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Discovery of Protease-Activated Receptor 4 (PAR4)-Tethered Ligand Antagonists Using Ultralarge Virtual Screening
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  • Shannon T. Smith
    Shannon T. Smith
    Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
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  • Jackson B. Cassada
    Jackson B. Cassada
    Department of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
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  • Lukas Von Bredow
    Lukas Von Bredow
    Warren Center for Neuroscience Drug Discovery, Nashville, Tennessee 37067, United States
    Institute for Drug Discovery, Leipzig University Medical School, Leipzig 04109, Germany
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  • Kevin Erreger
    Kevin Erreger
    Department of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
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    Orcidhttps://orcid.org/0000-0001-5801-2344
  • Emma M. Webb
    Emma M. Webb
    Department of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
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  • Trevor A. Trombley
    Trevor A. Trombley
    Warren Center for Neuroscience Drug Discovery, Nashville, Tennessee 37067, United States
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    Orcidhttps://orcid.org/0000-0003-0413-0913
  • Jacob J. Kalbfleisch
    Jacob J. Kalbfleisch
    Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
    Warren Center for Neuroscience Drug Discovery, Nashville, Tennessee 37067, United States
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  • Brian J. Bender
    Brian J. Bender
    Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California 94158, United States
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  • Irene Zagol-Ikapitte
    Irene Zagol-Ikapitte
    Warren Center for Neuroscience Drug Discovery, Nashville, Tennessee 37067, United States
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  • Valerie M. Kramlinger
    Valerie M. Kramlinger
    Department of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
    Warren Center for Neuroscience Drug Discovery, Nashville, Tennessee 37067, United States
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  • Jacob L. Bouchard
    Jacob L. Bouchard
    Warren Center for Neuroscience Drug Discovery, Nashville, Tennessee 37067, United States
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    Orcidhttps://orcid.org/0000-0002-5936-1244
  • Sidnee G. Mitchell
    Sidnee G. Mitchell
    Department of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
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  • Maik Tretbar
    Maik Tretbar
    Institute for Drug Discovery, Leipzig University Medical School, Leipzig 04109, Germany
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  • Brian K. Shoichet
    Brian K. Shoichet
    Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California 94158, United States
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    Orcidhttps://orcid.org/0000-0002-6098-7367
  • Craig W. Lindsley
    Craig W. Lindsley
    Department of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
    Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
    Warren Center for Neuroscience Drug Discovery, Nashville, Tennessee 37067, United States
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    Orcidhttps://orcid.org/0000-0003-0168-1445
  • Jens Meiler*
    Jens Meiler
    Department of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
    Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
    Institute for Drug Discovery, Leipzig University Medical School, Leipzig 04109, Germany
    *E-mail: [email protected]
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  • Heidi E. Hamm*
    Heidi E. Hamm
    Department of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
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ACS Pharmacology & Translational Science

Cite this: ACS Pharmacol. Transl. Sci. 2024, 7, 4, 1086–1100
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https://doi.org/10.1021/acsptsci.3c00378
Published March 21, 2024

Copyright © 2024 The Authors. Published by American Chemical Society. This publication is licensed under

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Abstract

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Here, we demonstrate a structure-based small molecule virtual screening and lead optimization pipeline using a homology model of a difficult-to-drug G-protein-coupled receptor (GPCR) target. Protease-activated receptor 4 (PAR4) is activated by thrombin cleavage, revealing a tethered ligand that activates the receptor, making PAR4 a challenging target. A virtual screen of a make-on-demand chemical library yielded a one-hit compound. From the single-hit compound, we developed a novel series of PAR4 antagonists. Subsequent lead optimization via simultaneous virtual library searches and structure-based rational design efforts led to potent antagonists of thrombin-induced activation. Interestingly, this series of antagonists was active against PAR4 activation by the native protease thrombin cleavage but not the synthetic PAR4 agonist peptide AYPGKF.

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PAR4 as a Therapeutic Target

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Thrombin, the major activator of platelets, activates protease-activated receptors PAR1 or PAR4 through the cleavage of the extracellular domain, revealing a tethered ligand (TL). (1−3) PAR1 is activated first by lower concentrations of thrombin, but PAR1 signaling is transient. (4) PAR1 activation is required for the initiation of hemostasis, but as PAR1-mediated platelet activation generates platelet prothrombinase on its surface, local thrombin concentrations increase. When local thrombin concentrations are high enough to activate PAR4, thrombin signaling is predominated by PAR4. PAR4 signaling is more sustained than PAR1 and drives yet more thrombin generation, fibrinogen cleavage to fibrin, and microparticle formation. (5)
While PAR4 has been most noted for the activation of platelets, PAR4 is also known to be expressed on diverse cell types with PAR4 expression higher in the context of inflammation than in control conditions. (6−14) PAR4 knockout mouse studies have demonstrated the role of PAR4 in neutrophil homing and invasion at the site of vascular insult. (6−8) PAR4 contributes to tissue damage after ischemia reperfusion injury in animal models of both myocardial infarction (15) and stroke, (16) as well as in inflammatory bowel disease, (9) lung inflammation and fibrosis, (6,10−12) and arthritis. (13)
PAR1 has a greater affinity for thrombin than PAR4, but despite early clinical promise, the addition of vorapaxar (the only licensed PAR1 antagonist) to standard care failed to meet its primary efficacy outcome in patients with acute coronary syndrome and was associated with an excess of major bleeding, especially intracranial hemorrhage, in phase 3 clinical trials. (17,18) Therefore, attention has since turned toward PAR4 as a potential thrombin receptor therapeutic target. Bristol–Myers–Squibb (BMS) developed PAR4 antagonists BMS-986120 (1) and BMS-986141 as antithrombotic agents with high potency and specificity for thrombin activation of PAR4 over PAR1. (19−21) PAR4 antagonists are hypothesized to have a lower bleeding risk than PAR1 antagonists because PAR4 acts at a later stage in the platelet activation process than PAR14. Importantly, in a cynomolgus monkey arterial thrombosis model, BMS-986120 (1) demonstrated potent and highly efficacious antithrombotic activity. (19) BMS-986120 (1) also exhibited a low bleeding liability and a markedly wider therapeutic window compared to that of clopidogrel tested in the same nonhuman primate model. In phase I human clinical trials, BMS-986120 (1) and BMS-986141 were safe and well tolerated in healthy participants over a wide range. (20,22) However, at this time, the BMS compounds have not advanced further in clinical development and larger clinical trials would ultimately be needed to establish safety.

Targeting PAR4

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Unlike most other GPCRs, PARs are not activated by the binding of a soluble ligand. Instead, they are triggered by proteases, which cleave a part of their N-terminus, exposing a new N-terminus that we refer to as the “tethered ligand” that binds intramolecularly to the receptor, activating it and inducing signal transduction (Figure 1). Experimentally, PAR4 can be activated either by the native protease thrombin cleavage event or by a synthetic soluble agonist peptide mimicking the tethered ligand (“PAR4-AP”).

Figure 1

Figure 1. PAR4 activation mechanism. PARs contain the highly conserved seven transmembrane helical bundle, an extracellular N-terminus, and an intracellular C-terminus, which binds to respective G-proteins to initiate downstream signaling. PAR activation is caused by thrombin-induced cleavage between residues Arg47/Gly48 on the exposed extracellular N-terminus, revealing a new N-terminus called the “tethered ligand (TL)”. The TL subsequently binds within the 7TM helical bundle to induce a conformational change to the receptor, prompting G-protein binding and propagate downstream signaling via Gq and G12/13.

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This unusual mechanism of action of PARs poses significant challenges in small molecule antagonist development. The native tethered ligand activator, being the newly cleaved N-terminus of the PAR, effectively exists at a high local concentration around the PAR binding site (estimated as high as 0.4 mM), making these receptors difficult to inhibit. (22,23) Several groups have successfully inhibited agonist peptide PAR4 activation including an indazole scaffold, (24) indole derivatives, (25−27) and a CNS-penetrant series; (28) however, these compounds do not robustly inhibit the physiologically relevant thrombin activation of PAR4. Bristol–Myers–Squibb published a patent describing a series of efficacious and bioavailable PAR4 antagonists (WO2013163244). The initial hit (Figure 2A, BMS-3 (2)) from the BMS campaign contains an imidazothiadiazole scaffold and is a selective and potent PAR4 thrombin antagonist. We synthesized this lead as a tool compound, “BMS-3” (2). (4) Chimerization of the imidazothiadiazole and indole (6) series has demonstrated weak antagonism against γ-thrombin (IC50 = 4.35 μM) (29) (Figure 2A). Specifically, using the minimum pharmacophore of 2 to be the imidazothiadiazole moiety (29) and systematic truncation of this molecule revealed a change in inhibition mode from noncompetitive to competitive. (4)

Figure 2

Figure 2. Previously identified PAR4 antagonists. (A) Red boxes follow lead optimization of 3 through 4 and 5 indole series; light blue designates imidazothiadiazole series from BMS starting with BMS-3 (2) from the HTS and BMS-986120 (1) in clinical trials; purple designates the chimerization series of the indole and imidazothiadiazole series (6). (B) Predicted binding mode of BMS-3 (green). Comparison to experimentally determined binding modes: vorapaxar (PAR1, PDB ID: 3vw7, cyan), AZ3451 (PAR2, PDB ID: 5NDZ, yellow), and AZ8838 (PAR2, PDB ID: 5NDD, magenta).

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There currently exist no experimentally determined structures of PAR4; however, extensive in silico modeling studies using homology models of PAR4 in combination with mutagenesis and known antagonists led us to propose a binding mode for BMS-3. Here, we structurally probe “make-on-demand” chemical space for novel compounds that can extend into this antagonist binding pocket.

Results and Discussion

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Despite not having an experimentally determined structure of PAR4, there currently exist structures of homologous proteins that can be used as templates for comparative modeling of PAR4. Here, we used deposited Protein Data Bank (30) crystal structures of PAR2, PAR1, chemokine receptor type 9 (CCR9), apelin receptor (APJ), and C5a receptor to build our model of PAR4. Templates were chosen based on the RosettaGPCR framework, which takes both sequence and structure-based GPCR alignments to select the optimal experimentally determined structure for individual regions to be used in multitemplate modeling. (30) Additionally, we chose to include only other endogenous peptide binding GPCRS to maintain a larger binding pocket and include a conserved ECL2 β turn motif. In addition to using the respective protein templates, we built the PAR4 binding pocket around the determined binding mode of vorapaxar within PAR1 to generate the PAR4 structural model. Using this homology model and docking studies, we predicted that BMS-3 binds in a pocket overlapping with vorapaxar in PAR1 (22) while extending further down into the transmembrane bundle (Figure 2B). Also shown for comparison are the structures of two antagonists against PAR2. (31) Independent findings based on hydrogen–deuterium exchange experiments demonstrate the mechanism of binding of the TL within the TM3/TM7 pocket, specifically noting the importance of T153 in the peptide binding event. (32) Our modeling studies of BMS-3 series demonstrate that the minimum pharmacophore binds near the residues involved in TL binding proposed by Han et al., (32) whereas the extended ring system of BMS-986120 (1) binds deeper into the TM bundle outside of the TL binding pocket (Figure 2B). As the imidazothiadiazole scaffold is the most effective compound against TL activation, we aimed to structurally probe for a new chemical space that can extend into this pocket.
Using this hypothesis, we demonstrate a structure-based virtual ultralarge library screen using DOCK to identify a new series of PAR4 antagonists. This method has shown previous success in identifying novel binders where the structure of a known active compound bound to the receptor has been experimentally determined, specifically AmpC β-lactamase, D4 dopamine receptor, (33) and melatonin receptor MT1. (33,34) Using comparative modeling in Rosetta (30,35) to obtain a structure for docking simulations and the lead-like subset from the ZINC database (https://zinc.docking.org/), (36−38) we screened 164 million compounds using the DOCK software to identify compounds distinct from the known PAR4 antagonist chemical space. (33,39,40) After identifying and validating a single-hit lead compound, we developed a series of analogs using the make-on-demand library and medicinal chemistry rational design.
Parallel testing in both human and mouse platelets explores new tool compounds for in vivo mouse studies as well as optimization for clinically relevant human PAR4 antagonists. Aside from testing for activity at mouse PAR4, this parallel testing in both human and mouse platelets with structural mapping of sequence variations was used as an additional structure-based approach for optimization. This pipeline is summarized in Figure 3.

Figure 3

Figure 3. Overview of the structure-based high-throughput screen and subsequent optimization.

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The initial virtual high-throughput screen of our PAR4 homology model was conducted, utilizing the lead-like subset of the ZINC ultralarge virtual library, composed of 164 million lead-like compounds. Virtual hit compounds were further filtered by DOCK score, transmembrane depth, visual analysis by medicinal chemists, and other chemical parameters. From the results of the virtual screen, 88 compounds were ordered from the Enamine make-on-demand library, and 79 were synthesized successfully.
To test the antagonistic effect of the compounds on PAR4, a flow cytometry assay of human platelets was employed. Each compound was screened at 10 μM in human platelets using flow cytometry for GPIIbIIIa activation (PAC1) and P-selectin (CD62p) expression as previously described. (4) Activation of GPIIbIIIa allows interaction with divalent fibrinogen or multivalent von Willebrand factor, leading to platelet aggregation. (41) PAR4 stimulation also triggers α-granule secretion containing P-selectin. (5) GPIIbIIIa and P-selectin flow cytometry signals exhibit strong correlation as readouts of PAR4 activation in platelets, (4,26) and therefore, antagonists of PAR4 are expected to reduce PAC1 (active GPIIbIIIa) and P-selectin (CD62p) detected by flow cytometry in platelets. Platelets were treated with each test compound or vehicle control DMSO before activating with γ-thrombin (100 nM) or PAR4 agonist peptide mimetic (200 μM, “PAR4-AP” AYPGKF) for 30 min. γ-thrombin activates PAR4 but not PAR1. (42) This screen identified one validated partial antagonist (“A8” (7), ZINC590833518) inhibiting the platelet activation by ∼50% against the protease γ-thrombin (Figure 4A) but not inhibiting the PAR4 agonist peptide (Figure 4B).

Figure 4

Figure 4. PAR4 platelet screen of enamine compounds generated from the vHTS conducted off the PAR4 homology model. Platelets activated 100 nM γ thrombin (A) or 200 μM PAR4 agonist peptide (AYPGKF) (B) with each compound normalized to vehicle DMSO control.

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The hit compound (7) was tested against several platelet activators including targeting PAR1, PAR4, and GPVI collagen receptors (Figure 5). 7 did not inhibit the platelet off-target convulxin activation of the GPVI receptor. 7 also did not inhibit the synthetic agonist peptides for PAR4-AP or PAR1AP but did inhibit the PAR4 component of α-thrombin activation defined as a response to thrombin in the presence of the PAR1 antagonist vorapaxar (Figure 5). One other compound from the screen (“C5″, ZINC348291734) partially inhibited γ-thrombin activation but failed the off-target screen by also inhibiting platelet activation by the GPVI receptor agonist convulxin (Figure 5). C5 also displayed differential activity against PAC1 (green) and P-selectin (red), which our group has previously observed to be an effect of off-target inhibition of convulxin, whereas PAR4-specific antagonists inhibit both readouts of PAR4 signaling with similar efficacy. Taken together, the inhibition of the off-target GPVI and the divergence of the inhibition of the γ-thrombin readouts PAC1 vs P-selectin are consistent with the C5 compound not being a specific antagonist of PAR4.

Figure 5

Figure 5. (A) Off-target controls: % of vehicle control for γ-thrombin (100 nM), PAR4 component of α-thrombin (5 nM α-thrombin +1 μM vorapaxar), PAR4 agonist peptide 200 μM AYPGKF, 200 μM PAR1 agonist peptide SFLLRN, and GPVI agonist convulxin 3.16 nM.

