
Tooluniverse Structural Proteomics
- 186 installs
- 1.6k repo stars
- Updated August 4, 2026
- mims-harvard/tooluniverse
Investigate protein structures and complexes via folding predictions, domain mapping, binding-site analysis, and structural comparison across homologs.
About
tooluniverse-structural-proteomics lets agents query and interpret protein 3D structures, compare conformations, annotate functional domains, and connect sequence variants to structural impact for mechanistic proteomics research.
- Structure retrieval and alignment
- Domain and motif annotation
- Binding-site identification
- Homolog structural comparison
- Mechanism-oriented summaries
Tooluniverse Structural Proteomics by the numbers
- 186 all-time installs (skills.sh)
- +5 installs in the week ending Aug 4, 2026 (Skillselion tracking)
- Ranked #677 of 2,064 Data Science & ML skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 186 |
|---|---|
| repo stars | ★ 1.6k |
| Last updated | August 4, 2026 |
| Repository | mims-harvard/tooluniverse ↗ |
What it does
Investigate protein structures and complexes via folding predictions, domain mapping, binding-site analysis, and structural comparison across homologs.
Files
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 ligandsFilter 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