichet. Structure-based discovery of opioid analgesics with reduced side effects. Nature 2016, 537 (7619) , 185-190. https://doi.org/10.1038/nature19112
  75. Celine Lacroix, Inbar Fish, Hayarpi Torosyan, Pranavan Parathaman, John J. Irwin, Brian K. Shoichet, Stephane Angers, . Identification of Novel Smoothened Ligands Using Structure-Based Docking. PLOS ONE 2016, 11 (8) , e0160365. https://doi.org/10.1371/journal.pone.0160365
  76. Nagarajan Vaidehi, Reinhard Grisshammer, Christopher G. Tate. How Can Mutations Thermostabilize G-Protein-Coupled Receptors?. Trends in Pharmacological Sciences 2016, 37 (1) , 37-46. https://doi.org/10.1016/j.tips.2015.09.005
  77. James Kean, Andrea Bortolato, Kaspar Hollenstein, Fiona H. Marshall, Ali Jazayeri. Conformational thermostabilisation of corticotropin releasing factor receptor 1. Scientific Reports 2015, 5 (1) https://doi.org/10.1038/srep11954
  78. Robert M. Cooke, Alastair J.H. Brown, Fiona H. Marshall, Jonathan S. Mason. Structures of G protein-coupled receptors reveal new opportunities for drug discovery. Drug Discovery Today 2015, 20 (11) , 1355-1364. https://doi.org/10.1016/j.drudis.2015.08.003
  79. Punita Kumari, Eshan Ghosh, Arun K. Shukla. Emerging Approaches to GPCR Ligand Screening for Drug Discovery. Trends in Molecular Medicine 2015, 21 (11) , 687-701. https://doi.org/10.1016/j.molmed.2015.09.002
  80. Ali Jazayeri, Joao M. Dias, Fiona H. Marshall. From G Protein-coupled Receptor Structure Resolution to Rational Drug Design. Journal of Biological Chemistry 2015, 290 (32) , 19489-19495. https://doi.org/10.1074/jbc.R115.668251
  81. Delia Preti, Pier Giovanni Baraldi, Allan R. Moorman, Pier Andrea Borea, Katia Varani. History and Perspectives of A 2A Adenosine Receptor Antagonists as Potential Therapeutic Agents. Medicinal Research Reviews 2015, 35 (4) , 790-848. https://doi.org/10.1002/med.21344
  82. Guillaume Lebon, Patricia C. Edwards, Andrew G. W. Leslie, Christopher G. Tate. Molecular Determinants of CGS21680 Binding to the Human Adenosine A 2A Receptor. Molecular Pharmacology 2015, 87 (6) , 907-915. https://doi.org/10.1124/mol.114.097360
  83. Kirstie A Bennett, Andrew S Doré, John A Christopher, Dahlia R Weiss, Fiona H Marshall. Structures of mGluRs shed light on the challenges of drug development of allosteric modulators. Current Opinion in Pharmacology 2015, 20 , 1-7. https://doi.org/10.1016/j.coph.2014.09.022
  84. Gengyang Yuan, Nicholas G Gedeon, Tanner C Jankins, Graham B Jones. Novel approaches for targeting the adenosine A 2A receptor. Expert Opinion on Drug Discovery 2015, 10 (1) , 63-80. https://doi.org/10.1517/17460441.2015.971006
  85. Thijs Beuming, Bart Lenselink, Daniele Pala, Fiona McRobb, Matt Repasky, Woody Sherman. Docking and Virtual Screening Strategies for GPCR Drug Discovery. 2015, 251-276. https://doi.org/10.1007/978-1-4939-2914-6_17
  86. Henrik Keränen, Johan Åqvist, Hugo Gutiérrez-de-Terán. Free energy calculations of A 2A adenosine receptor mutation effects on agonist binding. Chemical Communications 2015, 51 (17) , 3522-3525. https://doi.org/10.1039/C4CC09517K