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7 failed to directly inhibit thrombin in a fluorometric assay of in vitro thrombin activity, suggesting that 7 is inhibiting the PAR4 receptor rather than directly inhibiting thrombin enzymatic activity (Figure S1). The enamine-purchased 7 compound was confirmed using mass spectrometry and 1H NMR (data not shown) and then was independently resynthesized at Vanderbilt University (VU). The concentration–response curve (CRC) for 7 displayed an IC50 value of 2.3 μM (Figure S2B). These data show that at a higher concentration, 7 is a near-complete inhibitor for the γ-thrombin activation of PAR4.
Although it has a relatively low potency, 7 allowed for a starting point for structure–activity relationship (SAR) to be performed to improve potency. 7 (MW: 344 g/mol) is smaller than the clinical candidate compound BMS-986120 (1) (MW: 513 g/mol). The proposed binding mode of this compound in the homology model shows penetration deep in the transmembrane helical bundle pocket, similar to the proposed BMS compound and the TL (Figure S2C). These findings suggest that there are structural determinants of inhibiting the TL of PAR4 distinct from those inhibiting activation by the agonist peptide. This is also notable as previous efforts using conventional chemical library screening for PAR4 antagonists were relatively successful against the agonist peptide, while the TL was more challenging to inhibit. (24−28)

Hit Compound A8 Optimization and Structure–Activity Relationship Development

A parallel approach to antagonist development included both (1) common scaffold and similarity searches of the virtual make-on-demand library and (2) medicinal chemistry optimization (Figure S3).
Following the functional characteristics of 7, an additional screen was conducted of the Enamine database for structural similarity to that of 7. Compounds for this “Analog set 1” were selected based on 7 Tanimoto similarity from the make-on-demand Enamine library. Analog set 1 (Figure S3) resulted in 5 compounds with a <30% max response against thrombin (data not shown), and three of these compounds 9, 10, and 17 were shown to have improved IC50 in comparison to the hit 7 (Tables 1 and 2). Structurally, 7, 9, 10, and 17 all contain benzene off the pyrazole N (-R1 group), which is connected to another triazole via an amide bond. The second substitution of the pyrazole (-R2 group) showed the greatest variation within the hits. Thus, both small groups and large, sterically demanding groups were tolerated as well as differences in polarity. This prompted us to search the Enamine database for all compounds containing this same core as well as explore SAR through medicinal chemistry efforts.
Table 1. IC50 Values for Generation 1 Lead Optimization of 9 and 10a
a

PAC1 and P-selectin in human platelets, JonA and P-selectin in mouse platelets.

Table 2. IC50 Values for Generation 2 Lead Optimization of 17a
a

PAC1 and P-selectin in human platelets, JonA and P-selectin in mouse platelets.

After screening enamine for similar compounds with the same core as the previous compounds, 100 compounds were selected for testing (80 synthesized successfully), which we have termed “Analog set 2”. Expectedly, this more specific search yielded a higher hit rate with 11/80 tested with similar or greater potency than that of 7 (Figure S3). We also explored derivatives from Analog set 1 around the common scaffold of 9 and 10 and separately around the fused ring moiety of 17 by searching through the Enamine database for similar compounds.
For 9 derivatives, compounds that showed comparable or improved activity had an -R1 substitution of a methyl group in place of the benzene and small hydrocarbon (isopropyl, ethyl, or cyclopropyl) substitution on -R2 showed overall improved activity over 9 with the exception of 14. Compounds with an introduced aromatic ring on -R3 also showed improved potency (Table 1). 17 derivatives showed no activity upon the introduction of a difluoro-methoxy group on -R2. Halogen introduction at multiple sites around -R1 benzene led to a loss of activity (Table 2). Ring expansion of the fused ring system resulted in a cyclohexane series being the most potent series to date, leading to a nearly 2-fold IC50 improvement. Subsequent derivatization of -R1 and -R2 groups showed no obvious trends in halogen placement around the -R1 benzene or -R2 substitutions with different ring systems ranging from cyclopropane to para-cyanobenzene (Table 3). All efforts to replace the amide core with isosteric groups thus far have resulted in a loss of activity (data not shown). Replacing any triazole nitrogen with carbon or oxygen resulted in a complete loss of activity, as well (data not shown). Considering all medicinal chemistry efforts, a minimal pharmacophore consisting of a pyrazole linked by an amide bond and 1,2,4-triazole is obtained.
Table 3. IC50 Values for Generation 3 Lead Optimizationa
a

PAC1 and P-selectin in human platelets, JonA and P-selectin in mouse platelets.

Figure 6A shows the progress and correlation of compound activity against human versus mouse platelets. The PAR4 sequence identity between the mouse and human is 78%, with only two residue variations (Q214H and A236E) within 5 Å of the binding pocket (Figure S4). As exemplars of this study, we compared key pharmacology properties of one potent compound from the virtual make-on-demand library (12) and one compound designed and synthesized locally (31) (Figure 6B). As is typical for the overall set of compounds (see Figure 6A), both 12 and 31 had modest potency preference for human over mouse PAR4 (Figure 6C). The basis for the potency preference for humans over mice is not known, as the predicted binding modes of 31 and 12 do not show direct interactions with the said residues. Aside from the Q214H and A236E mutations in the binding site, there are certainly other mutations that may be causing activity differences. There are roughly 30 mutations within the transmembrane domain likely causing distal changes in PAR4, which in turn affect receptor dynamics. Additionally, there are also mutations within the ECLs that may affect accessibility to the orthosteric site, as well as ICL mutations including helix 8 that potentially change the G-protein interface, causing signaling bias or affecting dynamics. Systematic mutational analysis of these regions in tandem with parallel signaling assays to determine the recruitment of different G-proteins or arrestin will be necessary to further understand the dynamics of PAR4 signaling.

Figure 6

Figure 6. (A) Distribution of IC50 values for the inhibition of human PAR4 vs mouse PAR4. Red indicates the 2 compounds highlighted in panels (B–D). (B) Structures of exemplar compounds. (C) Concentration–response curves for human vs mouse PAR4. (D) Concentration–response curves for the inhibition of pharmacologically defined α-thrombin components for PAR1 (α-thrombin + BMS-3) vs PAR4 (α-thrombin + vorapaxar).

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α-thrombin activates both PAR1 and PAR4, and the individual PAR1 or PAR4 component can be isolated pharmacologically. Both 12 and 31 exhibit a preference for inhibiting PAR4 thrombin activity over PAR1 thrombin activity (Figure 6D). With 20 substitutions between PAR1 and PAR4 within 5 Å of the predicted 31 binding site, the basis for these selectivity differences is not known (Figure S4). As we continue to probe for a selective PAR4 antagonist, mutagenesis of individual residues will be conducted.
A Schild analysis was performed on compounds 12 and 31 to determine the mode of inhibition of these compounds. Both compounds exhibited a rightward shift in the Schild graph and a linear slope of about one on the Schild plot. This indicates that both compounds exhibit a competitive mode of inhibition against γ-thrombin activation (Figure 7).

Figure 7

Figure 7. Schild analysis progressive fold-shift experiments. (A) Platelets were pretreated with increasing concentrations of each antagonist for 20 min prior to activation with increasing concentrations of γ-thrombin. (B) Logarithmic transformation of the dose ratio plotted versus the logarithm of compound concentration. m = slope of linear regression.

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Structural Analysis of the Predicted Binding Modalities of the Antagonists

Structural analysis of our current most potent compound, 31, provides a hypothesis for further derivatization to improve potency (Figure 8). The current model shows the carbonyl oxygen from the amide core of the molecule forming hydrogen bonds with the hydroxyl groups of Tyr157 and His229, while Tyr322 forms hydrogen bonds with the triazole nitrogen adjacent to the amide; the fused cyclohexane group occupies a hydrophobic pocket near Trp241 and Phe245, and the benzene from off the pyrazole nitrogen extends down into the TM domain. 1,3-Difluorobenzene off the triazole points into the extracellular region.

Figure 8

Figure 8. Predicted binding mode of 31 in the PAR4 homology model. Noting key binding interactions: H-bonds (yellow), H-bonding residues HIS162, TYR255, and TYR90 (green), and hydrophobic interactions TRP147 and PHE178 (magenta).

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Overlaying 31 and BMS-3 (2) predicted binding modes shows that both molecules occupy the same pocket and interact with a nearly identical set of residues (Figure 9). A closer inspection of Figure 9A shows the triazole moiety overlaying the imidazothiadiazole of BMS-3 (2), whereas on the other side of the molecule, the phenyl off the pyrazole is overlaid onto the thiazole of BMS-3 (2). This supports previously published data, showing that the imidazothiadiazole of BMS-3 (2) is also the minimum pharmacophore. (4) Whereas His162 and Tyr255 interact with the core amide oxygen, these same residues are predicted to interact with the imidazothiadiazole.

Figure 9

Figure 9. Predicted binding mode of 31 and BMS-3. (A) Predicted docking of 31 (magenta) and 2 (green) in the PAR4 homology model, noting the binding pocket in yellow. (B) Key residue of PAR4 interacting with 31 and 2. (C) Binding interaction between key residues of PAR4 and 31 and 2.

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The ability of these compounds to inhibit the endogenous activation mechanism and not the peptide-mediated activation is still an important question to answer and requires more in-depth structural and signaling studies between the mechanisms of TL and PAR4-AP-induced activation. We propose two intertwined and testable hypotheses: the TL and PAR4-AP bind to separate sites or induce distinct structural receptor states upon binding, or the TL and PAR4-AP demonstrate distinct signaling patterns.
It has previously been shown that TL binding requires a complex arrangement of TM3, TM7, ECL2, and ECL3. (32) It is possible that our molecules bind to a receptor state that occludes binding of the TL but not binding of the PAR4-AP. We specifically screened for molecules that bind deeper within the transmembrane domain, which is the proposed binding site for the TL, whereas the lower-affinity PAR4-AP could bind further toward the ECLs. Aside from interactions with the extracellular domain, the conformational states of the 6-residue soluble peptide and the corresponding TL residues likely differ, therefore potentially resulting in distinct binding mechanisms. Another explanation lies in the intracellular domain, specifically whether the TL and PAR4-AP demonstrate distinct signaling biases and kinetics. Structural changes on the intracellular side may induce changes in the recruitment of different G-protein or β-arrestin, which is not seen in our assay. Detailed signaling analysis using more direct activity readouts of the ligands may help us with our understanding of the activation mechanisms. In-depth structural and signaling studies including mutational analysis in both the presence and absence of thrombin and free peptide will be necessary for understanding these PAR4 activation mechanisms.

Pharmacokinetic Properties of Top Compounds

To assess the druglikeness of some of these molecules, the in vitro intrinsic clearance (CLlint) and plasma protein binding (PPB) were measured in mouse and human matrices. The values associated with these initial measurements are listed in Table 4. Plasma protein binding was high in human plasma, ranging from 0.6 to 5.2% free. For the compounds measured in mouse plasma, the percent free ranged from 0.6% to 16.2%. The most potent compound, 31, exhibited an intrinsic clearance of 459 mL/min/kg and 19,894 mL/min/kg in human liver microsomes (HLMs) and mouse liver microsomes (MLMs), respectively. In HLM, similarly high intrinsic clearance values were measured for all of the other compounds tested, with the exception of 17, which returned a Clint value of 49.6 mL/min/kg. While this value is still high for translational developability of the molecule, it is distinct from the others that were measured. In vitro intrinsic clearances in MLM of 17 and 12 were 2443 and 11534 mL/min/kg, respectively. Given the high intrinsic clearance values, the calculated hepatic clearance values were all approaching liver blood flow values in both the human and mouse.
Table 4. In Vitro Drug Metabolism and Pharmacokinetic Parameters (fu, Unbound Fraction; PPB, Plasma Protein Binding)
   human (in vitro mL/min/kg)mouse (in vitro mL/min/kg)human PPBmouse PPB
compoundhuman PAR4 IC50 (nM)mouse PAR4 IC50 (nM)ClintCLhepClintCLhepfufu
17502333049.614.8244386.80.0290.006
319536745920.11989489.60.0150.044
1213415047820.11153489.30.0520.169
9583145040720NRNR0.036NR
3316029641620NRNR0.006NR
3212026928519.6NRNR0.007NR
It is of note that the experimental design for the initial round of microsomal clearance assays adds a test compound to microsomes before the reaction is started by the addition of nicotinamide adenine dinucleotide phosphate (NADPH). This design assumes that the major contributing drug-metabolizing enzymes are cytochrome P450s (CYPs). To investigate this further, the experimental design was modified where the reaction was initiated by the addition of a test compound and was carried out in the presence or absence of NADPH. The intrinsic clearances of 17, 9, and 12 were measured under these conditions (Table S1). By comparison of the measured intrinsic clearance in the presence and absence of NADPH, the contribution of non-CYP450 drug-metabolizing enzymes can be estimated.
Mammalian liver microsomes include CYPs, flavin monooxygenases (FMOs), glucuronosyltransferases (GSTs), and esterases as part of their enzyme composition. In the presence of NADPH, which serves as a cofactor for CYP- and FMO-dependent reactions, the observed activity can be attributed to these enzymes. Similarly, UGTs require the cofactor uridine diphosphate glucuronic acid (UDPGA). Therefore, when NADPH and UDPGA are absent, any reduction in the compound can be ascribed only to esterase activity or chemical instability. Incubations with 12, 17, and 31 in potassium phosphate (KPI) or Tyrode’s buffer (HP) only showed little loss of compound over time (Figures S5C,D, S6C,D, and S7C,D), so the loss of compound in the liver microsomal incubations in the absence of NADPH is likely due to esterase activity. These data support the notion that the in vitro potency measurements of the compounds are likely an accurate reflection of their inherent pharmacological activity at PAR4. This is based on two factors: (1) the stability of the compounds in HP buffer, which is used in the platelet assays, and (2) the low probability of drug metabolism occurring within the platelets themselves. Together, these minimize the chance that any metabolites with distinct PAR4 potencies could confound the results. By comparing the area under the curve of the compound metabolized from the microsomal incubations in the presence or absence of NADPH to the corresponding area under the curve of the buffer-only incubation, the percent contribution of CYPs and FMOs and esterases were estimated (Table S1). The major contributing enzyme toward the metabolism of 12 is esterases in both the human (96%) and mouse (98%). In contrast, the metabolism of 31 was primarily driven by CYPs and FMOs in both HLM (80%) and MLM (88%). Metabolism of 17 in MLM was solely attributed to the CYP/FMO activity, whereas in HLM, CYP/FMO contributed only 39%.

Conclusions

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This study has integrated multiple approaches to develop a potent PAR4 antagonist. Without an experimentally determined structure of PAR4, a virtual high-throughput screen was used on a homology model to generate an initial hit compound. Through subsequent computational screens and parallel medicinal chemistry efforts, SAR was conducted to generate several potent and specific PAR4 antagonists. Further SAR and optimization are ongoing to increase the potency and improve the DMPK properties. As thrombin is the endogenous activator of PAR4, it would be of interest to focus drug discovery efforts against the TL over the PAR4-AP. This series of compounds has antagonist effects against the endogenous activator of PAR4, thrombin, but not PAR4 activation by the agonist peptide mimetic (PAR4-AP, AYPGKF). The pharmacological differences between inhibition by thrombin and inhibition by the agonist peptide may be important for understanding the structural basis for the activation of PAR4 by the protease-generated tethered ligand.

