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.

PMID: 32915954
Funding: - Ministry of Science and Technology, Taiwan: 108-2627-E-002-001-, 109-2320-B-002-040-, 109-2926-I-002-506- - National Taiwan University, Taiwan: NTU-CC-109L892703 - Taiwan Food and Drug Administration: 109-FDA-D-114-000611