  87. Claudio N. Cavasotto, Damián Palomba. Expanding the horizons of G protein-coupled receptor structure-based ligand discovery and optimization using homology models. Chemical Communications 2015, 51 (71) , 13576-13594. https://doi.org/10.1039/C5CC05050B
  88. Henrik Keränen, Hugo Gutiérrez-de-Terán, Johan Åqvist, . Structural and Energetic Effects of A2A Adenosine Receptor Mutations on Agonist and Antagonist Binding. PLoS ONE 2014, 9 (10) , e108492. https://doi.org/10.1371/journal.pone.0108492
  89. Maria João Matos, Santiago Vilar, Sonja Kachler, André Fonseca, Lourdes Santana, Eugenio Uriarte, Fernanda Borges, Nicholas P. Tatonetti, Karl‐Norbert Klotz. Insight into the Interactions between Novel Coumarin Derivatives and Human A 3 Adenosine Receptors. ChemMedChem 2014, 9 (10) , 2245-2253. https://doi.org/10.1002/cmdc.201402205
  90. Albert J. Kooistra, Chris de Graaf, Henk Timmerman. The Receptor Concept in 3D: From Hypothesis and Metaphor to GPCR–Ligand Structures. Neurochemical Research 2014, 39 (10) , 1850-1861. https://doi.org/10.1007/s11064-014-1398-8
  91. David Rodríguez, José Brea, María Isabel Loza, Jens Carlsson. Structure-Based Discovery of Selective Serotonin 5-HT 1B Receptor Ligands. Structure 2014, 22 (8) , 1140-1151. https://doi.org/10.1016/j.str.2014.05.017
  92. Rangappa S. Keri, Srinivasa Budagumpi, Ranjith Krishna Pai, R. Geetha Balakrishna. Chromones as a privileged scaffold in drug discovery: A review. European Journal of Medicinal Chemistry 2014, 78 , 340-374. https://doi.org/10.1016/j.ejmech.2014.03.047
  93. Julia K. Archbold, Jennifer L. Martin, Matthew J. Sweet. Towards selective lysophospholipid GPCR modulators. Trends in Pharmacological Sciences 2014, 35 (5) , 219-226. https://doi.org/10.1016/j.tips.2014.03.004
  94. Sophie J Bradley, Sajjad A Riaz, Andrew B Tobin. Employing novel animal models in the design of clinically efficacious GPCR ligands. Current Opinion in Cell Biology 2014, 27 , 117-125. https://doi.org/10.1016/j.ceb.2013.12.002
  95. Hugo Gutiérrez-de-Terán. The roles of computational chemistry in the ligand design of G protein-coupled receptors: how far have we come and what should we expect?. Future Medicinal Chemistry 2014, 6 (3) , 251-254. https://doi.org/10.4155/fmc.13.209
  96. Stephen P. Andrews, Giles A. Brown, John A. Christopher. Structure‐Based and Fragment‐Based GPCR Drug Discovery. ChemMedChem 2014, 9 (2) , 256-275. https://doi.org/10.1002/cmdc.201300382
  97. Brian C. Shook. Adenosine A2A Receptor Antagonists. 2014, 1-42. https://doi.org/10.1007/7355_2014_67
  98. Albert J. Kooistra, Rob Leurs, Iwan J. P. de Esch, Chris de Graaf. From Three-Dimensional GPCR Structure to Rational Ligand Discovery. 2014, 129-157. https://doi.org/10.1007/978-94-007-7423-0_7
  99. Miles Congreve, João M. Dias, Fiona H. Marshall. Structure-Based Drug Design for G Protein-Coupled Receptors. 2014, 1-63. https://doi.org/10.1016/B978-0-444-63380-4.00001-9
  100. Lihui Zhang, Tianjun Liu, Xia Wang, Jinan Wang, Guohui Li, Yan Li, Ling Yang, Yonghua Wang. Insight into the binding mode and the structural features of the pyrimidine derivatives as human A2A adenosine receptor antagonists. Biosystems 2014, 115 , 13-22. https://doi.org/10.1016/j.biosystems.2013.04.003
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  • Abstract