Materials and Methods

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PAR4 Model Generation

Despite not having an experimentally determined structure of PAR4, there currently exist deposited structures of homologous proteins to use for comparative modeling of PAR4. Here, we used deposited Protein Data Bank crystal structures of PAR2, PAR1, chemokine receptor type 9 (CCR9), apelin receptor (APJ), and C5a to build our model of PAR4. In addition to using the respective protein templates, we built the PAR4 binding pocket around the determined binding mode of vorapaxar within PAR1 to generate 500 output models.
Inclusion of a ligand in the binding site has been shown to be crucial during homology model building due to binding pocket collapse. It would be ideal to obtain a known PAR4 antagonist that is chemically close to a ligand cocrystallized in another template. First, we took all known PAR4 binders and ran Tanimoto similarity to the ligands cocrystallized in PARs 1 and 2. If we found a known PAR4 antagonist that was chemically similar to a cocrystallized PAR1 or PAR2 ligand, we could align the compounds and use this in RosettaCM; however, there was no significant chemical overlap. We then looked at all known binders to PARs 1, 2, and 4 from ChEMBL and ran Tanimoto similarities to determine any chemical overlap; however, we still found nothing. We also tried breaking down vorapaxar into fragments and compared to known PAR4 antagonists, but there was still no chemical overlapping. We then decided to use the predicted binding mode of BMS-3 aligned with the templates to build PAR4 using RosettaCM. Tanimoto similarities in all cases were <0.3, and binder threshold was defined using a threshold IC50 < 200 nM.
We used all 500 receptor models from the comparative modeling step for receptor optimization by testing the enrichment rates when scoring using the DOCK algorithm when docking known binders and property-matched generated decoys from the DUD-E database. Looking at the docked structures of the known active compounds, while taking into account the enrichment of these actives, we selected a single PAR4 model to move forward into the ultralarge screen. From the ZINC database, (37) we opted to screen against the lead-like compound set containing 129 million compounds using the following criteria: MW: 300–350 Da, log P: −1–3.5, Rep: 3D, React: Standard, Purch: Wait OK, pH: ref Mid, Charge: 0. (Data set curated on December 4, 2019.)

Postscreen Filtering

An arbitrary cutoff of −40 DOCK score was used as a cutoff value for initial filtering; this resulted in ∼100 K DOCKed poses and is only the top ∼0.4% of screened compounds. Using Tanimoto similarities, we removed all compounds >0.35 Tc to known PAR4 binders, which reduced our compounds to ∼99.7 K. We then ran a filter that only selects compounds that docked below a certain threshold with respect to the membrane, resulting in ∼7000 remaining compounds to analyze. The transmembrane depth filter was put in place based on our hypothesis that we want a binder that penetrates deep into the transmembrane bundle to inhibit the TL mechanism. To ensure that compound poses are energetically favorable and below a certain internal strain threshold, we ran the resulting compounds through a conformer analysis with a threshold <1.5 TEU (arbitrary torsional strain units) and resulted in 4638 compounds. These compounds were then clustered based on the Tanimoto similarity to ensure we were testing a diverse set of compounds, resulting in 2101 cluster heads for further inspection. The resulting compounds with their respective predicted binding pose from the DOCK simulation were analyzed for interaction criteria using Rosetta’s InterfaceAnalyzer application, namely, to quantify the number of unsatisfied hydrogen bonds and the number of hydrogen bonds between the ligand and receptor. (43) This interface analysis was meant to guide visual inspection of the 2101 output models, in which we also manually inspected predicted interactions. All command-line protocol captures for these steps are detailed in the Supporting Information.

Hit-to-Lead Optimization Using SAR-by-Purchase Approach

To acquire “Analog set 1”, we screened the ZINC database for compounds similar to A8. As we were starting with a single hit, we wanted the next set to balance having enough similarity to our initial hit while obtaining a rich diversity of compounds to expand potential SAR. We screened the ZINC library to acquire compounds with a Tanimoto similarity of >0.75 relative to A8. These compounds were then clustered based on a Tanimoto similarity threshold of >0.75 with respect to each other to achieve a maximally diverse subset, resulting in 207 compound clusters. The compound with the lowest molecular weight from each of these clusters was selected to serve as a potential starting compound for testing and to be easier for subsequent optimization. These 207 resulting cluster representatives were aligned to the predicted binding mode (44,45) for 7, redocked using RosettaLigand, (46−48) and subsequently ranked based on score, number of unsaturated hydrogen bond donors and acceptors, and number of interface hydrogen bonds formed. These compounds were first screened at 10 μM; the dose–response curves were performed for the compounds that were full antagonists at this concentration. Of these 80 compounds, three had higher affinities than the original hit. All three analogs passed the same off-target screen we performed for the original hit, not inhibiting platelet activation by the GPVI receptor agonist convulxin nor the PAR1 agonist peptide (data not shown, n = 3).
“Analog set 2” was based on the common core of the resulting 3 higher affinity compounds. We rescreened the Enamine database for compounds containing this chemical motif (SMILES string: “c1nc(NC(═O)c2cc[nH]n2)n[nH]1”). This search resulted in 100 compounds that we then purchased from Enamine for testing. We also explored analogs around the Analogs set 1 hits 212B10, 212D8, and 212H3 individually. We repeated the steps above from A8 to obtain derivatives for B212D8 and B212H3, driving a 20-fold increased potency.

Chemicals and Reagents

All solvents were purchased from Sigma-Aldrich and were used without further purification. Unless otherwise stated, all reagents were purchased from commercial suppliers and used without further purification. 1H and 13C NMR spectra were recorded on a 400 MHz AMX Bruker NMR spectrometer and referenced internally to the deuterated solvent signal. Chemical shifts are reported downfield in δ-values (ppm). The signals are reported as follows: chemical shift, multiplicity (s = singlet, bs = broad singlet, d = doublet, t = triplet, q = quartet, dd = doublet of doublets, m = multiplet), coupling constant, and integration. Reversed-phase LCMS analysis was performed using an Agilent 1200 system composed of a binary pump with a degasser, a high-performance autosampler, a thermostated column compartment, a C18 column, a diode-array detector (DAD), and an Agilent 6150 MSD with the following parameters. The gradient conditions were 5–95% acetonitrile with the aqueous phase 0.1% TFA or 1 g NH4CO3/L in water over 1.4 min. Samples were separated on a Waters Acquity UPLC BEH C18 column (1.7 μm, 1.0 mm × 50 mm) at 0.5 mL/min, with column and solvent temperatures maintained at 55 °C. The DAD was set to scan from 190 to 300 nm, and the signals used were 220 and 254 nm (both with a bandwidth of 4 nm). The MS detector was configured with an electrospray ionization source, and the low-resolution mass spectra were acquired by scanning from 140 to 700 AMU with a step size of 0.2 AMU at 0.13 cycles/second and a peak width of 0.008 min. The drying gas flow was set to 13 L/min at 300 °C. and the nebulizer pressure was set to 30 psi. The capillary needle voltage was set at 3000 V, and the fragmentor voltage was set at 100 V. Data acquisition was performed with Agilent Chemstation and Analytical Studio Reviewer software. The purity of all compounds was greater than 95% according to LCMS. The starting benzamidoguanidines were synthesized according to. (49) The cyclization to the 3-amino-1H-1,2,4-triazoles was performed according to. (50) Fluorine containing carboxylic acids was prepared from the appropriate fluoroanilines according to. (51)

General Procedure for the Amide Coupling of the Final Compounds

Final compounds were prepared through a mixture of the carboxylic acid (1 equiv), triethylamine (1.5 equiv), 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride (1.5 equiv), 4-dimethylaminopyridine (1.5 equiv), and the corresponding 3-amino-1H-1,2,4-triazole (1.5 equiv) in dry acetonitrile (0.5 mL/0.1 mmol), a mixture of the carboxylic acid (1 equiv), triethylamine (1.5 equiv), hexafluorophosphate azabenzotriazole tetramethyl uronium (HATU) (1.5 equiv) in dry dimethylformamide (DMF) (0.5 mL/0.1 mmol), or a mixture of the carboxylic acid (1 equiv), triethylamine (1.5 equiv), 2-chloro-1-methylpyridinium iodide (1.2 equiv), and the corresponding 3-amino-1H-1,2,4-triazole (1.5 equiv) in dry dimethylformamide (DMF) (0.5 mL/0.1 mmol). The reactions were stirred overnight at room temperature. The solvent was then removed under pressure, and the crude reaction was purified on silica.

Human Platelet Activity Assay

Activation of washed human platelets was monitored by flow cytometry for the detection of PAC1 (GPIIbIIIa activation) and CD62p (P-selectin expression) binding as previously described. (4) Human platelets were obtained from healthy volunteers. The Vanderbilt University Internal Review Board approved these studies. Informed consent was obtained from all individuals prior to the blood draw. 10 mL of whole blood was collected into 0.32% Na citrate. Platelet-rich plasma was collected after room-temperature centrifugation at 1100 rpm for 15 min on a Thermo Forma 400 centrifuge (Thermo Fisher, Waltham, MA). 1/10 volume of acid citrate dextrose (ACD) buffer was added to the collected supernatant and incubated for 10 min. After centrifugation at 2400 rpm for 10 min, the platelet pellet was resuspended in 1 mL Tyrode’s buffer (“TBB”: 15 mM HEPES, 0.33 mM NaH2PO4, pH 6.4, 138 mM NaCl, 2.7 mM KCl, 1 mM MgCl2, 5.5 mM dextrose, 0.1% bovine serum albumin). Platelets were collected, counted on a Z1 Coulter Particle Counter (Beckman Coulter, Brea, CA), and diluted in TBB to 1.5 × 107 platelets/mL. 60 uL of platelets were aliquoted to each tube and preincubated with 40 μL of TBB with antibodies PAC1 FITC (Becton Dickinson, Franklin Lakes NJ) and P-selectin PE (Becton Dickinson) and either test compound antagonist or DMSO vehicle control for 20 min. Platelets were then activated with 100 nM γ-thrombin or other agonists for 30 min. Samples were then fixed by adding 100 μL of 2% paraformaldehyde. After 30 min, samples were diluted by adding 300 μL of phosphate-buffered saline (PBS) and stored at 4C. Data were collected on a three-laser BD LSRFortessa (Becton Dickinson) and analyzed with FlowJo software (FlowJo LLC, Ashland, OR). The mean fluorescence intensity (geometric) of PE and FITC was determined from 30,000 events after compensation correction. Data were normalized to vehicle (DMSO) controls.
Compounds were screened for inhibition activity against human PAR4 using 100 nM γ-thrombin or 200 μM PAR4-AP in a single-point assay (10 μM test compound). Dose–response curves were conducted for active compounds. Active compounds were also assessed at a single concentration of 10 μM against other agonists, including the PAR4 component of α-thrombin (5 nM α-thrombin +1 μM vorapaxar), the PAR1 agonist peptide (20 μM SFFLRN), and the GPVI receptor agonist convulxin (3 nM).

Mouse Platelet Activity Assay

For mouse PAR4 activity, washed mouse platelets were prepared by collecting cardiac puncture blood with 200 μL of 20 U/mL heparin. Following centrifugation at 1000 rpm for 15 min, plasma and buffy coat were collected into a separate tube and spiked with apyrase and PGE1. After centrifuging at 1000 rpm for 8 min, the platelet-rich plasma was transferred to another tube and centrifuged at 2200 rpm for 10 min to pellet the platelets. Platelets were resuspended in TBB + apyrase + PGE1. Platelets were counted and diluted in TBB to a final concentration of 2 × 107 platelets/mL. 50 μL of platelets were aliquoted to each tube preincubated with 50 μL of TBB with antibodies for mouse GPIIbIIIa and P-selectin (Emfret, Eibelstadt Germany) and either test compound antagonist or DMSO control. Platelets were activated with 316 nM γ-thrombin for 30 min. Samples were then fixed with 100 μL of 2% paraformaldehyde for 20 min and diluted with 300 μL of PBS. Flow cytometry counts are measured as for human platelets on a three-laser BD LSRFortessa (Becton Dickinson) and analyzed with FloJo software.

Drug Metabolism and Pharmacokinetics

Materials

Potassium phosphate, ammonium formate, formic acid, magnesium chloride, and carbamazepine were purchased from Sigma-Aldrich (St. Louis, MO). Human liver microsomes (HLMs) and mouse liver microsomes (MLMs) were obtained from BD Biosciences (Billerica, MA), with pooled gender at a concentration of 20 mg/mL protein. Microsomes were stored in a −80 °C freezer. All solvents used for bioanalysis were purchased from Sigma-Aldrich or Fisher Scientific (Waltham, MA) and were of high-performance liquid chromatography (HPLC) grade.

Microsomal Stability

The metabolic stability of each compound was investigated in mouse and human hepatic microsomes using substrate depletion methodology (% parent compound remaining). Prior to use, microsomes were removed from the freezer and allowed to thaw in a 37 °C water bath before being placed on wet ice.
In separate 96-well plates for each time point, a mixture of 0.1 M potassium phosphate-buffered (pH 7.4), 3 mM MgCl2, and 2.5 mg/mL microsomes were prewarmed at 37 °C for 5 min. Following the preincubation, a 1 μM test compound and 1 mM NADPH (for 3, 7, 15, 25, or 45 min time points) or buffer (for 0 min time point) were added to initiate the reaction. Under standard conditions, reactions were initiated with the addition of NADPH. To delineate the contribution of esterases, some experiments were initiated by the addition of substrate in the presence or absence of NADPH. Plates were incubated at 37 °C under ambient oxygenation. At the respective times, each plate’s reaction was precipitated by the addition of 2 volumes of ice-cold acetonitrile containing an internal standard (carbamazepine, 50 nM). The plates were centrifuged at 4000 rcf (4 °C) for 5 min. The resulting supernatants were transferred and diluted 1:1 (supernatant:water) into new 96-well plates in preparation for LC/MS/MS analysis. Each compound was assayed in triplicate within the same 96-well plate. The in vitro half-life (t1/2, min, eq 1), intrinsic clearance (CLint, mL/min/kg, eq 2), and subsequent predicted hepatic clearance (CLhep, mL/min/kg, eq 3) were determined employing the following equations
t1/2=ln(2)/k;wherekrepresentstheslopefromlinearregressionanalysis(%testcompoundremaining)
(1)
CLint=(0.693/t1/2)(reactionvolume/mgmicrosomes)(45mgmicrosomes/gramofliver)(20agmofliver/kgbodyweight);sacale‐upfactorsof20(human)and45(mouse)
(2)
CLhep=QCLintQ+CLint
(3)
The estimated percent contribution to metabolism was calculated from the percent substrate lost plots generated from metabolic incubations initiated with the addition of a test compound. Calculations compared the area under the curve (AUC) from conditions in the presence and absence of NADPH and buffer-only (control) conditions (eqs 4 and 5). AUC was calculated by using the trapezoidal method
CYPandFMOcontribution=AUCmic,+NADPH−AUCmic,−NADPH−AUCbufferonlyAUCmic,+NADPH−AUCbufferonly
(4)
esterasecontribution=AUCmic,−NADPHAUCmic,+NADPH−AUCbufferonly
(5)

Plasma Protein Binding

The protein binding of each compound was determined in mouse plasma via equilibrium dialysis employing single-use RED plates with inserts (Thermo Fisher Scientific, Rochester, NY). Plasma (220 μL) was added to a 96-well plate containing test compound (5 μL) and mixed thoroughly. Subsequently, 200 μL of the plasma–compound mixture was transferred to the cis chamber (red) of the RED plate with an accompanying 350 μL of phosphate buffer (25 mM, pH 7.4) in the trans chamber. The RED plate was sealed and incubated for 4 h at 37 °C with shaking. At completion, 50 μL aliquots from each chamber were diluted 1:1 (50 μL) with either plasma (cis) or buffer (trans) and transferred to a new 96-well plate, at which time ice-cold acetonitrile (2 volumes) was added to extract the matrices. The plate was centrifuged (3000 rpm, 10 min), and supernatants were transferred and diluted 1:1 (supernatant: water) into a new 96-well plate, which was then sealed in preparation for LC/MS/MS analysis. Each compound was assayed in triplicate within the same 96-well plate.

