Browse
Submit Data
Databases
API
Help

Dataset Information

23 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Virtual screening leads to the discovery of novel non-nucleotide P2Y? receptor antagonists.

ABSTRACT: The P2Y(1) receptor (P2Y(1)R) is a G protein-coupled receptor naturally activated by extracellular ADP. Its stimulation is an essential requirement of ADP-induced platelet aggregation, thus making antagonists highly sought compounds for the development of antithrombotic agents. Here, through a virtual screening campaign based on a pharmacophoric representation of the common characteristics of known P2Y(1)R ligands and the putative shape and size of the receptor binding pocket, we have identified novel antagonist hits of ?M affinity derived from a N,N'-bis-arylurea chemotype. Unlike the vast majority of known P2Y(1)R antagonists, these drug-like compounds do not have a nucleotidic scaffold or highly negatively charged phosphate groups. Hence, our compounds may provide a direction for the development of receptor probes with altered physicochemical properties.

SUBMITTER: Costanzi S

PROVIDER: S-EPMC3420346 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Virtual screening leads to the discovery of novel non-nucleotide P2Y₁ receptor antagonists.

Costanzi Stefano S Santhosh Kumar T T Balasubramanian Ramachandran R Kendall Harden T T Jacobson Kenneth A KA

Bioorganic & medicinal chemistry 20120703 17

The P2Y(1) receptor (P2Y(1)R) is a G protein-coupled receptor naturally activated by extracellular ADP. Its stimulation is an essential requirement of ADP-induced platelet aggregation, thus making antagonists highly sought compounds for the development of antithrombotic agents. Here, through a virtual screening campaign based on a pharmacophoric representation of the common characteristics of known P2Y(1)R ligands and the putative shape and size of the receptor binding pocket, we have identified ...[more]

PMID: 22831801

Similar Datasets

Discovery of novel agonists and antagonists of the free fatty acid receptor 1 (FFAR1) using virtual screening.

Project description:The G-protein-coupled receptor free fatty acid receptor 1 (FFAR1), previously named GPR40, is a possible novel target for the treatment of type 2 diabetes. In an attempt to identify new ligands for this receptor, we performed virtual screening (VS) based on two-dimensional (2D) similarity, three-dimensional (3D) pharmacophore searches, and docking studies by using the structure of known agonists and our model of the ligand binding site, which was validated by mutagenesis. VS of a database of 2.6 million compounds followed by extraction of structural neighbors of functionally confirmed hits resulted in identification of 15 compounds active at FFAR1 either as full agonists, partial agonists, or pure antagonists. Site-directed mutagenesis and docking studies revealed different patterns of ligand-receptor interactions and provided important information on the role of specific amino acids in binding and activation of FFAR1.

| S-EPMC3711565 | biostudies-literature

Discovery of Natural Products as Novel and Potent FXR Antagonists by Virtual Screening.

Project description:Farnesoid X receptor (FXR) is a member of nuclear receptor family involved in multiple physiological processes through regulating specific target genes. The critical role of FXR as a transcriptional regulator makes it a promising target for diverse diseases, especially those related to metabolic disorders such as diabetes and cholestasis. However, the underlying activation mechanism of FXR is still a blur owing to the absence of proper FXR modulators. To identify potential FXR modulators, an in-house natural product database (NPD) containing over 4,000 compounds was screened by structure-based virtual screening strategy and subsequent hit-based similarity searching method. After the yeast two-hybrid (Y2H) assay, six natural products were identified as FXR antagonists which blocked the CDCA-induced SRC-1 association. The IC50 values of compounds 2a, a diterpene bearing polycyclic skeleton, and 3a, named daphneone with chain scaffold, are as low as 1.29 and 1.79 ?M, respectively. Compared to the control compound guggulsterone (IC50 = 6.47 ?M), compounds 2a and 3a displayed 5- and 3-fold higher antagonistic activities against FXR, respectively. Remarkably, the two representative compounds shared low topological similarities with other reported FXR antagonists. According to the putative binding poses, the molecular basis of these antagonists against FXR was also elucidated in this report.

| S-EPMC5936786 | biostudies-literature

Identification of novel adenosine A(2A) receptor antagonists by virtual screening.

Project description:Virtual screening was performed against experimentally enabled homology models of the adenosine A(2A) receptor, identifying a diverse range of ligand efficient antagonists (hit rate 9%). By use of ligand docking and Biophysical Mapping (BPM), hits 1 and 5 were optimized to potent and selective lead molecules (11-13 from 5, pK(I) = 7.5-8.5, 13- to >100-fold selective versus adenosine A(1); 14-16 from 1, pK(I) = 7.9-9.0, 19- to 59-fold selective).

| S-EPMC3308209 | biostudies-literature

Comparative Analysis of Virtual Screening Approaches in the Search for Novel EphA2 Receptor Antagonists.

