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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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mims-harvard/ToolUniverse
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16 septembre 2026 à 05:14
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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` - Protein-protein binding mutations (ddG): `SKEMPI_search_by_structure`/`SKEMPI_search_by_protein`/`SKEMPI_get_mutation` -- BindingDB's protein-protein equivalent, for interface-mutation affinity effects rather than small-molecule ligands - 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` (comp_id) ### 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/summary` | `uniprot_id` | `qualifier` | | `GPCRdb_get_protein` | `gene_name` | `protein` | | `PDBeSIFTS_get_best_structures` | gene symbol | `uniprot_id` (e.g., "P04637") | | `Foldseek_search_structure` | `mode="3diaa"` | `mode="tmalign"` | | `SAbDab_search_structures` | `name` | `query` or `antigen` | | `RCSB_get_chemical_component` | `ligand_id` | `comp_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
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