Liquid Chromatography-Mass Spectrometry Analysis

The analysis of in vitro samples from microsomal stability or plasma protein binding experiments was determined by employing LCMS/MS with an electrospray ionization-enabled 5500 Sciex instrument (Sciex, Foster City, CA) that was coupled to Agilent HPLC pumps and an autosampler (Agilent Technologies, Santa Clara, CA). Analytes were separated by gradient elution using a Kinetex C18 column (2.1 mm × 50 mm, 5 μm; Phenomenex, Torrance, CA) warmed to 35 °C. Mobile phase A was 0.5% formic acid in water, and mobile phase B was 0.5% formic acid in acetonitrile. The gradient started at 5% B after a 0.2 min hold and was linearly increased to 95% B over 1.5 min, held at 95% B for 0.2 min, and returned to 5% B in 0.1 min, followed by a re-equilibration (0.3 min). The total run time was 2.8 min, and the HPLC flow rate was 0.5 mL/min. Mass spectral analyses were performed using multiple reaction monitoring, with transitions and voltages specific for each analyte using a Turbo Ion Spray source (source temperature of 500 °C) in positive ionization mode (5.0 kV spray voltage). Multiple reaction monitoring transitions were the following: 6110B8 (m/z 345.02 → 149.03, DP = 31; EP = 10; CE = 37 and CXP = 14), 6G3R (m/z 421.06 → 225.12, DP = 91; EP = 10; CE = 39 and CXP = 12), 5D22 (m/z 309.068 → 211.16, DP = 131; EP = 10; CE = 29 and CXP = 12), and carbamazepine (m/z 237 → 194, DP = 96; EP = 10; CE = 25 and CXP = 8). Data were analyzed using OS Sciex Analyst 1.5.1 (SCIEX, Framingham, MA), Graphpad Prism 10.1.0 (GraphPad Software, Boston, Massachusetts), and Microsoft Excel version 2310 (Microsoft, Redmond, WA).

Supporting Information

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsptsci.3c00378.

  • Compound synthesis analytical data with NMR spectra and computational protocol captures (PDF)

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Author Information

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  • Corresponding Authors
    • Jens Meiler - Department of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States;  Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States;  Institute for Drug Discovery, Leipzig University Medical School, Leipzig 04109, Germany;  Email: [email protected]
    • Heidi E. Hamm - Department of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States;  Email: [email protected]
  • Authors
    • Shannon T. Smith - Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
    • Jackson B. Cassada - Department of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
    • Lukas Von Bredow - Warren Center for Neuroscience Drug Discovery, Nashville, Tennessee 37067, United States;  Institute for Drug Discovery, Leipzig University Medical School, Leipzig 04109, Germany
    • Kevin Erreger - Department of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States;  Orcidhttps://orcid.org/0000-0001-5801-2344
    • Emma M. Webb - Department of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
    • Trevor A. Trombley - Warren Center for Neuroscience Drug Discovery, Nashville, Tennessee 37067, United States;  Orcidhttps://orcid.org/0000-0003-0413-0913
    • Jacob J. Kalbfleisch - Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States;  Warren Center for Neuroscience Drug Discovery, Nashville, Tennessee 37067, United States
    • Brian J. Bender - Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California 94158, United States
    • Irene Zagol-Ikapitte - Warren Center for Neuroscience Drug Discovery, Nashville, Tennessee 37067, United States
    • Valerie M. Kramlinger - Department of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States;  Warren Center for Neuroscience Drug Discovery, Nashville, Tennessee 37067, United States
    • Jacob L. Bouchard - Warren Center for Neuroscience Drug Discovery, Nashville, Tennessee 37067, United States;  Orcidhttps://orcid.org/0000-0002-5936-1244
    • Sidnee G. Mitchell - Department of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
    • Maik Tretbar - Institute for Drug Discovery, Leipzig University Medical School, Leipzig 04109, Germany
    • Brian K. Shoichet - Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California 94158, United States;  Orcidhttps://orcid.org/0000-0002-6098-7367
    • Craig W. Lindsley - Department of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States;  Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States;  Warren Center for Neuroscience Drug Discovery, Nashville, Tennessee 37067, United States;  Orcidhttps://orcid.org/0000-0003-0168-1445
  • Notes
    The authors declare no competing financial interest.

Acknowledgments

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H.E.H. acknowledges that this work was supported, in part, by NIH grants R01NS081669, R01 NS082198, R01HL133923, R21AG073891, and R01AG068623. J.M. acknowledges funding by the Deutsche Forschungsgemeinschaft (DFG) through SFB1423 (421152132), SFB 1052 (209933838), and SPP 2363 (460865652). J.M. is supported by a Humboldt Professorship of the Alexander von Humboldt Foundation. Work in the Meiler laboratory is further supported through the NIH (R01 HL122010, R01 DA046138, R01CA227833, S10 OD016216, S10 OD020154, S10 OD032234). C.W.L. acknowledges support from the William K. Warren Foundation.