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    Figure 1

    Figure 1. Structures of virtual screening hits.

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    Scheme 1

    Scheme 1. Optimization of Chromone Hit 5
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    Figure 2

    Figure 2. Docking of the chromone 12, showing the BPM fingerprint color coded onto the binding site residues and in graphical form as change in pKD. Nonbinding is shown in red (N253A, H250A). Next largest effect is in dark orange (L85A), second largest in amber (N181A, Y271A, I66A), an increase in binding in green (S277A). H-bonding between the nitrogen of the thiazole and the aromatic C–H of the chromone is predicted to Asn2536.55. Selected BPM data are tabulated showing the change in pKD of each binding site mutation.

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    Scheme 2

    Scheme 2. Optimization of Triazine Hit 1
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    Figure 3

    Figure 3. Docking of the triazine 15, showing the BPM fingerprint color coded onto the binding site residues and in graphical form as change in pKD. Nonbinding is shown in red (N253A, H250A). Next largest effect is in dark orange (L85A, S277A). H-bonding between the nitrogen of the triazine and the phenol is predicted to Asn2536.55. The polar piperazine substituent is proposed to reach into the region of the binding site occupied by ribose in the natural agonist ligand adenosine and may be the driver of selectivity versus the A1 receptor, as this region of the binding site contains some amino acid differences comparing the two receptors. (14) Selected BPM data are tabulated showing the change in pKD of each binding site mutation.

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    Scheme 3

    Scheme 3. Synthesis of 15 and 16a
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    Scheme aReagents and conditions: (a) THF, iPr2EtN, NH3. (b) For 16: (i) 3-(4-methoxypiperidin-1-yl)phenylboronic acid, Na2CO3, 1,4-dioxane/H2O, Pd(PPh3)4, 90 °C, then (ii) 2-hydroxylphenylboronic acid, Na2CO3, 1,4-dioxane/H2O, Pd(PPh3)4, 90 °C. (c) For 17: (i) 2-benzyloxyphenylboronic acid, Na2CO3, 1,4-dioxane/H2O, Pd(PPh3)4, 70 °C, then (ii) 3-(4-methylpiperazine-1-carbonyl)phenylboronic acid hydrochloride, Na2CO3, 1,4-dioxane/H2O, Pd(PPh3)4, 90 °C; (d) 17, EtOAc, Pd(OH)2/C, 1,4-cyclohexadiene, 140 °C (microwave).

  • References

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    This article references 25 other publications.

    1. 1
      Pinna, A. Novel investigational adenosine A2A receptor antagonists for Parkinson’s disease Expert Opin. Invest. Drugs 2009, 18, 1619– 1631
      Google Scholar
      There is no corresponding record for this reference.
    2. 2
      Blagg, J. Structural Alerts for Toxicity. In Burger’s Medicinal Chemistry, Drug Discovery and Development, 7th ed.; Abraham, D. J.; Rotella, D. P., Eds.; Wiley: Hoboken, NJ, 2010; pp 301– 344.
      Google Scholar
      There is no corresponding record for this reference.
    3. 3
      Warne, A.; Serrano-Vega, M. J.; Baker, J. G.; Moukhametzianov, R.; Edwards, P. C.; Henderson, R.; Leslie, A. G. W.; Tate, C. G.; Schertler, G. F. X. Structure of the beta1-adrenergic G protein-coupled receptor Nature 2008, 454, 486– 491

      (PDB code 2VT4)