Project description:The EphA2 receptor and its ephrin-A1 ligand form a key cell communication system, which has been found overexpressed in many cancer types and involved in tumor growth. Recent medicinal chemistry efforts have identified bile acid derivatives as low micromolar binders of the EphA2 receptor. However, these compounds suffer from poor physicochemical properties, hampering their use in vivo. The identification of compounds able to disrupt the EphA2-ephrin-A1 complex lacking the bile acid scaffold may lead to new pharmacological tools suitable for in vivo studies. To identify the most promising virtual screening (VS) protocol aimed at finding novel EphA2 antagonists, we investigated the ability of both ligand-based and structure-based approaches to retrieve known EphA2 antagonists from libraries of decoys with similar molecular properties. While ligand-based VSs were conducted using UniPR129 and ephrin-A1 ligand as reference structures, structure-based VSs were performed with Glide, using the X-ray structure of the EphA2 receptor/ephrin-A1 complex. A comparison of enrichment factors showed that ligand-based approaches outperformed the structure-based ones, suggesting ligand-based methods using the G-H loop of ephrin-A1 ligand as template as the most promising protocols to search for novel EphA2 antagonists.

| S-EPMC6331951 | biostudies-literature

Virtual High-Throughput Screening To Identify Novel Activin Antagonists.

Project description:Activin belongs to the TGFβ superfamily, which is associated with several disease conditions, including cancer-related cachexia, preterm labor with delivery, and osteoporosis. Targeting activin and its related signaling pathways holds promise as a therapeutic approach to these diseases. A small-molecule ligand-binding groove was identified in the interface between the two activin βA subunits and was used for a virtual high-throughput in silico screening of the ZINC database to identify hits. Thirty-nine compounds without significant toxicity were tested in two well-established activin assays: FSHβ transcription and HepG2 cell apoptosis. This screening workflow resulted in two lead compounds: NUCC-474 and NUCC-555. These potential activin antagonists were then shown to inhibit activin A-mediated cell proliferation in ex vivo ovary cultures. In vivo testing showed that our most potent compound (NUCC-555) caused a dose-dependent decrease in FSH levels in ovariectomized mice. The Blitz competition binding assay confirmed target binding of NUCC-555 to the activin A:ActRII that disrupts the activin A:ActRII complex's binding with ALK4-ECD-Fc in a dose-dependent manner. The NUCC-555 also specifically binds to activin A compared with other TGFβ superfamily member myostatin (GDF8). These data demonstrate a new in silico-based strategy for identifying small-molecule activin antagonists. Our approach is the first to identify a first-in-class small-molecule antagonist of activin binding to ALK4, which opens a completely new approach to inhibiting the activity of TGFβ receptor superfamily members. in addition, the lead compound can serve as a starting point for lead optimization toward the goal of a compound that may be effective in activin-mediated diseases.

| S-EPMC4635973 | biostudies-literature

Novel Transforming Growth Factor-Beta Receptor 1 Antagonists through a Pharmacophore-Based Virtual Screening Approach.

Project description:As new drugs for the treatment of malignant tumors, transforming growth factor-beta receptor 1 (TGF?R1) antagonists have attracted wide attention. Based on the crystal structure of TGF?R1-BMS22 complex, the pharmacophore model A02 with two hydrogen bond acceptors (HBAs) and four hydrophobic (HYD) properties was constructed. From the common features of active ligands reported in the literature, pharmacophore model B10 was also generated, which has two aromatic ring centers (RAs) and two HYD properties. The two models have high sensitivity and specificity to the training set, and they are highly consistent in spatial structure. Combining the two pharmacophore models, two novel skeleton structures with potential activity were selected by virtual screening from the DruglikeDiverse, MiniMaybridge, and ZINC Drug-Like databases. Four compounds (YXY01?YXY04) with potential anti-TGF?R1 activity were designed based on the new skeleton structures. In combination with Lipinski's rules; absorption, distribution, metabolism, excretion, and toxicity (ADMET); and, toxicological properties predicted in the study, YXY01-03 with the novel skeleton, good drug-like properties, and potential activity were finally discovered and may have higher safety relative to BMS22, which may be valuable for further research.

| S-EPMC6278322 | biostudies-literature

Identification of Novel Antagonists Targeting Cannabinoid Receptor 2 Using a Multi-Step Virtual Screening Strategy