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    Braga, A. D.; Miranda, J. P.; Ferreira, G. M.; Bilheiro, R. P.; Duarte, I. D.; Francischi, J. N.; Klein, A. Blockade of proteinase-activated receptor-4 inhibits the eosinophil recruitment induced by eotaxin-1 in the pleural cavity of mice. Pharmacology 2010, 86 (4), 224– 230,  DOI: 10.1159/000319751
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    Peters, T.; Henry, P. J. Protease-activated receptors and prostaglandins in inflammatory lung disease. Br. J. Pharmacol. 2009, 158 (4), 1017– 1033,  DOI: 10.1111/j.1476-5381.2009.00449.x
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    Sokolova, E.; Reiser, G. A novel therapeutic target in various lung diseases: airway proteases and protease-activated receptors. Pharmacol. Ther. 2007, 115 (1), 70– 83,  DOI: 10.1016/j.pharmthera.2007.04.002
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    McDougall, J. J.; Zhang, C.; Cellars, L.; Joubert, E.; Dixon, C. M.; Vergnolle, N. Triggering of proteinase-activated receptor 4 leads to joint pain and inflammation in mice. Arthritis Rheum. 2009, 60 (3), 728– 737,  DOI: 10.1002/art.24300
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    Fender, A. C.; Rauch, B. H.; Geisler, T.; Schror, K. Protease-Activated Receptor PAR-4: An Inducible Switch between Thrombosis and Vascular Inflammation?. Thromb. Haemostasis 2017, 117 (11), 2013– 2025,  DOI: 10.1160/TH17-03-0219
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    Kolpakov, M. A.; Rafiq, K.; Guo, X.; Hooshdaran, B.; Wang, T.; Vlasenko, L.; Bashkirova, Y. V.; Zhang, X.; Chen, X.; Iftikhar, S.; Libonati, J. R.; Kunapuli, S. P.; Sabri, A. Protease-activated receptor 4 deficiency offers cardioprotection after acute ischemia reperfusion injury. J. Mol. Cell. Cardiol. 2016, 90, 21– 29,  DOI: 10.1016/j.yjmcc.2015.11.030
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    15
    Protease-activated receptor 4 deficiency offers cardioprotection after acute ischemia reperfusion injury
    Kolpakov, Mikhail A.; Rafiq, Khadija; Guo, Xinji; Hooshdaran, Bahman; Wang, Tao; Vlasenko, Liudmila; Bashkirova, Yulia V.; Zhang, Xiaoxiao; Chen, Xiongwen; Iftikhar, Sahar; Libonati, Joseph R.; Kunapuli, Satya P.; Sabri, Abdelkarim
    Journal of Molecular and Cellular Cardiology (2016), 90 (), 21-29CODEN: JMCDAY; ISSN:0022-2828. (Elsevier B.V.)
    Protease-activated receptor (PAR)4 is a low affinity thrombin receptor with less understood function relative to PAR1. PAR4 is involved in platelet activation and hemostasis, but its specific actions on myocyte growth and cardiac function remain unknown. This study examd. the role of PAR4 deficiency on cardioprotection after myocardial ischemia-reperfusion (IR) injury in mice. When challenged by in vivo or ex vivo IR, PAR4 knockout (KO) mice exhibited increased tolerance to injury, which was manifest as reduced infarct size and a more robust functional recovery compared to wild-type mice. PAR4 KO mice also showed reduced cardiomyocyte apoptosis and putative signaling shifts in survival pathways in response to IR. Inhibition of PAR4 expression in isolated cardiomyocytes by shRNA offered protection against thrombin and PAR4-agonist peptide-induced apoptosis, while overexpression of wild-type PAR4 significantly enhanced the susceptibility of cardiomyocytes to apoptosis, even under low thrombin concns. Further studies implicate Src- and epidermal growth factor receptor-dependent activation of JNK on the proapoptotic effect of PAR4 in cardiomyocytes. These findings reveal a pivotal role for PAR4 as a regulator of cardiomyocyte survival and point to PAR4 inhibition as a therapeutic target offering cardioprotection after acute IR injury.
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    Mao, Y.; Zhang, M.; Tuma, R. F.; Kunapuli, S. P. Deficiency of PAR4 attenuates cerebral ischemia/reperfusion injury in mice. J. Cereb. Blood Flow Metab. 2010, 30 (5), 1044– 1052,  DOI: 10.1038/jcbfm.2009.283
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    Morrow, D. A.; Braunwald, E.; Bonaca, M. P.; Ameriso, S. F.; Dalby, A. J.; Fish, M. P.; Fox, K. A.; Lipka, L. J.; Liu, X.; Nicolau, J. C.; Ophuis, A. J.; Paolasso, E.; Scirica, B. M.; Spinar, J.; Theroux, P.; Wiviott, S. D.; Strony, J.; Murphy, S. A.; Committee, T. P. T. S.; Investigators Vorapaxar in the secondary prevention of atherothrombotic events. N. Engl. J. Med. 2012, 366 (15), 1404– 1413,  DOI: 10.1056/NEJMoa1200933
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    17
    Vorapaxar in the secondary prevention of atherothrombotic events
    Morrow, David A.; Braunwald, Eugene; Bonaca, Marc P.; Ameriso, Sebastian F.; Dalby, Anthony J.; Fish, Mary Polly; Fox, Keith A. A.; Lipka, Leslie J.; Liu, Xuan; Nicolau, Jose Carlos; Oude Ophuis, A. J.; Paolasso, Ernesto; Scirica, Benjamin M.; Spinar, Jindrich; Theroux, Pierre; Wiviott, Stephen D.; Strony, John; Murphy, Sabina A.
    New England Journal of Medicine (2012), 366 (15), 1404-1413CODEN: NEJMAG; ISSN:0028-4793. (Massachusetts Medical Society)
    Thrombin potently activates platelets through the protease-activated receptor PAR-1. Vorapaxar is a novel antiplatelet agent that selectively inhibits the cellular actions of thrombin through antagonism of PAR-1. We randomly assigned 26,449 patients who had a history of myocardial infarction, ischemic stroke, or peripheral arterial disease to receive vorapaxar (2.5 mg daily) or matching placebo and followed them for a median of 30 mo. The primary efficacy end point was the composite of death from cardiovascular causes, myocardial infarction, or stroke. After 2 years, the data and safety monitoring board recommended discontinuation of the study treatment in patients with a history of stroke owing to the risk of intracranial hemorrhage. At 3 years, the primary end point had occurred in 1028 patients (9.3%) in the vorapaxar group and in 1176 patients (10.5%) in the placebo group (hazard ratio for the vorapaxar group, 0.87; 95% confidence interval [CI], 0.80 to 0.94; P<0.001). Cardiovascular death, myocardial infarction, stroke, or recurrent ischemia leading to revascularization occurred in 1259 patients (11.2%) in the vorapaxar group and 1417 patients (12.4%) in the placebo group (hazard ratio, 0.88; 95% CI, 0.82 to 0.95; P = 0.001). Moderate or severe bleeding occurred in 4.2% of patients who received vorapaxar and 2.5% of those who received placebo (hazard ratio, 1.66; 95% CI, 1.43 to 1.93; P<0.001). There was an increase in the rate of intracranial hemorrhage in the vorapaxar group (1.0%, vs. 0.5% in the placebo group; P<0.001). Inhibition of PAR-1 with vorapaxar reduced the risk of cardiovascular death or ischemic events in patients with stable atherosclerosis who were receiving std. therapy. However, it increased the risk of moderate or severe bleeding, including intracranial hemorrhage.
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  18. 18
    Tricoci, P.; Huang, Z.; Held, C.; Moliterno, D. J.; Armstrong, P. W.; Van de Werf, F.; White, H. D.; Aylward, P. E.; Wallentin, L.; Chen, E.; Lokhnygina, Y.; Pei, J.; Leonardi, S.; Rorick, T. L.; Kilian, A. M.; Jennings, L. H.; Ambrosio, G.; Bode, C.; Cequier, A.; Cornel, J. H.; Diaz, R.; Erkan, A.; Huber, K.; Hudson, M. P.; Jiang, L.; Jukema, J. W.; Lewis, B. S.; Lincoff, A. M.; Montalescot, G.; Nicolau, J. C.; Ogawa, H.; Pfisterer, M.; Prieto, J. C.; Ruzyllo, W.; Sinnaeve, P. R.; Storey, R. F.; Valgimigli, M.; Whellan, D. J.; Widimsky, P.; Strony, J.; Harrington, R. A.; Mahaffey, K. W.; Investigators, T. Thrombin-receptor antagonist vorapaxar in acute coronary syndromes. N. Engl. J. Med. 2012, 366 (1), 20– 33,  DOI: 10.1056/NEJMoa1109719
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    Thrombin-receptor antagonist vorapaxar in acute coronary syndromes
    Tricoci, Pierluigi; Huang, Zhen; Held, Claes; Moliterno, David J.; Armstrong, Paul W.; Van de Werf, Frans; White, Harvey D.; Aylward, Philip E.; Wallentin, Lars; Chen, Edmond; Lokhnygina, Yuliya; Pei, Jinglan; Leonardi, Sergio; Rorick, Tyrus L.; Kilian, Ann M.; Jennings, Lisa H. K.; Ambrosio, Giuseppe; Bode, Christoph; Cequier, Angel; Cornel, Jan H.; Diaz, Rafael; Erkan, Aycan; Huber, Kurt; Hudson, Michael P.; Jiang, Lixin; Jukema, J. Wouter; Lewis, Basil S.; Lincoff, A. Michael; Montalescot, Gilles; Nicolau, Jose Carlos; Ogawa, Hisao; Pfisterer, Matthias; Prieto, Juan Carlos; Ruzyllo, Witold; Sinnaeve, Peter R.; Storey, Robert F.; Valgimigli, Marco; Whellan, David J.; Widimsky, Petr; Strony, John; Harrington, Robert A.
    New England Journal of Medicine (2012), 366 (1), 20-33CODEN: NEJMAG; ISSN:0028-4793. (Massachusetts Medical Society)
    Vorapaxar is a new oral protease-activated-receptor 1 (PAR-1) antagonist that inhibits thrombin-induced platelet activation. In this multinational, double-blind, randomized trial, we compared vorapaxar with placebo in 12,944 patients who had acute coronary syndromes without ST-segment elevation. The primary end point was a composite of death from cardiovascular causes, myocardial infarction, stroke, recurrent ischemia with rehospitalization, or urgent coronary revascularization. Follow-up in the trial was terminated early after a safety review. After a median follow-up of 502 days (interquartile range, 349 to 667), the primary end point occurred in 1031 of 6473 patients receiving vorapaxar vs. 1102 of 6471 patients receiving placebo (Kaplan-Meier 2-yr rate, 18.5% vs. 19.9%; hazard ratio, 0.92; 95% confidence interval [CI], 0.85 to 1.01; P = 0.07). A composite of death from cardiovascular causes, myocardial infarction, or stroke occurred in 822 patients. in the vorapaxar group vs. 910 in the placebo group (14.7% and 16.4%, resp.; hazard ratio, 0.89; 95% CI, 0.81 to 0.98; P = 0.02). Rates of moderate and severe bleeding were 7.2% in the vorapaxar group and 5.2% in the placebo group (hazard ratio, 1.35; 95% CI, 1.16 to 1.58; P < 0.001). Intracranial hemorrhage rates were 1.1% and 0.2%, resp. (hazard ratio, 3.39; 95% CI, 1.78 to 6.45; P < 0.001). Rates of nonhemorrhagic adverse events were similar in the two groups. In patients with acute coronary syndromes, the addn. of vorapaxar to std. therapy did not significantly reduce the primary composite end point but significantly increased the risk of major bleeding, including intracranial hemorrhage.
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    Wong, P. C.; Seiffert, D.; Bird, J. E.; Watson, C. A.; Bostwick, J. S.; Giancarli, M.; Allegretto, N.; Hua, J.; Harden, D.; Guay, J.; Callejo, M.; Miller, M. M.; Lawrence, R. M.; Banville, J.; Guy, J.; Maxwell, B. D.; Priestley, E. S.; Marinier, A.; Wexler, R. R.; Bouvier, M.; Gordon, D. A.; Schumacher, W. A.; Yang, J. Blockade of protease-activated receptor-4 (PAR4) provides robust antithrombotic activity with low bleeding. Sci. Transl. Med. 2017, 9 (371), eaaf5294  DOI: 10.1126/scitranslmed.aaf5294
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    Merali, S.; Wang, Z.; Frost, C.; Callejo, M.; Hedrick, M.; Hui, L.; Meadows Shropshire, S.; Xu, K.; Bouvier, M.; DeSouza, M. M.; Yang, J. New oral protease-activated receptor 4 antagonist BMS-986120: tolerability, pharmacokinetics, pharmacodynamics, and gene variant effects in humans. Platelets 2022, 33, 969– 978,  DOI: 10.1080/09537104.2022.2088719
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    Priestley, E. S.; Banville, J.; Deon, D.; Dube, L.; Gagnon, M.; Guy, J.; Lapointe, P.; Lavallee, J. F.; Martel, A.; Plamondon, S.; Remillard, R.; Ruediger, E.; Tremblay, F.; Posy, S. L.; Guarino, V. R.; Richter, J. M.; Li, J.; Gupta, A.; Vetrichelvan, M.; Balapragalathan, T. J.; Mathur, A.; Hua, J.; Callejo, M.; Guay, J.; Sum, C. S.; Cvijic, M. E.; Watson, C.; Wong, P.; Yang, J.; Bouvier, M.; Gordon, D. A.; Wexler, R. R.; Marinier, A. Discovery of Two Novel Antiplatelet Clinical Candidates (BMS-986120 and BMS-986141) That Antagonize Protease-Activated Receptor 4. J. Med. Chem. 2022, 65 (13), 8843– 8854,  DOI: 10.1021/acs.jmedchem.2c00359
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    Zhang, C.; Srinivasan, Y.; Arlow, D. H.; Fung, J. J.; Palmer, D.; Zheng, Y.; Green, H. F.; Pandey, A.; Dror, R. O.; Shaw, D. E.; Weis, W. I.; Coughlin, S. R.; Kobilka, B. K. High-resolution crystal structure of human protease-activated receptor 1. Nature 2012, 492 (7429), 387– 392,  DOI: 10.1038/nature11701
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    High-resolution crystal structure of human protease-activated receptor 1
    Zhang, Cheng; Srinivasan, Yoga; Arlow, Daniel H.; Fung, Juan Jose; Palmer, Daniel; Zheng, Yaowu; Green, Hillary F.; Pandey, Anjali; Dror, Ron O.; Shaw, David E.; Weis, William I.; Coughlin, Shaun R.; Kobilka, Brian K.
    Nature (London, United Kingdom) (2012), 492 (7429), 387-392CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
    Protease-activated receptor 1 (PAR1) is the prototypical member of a family of G-protein-coupled receptors (GPCRs) that mediate cellular responses to thrombin and related proteases. Thrombin irreversibly activates PAR1 by cleaving the amino-terminal exodomain of the receptor, which exposes a tethered peptide ligand that binds the heptahelical bundle of the receptor to affect G-protein activation. Here we report the 2.2-Å-resoln. crystal structure of human PAR1 bound to vorapaxar, a PAR1 antagonist. The structure reveals an unusual mode of drug binding that explains how a small mol. binds virtually irreversibly to inhibit receptor activation by the tethered ligand of PAR1. In contrast to deep, solvent-exposed binding pockets obsd. in other peptide-activated G-protein-coupled receptors, the vorapaxar-binding pocket is superficial but has little surface exposed to the aq. solvent. Protease-activated receptors are important targets for drug development. The structure reported here will aid the development of improved PAR1 antagonists and the discovery of antagonists to other members of this receptor family.
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    Ramachandran, R.; Noorbakhsh, F.; Defea, K.; Hollenberg, M. D. Targeting proteinase-activated receptors: therapeutic potential and challenges. Nat. Rev. Drug Discovery 2012, 11 (1), 69– 86,  DOI: 10.1038/nrd3615
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    Targeting proteinase-activated receptors: therapeutic potential and challenges
    Ramachandran, Rithwik; Noorbakhsh, Farshid; DeFea, Kathryn; Hollenberg, Morley D.
    Nature Reviews Drug Discovery (2012), 11 (1), 69-86CODEN: NRDDAG; ISSN:1474-1776. (Nature Publishing Group)
    A review. Proteinase-activated receptors (PARs), a family of four seven-transmembrane G protein-coupled receptors, act as targets for signalling by various proteolytic enzymes. PARs are characterized by a unique activation mechanism involving the proteolytic unmasking of a tethered ligand that stimulates the receptor. Given the emerging roles of these receptors in cancer as well as in disorders of the cardiovascular, musculoskeletal, gastrointestinal, respiratory and central nervous system, PARs have become attractive targets for the development of novel therapeutics. In this Review we summarize the mechanisms by which PARs modulate cell function and the roles they can have in physiol. and diseases. Furthermore, we provide an overview of possible strategies for developing PAR antagonists.
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    Wu, C. C.; Hwang, T. L.; Liao, C. H.; Kuo, S. C.; Lee, F. Y.; Lee, C. Y.; Teng, C. M. Selective inhibition of protease-activated receptor 4-dependent platelet activation by YD-3. Thromb. Haemostasis 2002, 87 (6), 1026– 1033,  DOI: 10.1055/s-0037-1613128
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    Young, S. E.; Duvernay, M. T.; Schulte, M. L.; Nance, K. D.; Melancon, B. J.; Engers, J.; Wood, M. R.; Hamm, H. E.; Lindsley, C. W. A Novel and Selective PAR4 Antagonist: ML354. In Probe Reports from the NIH Molecular Libraries Program; Bethesda: MD, 2010.
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    Young, S. E.; Duvernay, M. T.; Schulte, M. L.; Lindsley, C. W.; Hamm, H. E. Synthesis of indole derived protease-activated receptor 4 antagonists and characterization in human platelets. PLoS One 2013, 8 (6), e65528  DOI: 10.1371/journal.pone.0065528
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    Wen, W.; Young, S. E.; Duvernay, M. T.; Schulte, M. L.; Nance, K. D.; Melancon, B. J.; Engers, J.; Locuson, C. W., 2nd; Wood, M. R.; Daniels, J. S.; Wu, W.; Lindsley, C. W.; Hamm, H. E.; Stauffer, S. R. Substituted indoles as selective protease activated receptor 4 (PAR-4) antagonists: Discovery and SAR of ML354. Bioorg. Med. Chem. Lett. 2014, 24 (19), 4708– 4713,  DOI: 10.1016/j.bmcl.2014.08.021
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    27
    Substituted indoles as selective protease activated receptor 4 (PAR-4) antagonists: Discovery and SAR of ML354
    Wen, Wandong; Young, Summer E.; Duvernay, Matthew T.; Schulte, Michael L.; Nance, Kellie D.; Melancon, Bruce J.; Engers, Julie; Locuson, Charles W.; Wood, Michael R.; Daniels, J. Scott; Wu, Wenjun; Lindsley, Craig W.; Hamm, Heidi E.; Stauffer, Shaun R.
    Bioorganic & Medicinal Chemistry Letters (2014), 24 (19), 4708-4713CODEN: BMCLE8; ISSN:0960-894X. (Elsevier B.V.)
    Herein the authors report the discovery and SAR of an indole-based protease activated receptor-4 (PAR-4) antagonist scaffold derived from a similarity search of the Vanderbilt HTS collection, leading to MLPCN probe ML354 I (VU0099704). Using a novel PAC-1 fluorescent αIIbβ3 activation assay this probe mol. antagonist was found to have an IC50 of 140 nM for PAR-4 with 71-fold selectivity vs. PAR-1 (IC50 = 10 μM).
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    Bertron, J. L.; Duvernay, M. T.; Mitchell, S. G.; Smith, S. T.; Maeng, J. G.; Blobaum, A. L.; Davis, D. C.; Meiler, J.; Hamm, H. E.; Lindsley, C. W. Discovery and Optimization of a Novel Series of Competitive and Central Nervous System-Penetrant Protease-Activated Receptor 4 (PAR4) Inhibitors. ACS Chem. Neurosci. 2021, 12 (24), 4524– 4534,  DOI: 10.1021/acschemneuro.1c00557
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    Temple, K. J.; Duvernay, M. T.; Maeng, J. G.; Blobaum, A. L.; Stauffer, S. R.; Hamm, H. E.; Lindsley, C. W. Identification of the minimum PAR4 inhibitor pharmacophore and optimization of a series of 2-methoxy-6-arylimidazo[2,1-b][1,3,4]thiadiazoles. Bioorg. Med. Chem. Lett. 2016, 26 (22), 5481– 5486,  DOI: 10.1016/j.bmcl.2016.10.020
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    Bender, B. J.; Marlow, B.; Meiler, J. Improving homology modeling from low-sequence identity templates in Rosetta: A case study in GPCRs. PLoS Comput. Biol. 2020, 16 (10), e1007597  DOI: 10.1371/journal.pcbi.1007597
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    Cheng, R. K. Y.; Fiez-Vandal, C.; Schlenker, O.; Edman, K.; Aggeler, B.; Brown, D. G.; Brown, G. A.; Cooke, R. M.; Dumelin, C. E.; Doré, A. S.; Geschwindner, S.; Grebner, C.; Hermansson, N. O.; Jazayeri, A.; Johansson, P.; Leong, L.; Prihandoko, R.; Rappas, M.; Soutter, H.; Snijder, A.; Sundström, L.; Tehan, B.; Thornton, P.; Troast, D.; Wiggin, G.; Zhukov, A.; Marshall, F. H.; Dekker, N. Structural insight into allosteric modulation of protease-activated receptor 2. Nature 2017, 545 (7652), 112– 115,  DOI: 10.1038/nature22309
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    Structural insight into allosteric modulation of protease-activated receptor 2
    Cheng, Robert K. Y.; Fiez-Vandal, Cedric; Schlenker, Oliver; Edman, Karl; Aggeler, Birte; Brown, Dean G.; Brown, Giles A.; Cooke, Robert M.; Dumelin, Christoph E.; Dore, Andrew S.; Geschwindner, Stefan; Grebner, Christoph; Hermansson, Nils-Olov; Jazayeri, Ali; Johansson, Patrik; Leong, Louis; Prihandoko, Rudi; Rappas, Mathieu; Soutter, Holly; Snijder, Arjan; Sundstrom, Linda; Tehan, Benjamin; Thornton, Peter; Troast, Dawn; Wiggin, Giselle; Zhukov, Andrei; Marshall, Fiona H.; Dekker, Niek
    Nature (London, United Kingdom) (2017), 545 (7652), 112-115CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
    Protease-activated receptors (PARs) are a family of G-protein-coupled receptors (GPCRs) that are irreversibly activated by proteolytic cleavage of the N-terminus, which unmasks a tethered peptide ligand that binds and activates the transmembrane receptor domain, eliciting a cellular cascade in response to inflammatory signals and other stimuli. PARs are implicated in a wide range of diseases, such as cancer and inflammation. PARs have been the subject of major pharmaceutical research efforts, but the discovery of small-mol. antagonists that effectively bind them has proved challenging. The only marketed drug targeting a PAR is vorapaxar, a selective antagonist of PAR1 used to prevent thrombosis. The structure of PAR1 in complex with vorapaxar has been reported previously. Despite sequence homol. across the PAR isoforms, discovery of PAR2 antagonists has been less successful, although GB88 has been described as a weak antagonist. Here, we report crystal structures of PAR2 in complex with two distinct antagonists and a blocking antibody. The antagonist AZ8838 binds in a fully occluded pocket near the extracellular surface. Functional and binding studies reveal that AZ8838 exhibits slow binding kinetics, which is an attractive feature for a PAR2 antagonist competing against a tethered ligand. Antagonist AZ3451 binds to a remote allosteric site outside the helical bundle. We propose that antagonist binding prevents structural rearrangements required for receptor activation and signalling. We also show that a blocking antibody antigen-binding fragment binds to the extracellular surface of PAR2, preventing access of the tethered ligand to the peptide-binding site. These structures provide a basis for the development of selective PAR2 antagonists for a range of therapeutic uses.
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    Han, X.; Hofmann, L.; de la Fuente, M.; Alexander, N.; Palczewski, K.; Nieman, M. T.; Consortium, I. PAR4 activation involves extracellular loop 3 and transmembrane residue Thr153. Blood 2020, 136 (19), 2217– 2228,  DOI: 10.1182/blood.2019004634
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    Lyu, J.; Wang, S.; Balius, T. E.; Singh, I.; Levit, A.; Moroz, Y. S.; O’Meara, M. J.; Che, T.; Algaa, E.; Tolmachova, K.; Tolmachev, A. A.; Shoichet, B. K.; Roth, B. L.; Irwin, J. J. Ultra-large library docking for discovering new chemotypes. Nature 2019, 566 (7743), 224– 229,  DOI: 10.1038/s41586-019-0917-9
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    Ultra-large library docking for discovering new chemotypes
    Lyu, Jiankun; Wang, Sheng; Balius, Trent E.; Singh, Isha; Levit, Anat; Moroz, Yurii S.; O'Meara, Matthew J.; Che, Tao; Algaa, Enkhjargal; Tolmachova, Kateryna; Tolmachev, Andrey A.; Shoichet, Brian K.; Roth, Bryan L.; Irwin, John J.
    Nature (London, United Kingdom) (2019), 566 (7743), 224-229CODEN: NATUAS; ISSN:0028-0836. (Nature Research)
    Despite intense interest in expanding chem. space, libraries contg. hundreds-of-millions to billions of diverse mols. have remained inaccessible. Here we investigate structure-based docking of 170 million make-on-demand compds. from 130 well-characterized reactions. The resulting library is diverse, representing over 10.7 million scaffolds that are otherwise unavailable. For each compd. in the library, docking against AmpC β-lactamase (AmpC) and the D4 dopamine receptor were simulated. From the top-ranking mols., 44 and 549 compds. were synthesized and tested for interactions with AmpC and the D4 dopamine receptor, resp. We found a phenolate inhibitor of AmpC, which revealed a group of inhibitors without known precedent. This mol. was optimized to 77 nM, which places it among the most potent non-covalent AmpC inhibitors known. Crystal structures of this and other AmpC inhibitors confirmed the docking predictions. Against the D4 dopamine receptor, hit rates fell almost monotonically with docking score, and a hit-rate vs. score curve predicted that the library contained 453,000 ligands for the D4 dopamine receptor. Of 81 new chemotypes discovered, 30 showed submicromolar activity, including a 180-pM subtype-selective agonist of the D4 dopamine receptor.
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  • Abstract

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    Figure 1

    Figure 1. PAR4 activation mechanism. PARs contain the highly conserved seven transmembrane helical bundle, an extracellular N-terminus, and an intracellular C-terminus, which binds to respective G-proteins to initiate downstream signaling. PAR activation is caused by thrombin-induced cleavage between residues Arg47/Gly48 on the exposed extracellular N-terminus, revealing a new N-terminus called the “tethered ligand (TL)”. The TL subsequently binds within the 7TM helical bundle to induce a conformational change to the receptor, prompting G-protein binding and propagate downstream signaling via Gq and G12/13.

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    Figure 2

    Figure 2. Previously identified PAR4 antagonists. (A) Red boxes follow lead optimization of 3 through 4 and 5 indole series; light blue designates imidazothiadiazole series from BMS starting with BMS-3 (2) from the HTS and BMS-986120 (1) in clinical trials; purple designates the chimerization series of the indole and imidazothiadiazole series (6). (B) Predicted binding mode of BMS-3 (green). Comparison to experimentally determined binding modes: vorapaxar (PAR1, PDB ID: 3vw7, cyan), AZ3451 (PAR2, PDB ID: 5NDZ, yellow), and AZ8838 (PAR2, PDB ID: 5NDD, magenta).