      Google Scholar
      There is no corresponding record for this reference.
    4. 4
      Zhukov, A.; Andrews, S. P.; Errey, J. C.; Robertson, N.; Tehan, B.; Mason, J. S.; Marshall, F. H.; Weir, M.; Congreve, M. Biophysical Mapping of the adenosine A2A receptor J. Med. Chem. 2011, 54, 4312– 4323
      Google Scholar
      4
      Biophysical Mapping of the Adenosine A2A Receptor
      Zhukov, Andrei; Andrews, Stephen P.; Errey, James C.; Robertson, Nathan; Tehan, Benjamin; Mason, Jonathan S.; Marshall, Fiona H.; Weir, Malcolm; Congreve, Miles
      Journal of Medicinal Chemistry (2011), 54 (13), 4312-4323CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
      A new approach to generating information on ligand receptor interactions within the binding pocket of G protein-coupled receptors has been developed, called Biophys. Mapping (BPM). Starting from a stabilized receptor (StaR), minimally engineered for thermostability, addnl. single mutations are then added at positions that could be involved in small mol. interactions. The StaR and a panel of binding site mutants are captured onto Biacore chips to enable characterization of the binding of small mol. ligands using surface plasmon resonance (SPR) measurement. A matrix of binding data for a set of ligands vs. each active site mutation is then generated, providing specific affinity and kinetic information (KD, kon, and koff) of receptor-ligand interactions. This data set, in combination with mol. modeling and docking, is used to map the small mol. binding site for each class of compds. Taken together, the many constraints provided by these data identify key protein-ligand interactions and allow the shape of the site to be refined to produce a high quality three-dimensional picture of ligand binding, thereby facilitating structure based drug design. Results of biophys. mapping of the adenosine A2A receptor are presented.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXnt12nsb0%253D&md5=d7f5c6bf24f9093d823bb41db9867c09
    5. 5
      Halgren, T. A.; Murphy, R. B.; Friesner, R. A.; Beard, H. S.; Frye, L. L.; Pollard, W. T.; Banks, J. L. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening J. Med. Chem. 2004, 47, 1750– 1759
      Google Scholar
      5
      Glide: A new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening
      Halgren, Thomas A.; Murphy, Robert B.; Friesner, Richard A.; Beard, Hege S.; Frye, Leah L.; Pollard, W. Thomas; Banks, Jay L.
      Journal of Medicinal Chemistry (2004), 47 (7), 1750-1759CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
      A review. Glide's ability to identify active compds. in a database screen is characterized by applying Glide to a diverse set of nine protein receptors. In many cases, two, or even three, protein sites are employed to probe the sensitivity of the results to the site geometry. To make the database screens as realistic as possible, the screens use sets of "druglike" decoy ligands that have been selected to be representative of what we believe is likely to be found in the compd. collection of a pharmaceutical or biotechnol. company. Results are presented for releases 1.8, 2.0, and 2.5 of Glide. The comparisons show that av. measures for both "early" and "global" enrichment for Glide 2.5 are 3 times higher than for Glide 1.8 and more than 2 times higher than for Glide 2.0 because of better results for the least well-handled screens. This improvement in enrichment stems largely from the better balance of the more widely parametrized GlideScore 2.5 function and the inclusion of terms that penalize ligand-protein interactions that violate established principles of phys. chem., particularly as it concerns the exposure to solvent of charged protein and ligand groups. Comparisons to results for the thymidine kinase and estrogen receptors published by Rognan and co-workers (J. Med. Chem. 2000, 43, 4759-4767) show that Glide 2.5 performs better than GOLD 1.1, FlexX 1.8, or DOCK 4.01.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXhsFyit78%253D&md5=33d68dd968e65626b449df61e44e37be
    6. 6
      Congreve, M.; Andrews, S. P.; Dore, A. S.; Hollenstein, K.; Hurrell, E.; Langmead, C. J.; Mason, J. S.; Ng, I. W.; Tehan, B.; Zhukov, A.; Weir, M.; Marshall, F. H. Discovery of 1,2,4-triazine derivatives as adenosine A2A antagonists using structure based drug design. J. Med. Chem. [Online early access]. DOI:  DOI: 10.1021/jm201376w . Published Online: Jan 5, 2012.
      Google Scholar
      There is no corresponding record for this reference.
