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Get Started Free →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.
.claude/skills/mims-harvard-tooluniverse-structural-proteomics/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 224% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 120% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 5% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 35% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 81% | 0% |
Comprehensive structural data integration using ToolUniverse tools across PDB, AlphaFold, GPCRdb, SAbDab, and proteomics databases for drug target validation.
PDBeSIFTS_get_best_structures and RCSBGraphQL_get_structure_summaryalphafold_get_summaryPDBe_get_structure_ligands and BindingDB_get_ligands_by_uniprotSKEMPI_search_by_structure/SKEMPI_search_by_protein/SKEMPI_get_mutation -- BindingDB's protein-protein equivalent, for interface-mutation affinity effects rather than small-molecule ligandsProteinsPlus_predict_binding_sitesWhen 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.
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.
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)
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)
PDBePISA_get_interfaces (pdb_id), PDBePISA_get_assemblies (pdb_id)
alphafold_get_prediction (qualifier=UniProt), alphafold_get_summary (qualifier), alphafold_get_annotations (qualifier)
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_search_structure (sequence, mode="tmalign"), Foldseek_get_result (ticket)
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_search_structures (query/antigen), SAbDab_get_structure (pdb_id), TheraSAbDab_search_therapeutics (query), TheraSAbDab_search_by_target (target)
InterPro_get_protein_domains (uniprot_id), Pfam_get_protein_annotations (uniprot_id), UniProt_get_entry_by_accession (accession)
ProteomeXchange_search_datasets (query), ProteomeXchange_get_dataset (dataset_id)
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.
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 ligandsFilter artifacts: GOL, EDO, SO4, PEG, ACT, CL, NA. Keep cofactors (ATP, NAD, HEM) and catalytic metals (ZN, MG) if relevant.
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 | 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 |
| 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 |
| 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.
operation is internal, not a public parameter| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-02 | fail→fail | 27,532 | 9,109 | -67% | 1 | 1 | 0% | 5,567 | 2,723 | -51% | 0 | 0 | — |
case-18 | pass→pass | 15,527 | 16,956 | +9% | 1 | 1 | 0% | 2,237 | 4,445 | +99% | 0 | 0 | — |
case-01 | pass→fail | 20,167 | 8,154 | -60% | 1 | 1 | 0% | 3,983 | 2,734 | -31% | 0 | 0 | — |
case-03 | fail→fail | 32,513 | 8,860 | -73% | 1 | 1 | 0% | 6,227 | 2,708 | -57% | 0 | 0 | — |
case-04 | fail→pass | 3,990 | 65,049 | +1530% | 1 | 1 | 0% | 784 | 2,541 | +224% | 0 | 0 | — |
case-05 | fail→pass | 5,921 | 2,566 | -57% | 1 | 1 | 0% | 1,114 | 2,449 | +120% | 0 | 0 | — |
case-06 | pass→pass | 3,637 | 2,667 | -27% | 1 | 1 | 0% | 688 | 2,437 | +254% | 0 | 0 | — |
case-19 | pass→pass | 9,909 | 16,115 | +63% | 1 | 1 | 0% | 1,620 | 4,394 | +171% | 0 | 0 | — |
case-07 | fail→pass | 12,619 | 2,515 | -80% | 1 | 1 | 0% | 2,276 | 2,385 | +5% | 0 | 0 | — |
case-08 | pass→fail | 12,539 | 4,842 | -61% | 1 | 1 | 0% | 2,308 | 2,335 | +1% | 0 | 0 | — |
case-09 | fail→pass | 11,931 | 4,046 | -66% | 1 | 1 | 0% | 2,001 | 2,694 | +35% | 0 | 0 | — |
case-10 | pass→pass | 9,114 | 4,117 | -55% | 1 | 1 | 0% | 1,714 | 2,865 | +67% | 0 | 0 | — |
case-11 | fail→pass | 12,452 | 11,328 | -9% | 1 | 1 | 0% | 2,293 | 4,152 | +81% | 0 | 0 | — |
case-12 | pass→pass | 13,810 | 15,235 | +10% | 1 | 1 | 0% | 2,320 | 4,719 | +103% | 0 | 0 | — |
case-13 | pass→pass | 4,283 | 2,388 | -44% | 1 | 1 | 0% | 822 | 2,409 | +193% | 0 | 0 | — |
case-14 | fail→pass | 9,367 | 3,204 | -66% | 1 | 1 | 0% | 1,680 | 2,575 | +53% | 0 | 0 | — |
case-15 | pass→pass | 7,693 | 7,976 | +4% | 1 | 1 | 0% | 1,575 | 3,500 | +122% | 0 | 0 | — |
case-16 | pass→pass | 16,622 | 21,104 | +27% | 1 | 1 | 0% | 2,245 | 4,926 | +119% | 0 | 0 | — |
case-17 | pass→fail | 17,727 | 11,364 | -36% | 1 | 1 | 0% | 2,431 | 2,799 | +15% | 0 | 0 | — |
case-20 | pass→pass | 11,767 | 11,150 | -5% | 1 | 1 | 0% | 1,953 | 3,648 | +87% | 0 | 0 | — |
case-21 | pass→pass | 10,327 | 8,427 | -18% | 1 | 1 | 0% | 1,491 | 3,292 | +121% | 0 | 0 | — |
case-22 | pass→pass | 14,223 | 21,706 | +53% | 1 | 1 | 0% | 2,536 | 6,150 | +143% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 17 counted toward the lift figure. The other 5 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +14 percentage points is the difference between those two pass rates over the 17 comparable cases. 4 cases got worse with the skill loaded, and they are included in that figure.
The publisher has shipped newer versions since this run, so these numbers describe v1, not the version currently listed.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.