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    Figure 3

    Figure 3. Overview of the structure-based high-throughput screen and subsequent optimization.

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    Figure 4

    Figure 4. PAR4 platelet screen of enamine compounds generated from the vHTS conducted off the PAR4 homology model. Platelets activated 100 nM γ thrombin (A) or 200 μM PAR4 agonist peptide (AYPGKF) (B) with each compound normalized to vehicle DMSO control.

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    Figure 5

    Figure 5. (A) Off-target controls: % of vehicle control for γ-thrombin (100 nM), PAR4 component of α-thrombin (5 nM α-thrombin +1 μM vorapaxar), PAR4 agonist peptide 200 μM AYPGKF, 200 μM PAR1 agonist peptide SFLLRN, and GPVI agonist convulxin 3.16 nM.

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    Figure 6

    Figure 6. (A) Distribution of IC50 values for the inhibition of human PAR4 vs mouse PAR4. Red indicates the 2 compounds highlighted in panels (B–D). (B) Structures of exemplar compounds. (C) Concentration–response curves for human vs mouse PAR4. (D) Concentration–response curves for the inhibition of pharmacologically defined α-thrombin components for PAR1 (α-thrombin + BMS-3) vs PAR4 (α-thrombin + vorapaxar).

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    Figure 7

    Figure 7. Schild analysis progressive fold-shift experiments. (A) Platelets were pretreated with increasing concentrations of each antagonist for 20 min prior to activation with increasing concentrations of γ-thrombin. (B) Logarithmic transformation of the dose ratio plotted versus the logarithm of compound concentration. m = slope of linear regression.

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    Figure 8

    Figure 8. Predicted binding mode of 31 in the PAR4 homology model. Noting key binding interactions: H-bonds (yellow), H-bonding residues HIS162, TYR255, and TYR90 (green), and hydrophobic interactions TRP147 and PHE178 (magenta).

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    Figure 9

    Figure 9. Predicted binding mode of 31 and BMS-3. (A) Predicted docking of 31 (magenta) and 2 (green) in the PAR4 homology model, noting the binding pocket in yellow. (B) Key residue of PAR4 interacting with 31 and 2. (C) Binding interaction between key residues of PAR4 and 31 and 2.