    7. 7
      Hopkins, A. L.; Groom, C. R.; Alex, A. Ligand efficiency: a useful metric for lead selection Drug Discovery Today 2004, 9, 430– 431
      Google Scholar
      7
      Ligand efficiency: a useful metric for lead selection
      Hopkins Andrew L; Groom Colin R; Alex Alexander
      Drug discovery today (2004), 9 (10), 430-1 ISSN:1359-6446.
      There is no expanded citation for this reference.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD2c3ht1ygtw%253D%253D&md5=eb19a1bca53247d0bd9adcb99d5817e6
    8. 8
      Leeson, P. D.; Springthorpe, B. The influence of drug-like concepts on decision-making in medicinal chemistry Nat. Rev. Drug Discovery 2007, 6, 881– 890
      Google Scholar
      8
      The influence of drug-like concepts on decision-making in medicinal chemistry
      Leeson, Paul D.; Springthorpe, Brian
      Nature Reviews Drug Discovery (2007), 6 (11), 881-890CODEN: NRDDAG; ISSN:1474-1776. (Nature Publishing Group)
      A review. Despite the wide acceptance of drug-like principles such as the 'rule of five', this anal. of mols. currently being synthesized in leading pharmaceutical companies reveals that their phys. properties differ significantly from those of recently discovered oral drugs. The marked increase in lipophilicity in particular could increase the likelihood of attrition in drug development. The application of guidelines linked to the concept of drug-likeness, such as the 'rule of five', has gained wide acceptance as an approach to reduce attrition in drug discovery and development. However, despite this acceptance, anal. of recent trends reveals that the phys. properties of mols. that are currently being synthesized in leading drug discovery companies differ significantly from those of recently discovered oral drugs and compds. in clin. development. The consequences of the marked increase in lipophilicity - the most important drug-like phys. property - include a greater likelihood of lack of selectivity and attrition in drug development. Tackling the threat of compd.-related toxicol. attrition needs to move to the mainstream of medicinal chem. decision-making.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXht1els7rL&md5=5665a9e2dc266a1ee096e20625757ef7
    9. 9
      Katritch, V.; Jaakola, V.-P.; Lane, J. R.; Lin, J.; Izerman, A. P.; Yaeger, M.; Kufarena, I.; Stevens, R. C.; Abagyan, R. Structure-based discovery of novel chemotypes for adenosine A2a receptor antagonists J. Med. Chem. 2010, 53, 1799– 1809
      Google Scholar
      9
      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.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXptlKqsw%253D%253D&md5=5a54df43f6edd20d83e7e5942e2f9811
    10. 10
      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
      Google Scholar
      10
      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.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXkvFaqsL8%253D&md5=c36c941d52d2cec06387d79c4c423d46
    11. 11
      Goodford, P. J. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules J. Med. Chem. 1985, 28 (7) 849– 857
      Google Scholar
      11
      A computational procedure for determining energetically favorable binding sites on biologically important macromolecules
      Goodford, P. J.
      Journal of Medicinal Chemistry (1985), 28 (7), 849-57CODEN: JMCMAR; ISSN:0022-2623.
      The interaction of a probe group with a protein of known structure is computed at sample positions throughout and around the macromol., giving an array of energy values. The probe include H2O, the Me group, amine-N, carboxy-O, and OH. Contour surfaces at appropriate energy levels are calcd. for each probe and displayed by computer graphics together with the protein structure. Contours at neg. energy levels delineate regions of attraction between probe and protein and are found at known ligand binding clefts in particular. The contours also enable other regions of attraction to be identified and facilitate the interpretation of protein-ligand energetics. They may, therefore, be of value for drug design.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaL2MXktVeit7c%253D&md5=03e9d22787f16c760ef0dc29ca02aa88
    12. 12
      Higgs, C.; Beuming, T.; Sherman, W. Hydration site thermodynamics explain SARs for triazolylpurines analogues binding to the A2A receptor ACS Med. Chem. Lett. 2010, 1, 160– 164
      Google Scholar
      There is no corresponding record for this reference.
    13. 13
