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tooluniverse-structural-proteomics

Structural biology plus proteomics integration for drug target validation. Combines PDB experimental structures, AlphaFold predictions, GPCRdb, SAbDab antibody structures, ProteinsPlus binding-site prediction, BindingDB ligand-affinity data, BMRB NMR data, CATH/InterPro fold classification, intrinsic-disorder prediction (MobiDB/DisProt/IUPred3), membrane-protein topology (OPM/TopDB/PDBTM/ChannelsDB), enzyme catalytic-site data (M-CSA), PDBe ligand/compound chemistry, and MD simulation trajectory metadata (MDDB). Use for druggability assessment, binding-site characterization, ligand-pocket analysis, structural-confidence scoring (resolution, pLDDT), antibody-target interface analysis, disordered-region assessment, membrane-protein topology, catalytic-mechanism lookup, and finding published MD simulations for a target.

plugins/tooluniverse/skills/tooluniverse-structural-proteomics/SKILL.md

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Repository stars (not skill ratings): 1,778
Repository last updated: Nov 8, 2026
License: Apache-2.0

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# 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`
- Protein-protein binding mutations (ddG): `SKEMPI_search_by_structure`/`SKEMPI_search_by_protein`/`SKEMPI_get_mutation` -- BindingDB's protein-protein equivalent, for interface-mutation affinity effects rather than small-molecule ligands
- 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.