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tooluniverse-structural-proteomics Structural biology plus proteomics integration for drug target validation. Combines PDB experimental structures, AlphaFold predictions, GPCRdb, SAbDab antibody structures, ProteinsPlus binding-site prediction, and BindingDB ligand-affinity data. Use for druggability assessment, binding-site characterization, ligand-pocket analysis, structural-confidence scoring (resolution, pLDDT), and antibody-target interface analysis.
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name tooluniverse-structural-proteomics description Structural biology plus proteomics integration for drug target validation. Combines PDB experimental structures, AlphaFold predictions, GPCRdb, SAbDab antibody structures, ProteinsPlus binding-site prediction, and BindingDB ligand-affinity data. Use for druggability assessment, binding-site characterization, ligand-pocket analysis, structural-confidence scoring (resolution, pLDDT), and antibody-target interface analysis.
Structural Proteomics for Drug Target Validation
Comprehensive structural data integration using ToolUniverse tools across PDB, AlphaFold, GPCRdb, SAbDab, and proteomics databases for drug target validation.
LOOK UP DON'T GUESS
PDB structures/resolutions: PDBeSIFTS_get_best_structures and RCSBGraphQL_get_structure_summary
AlphaFold confidence: alphafold_get_summary
Ligands/affinities: PDBe_get_structure_ligands and BindingDB_get_ligands_by_uniprot
Druggability: ProteinsPlus_predict_binding_sites
COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
Domain Reasoning
Resolution determines valid conclusions: <2A = atom positions visible; 2-3A = side chains reliable, drug design supported; >3A = backbone only, binding site unreliable. Do not over-interpret low-resolution structures.
Tool Inventory
PDB (RCSB)
RCSBAdvSearch_search_structures (query_type, query_value, rows), RCSBData_get_entry (entry_id), RCSBGraphQL_get_structure_summary (pdb_id), RCSBGraphQL_get_ligand_info (pdb_id),
RCSB_get_chemical_component
PDB (PDBe) pdbe_get_entry_summary (pdb_id), PDBe_get_structure_ligands (pdb_id), PDBe_get_bound_molecules (pdb_id), PDBeSearch_search_structures (query, rows), PDBeSIFTS_get_best_structures (uniprot_id), PDBeSIFTS_get_all_structures (uniprot_id), PDBe_KB_get_ligand_sites (pdb_id), PDBe_KB_get_interface_residues (pdb_id), PDBeValidation_get_quality_scores (pdb_id)
PDBe PISA PDBePISA_get_interfaces (pdb_id), PDBePISA_get_assemblies (pdb_id)
AlphaFold alphafold_get_prediction (qualifier=UniProt), alphafold_get_summary (qualifier), alphafold_get_annotations (qualifier)
Binding Sites ProteinsPlus_predict_binding_sites (pdb_id, chain), BindingDB_get_ligands_by_uniprot (uniprot_id), BindingDB_get_ligands_by_pdb (pdb_id), BindingDB_get_targets_by_compound (smiles)
Foldseek Foldseek_search_structure (sequence, mode="tmalign"), Foldseek_get_result (ticket)
GPCRdb GPCRdb_get_protein (protein), GPCRdb_get_structures (protein), GPCRdb_get_ligands (protein), GPCRdb_get_mutations (protein). Accepts entry names, gene symbols (auto-converted to {symbol.lower()}_human), or UniProt accessions.
SAbDab SAbDab_search_structures (query/antigen), SAbDab_get_structure (pdb_id), TheraSAbDab_search_therapeutics (query), TheraSAbDab_search_by_target (target)
Domains InterPro_get_protein_domains (uniprot_id), Pfam_get_protein_annotations (uniprot_id), UniProt_get_entry_by_accession (accession)
Proteomics ProteomeXchange_search_datasets (query), ProteomeXchange_get_dataset (dataset_id)
Workflow 1: Find All Structures for a Drug Target Phase 0: Resolve protein → UniProt ID, gene symbol, organism
Phase 1: PDBeSIFTS_get_best_structures → RCSBGraphQL_get_structure_summary → PDBeValidation
Phase 2: alphafold_get_prediction/summary → compare pLDDT with experimental coverage
Phase 3: IF GPCR → GPCRdb; IF antibody target → SAbDab/TheraSAbDab
Phase 4: InterPro/Pfam domain mapping → identify unresolved regions
Phase 5: Summary table (PDB ID, method, resolution, ligands, coverage, quality)
Decisions : Resolution <2.5A for drug design. X-ray > Cryo-EM > NMR > AlphaFold for binding sites. Holo > apo structures.
