GPCR Panel
GPCR Panel predicts interactions between small molecules and human G protein-coupled receptors (GPCRs) using docking and integrates tissue expression and SIDER side-effect data to prioritize targets and repurposing opportunities for drug discovery.
Key Features:
- GPCR Target Prediction: Uses docking against a curated library of 36 experimentally determined human GPCR crystal structures covering 46 distinct docking sites to generate a ranked list of potential targets by binding affinity.
- Expression Location Mapping: Maps predicted GPCR targets to tissue expression locations to indicate likely in vivo interaction sites.
- Side Effect Prediction: Integrates SIDER (Side-Effect Resource) data and maps predicted off-targets to side effects across 45 tissues and organs using expressed sequence tag profiles.
- Drug Repurposing Potential: Evaluates binding affinities and tissue expression profiles to identify opportunities for repurposing existing compounds.
Scientific Applications:
- Facilitating Drug Discovery: Predicts GPCR targets and ligand interactions to prioritize therapeutic candidates for further development.
- Minimizing Side Effects: Identifies potential off-target interactions and associated side effects to inform safer drug design.
- Enhancing Drug Repurposing Efforts: Highlights new uses for existing compounds by combining binding affinity and expression profile data.
Methodology:
Docking of compounds against a curated library of 36 experimentally determined human GPCR crystal structures (46 docking sites) to produce a ranked list by binding affinity, with cross-referencing of predicted off-targets to tissue expression data and SIDER side-effect information using expressed sequence tag profiles mapped across 45 tissues and organs.
Topics
Collections
Details
- Tool Type:
- web application
- Added:
- 2/8/2024
- Last Updated:
- 11/24/2024
Operations
Publications
Liu L, Ho M, Su B, Wang S, Hsu M, Tseng YJ. PanGPCR: predictions for multiple targets, repurposing and side effects. Bioinformatics. 2020;37(8):1184-1186. doi:10.1093/bioinformatics/btaa766. PMID:32915954.