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.

4 genomes 16557 proteins

Methodology

Data sources

Full reference

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.

FastTarget DIAMOND BLASTP DEG PSORTb v3

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.

Protein Data Bank AlphaFold DB ColabFold

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.

FPocket v4 P2Rank v2

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.

InterProScan v5 UniProt Gene Ontology

Ligand evidence

LigQ_2 is the internal ligand-evidence step. It retrieves co-crystallized ligands from PDB, measured bioactive compounds from ChEMBL, and proposed ZINC compounds by chemical similarity. Direct and homology-transferred evidence are distinguished.

LigQ_2 PDB ChEMBL ZINC

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.

RDKit Lipinski Ro5 PAINS

About us

Developed at FCEN-UBA

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Target Pathogen is developed at the Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, within a public and tuition-free university ecosystem. The project brings biology, chemistry, bioinformatics, and software development together to make pathogen target prioritization traceable and reviewable.