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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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Listed on Skillselion
Installs186
repo stars1.6k
Last updatedAugust 4, 2026
Repositorymims-harvard/tooluniverse

What it does

Investigate protein structures and complexes via folding predictions, domain mapping, binding-site analysis, and structural comparison across homologs.

Files

SKILL.mdMarkdownGitHub ↗

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.

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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

ToolMistakeCorrect
alphafold_get_prediction/summaryuniprot_idqualifier
GPCRdb_get_proteingene_nameprotein
PDBeSIFTS_get_best_structuresgene symboluniprot_id (e.g., "P04637")
Foldseek_search_structuremode="3diaa"mode="tmalign"
SAbDab_search_structuresnamequery or antigen
RCSB_get_chemical_componentligand_idcomp_id

---

Evidence Grading

TierConfidence
T1Co-crystal (<2.5A) + binding affinity data
T2Experimental structure + computational prediction
T3AlphaFold + pocket analysis + known ligand analogs
T4Homology model or low-resolution only

Interpretation

MetricHighAcceptableCaution
Resolution<2.0A (X-ray) / <3.0A (cryo-EM)2.0-2.5A / 3.0-4.0A>3.0A / >4.5A
R-free<0.250.25-0.30>0.30
AlphaFold pLDDT>9070-90<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

Related skills

Data Science & MLpipelinesanalyticsetl

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