      Congreve, M.; Langmead, C. J.; Mason, J. S.; Marshall, F. H. Progress in structure based drug design for G protein-coupled receptors J. Med. Chem. 2011, 54, 4283– 4311
      Google Scholar
      13
      Progress in Structure Based Drug Design for G Protein-Coupled Receptors
      Congreve, Miles; Langmead, Christopher J.; Mason, Jonathan S.; Marshall, Fiona H.
      Journal of Medicinal Chemistry (2011), 54 (13), 4283-4311CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
      A review. The field of GPCR drug discovery is set to have a new lease of life, invigorated by a quantum leap of progress in structural biol. over the past 3 years. The new era of SBDD for GPCRS should now allow even some of the most challenging targets to yield to the efforts of medicinal chemists. This very well established drug family can expect to see many more first in class agents and better mols. with improved physicochem. properties and selectivity profiles, derived from the careful, rational optimization of hits using structure based and fragment based methods. In particular, a disciplined emphasis on maintaining high ligand efficiency and ligand lipophilicity efficiency should allow the next generation of research on this target family to profit from improved clin. success rates and lead to safe and efficacious medicines over the next decade.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXns1emtr0%253D&md5=2610591be5473dad558145b6dea84c2b
    14. 14
      Lebon, G.; Warne, T.; Edwards, P. C.; Bennett, K.; Langmead, C. J.; Leslie, A. G. W.; Tate, C. G. Agonist-bound adenosine A2A receptor structures reveal common features of GPCR activation Nature 2011, 474, 521– 523
      Google Scholar
      14
      Agonist-bound adenosine A2A receptor structures reveal common features of GPCR activation
      Lebon, Guillaume; Warne, Tony; Edwards, Patricia C.; Bennett, Kirstie; Langmead, Christopher J.; Leslie, Andrew G. W.; Tate, Christopher G.
      Nature (London, United Kingdom) (2011), 474 (7352), 521-525CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
      Adenosine receptors and β-adrenoceptors are G-protein-coupled receptors (GPCRs) that activate intracellular G proteins on binding the agonists adenosine or noradrenaline, resp. GPCRs have similar structures consisting of seven transmembrane helixes that contain well-conserved sequence motifs, indicating that they are probably activated by a common mechanism. Recent structures of β-adrenoceptors highlight residues in transmembrane region 5 that initially bind specifically to agonists rather than to antagonists, indicating that these residues have an important role in agonist-induced activation of receptors. Here we present two crystal structures of the thermostabilized human adenosine A2A receptor (A2AR-GL31) bound to its endogenous agonist adenosine and the synthetic agonist NECA. The structures represent an intermediate conformation between the inactive and active states, because they share all the features of GPCRs that are thought to be in a fully activated state, except that the cytoplasmic end of transmembrane helix 6 partially occludes the G-protein-binding site. The adenine substituent of the agonists binds in a similar fashion to the chem. related region of the inverse agonist ZM241385 (ref. 8). Both agonists contain a ribose group, not found in ZM241385, which extends deep into the ligand-binding pocket where it makes polar interactions with conserved residues in H7 (Ser 2777.42 and His 2787.43; superscripts refer to Ballesteros-Weinstein numbering) and non-polar interactions with residues in H3. In contrast, the inverse agonist ZM241385 does not interact with any of these residues and comparison with the agonist-bound structures indicates that ZM241385 sterically prevents the conformational change in H5 and therefore it acts as an inverse agonist. Comparison of the agonist-bound structures of A2AR with the agonist-bound structures of β-adrenoceptors indicates that the contraction of the ligand-binding pocket caused by the inward motion of helixes 3, 5 and 7 may be a common feature in the activation of all GPCRs.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXmsFyqsrg%253D&md5=1b8161216b878db48ad33d7e8e007dc7
    15. 15
      “Chemicals Available for Purchase” database. Available from http://www.accelrys.com.
      Google Scholar
      There is no corresponding record for this reference.
    16. 16
      Berman, H. M; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T. N.; Weissig, H.; Shindyalov, I. N.; Bourne, P. E. The Protein Data Bank Nucleic Acids Res. 2000, 28, 235– 242