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      Vergnolle, N.; Derian, C. K.; D’Andrea, M. R.; Steinhoff, M.; Andrade-Gordon, P. Characterization of thrombin-induced leukocyte rolling and adherence: a potential proinflammatory role for proteinase-activated receptor-4. J. Immunol. 2002, 169 (3), 1467– 1473,  DOI: 10.4049/jimmunol.169.3.1467
      There is no corresponding record for this reference.
    8. 8
      Houle, S.; Papez, M. D.; Ferazzini, M.; Hollenberg, M. D.; Vergnolle, N. Neutrophils and the kallikrein-kinin system in proteinase-activated receptor 4-mediated inflammation in rodents. Br. J. Pharmacol. 2005, 146 (5), 670– 678,  DOI: 10.1038/sj.bjp.0706371
      There is no corresponding record for this reference.
    9. 9
      Dabek, M.; Ferrier, L.; Roka, R.; Gecse, K.; Annahazi, A.; Moreau, J.; Escourrou, J.; Cartier, C.; Chaumaz, G.; Leveque, M.; Ait-Belgnaoui, A.; Wittmann, T.; Theodorou, V.; Bueno, L. Luminal cathepsin g and protease-activated receptor 4: a duet involved in alterations of the colonic epithelial barrier in ulcerative colitis. Am. J. Pathol. 2009, 175 (1), 207– 214,  DOI: 10.2353/ajpath.2009.080986
      There is no corresponding record for this reference.
    10. 10
      Braga, A. D.; Miranda, J. P.; Ferreira, G. M.; Bilheiro, R. P.; Duarte, I. D.; Francischi, J. N.; Klein, A. Blockade of proteinase-activated receptor-4 inhibits the eosinophil recruitment induced by eotaxin-1 in the pleural cavity of mice. Pharmacology 2010, 86 (4), 224– 230,  DOI: 10.1159/000319751
      There is no corresponding record for this reference.
    11. 11
      Peters, T.; Henry, P. J. Protease-activated receptors and prostaglandins in inflammatory lung disease. Br. J. Pharmacol. 2009, 158 (4), 1017– 1033,  DOI: 10.1111/j.1476-5381.2009.00449.x
      There is no corresponding record for this reference.
    12. 12
      Sokolova, E.; Reiser, G. A novel therapeutic target in various lung diseases: airway proteases and protease-activated receptors. Pharmacol. Ther. 2007, 115 (1), 70– 83,  DOI: 10.1016/j.pharmthera.2007.04.002
      There is no corresponding record for this reference.
    13. 13
      McDougall, J. J.; Zhang, C.; Cellars, L.; Joubert, E.; Dixon, C. M.; Vergnolle, N. Triggering of proteinase-activated receptor 4 leads to joint pain and inflammation in mice. Arthritis Rheum. 2009, 60 (3), 728– 737,  DOI: 10.1002/art.24300
      There is no corresponding record for this reference.
    14. 14
      Fender, A. C.; Rauch, B. H.; Geisler, T.; Schror, K. Protease-Activated Receptor PAR-4: An Inducible Switch between Thrombosis and Vascular Inflammation?. Thromb. Haemostasis 2017, 117 (11), 2013– 2025,  DOI: 10.1160/TH17-03-0219
      There is no corresponding record for this reference.
    15. 15
      Kolpakov, M. A.; Rafiq, K.; Guo, X.; Hooshdaran, B.; Wang, T.; Vlasenko, L.; Bashkirova, Y. V.; Zhang, X.; Chen, X.; Iftikhar, S.; Libonati, J. R.; Kunapuli, S. P.; Sabri, A. Protease-activated receptor 4 deficiency offers cardioprotection after acute ischemia reperfusion injury. J. Mol. Cell. Cardiol. 2016, 90, 21– 29,  DOI: 10.1016/j.yjmcc.2015.11.030
      15
      Protease-activated receptor 4 deficiency offers cardioprotection after acute ischemia reperfusion injury
      Kolpakov, Mikhail A.; Rafiq, Khadija; Guo, Xinji; Hooshdaran, Bahman; Wang, Tao; Vlasenko, Liudmila; Bashkirova, Yulia V.; Zhang, Xiaoxiao; Chen, Xiongwen; Iftikhar, Sahar; Libonati, Joseph R.; Kunapuli, Satya P.; Sabri, Abdelkarim
      Journal of Molecular and Cellular Cardiology (2016), 90 (), 21-29CODEN: JMCDAY; ISSN:0022-2828. (Elsevier B.V.)
      Protease-activated receptor (PAR)4 is a low affinity thrombin receptor with less understood function relative to PAR1. PAR4 is involved in platelet activation and hemostasis, but its specific actions on myocyte growth and cardiac function remain unknown. This study examd. the role of PAR4 deficiency on cardioprotection after myocardial ischemia-reperfusion (IR) injury in mice. When challenged by in vivo or ex vivo IR, PAR4 knockout (KO) mice exhibited increased tolerance to injury, which was manifest as reduced infarct size and a more robust functional recovery compared to wild-type mice. PAR4 KO mice also showed reduced cardiomyocyte apoptosis and putative signaling shifts in survival pathways in response to IR. Inhibition of PAR4 expression in isolated cardiomyocytes by shRNA offered protection against thrombin and PAR4-agonist peptide-induced apoptosis, while overexpression of wild-type PAR4 significantly enhanced the susceptibility of cardiomyocytes to apoptosis, even under low thrombin concns. Further studies implicate Src- and epidermal growth factor receptor-dependent activation of JNK on the proapoptotic effect of PAR4 in cardiomyocytes. These findings reveal a pivotal role for PAR4 as a regulator of cardiomyocyte survival and point to PAR4 inhibition as a therapeutic target offering cardioprotection after acute IR injury.
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    16. 16
      Mao, Y.; Zhang, M.; Tuma, R. F.; Kunapuli, S. P. Deficiency of PAR4 attenuates cerebral ischemia/reperfusion injury in mice. J. Cereb. Blood Flow Metab. 2010, 30 (5), 1044– 1052,  DOI: 10.1038/jcbfm.2009.283
      There is no corresponding record for this reference.
    17. 17
      Morrow, D. A.; Braunwald, E.; Bonaca, M. P.; Ameriso, S. F.; Dalby, A. J.; Fish, M. P.; Fox, K. A.; Lipka, L. J.; Liu, X.; Nicolau, J. C.; Ophuis, A. J.; Paolasso, E.; Scirica, B. M.; Spinar, J.; Theroux, P.; Wiviott, S. D.; Strony, J.; Murphy, S. A.; Committee, T. P. T. S.; Investigators Vorapaxar in the secondary prevention of atherothrombotic events. N. Engl. J. Med. 2012, 366 (15), 1404– 1413,  DOI: 10.1056/NEJMoa1200933
      17
      Vorapaxar in the secondary prevention of atherothrombotic events
      Morrow, David A.; Braunwald, Eugene; Bonaca, Marc P.; Ameriso, Sebastian F.; Dalby, Anthony J.; Fish, Mary Polly; Fox, Keith A. A.; Lipka, Leslie J.; Liu, Xuan; Nicolau, Jose Carlos; Oude Ophuis, A. J.; Paolasso, Ernesto; Scirica, Benjamin M.; Spinar, Jindrich; Theroux, Pierre; Wiviott, Stephen D.; Strony, John; Murphy, Sabina A.
      New England Journal of Medicine (2012), 366 (15), 1404-1413CODEN: NEJMAG; ISSN:0028-4793. (Massachusetts Medical Society)
      Thrombin potently activates platelets through the protease-activated receptor PAR-1. Vorapaxar is a novel antiplatelet agent that selectively inhibits the cellular actions of thrombin through antagonism of PAR-1. We randomly assigned 26,449 patients who had a history of myocardial infarction, ischemic stroke, or peripheral arterial disease to receive vorapaxar (2.5 mg daily) or matching placebo and followed them for a median of 30 mo. The primary efficacy end point was the composite of death from cardiovascular causes, myocardial infarction, or stroke. After 2 years, the data and safety monitoring board recommended discontinuation of the study treatment in patients with a history of stroke owing to the risk of intracranial hemorrhage. At 3 years, the primary end point had occurred in 1028 patients (9.3%) in the vorapaxar group and in 1176 patients (10.5%) in the placebo group (hazard ratio for the vorapaxar group, 0.87; 95% confidence interval [CI], 0.80 to 0.94; P<0.001). Cardiovascular death, myocardial infarction, stroke, or recurrent ischemia leading to revascularization occurred in 1259 patients (11.2%) in the vorapaxar group and 1417 patients (12.4%) in the placebo group (hazard ratio, 0.88; 95% CI, 0.82 to 0.95; P = 0.001). Moderate or severe bleeding occurred in 4.2% of patients who received vorapaxar and 2.5% of those who received placebo (hazard ratio, 1.66; 95% CI, 1.43 to 1.93; P<0.001). There was an increase in the rate of intracranial hemorrhage in the vorapaxar group (1.0%, vs. 0.5% in the placebo group; P<0.001). Inhibition of PAR-1 with vorapaxar reduced the risk of cardiovascular death or ischemic events in patients with stable atherosclerosis who were receiving std. therapy. However, it increased the risk of moderate or severe bleeding, including intracranial hemorrhage.
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    18. 18
      Tricoci, P.; Huang, Z.; Held, C.; Moliterno, D. J.; Armstrong, P. W.; Van de Werf, F.; White, H. D.; Aylward, P. E.; Wallentin, L.; Chen, E.; Lokhnygina, Y.; Pei, J.; Leonardi, S.; Rorick, T. L.; Kilian, A. M.; Jennings, L. H.; Ambrosio, G.; Bode, C.; Cequier, A.; Cornel, J. H.; Diaz, R.; Erkan, A.; Huber, K.; Hudson, M. P.; Jiang, L.; Jukema, J. W.; Lewis, B. S.; Lincoff, A. M.; Montalescot, G.; Nicolau, J. C.; Ogawa, H.; Pfisterer, M.; Prieto, J. C.; Ruzyllo, W.; Sinnaeve, P. R.; Storey, R. F.; Valgimigli, M.; Whellan, D. J.; Widimsky, P.; Strony, J.; Harrington, R. A.; Mahaffey, K. W.; Investigators, T. Thrombin-receptor antagonist vorapaxar in acute coronary syndromes. N. Engl. J. Med. 2012, 366 (1), 20– 33,  DOI: 10.1056/NEJMoa1109719
      18
      Thrombin-receptor antagonist vorapaxar in acute coronary syndromes
      Tricoci, Pierluigi; Huang, Zhen; Held, Claes; Moliterno, David J.; Armstrong, Paul W.; Van de Werf, Frans; White, Harvey D.; Aylward, Philip E.; Wallentin, Lars; Chen, Edmond; Lokhnygina, Yuliya; Pei, Jinglan; Leonardi, Sergio; Rorick, Tyrus L.; Kilian, Ann M.; Jennings, Lisa H. K.; Ambrosio, Giuseppe; Bode, Christoph; Cequier, Angel; Cornel, Jan H.; Diaz, Rafael; Erkan, Aycan; Huber, Kurt; Hudson, Michael P.; Jiang, Lixin; Jukema, J. Wouter; Lewis, Basil S.; Lincoff, A. Michael; Montalescot, Gilles; Nicolau, Jose Carlos; Ogawa, Hisao; Pfisterer, Matthias; Prieto, Juan Carlos; Ruzyllo, Witold; Sinnaeve, Peter R.; Storey, Robert F.; Valgimigli, Marco; Whellan, David J.; Widimsky, Petr; Strony, John; Harrington, Robert A.
      New England Journal of Medicine (2012), 366 (1), 20-33CODEN: NEJMAG; ISSN:0028-4793. (Massachusetts Medical Society)
      Vorapaxar is a new oral protease-activated-receptor 1 (PAR-1) antagonist that inhibits thrombin-induced platelet activation. In this multinational, double-blind, randomized trial, we compared vorapaxar with placebo in 12,944 patients who had acute coronary syndromes without ST-segment elevation. The primary end point was a composite of death from cardiovascular causes, myocardial infarction, stroke, recurrent ischemia with rehospitalization, or urgent coronary revascularization. Follow-up in the trial was terminated early after a safety review. After a median follow-up of 502 days (interquartile range, 349 to 667), the primary end point occurred in 1031 of 6473 patients receiving vorapaxar vs. 1102 of 6471 patients receiving placebo (Kaplan-Meier 2-yr rate, 18.5% vs. 19.9%; hazard ratio, 0.92; 95% confidence interval [CI], 0.85 to 1.01; P = 0.07). A composite of death from cardiovascular causes, myocardial infarction, or stroke occurred in 822 patients. in the vorapaxar group vs. 910 in the placebo group (14.7% and 16.4%, resp.; hazard ratio, 0.89; 95% CI, 0.81 to 0.98; P = 0.02). Rates of moderate and severe bleeding were 7.2% in the vorapaxar group and 5.2% in the placebo group (hazard ratio, 1.35; 95% CI, 1.16 to 1.58; P < 0.001). Intracranial hemorrhage rates were 1.1% and 0.2%, resp. (hazard ratio, 3.39; 95% CI, 1.78 to 6.45; P < 0.001). Rates of nonhemorrhagic adverse events were similar in the two groups. In patients with acute coronary syndromes, the addn. of vorapaxar to std. therapy did not significantly reduce the primary composite end point but significantly increased the risk of major bleeding, including intracranial hemorrhage.
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    19. 19
      Wong, P. C.; Seiffert, D.; Bird, J. E.; Watson, C. A.; Bostwick, J. S.; Giancarli, M.; Allegretto, N.; Hua, J.; Harden, D.; Guay, J.; Callejo, M.; Miller, M. M.; Lawrence, R. M.; Banville, J.; Guy, J.; Maxwell, B. D.; Priestley, E. S.; Marinier, A.; Wexler, R. R.; Bouvier, M.; Gordon, D. A.; Schumacher, W. A.; Yang, J. Blockade of protease-activated receptor-4 (PAR4) provides robust antithrombotic activity with low bleeding. Sci. Transl. Med. 2017, 9 (371), eaaf5294  DOI: 10.1126/scitranslmed.aaf5294
      There is no corresponding record for this reference.
    20. 20
      Merali, S.; Wang, Z.; Frost, C.; Callejo, M.; Hedrick, M.; Hui, L.; Meadows Shropshire, S.; Xu, K.; Bouvier, M.; DeSouza, M. M.; Yang, J. New oral protease-activated receptor 4 antagonist BMS-986120: tolerability, pharmacokinetics, pharmacodynamics, and gene variant effects in humans. Platelets 2022, 33, 969– 978,  DOI: 10.1080/09537104.2022.2088719
      There is no corresponding record for this reference.
    21. 21
      Priestley, E. S.; Banville, J.; Deon, D.; Dube, L.; Gagnon, M.; Guy, J.; Lapointe, P.; Lavallee, J. F.; Martel, A.; Plamondon, S.; Remillard, R.; Ruediger, E.; Tremblay, F.; Posy, S. L.; Guarino, V. R.; Richter, J. M.; Li, J.; Gupta, A.; Vetrichelvan, M.; Balapragalathan, T. J.; Mathur, A.; Hua, J.; Callejo, M.; Guay, J.; Sum, C. S.; Cvijic, M. E.; Watson, C.; Wong, P.; Yang, J.; Bouvier, M.; Gordon, D. A.; Wexler, R. R.; Marinier, A. Discovery of Two Novel Antiplatelet Clinical Candidates (BMS-986120 and BMS-986141) That Antagonize Protease-Activated Receptor 4. J. Med. Chem. 2022, 65 (13), 8843– 8854,  DOI: 10.1021/acs.jmedchem.2c00359
      There is no corresponding record for this reference.
    22. 22
      Zhang, C.; Srinivasan, Y.; Arlow, D. H.; Fung, J. J.; Palmer, D.; Zheng, Y.; Green, H. F.; Pandey, A.; Dror, R. O.; Shaw, D. E.; Weis, W. I.; Coughlin, S. R.; Kobilka, B. K. High-resolution crystal structure of human protease-activated receptor 1. Nature 2012, 492 (7429), 387– 392,  DOI: 10.1038/nature11701
      22
      High-resolution crystal structure of human protease-activated receptor 1
      Zhang, Cheng; Srinivasan, Yoga; Arlow, Daniel H.; Fung, Juan Jose; Palmer, Daniel; Zheng, Yaowu; Green, Hillary F.; Pandey, Anjali; Dror, Ron O.; Shaw, David E.; Weis, William I.; Coughlin, Shaun R.; Kobilka, Brian K.
      Nature (London, United Kingdom) (2012), 492 (7429), 387-392CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
      Protease-activated receptor 1 (PAR1) is the prototypical member of a family of G-protein-coupled receptors (GPCRs) that mediate cellular responses to thrombin and related proteases. Thrombin irreversibly activates PAR1 by cleaving the amino-terminal exodomain of the receptor, which exposes a tethered peptide ligand that binds the heptahelical bundle of the receptor to affect G-protein activation. Here we report the 2.2-Å-resoln. crystal structure of human PAR1 bound to vorapaxar, a PAR1 antagonist. The structure reveals an unusual mode of drug binding that explains how a small mol. binds virtually irreversibly to inhibit receptor activation by the tethered ligand of PAR1. In contrast to deep, solvent-exposed binding pockets obsd. in other peptide-activated G-protein-coupled receptors, the vorapaxar-binding pocket is superficial but has little surface exposed to the aq. solvent. Protease-activated receptors are important targets for drug development. The structure reported here will aid the development of improved PAR1 antagonists and the discovery of antagonists to other members of this receptor family.
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    23. 23
      Ramachandran, R.; Noorbakhsh, F.; Defea, K.; Hollenberg, M. D. Targeting proteinase-activated receptors: therapeutic potential and challenges. Nat. Rev. Drug Discovery 2012, 11 (1), 69– 86,  DOI: 10.1038/nrd3615
      23
      Targeting proteinase-activated receptors: therapeutic potential and challenges
      Ramachandran, Rithwik; Noorbakhsh, Farshid; DeFea, Kathryn; Hollenberg, Morley D.
      Nature Reviews Drug Discovery (2012), 11 (1), 69-86CODEN: NRDDAG; ISSN:1474-1776. (Nature Publishing Group)
      A review. Proteinase-activated receptors (PARs), a family of four seven-transmembrane G protein-coupled receptors, act as targets for signalling by various proteolytic enzymes. PARs are characterized by a unique activation mechanism involving the proteolytic unmasking of a tethered ligand that stimulates the receptor. Given the emerging roles of these receptors in cancer as well as in disorders of the cardiovascular, musculoskeletal, gastrointestinal, respiratory and central nervous system, PARs have become attractive targets for the development of novel therapeutics. In this Review we summarize the mechanisms by which PARs modulate cell function and the roles they can have in physiol. and diseases. Furthermore, we provide an overview of possible strategies for developing PAR antagonists.
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    24. 24
      Wu, C. C.; Hwang, T. L.; Liao, C. H.; Kuo, S. C.; Lee, F. Y.; Lee, C. Y.; Teng, C. M. Selective inhibition of protease-activated receptor 4-dependent platelet activation by YD-3. Thromb. Haemostasis 2002, 87 (6), 1026– 1033,  DOI: 10.1055/s-0037-1613128
      There is no corresponding record for this reference.
    25. 25
      Young, S. E.; Duvernay, M. T.; Schulte, M. L.; Nance, K. D.; Melancon, B. J.; Engers, J.; Wood, M. R.; Hamm, H. E.; Lindsley, C. W. A Novel and Selective PAR4 Antagonist: ML354. In Probe Reports from the NIH Molecular Libraries Program; Bethesda: MD, 2010.
      There is no corresponding record for this reference.
    26. 26
      Young, S. E.; Duvernay, M. T.; Schulte, M. L.; Lindsley, C. W.; Hamm, H. E. Synthesis of indole derived protease-activated receptor 4 antagonists and characterization in human platelets. PLoS One 2013, 8 (6), e65528  DOI: 10.1371/journal.pone.0065528
      There is no corresponding record for this reference.
    27. 27
      Wen, W.; Young, S. E.; Duvernay, M. T.; Schulte, M. L.; Nance, K. D.; Melancon, B. J.; Engers, J.; Locuson, C. W., 2nd; Wood, M. R.; Daniels, J. S.; Wu, W.; Lindsley, C. W.; Hamm, H. E.; Stauffer, S. R. Substituted indoles as selective protease activated receptor 4 (PAR-4) antagonists: Discovery and SAR of ML354. Bioorg. Med. Chem. Lett. 2014, 24 (19), 4708– 4713,  DOI: 10.1016/j.bmcl.2014.08.021
      27
      Substituted indoles as selective protease activated receptor 4 (PAR-4) antagonists: Discovery and SAR of ML354
      Wen, Wandong; Young, Summer E.; Duvernay, Matthew T.; Schulte, Michael L.; Nance, Kellie D.; Melancon, Bruce J.; Engers, Julie; Locuson, Charles W.; Wood, Michael R.; Daniels, J. Scott; Wu, Wenjun; Lindsley, Craig W.; Hamm, Heidi E.; Stauffer, Shaun R.
      Bioorganic & Medicinal Chemistry Letters (2014), 24 (19), 4708-4713CODEN: BMCLE8; ISSN:0960-894X. (Elsevier B.V.)
      Herein the authors report the discovery and SAR of an indole-based protease activated receptor-4 (PAR-4) antagonist scaffold derived from a similarity search of the Vanderbilt HTS collection, leading to MLPCN probe ML354 I (VU0099704). Using a novel PAC-1 fluorescent αIIbβ3 activation assay this probe mol. antagonist was found to have an IC50 of 140 nM for PAR-4 with 71-fold selectivity vs. PAR-1 (IC50 = 10 μM).
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    28. 28
      Bertron, J. L.; Duvernay, M. T.; Mitchell, S. G.; Smith, S. T.; Maeng, J. G.; Blobaum, A. L.; Davis, D. C.; Meiler, J.; Hamm, H. E.; Lindsley, C. W. Discovery and Optimization of a Novel Series of Competitive and Central Nervous System-Penetrant Protease-Activated Receptor 4 (PAR4) Inhibitors. ACS Chem. Neurosci. 2021, 12 (24), 4524– 4534,  DOI: 10.1021/acschemneuro.1c00557
      There is no corresponding record for this reference.
    29. 29
      Temple, K. J.; Duvernay, M. T.; Maeng, J. G.; Blobaum, A. L.; Stauffer, S. R.; Hamm, H. E.; Lindsley, C. W. Identification of the minimum PAR4 inhibitor pharmacophore and optimization of a series of 2-methoxy-6-arylimidazo[2,1-b][1,3,4]thiadiazoles. Bioorg. Med. Chem. Lett. 2016, 26 (22), 5481– 5486,  DOI: 10.1016/j.bmcl.2016.10.020
      There is no corresponding record for this reference.
    30. 30
      Bender, B. J.; Marlow, B.; Meiler, J. Improving homology modeling from low-sequence identity templates in Rosetta: A case study in GPCRs. PLoS Comput. Biol. 2020, 16 (10), e1007597  DOI: 10.1371/journal.pcbi.1007597
      There is no corresponding record for this reference.
    31. 31
      Cheng, R. K. Y.; Fiez-Vandal, C.; Schlenker, O.; Edman, K.; Aggeler, B.; Brown, D. G.; Brown, G. A.; Cooke, R. M.; Dumelin, C. E.; Doré, A. S.; Geschwindner, S.; Grebner, C.; Hermansson, N. O.; Jazayeri, A.; Johansson, P.; Leong, L.; Prihandoko, R.; Rappas, M.; Soutter, H.; Snijder, A.; Sundström, L.; Tehan, B.; Thornton, P.; Troast, D.; Wiggin, G.; Zhukov, A.; Marshall, F. H.; Dekker, N. Structural insight into allosteric modulation of protease-activated receptor 2. Nature 2017, 545 (7652), 112– 115,  DOI: 10.1038/nature22309
      31
      Structural insight into allosteric modulation of protease-activated receptor 2
      Cheng, Robert K. Y.; Fiez-Vandal, Cedric; Schlenker, Oliver; Edman, Karl; Aggeler, Birte; Brown, Dean G.; Brown, Giles A.; Cooke, Robert M.; Dumelin, Christoph E.; Dore, Andrew S.; Geschwindner, Stefan; Grebner, Christoph; Hermansson, Nils-Olov; Jazayeri, Ali; Johansson, Patrik; Leong, Louis; Prihandoko, Rudi; Rappas, Mathieu; Soutter, Holly; Snijder, Arjan; Sundstrom, Linda; Tehan, Benjamin; Thornton, Peter; Troast, Dawn; Wiggin, Giselle; Zhukov, Andrei; Marshall, Fiona H.; Dekker, Niek
      Nature (London, United Kingdom) (2017), 545 (7652), 112-115CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
