Drug Target Discovery Platform
A bioinformatics platform for prioritizing protein targets in pathogens.
Target Pathogen integrates genomic annotation, structural evidence, functional annotation, and project-specific scoring to support the identification and prioritization of candidate protein targets.
Operations
Current workflow status
Bioinformatics stage 24 / 24 · Loading LigQ_2 binder evidence
Methodology
Data sources
Evidence for each protein is assembled through a 24-stage bioinformatics pipeline, combining external databases, comparative genomics, structural modeling, and machine learning.
Target profile
Proteins are assessed for selectivity against the human proteome and gut microbiome, and for essentiality using the Database of Essential Genes. Subcellular localization is predicted by PSORTb to evaluate target accessibility.
3D Structure
Structures are assigned by priority: experimental PDB entries first, then AlphaFold Database models, then ColabFold predictions for proteins without other coverage. Per-residue pLDDT scores indicate model confidence.
Binding sites
FPocket detects surface cavities using Voronoi spheres and reports a druggability score from 0 to 1. P2Rank applies a random forest trained on ~15,000 PDB structures to rank predicted binding site probability.
Functional annotation
InterProScan scans each protein against 20 databases including Pfam, HAMAP, and PANTHER to assign domains, GO terms, and EC numbers. UniProt provides additional curated GO and enzyme data for mapped accessions.
Ligand evidence
LigQ_2 retrieves co-crystallized ligands from PDB, bioactive compounds from ChEMBL (ranked by potency), and ZINC candidates by chemical similarity. Direct and homology-transferred evidence are distinguished.
Drug-likeness
Physicochemical properties including MW, LogP, and TPSA are computed from SMILES using RDKit. Lipinski Rule of Five compliance and PAINS substructure filters are applied to flag compounds prone to assay interference.