Project description:The endocannabinoid system plays an essential role in the regulation of analgesia and human immunity, and Cannabinoid Receptor 2 (CB2) has been proved to be an ideal target for the treatment of liver diseases and some cancers. In this study, we identified CB2 antagonists using a three-step “deep learning–pharmacophore–molecular docking” virtual screening approach. From the ChemDiv database (1,178,506 compounds), 15 hits were selected and tested by radioligand binding assays and cAMP functional assays. A total of 7 out of the 15 hits were found to exhibit binding affinities in the radioligand binding assays against CB2 receptor, with a pKi of 5.15–6.66, among which five compounds showed antagonistic activities with pIC50 of 5.25–6.93 in the cAMP functional assays. Among these hits, Compound 8 with the 4H-pyrido[1,2-a]pyrimidin-4-one scaffold showed the best binding affinity and antagonistic activity with a pKi of 6.66 and pIC50 of 6.93, respectively. The new scaffold could serve as a lead for further development of CB2 drugs. Additionally, we hope that the model in this study could be further utilized to identify more novel CB2 receptor antagonists, and the developed approach could also be used to design potent ligands for other therapeutic targets.

| S-EPMC8587544 | biostudies-literature

Discovery of human macrophage migration inhibitory factor (MIF)-CD74 antagonists via virtual screening.

Project description:Macrophage migration inhibitory factor (MIF) is a cytokine that is involved in the regulation of inflammation as well as cell proliferation and differentiation. Deactivation of MIF by antibodies or inhibition of MIF binding to its receptor, CD74, attenuates tumor growth and angiogenesis. To discover small-molecule inhibitors of MIF's biological activity, virtual screening was performed by docking 2.1 million compounds into the MIF tautomerase active site. After visual inspection of 1200 top-ranked MIF-ligand complexes, 26 possible inhibitors were selected and purchased and 23 of them were assayed. The in vitro binding assay for MIF with CD74 revealed that 11 of the compounds have inhibitory activity in the micromolar regime, including four compounds with IC(50) values below 5 microM. Inhibition of MIF tautomerase activity was also established for many of the compounds with IC(50) values as low as 0.5 microM; Michaelis-Menten analysis was performed for two cases and confirmed the competitive inhibition.

| S-EPMC2680181 | biostudies-literature

Discovery of CNS-Like D3R-Selective Antagonists Using 3D Pharmacophore Guided Virtual Screening.

Project description:The dopamine D3 receptor is an important CNS target for the treatment of a variety of neurological diseases. Selective dopamine D3 receptor antagonists modulate the improvement of psychostimulant addiction and relapse. In this study, five and six featured pharmacophore models of D3R antagonists were generated and evaluated with the post-hoc score combining two survival scores of active and inactive. Among the Top 10 models, APRRR215 and AHPRRR104 were chosen based on the coefficient of determination (APRRR215: R²training = 0.80; AHPRRR104: R²training = 0.82) and predictability (APRRR215: Q²test = 0.73, R²predictive = 0.82; AHPRRR104: Q²test = 0.86, R²predictive = 0.74) of their 3D-quantitative structure?activity relationship models. Pharmacophore-based virtual screening of a large compound library from eMolecules (>3 million compounds) using two optimal models expedited the search process by a 100-fold speed increase compared to the docking-based screening (HTVS scoring function in Glide) and identified a series of hit compounds having promising novel scaffolds. After the screening, docking scores, as an adjuvant predictor, were added to two fitness scores (from the pharmacophore models) and predicted Ki (from PLSs of the QSAR models) to improve accuracy. Final selection of the most promising hit compounds were also evaluated for CNS-like properties as well as expected D3R antagonism.

| S-EPMC6222863 | biostudies-literature

Discovery of Novel Androgen Receptor Ligands by Structure-based Virtual Screening and Bioassays.

Project description:Androgen receptor (AR) is a ligand-activated transcription factor that plays a pivotal role in the development and progression of many severe diseases such as prostate cancer, muscle atrophy, and osteoporosis. Binding of ligands to AR triggers the conformational changes in AR that may affect the recruitment of coactivators and downstream response of AR signaling pathway. Therefore, AR ligands have great potential to treat these diseases. In this study, we searched for novel AR ligands by performing a docking-based virtual screening (VS) on the basis of the crystal structure of the AR ligand binding domain (LBD) in complex with its agonist. A total of 58 structurally diverse compounds were selected and subjected to LBD affinity assay, with five of them (HBP1-3, HBP1-17, HBP1-38, HBP1-51, and HBP1-58) exhibiting strong binding to AR-LBD. The IC50 values of HBP1-51 and HBP1-58 are 3.96?µM and 4.92?µM, respectively, which are even lower than that of enzalutamide (Enz, IC50?=?13.87?µM), a marketed second-generation AR antagonist. Further bioactivity assays suggest that HBP1-51 is an AR agonist, whereas HBP1-58 is an AR antagonist. In addition, molecular dynamics (MD) simulations and principal components analysis (PCA) were carried out to reveal the binding principle of the newly-identified AR ligands toward AR. Our modeling results indicate that the conformational changes of helix 12 induced by the bindings of antagonist and agonist are visibly different. In summary, the current study provides a highly efficient way to discover novel AR ligands, which could serve as the starting point for development of new therapeutics for AR-related diseases.

| S-EPMC6411960 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data