TCRD

TCRD aggregates and organizes human protein target data to support identification and prioritization of druggable genome targets amenable to modulation by small molecules or biologics, with emphasis on GPCRs, kinases, ion channels, nuclear receptors, and olfactory GPCRs.

Key Features:

• Central repository: Aggregates comprehensive information on human protein targets relevant to drug discovery.
• Target families: Focuses specifically on G-protein coupled receptors (GPCRs), kinases, ion channels, nuclear receptors, and treats olfactory GPCRs distinctly.
• Target Development Levels (TDLs): Classifies targets into four distinct TDL categories to indicate development status and druggability.
• Data integration: Incorporates data from over 25 external sources, expanding coverage since the 2017 release.
• Interaction and association data: Includes human and viral-human protein-protein interactions and associations between proteins and diseases or phenotypes.
• Drug perturbation signatures: Integrates drug-induced gene signatures linked to protein targets.
• Machine learning-ready formatting: Provides aggregated data formatted for machine learning applications via Pharos.

Scientific Applications:

• Target prioritization: Enables prioritization of targets for therapeutic development based on TDLs and aggregated evidence.
• Illuminating understudied proteins: Supports exploration and characterization of poorly annotated proteins within the druggable genome.
• Interaction and disease biology: Facilitates analysis of protein-protein interactions and protein–disease or phenotype associations.
• Drug perturbation analysis: Allows investigation of drug-induced gene expression signatures in relation to targets.
• Machine learning-driven discovery: Supplies machine learning-ready datasets for target discovery and hypothesis generation.

Methodology:

Aggregates and integrates data from over 25 sources, categorizes targets into four TDLs, incorporates human and viral-human protein-protein interactions, protein–disease/phenotype associations, and drug-induced gene signatures, and formats aggregated data for machine learning via Pharos.