Workflow 2: Identify Binding Pocket Ligands Phase 1: PDBe_get_structure_ligands + RCSBGraphQL_get_ligand_info + PDBe_KB_get_ligand_sites
Phase 2: ProteinsPlus_predict_binding_sites → druggability score, pocket residues
Phase 3: BindingDB_get_ligands_by_pdb/uniprot → Ki, Kd, IC50
Phase 4: RCSB_get_chemical_component for key ligands
Filter artifacts : GOL, EDO, SO4, PEG, ACT, CL, NA. Keep cofactors (ATP, NAD, HEM) and catalytic metals (ZN, MG) if relevant.
Workflow 3: Cross-Validate Drug Binding Phase 1: Find co-crystal structures → filter for drug/analogs
Phase 2: BindingDB affinity data (Ki, Kd, IC50)
Phase 3: ProteinsPlus + PDBe-KB binding site characterization
Phase 4: PDBeValidation quality → binding site well-resolved?
Phase 5: AlphaFold + Foldseek structural comparison
Phase 6: GPCR-specific (if applicable) → active/inactive states, pharmacology, resistance mutations
Phase 7: Antibody-specific (if applicable) → epitope mapping
Phase 8: Evidence integration
Tool Parameter Gotchas Tool Mistake Correct alphafold_get_prediction/summaryuniprot_idqualifierGPCRdb_get_proteingene_nameproteinPDBeSIFTS_get_best_structuresgene symbol uniprot_id (e.g., "P04637")Foldseek_search_structuremode="3diaa"mode="tmalign"SAbDab_search_structuresnamequery or antigenRCSB_get_chemical_componentligand_idcomp_id
Evidence Grading Tier Confidence T1 Co-crystal (<2.5A) + binding affinity data T2 Experimental structure + computational prediction T3 AlphaFold + pocket analysis + known ligand analogs T4 Homology model or low-resolution only
Interpretation Metric High Acceptable Caution Resolution <2.0A (X-ray) / <3.0A (cryo-EM) 2.0-2.5A / 3.0-4.0A >3.0A / >4.5A R-free <0.25 0.25-0.30 >0.30 AlphaFold pLDDT >90 70-90 <70 (disordered)
DoGSiteScorer >0.6 = druggable; <0.4 = unlikely druggable. PISA assemblies should be cross-validated with SEC-MALS/native MS.
Limitations
BindingDB: 60s+ for popular targets
AlphaFold: lacks ligand context
GPCRdb: Class A-F GPCRs only
PDBePISA: operation is internal, not a public parameter
المزيد من هذا المستودع tooluniverse-drug-research Comprehensive drug profiling — mechanism, primary/secondary targets, drug interactions, clinical-trial status, adverse events (FAERS), pharmacogenomics, and approval history. Use for full drug investigation reports, 'tell me about drug X' queries, and assembling drug profiles for clinicians, researchers, or regulatory work.
tooluniverse-gene-enrichment Gene-set enrichment analysis — GO (Biological Process, Molecular Function, Cellular Component), KEGG, Reactome pathway enrichment via clusterProfiler, gseapy, ORA, GSEA. Use for interpreting DEG lists, screen hit lists, or any gene-list-to-pathways query. Includes simplify-cutoff handling and union-vs-total denominator conventions for percent-DE questions.
tooluniverse-multiomic-disease-characterization Comprehensive disease characterization across genomics, transcriptomics, proteomics, and pathways for systems-level understanding. Identifies therapeutic opportunities and biomarker candidates by integrating multi-layer molecular data. Use for full-omics disease deep-dive reports, mechanism mapping, and biomarker-and-target identification from multi-omics data.