      (http://www.rcsb.org/pdb/static.do?p=general_information/about_pdb/policies_references.html)

      Google Scholar
      16
      The Protein Data Bank
      Berman, Helen M.; Westbrook, John; Feng, Zukang; Gilliland, Gary; Bhat, T. N.; Weissig, Helge; Shindyalov, Ilya N.; Bourne, Philip E.
      Nucleic Acids Research (2000), 28 (1), 235-242CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)
      The Protein Data Bank (PDB; http://www.rcsb.org/pdb/)is the single worldwide archive of structural data of biol. macromols. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3cXhvVKjt7w%253D&md5=227fb393f754be2be375ab727bfd05dc
    17. 17
      Sali, A.; Blundell, T. L. Comparative protein modeling by satisfaction of spatial restraints J. Mol. Biol. 1993, 234, 779– 815
      Google Scholar
      17
      Comparative protein modeling by satisfaction of spatial restraints
      Sali, Andrej; Blundell, Tom L.
      Journal of Molecular Biology (1993), 234 (3), 779-815CODEN: JMOBAK; ISSN:0022-2836.
      The authors describe a comparative protein modeling method designed to find the most probable structure for a sequence given its alignment with related structures. The three-dimensional (3D) model is obtained by optimally satisfying spatial restraints derived from the alignment and expressed as probability d. functions (pdfs) for the features restrained. For example, the probabilities for main-chain conformations of a modelled residue may be restrained by its residue type, main-chain conformation of an equiv. residue in a related protein, and the local similarity between the two sequences. Several such pdfs are obtained from the correlations between structural features in 17 families of homologous proteins which have been aligned on the basis of their 3D structures. The pdfs restrain Cα-Cα distances, main-chain N-O distances, main-chain and side-chain dihedral angles. A smoothing procedure is used in the derivation of these relationships to minimize the problem of a sparse database. The 3D model of a protein is obtained by optimization of the mol. pdf such that the model violates the input restraints as little as possible. The mol. pdf is derived as a combination of pdfs restraining individual spatial features of the whole mol. The optimization procedure is a variable target function method that applies the conjugate gradients algorithm to positions of all non-hydrogen atoms. The method is automated and is illustrated by the modeling of trypsin from two other serine proteinases.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2cXnt1ylug%253D%253D&md5=d4a3c39b2205e36221dc187a3d1a478b
    18. 18
      Eswar, N.; Marti-Renom, M. A.; Webb, B.; Madhusudhan, M. S.; Eramian, D.; Shen, M.; Pieper, U.; Sali, A. Comparative Protein Structure Modeling with MODELLER. Current Protocols in Bioinformatics; Wiley: New York, 2006; Suppl. 15, pp 5.6.1– 5.6.30.
      Google Scholar
      There is no corresponding record for this reference.
    19. 19
      Molecular Operating Environment (MOE), version 2008.10; Chemical Computing Group, Inc.: Montreal, Quebec, Canada, 2008; www.chemcomp.com.
      Google Scholar
      There is no corresponding record for this reference.
    20. 20
      Thompson, J. D.; Higgins, D. G.; Gibson, T. J. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice Nucleic Acids Res. 1994, 22, 4673– 4680
      Google Scholar
      20
      CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice
      Thompson, Julie D.; Higgins, Desmond G.; Gibson, Toby J.
      Nucleic Acids Research (1994), 22 (22), 4673-80CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)
      The sensitivity of the commonly used progressive multiple sequence alignment method has been greatly improved for the alignment of divergent protein sequences. Firstly, individual wts. are assigned to each sequence in a partial alignment in order to down-wt. near-duplicate sequences and up-wt. the most divergent ones. Secondly, amino acid substitution matrixes are varied at different alignment stages according to the divergence of the sequences to be aligned. Thirdly, residue-specific gap penalties and locally reduced gap penalties in hydrophilic regions encourage new gaps in potential loop regions rather than regular secondary structure. Fourthly, positions in early alignments where gaps have been opened receive locally reduced gap penalties to encourage the opening of new gaps at these positions. These modifications are incorporated into a new program, CLUSTAL W which is freely available.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2MXitlSgu74%253D&md5=b219f8f0a8ea118b20a3e23461b99ccf
