mims-harvard/tooluniverse-structural-proteomics
plugin/skills/tooluniverse-structural-proteomics/SKILL.md
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.
$ install --global
skillsauthnpx skillsauth add mims-harvard/tooluniverse tooluniverse-structural-proteomicsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- 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.
- disable-model-invocation:
- true
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_structuresandRCSBGraphQL_get_structure_summary - AlphaFold confidence:
alphafold_get_summary - Ligands/affinities:
PDBe_get_structure_ligandsandBindingDB_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 (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:
operationis internal, not a public parameter
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