tooluniverse-structural-proteomics

What this skill does

# 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.

---

## 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