      Protease-activated receptors (PARs) are a family of G-protein-coupled receptors (GPCRs) that are irreversibly activated by proteolytic cleavage of the N-terminus, which unmasks a tethered peptide ligand that binds and activates the transmembrane receptor domain, eliciting a cellular cascade in response to inflammatory signals and other stimuli. PARs are implicated in a wide range of diseases, such as cancer and inflammation. PARs have been the subject of major pharmaceutical research efforts, but the discovery of small-mol. antagonists that effectively bind them has proved challenging. The only marketed drug targeting a PAR is vorapaxar, a selective antagonist of PAR1 used to prevent thrombosis. The structure of PAR1 in complex with vorapaxar has been reported previously. Despite sequence homol. across the PAR isoforms, discovery of PAR2 antagonists has been less successful, although GB88 has been described as a weak antagonist. Here, we report crystal structures of PAR2 in complex with two distinct antagonists and a blocking antibody. The antagonist AZ8838 binds in a fully occluded pocket near the extracellular surface. Functional and binding studies reveal that AZ8838 exhibits slow binding kinetics, which is an attractive feature for a PAR2 antagonist competing against a tethered ligand. Antagonist AZ3451 binds to a remote allosteric site outside the helical bundle. We propose that antagonist binding prevents structural rearrangements required for receptor activation and signalling. We also show that a blocking antibody antigen-binding fragment binds to the extracellular surface of PAR2, preventing access of the tethered ligand to the peptide-binding site. These structures provide a basis for the development of selective PAR2 antagonists for a range of therapeutic uses.
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    32. 32
      Han, X.; Hofmann, L.; de la Fuente, M.; Alexander, N.; Palczewski, K.; Nieman, M. T.; Consortium, I. PAR4 activation involves extracellular loop 3 and transmembrane residue Thr153. Blood 2020, 136 (19), 2217– 2228,  DOI: 10.1182/blood.2019004634
      There is no corresponding record for this reference.
    33. 33
      Lyu, J.; Wang, S.; Balius, T. E.; Singh, I.; Levit, A.; Moroz, Y. S.; O’Meara, M. J.; Che, T.; Algaa, E.; Tolmachova, K.; Tolmachev, A. A.; Shoichet, B. K.; Roth, B. L.; Irwin, J. J. Ultra-large library docking for discovering new chemotypes. Nature 2019, 566 (7743), 224– 229,  DOI: 10.1038/s41586-019-0917-9
      33
      Ultra-large library docking for discovering new chemotypes
      Lyu, Jiankun; Wang, Sheng; Balius, Trent E.; Singh, Isha; Levit, Anat; Moroz, Yurii S.; O'Meara, Matthew J.; Che, Tao; Algaa, Enkhjargal; Tolmachova, Kateryna; Tolmachev, Andrey A.; Shoichet, Brian K.; Roth, Bryan L.; Irwin, John J.
      Nature (London, United Kingdom) (2019), 566 (7743), 224-229CODEN: NATUAS; ISSN:0028-0836. (Nature Research)
      Despite intense interest in expanding chem. space, libraries contg. hundreds-of-millions to billions of diverse mols. have remained inaccessible. Here we investigate structure-based docking of 170 million make-on-demand compds. from 130 well-characterized reactions. The resulting library is diverse, representing over 10.7 million scaffolds that are otherwise unavailable. For each compd. in the library, docking against AmpC β-lactamase (AmpC) and the D4 dopamine receptor were simulated. From the top-ranking mols., 44 and 549 compds. were synthesized and tested for interactions with AmpC and the D4 dopamine receptor, resp. We found a phenolate inhibitor of AmpC, which revealed a group of inhibitors without known precedent. This mol. was optimized to 77 nM, which places it among the most potent non-covalent AmpC inhibitors known. Crystal structures of this and other AmpC inhibitors confirmed the docking predictions. Against the D4 dopamine receptor, hit rates fell almost monotonically with docking score, and a hit-rate vs. score curve predicted that the library contained 453,000 ligands for the D4 dopamine receptor. Of 81 new chemotypes discovered, 30 showed submicromolar activity, including a 180-pM subtype-selective agonist of the D4 dopamine receptor.
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      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXmt1yns70%253D&md5=589fb81ef11f9cbec645b9a3818648d1
    34. 34
      Stein, R. M.; Kang, H. J.; McCorvy, J. D.; Glatfelter, G. C.; Jones, A. J.; Che, T.; Slocum, S.; Huang, X. P.; Savych, O.; Moroz, Y. S.; Stauch, B.; Johansson, L. C.; Cherezov, V.; Kenakin, T.; Irwin, J. J.; Shoichet, B. K.; Roth, B. L.; Dubocovich, M. L. Virtual discovery of melatonin receptor ligands to modulate circadian rhythms. Nature 2020, 579 (7800), 609– 614,  DOI: 10.1038/s41586-020-2027-0
      34
      Virtual discovery of melatonin receptor ligands to modulate circadian rhythms
      Stein, Reed M.; Kang, Hye Jin; McCorvy, John D.; Glatfelter, Grant C.; Jones, Anthony J.; Che, Tao; Slocum, Samuel; Huang, Xi-Ping; Savych, Olena; Moroz, Yurii S.; Stauch, Benjamin; Johansson, Linda C.; Cherezov, Vadim; Kenakin, Terry; Irwin, John J.; Shoichet, Brian K.; Roth, Bryan L.; Dubocovich, Margarita L.
      Nature (London, United Kingdom) (2020), 579 (7800), 609-614CODEN: NATUAS; ISSN:0028-0836. (Nature Research)
      The neuromodulator melatonin synchronizes circadian rhythms and related physiol. functions through the actions of two G-protein-coupled receptors: MT1 and MT2. Circadian release of melatonin at night from the pineal gland activates melatonin receptors in the suprachiasmatic nucleus of the hypothalamus, synchronizing the physiol. and behavior of animals to the light-dark cycle. The two receptors are established drug targets for aligning circadian phase to this cycle in disorders of sleep5,6 and depression. Despite their importance, few in vivo active MT1-selective ligands have been reported, hampering both the understanding of circadian biol. and the development of targeted therapeutics. Here we docked more than 150 million virtual mols. to an MT1 crystal structure, prioritizing structural fit and chem. novelty. Of these compds., 38 high-ranking mols. were synthesized and tested, revealing ligands with potencies ranging from 470 picomolar to 6 micromolar. Structure-based optimization led to two selective MT1 inverse agonists-which were topol. unrelated to previously explored chemotypes-that acted as inverse agonists in a mouse model of circadian re-entrainment. Notably, we found that these MT1-selective inverse agonists advanced the phase of the mouse circadian clock by 1.3-1.5 h when given at subjective dusk, an agonist-like effect that was eliminated in MT1- but not in MT2-knockout mice. This study illustrates the opportunities for modulating melatonin receptor biol. through MT1-selective ligands and for the discovery of previously undescribed, in vivo active chemotypes from structure-based screens of diverse, ultralarge libraries.
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      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXltF2jsbo%253D&md5=0627b43b7c075b057af6ac393c358421
    35. 35
      Song, Y.; DiMaio, F.; Wang, R. Y.; Kim, D.; Miles, C.; Brunette, T.; Thompson, J.; Baker, D. High-resolution comparative modeling with RosettaCM. Structure 2013, 21 (10), 1735– 1742,  DOI: 10.1016/j.str.2013.08.005
      35
      High-Resolution Comparative Modeling with RosettaCM
      Song, Yifan; DiMaio, Frank; Wang, Ray Yu-Ruei; Kim, David; Miles, Chris; Brunette, T. J.; Thompson, James; Baker, David
      Structure (Oxford, United Kingdom) (2013), 21 (10), 1735-1742CODEN: STRUE6; ISSN:0969-2126. (Elsevier Ltd.)
      We describe an improved method for comparative modeling, RosettaCM, which optimizes a phys. realistic all-atom energy function over the conformational space defined by homologous structures. Given a set of sequence alignments, RosettaCM assembles topologies by recombining aligned segments in Cartesian space and building unaligned regions de novo in torsion space. The junctions between segments are regularized using a loop closure method combining fragment superposition with gradient-based minimization. The energies of the resulting models are optimized by all-atom refinement, and the most representative low-energy model is selected. The CASP10 expt. suggests that RosettaCM yields models with more accurate side-chain and backbone conformations than other methods when the sequence identity to the templates is greater than ∼15%.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhsVCltrrP&md5=299d5b1f30a1fdd0d311804158cc5053
    36. 36
      Irwin, J. J.; Shoichet, B. K. ZINC--a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model. 2005, 45 (1), 177– 182,  DOI: 10.1021/ci049714
      There is no corresponding record for this reference.
    37. 37
      Irwin, J. J.; Sterling, T.; Mysinger, M. M.; Bolstad, E. S.; Coleman, R. G. ZINC: a free tool to discover chemistry for biology. J. Chem. Inf. Model. 2012, 52 (7), 1757– 1768,  DOI: 10.1021/ci3001277
      37
      ZINC: A Free Tool to Discover Chemistry for Biology
      Irwin, John J.; Sterling, Teague; Mysinger, Michael M.; Bolstad, Erin S.; Coleman, Ryan G.
      Journal of Chemical Information and Modeling (2012), 52 (7), 1757-1768CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
      ZINC is a free public resource for ligand discovery. The database contains over twenty million com. available mols. in biol. relevant representations that may be downloaded in popular ready-to-dock formats and subsets. The Web site also enables searches by structure, biol. activity, phys. property, vendor, catalog no., name, and CAS no. Small custom subsets may be created, edited, shared, docked, downloaded, and conveyed to a vendor for purchase. The database is maintained and curated for a high purchasing success rate and is freely available at zinc.docking.org.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XmvFGnsrg%253D&md5=97f2ede64afc6b5e3ea2f279e38e32a0
    38. 38
      Sterling, T.; Irwin, J. J. ZINC 15--Ligand Discovery for Everyone. J. Chem. Inf. Model. 2015, 55 (11), 2324– 2337,  DOI: 10.1021/acs.jcim.5b00559
      38
      ZINC 15 - Ligand Discovery for Everyone
      Sterling, Teague; Irwin, John J.
      Journal of Chemical Information and Modeling (2015), 55 (11), 2324-2337CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
      Many questions about the biol. activity and availability of small mols. remain inaccessible to investigators who could most benefit from their answers. To narrow the gap between chemoinformatics and biol., we have developed a suite of ligand annotation, purchasability, target, and biol. assocn. tools, incorporated into ZINC and meant for investigators who are not computer specialists. The new version contains over 120 million purchasable "drug-like" compds. - effectively all org. mols. that are for sale - a quarter of which are available for immediate delivery. ZINC connects purchasable compds. to high-value ones such as metabolites, drugs, natural products, and annotated compds. from the literature. Compds. may be accessed by the genes for which they are annotated as well as the major and minor target classes to which those genes belong. It offers new anal. tools that are easy for nonspecialists yet with few limitations for experts. ZINC retains its original 3D roots - all mols. are available in biol. relevant, ready-to-dock formats. ZINC is freely available at http://zinc15.docking.org.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhs1OhurbF&md5=e767c26c1d0aff7432c403c59a5d2b8b
    39. 39
      Coleman, R. G.; Carchia, M.; Sterling, T.; Irwin, J. J.; Shoichet, B. K. Ligand pose and orientational sampling in molecular docking. PLoS One 2013, 8 (10), e75992  DOI: 10.1371/journal.pone.0075992
      39
      Ligand pose and orientational sampling in molecular docking
      Coleman, Ryan G.; Carchia, Michael; Sterling, Teague; Irwin, John J.; Shoichet, Brian K.
      PLoS One (2013), 8 (10), e75992CODEN: POLNCL; ISSN:1932-6203. (Public Library of Science)
      Mol. docking remains an important tool for structure-based screening to find new ligands and chem. probes. As docking ambitions grow to include new scoring function terms and to address ever more targets, the reliability and extendability of the orientation sampling and the throughput of the method, become pressing. Here we explore sampling techniques that eliminate stochastic behavior in DOCK3.6, allowing us to optimize the method for regularly variable sampling of orientations. This also enabled a focused effort to optimize the code for efficiency, with a three-fold increase in the speed of the program. This, in turn, facilitated extensive testing of the method on the 102 targets, 22,805 ligands and 1,411,214 decoys of the Directory of Useful Decoys - Enhanced (DUD-E) benchmarking set, at multiple levels of sampling. Encouragingly, we observe that as sampling increases from 50 to 500 to 2000 to 5000 to 20000 mol. orientations in the binding site (and so from about 1 × 1010 to 4 × 1010 to 1 × 1011 to 2 × 1011 to 5 × 1011 mean atoms scored per target, since multiple conformations are sampled per orientation), the enrichment of ligands over decoys monotonically increases for most DUD-E targets. Meanwhile, including internal electrostatics in the evaluation ligand conformational energies and restricting arom. hydroxyls to low energy rotamers, further improved enrichment values. Several of the strategies used here to improve the efficiency of the code are broadly applicable in the field.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhsFyrtbvE&md5=14ee7e84b0bfab5e646c0a7fea8a20d2
    40. 40
      Roth, B. L.; Irwin, J. J.; Shoichet, B. K. Discovery of new GPCR ligands to illuminate new biology. Nat. Chem. Biol. 2017, 13 (11), 1143– 1151,  DOI: 10.1038/nchembio.2490
      There is no corresponding record for this reference.
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      Savage, B.; Cattaneo, M.; Ruggeri, Z. M. Mechanisms of platelet aggregation. Curr. Opin Hematol 2001, 8 (5), 270– 276,  DOI: 10.1097/00062752-200109000-00002
      There is no corresponding record for this reference.
    42. 42
      Soslau, G.; Goldenberg, S. J.; Class, R.; Jameson, B. Differential activation and inhibition of human platelet thrombin receptors by structurally distinct alpha-, beta- and gamma-thrombin. Platelets 2004, 15 (3), 155– 166,  DOI: 10.1080/0953710042000199848
      There is no corresponding record for this reference.
    43. 43
      Fleishman, S. J.; Leaver-Fay, A.; Corn, J. E.; Strauch, E. M.; Khare, S. D.; Koga, N.; Ashworth, J.; Murphy, P.; Richter, F.; Lemmon, G.; Meiler, J.; Baker, D. RosettaScripts: a scripting language interface to the Rosetta macromolecular modeling suite. PLoS One 2011, 6 (6), e20161  DOI: 10.1371/journal.pone.0020161
      43
      RosettaScripts: a scripting language interface to the Rosetta macromolecular modeling suite
      Fleishman, Sarel J.; Leaver-Fay, Andrew; Corn, Jacob E.; Strauch, Eva-Maria; Khare, Sagar D.; Koga, Nobuyasu; Ashworth, Justin; Murphy, Paul; Richter, Florian; Lemmon, Gordon; Meiler, Jens; Baker, David
      PLoS One (2011), 6 (6), e20161CODEN: POLNCL; ISSN:1932-6203. (Public Library of Science)
      Macromol. modeling and design are increasingly useful in basic research, biotechnol., and teaching. However, the absence of a user-friendly modeling framework that provides access to a wide range of modeling capabilities is hampering the wider adoption of computational methods by non-experts. RosettaScripts is an XML-like language for specifying modeling tasks in the Rosetta framework. RosettaScripts provides access to protocol-level functionalities, such as rigid-body docking and sequence redesign, and allows fast testing and deployment of complex protocols without need for modifying or recompiling the underlying C++ code. We illustrate these capabilities with RosettaScripts protocols for the stabilization of proteins, the generation of computationally constrained libraries for exptl. selection of higher-affinity binding proteins, loop remodeling, small-mol. ligand docking, design of ligand-binding proteins, and specificity redesign in DNA-binding proteins.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXot1yktbY%253D&md5=556e89d2cc74e36f607882ca69be620d
    44. 44
      Brown, B. P.; Mendenhall, J.; Meiler, J. BCL::MolAlign: Three-Dimensional Small Molecule Alignment for Pharmacophore Mapping. J. Chem. Inf. Model. 2019, 59 (2), 689– 701,  DOI: 10.1021/acs.jcim.9b00020
      There is no corresponding record for this reference.
    45. 45
      Chan, S. L. MolAlign: an algorithm for aligning multiple small molecules. J. Comput.-Aided Mol. Des. 2017, 31 (6), 523– 546,  DOI: 10.1007/s10822-017-0023-8
      There is no corresponding record for this reference.
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      DeLuca, S.; Khar, K.; Meiler, J. Fully Flexible Docking of Medium Sized Ligand Libraries with RosettaLigand. PLoS One 2015, 10 (7), e0132508  DOI: 10.1371/journal.pone.0132508
      46
      Fully flexible docking of medium sized ligand libraries with RosettaLigand
      DeLuca, Samuel; Khar, Karen; Meiler, Jens
      PLoS One (2015), 10 (7), e0132508/1-e0132508/19CODEN: POLNCL; ISSN:1932-6203. (Public Library of Science)
      RosettaLigand has been successfully used to predict binding poses in protein-small mol. complexes. However, the RosettaLigand docking protocol is comparatively slow in identifying an initial starting pose for the small mol. (ligand) making it unfeasible for use in virtual High Throughput Screening (vHTS). To overcome this limitation, we developed a new sampling approach for placing the ligand in the protein binding site during the initial 'low-resoln.' docking step. It combines the translational and rotational adjustments to the ligand pose in a single transformation step. The new algorithm is both more accurate and more time-efficient. The docking success rate is improved by 10-15% in a benchmark set of 43 protein/ligand complexes, reducing the no. of models that typically need to be generated from 1000 to 150. The av. time to generate a model is reduced from 50 s to 10 s. As a result we observe an effective 30-fold speed increase, making Rosetta- Ligand appropriate for docking medium sized ligand libraries. We demonstrate that this improved initial placement of the ligand is crit. for successful prediction of an accurate binding position in the 'high-resoln.' full atom refinement step.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhsV2rtLzJ&md5=1449579925b76a78e66a5e0a0ff94b58
    47. 47
      Davis, I. W.; Baker, D. RosettaLigand docking with full ligand and receptor flexibility. J. Mol. Biol. 2009, 385 (2), 381– 392,  DOI: 10.1016/j.jmb.2008.11.010
      47
      RL Docking with Full Ligand and Receptor Flexibility
      Davis, Ian W.; Baker, David
      Journal of Molecular Biology (2009), 385 (2), 381-392CODEN: JMOBAK; ISSN:0022-2836. (Elsevier Ltd.)
      Summary: Computational docking of small-mol. ligands into protein receptors is an important tool for modern drug discovery. Although conformational adjustments are frequently obsd. between the free and ligand-bound states, the conformational flexibility of the protein is typically ignored in protein-small mol. docking programs. We previously described the program RL, which leverages the Rosetta energy function and side-chain repacking algorithm to account for flexibility of all side chains in the binding site. Here we present extensions to RL that incorporate full ligand flexibility as well as receptor backbone flexibility. Including receptor backbone flexibility is found to produce more correct docked complexes and to lower the av. RMSD of the best-scoring docked poses relative to the rigid-backbone results. On a challenging set of retrospective and prospective cross-docking tests, we find that the top-scoring ligand pose is correctly positioned within 2 Å RMSD for 64% (54/85) of cases overall.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXkt1WhsQ%253D%253D&md5=c8ff49fe105488dce94e1190346d0bb7
    48. 48
      Meiler, J.; Baker, D. ROSETTALIGAND: protein-small molecule docking with full side-chain flexibility. Proteins 2006, 65 (3), 538– 548,  DOI: 10.1002/prot.21086
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      Hoggarth, E. Compounds related to thiosemicarbazide. J. Chem. Soc. 1950, 612– 614,  DOI: 10.1039/jr9500000612
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      Dolzhenko, A. V.; Tan, G. K.; Koh, L. L.; Dolzhenko, A. V.; Chui, W. K. N-Carbethoxy-N′-[3-(4-methylphenyl)-1H-1,2,4-triazol-5-yl]thiourea. Acta Crystallogr., Sect. E: Struct. Rep. Online 2010, 66 (Pt 3), 549– 550,  DOI: 10.1107/S1600536810004289
      There is no corresponding record for this reference.
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      Liu, J.; Nie, M.; Wang, Y.; Hu, J.; Zhang, F.; Gao, Y.; Liu, Y.; Gong, P. Design, synthesis and structure-activity relationships of novel 4-phenoxyquinoline derivatives containing 1,2,4-triazolone moiety as c-Met kinase inhibitors. Eur. J. Med. Chem. 2016, 123, 431– 446,  DOI: 10.1016/j.ejmech.2016.07.059
      There is no corresponding record for this reference.

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