    21. 21
      Mirzadegan, T.; Benko, G. Sequence analyses of G-protein coupled receptor: similarities to rhodopsin Biochemistry 2003, 42 (10) 2759– 2767
      Google Scholar
      21
      Sequence Analyses of G-Protein-Coupled Receptors: Similarities to Rhodopsin
      Mirzadegan, Tara; Benko, Gil; Filipek, Slawomir; Palczewski, Krzysztof
      Biochemistry (2003), 42 (10), 2759-2767CODEN: BICHAW; ISSN:0006-2960. (American Chemical Society)
      The authors performed a comprehensive anal. of the primary sequences of G-protein-coupled receptors (GPCRs) from family A. They demonstrate that the extracellular domain is the least conserved, while GPCRs display considerable conservation toward the cytoplasmic side. Interestingly, the lengths of connecting loops also suggest an importance of these elements in retaining a similar general structure of these receptors. These structural similarities may suggest a common mechanism by which GPCRs activate G-proteins and how these receptors may self-organize within native membranes.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3sXptlCjtw%253D%253D&md5=6a33e7f8bf10b11627ad8aaf36290305
    22. 22
      Kim, J.; Wess, J.; van Rhee, M.; Schoneberg, T.; Jacobson, K. A. Site-directed mutagenesis identifies residues involved in ligand recognition in the human A2A adenosine receptor J. Biol. Chem. 1995, 270 (23) 13987– 13997
      Google Scholar
      There is no corresponding record for this reference.
    23. 23
      Available from Schrödinger, LLC, New York (http://www.schrodinger.com).
      Google Scholar
      There is no corresponding record for this reference.
    24. 24
      Pajouhesh, H.; Lenz, G. R. Medicinal chemical properties of successful central nervous system drugs NeuroRx 2005, 2 (4) 541– 553
      Google Scholar
      24
      Medicinal chemical properties of successful central nervous system drugs
      Pajouhesh Hassan; Lenz George R
      NeuroRx : the journal of the American Society for Experimental NeuroTherapeutics (2005), 2 (4), 541-53 ISSN:1545-5343.
      Fundamental physiochemical features of CNS drugs are related to their ability to penetrate the blood-brain barrier affinity and exhibit CNS activity. Factors relevant to the success of CNS drugs are reviewed. CNS drugs show values of molecular weight, lipophilicity, and hydrogen bond donor and acceptor that in general have a smaller range than general therapeutics. Pharmacokinetic properties can be manipulated by the medicinal chemist to a significant extent. The solubility, permeability, metabolic stability, protein binding, and human ether-ago-go-related gene inhibition of CNS compounds need to be optimized simultaneously with potency, selectivity, and other biological parameters. The balance between optimizing the physiochemical and pharmacokinetic properties to make the best compromises in properties is critical for designing new drugs likely to penetrate the blood brain barrier and affect relevant biological systems. This review is intended as a guide to designing CNS therapeutic agents with better drug-like properties.
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    25. 25
      Dal Ben, D.; Lambertucci, C.; Marucci, G.; Volpini, R.; Cristalli, G. Adenosine Receptor modeling: What does the A2A crystal structure tell us? Curr. Top. Med. Chem. 2010, 93, 993– 1018
      Google Scholar
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    Chemical synthesis protocols, QC data and binding curves for the top 10 hits, more detailed computational methods including virtual screening workflows, and a table of calculated blood–brain barrier prediction and the closest published adenosine A2A antagonist to each of the hits. This material is available free of charge via the Internet at http://pubs.acs.org.


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