TO

tooluniverse-protein-structure-retrieval

Fetch and evaluate protein structures from major databases using experimental resolution or pLDDT quality metrics.

Install

mkdir -p .claude/skills/tooluniverse-protein-structure-retrieval && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/6365" && unzip -o skill.zip -d .claude/skills/tooluniverse-protein-structure-retrieval && rm skill.zip

Installs to .claude/skills/tooluniverse-protein-structure-retrieval

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Protein structure retrieval from RCSB PDB, PDBe, and AlphaFold with disambiguation, quality assessment (resolution, R-factor, pLDDT), and metadata. Distinguishes high-quality experimental (X-ray under 2 Angstrom) vs predicted vs medium-quality structures. Use for fetching protein structures, structure-quality comparison, and selecting structures for drug design or modeling.
376 chars✓ has a “when” triggerlonger than Claude Code's old 250-char listing cap (fine on current versions)
Advanced

Key capabilities

  • Disambiguate proteins by organism
  • Fetch structure metadata from RCSB/PDBe
  • Assess structural quality via resolution or pLDDT
  • Retrieve ligand information
  • Compare experimental and predicted models

How it works

It queries standardized biological databases (RCSB, PDBe, AlphaFold) and filters results based on defined quality metrics (resolution/pLDDT).

Inputs & outputs

You give it
Protein name, UniProt ID, or PDB ID
You get back
Protein structural profile and quality report

When to use tooluniverse-protein-structure-retrieval

  • Select high-resolution structures for drug design
  • Compare experimental vs. predicted protein models
  • Retrieve ligand information for specific protein IDs
  • Validate structural quality using pLDDT or resolution data

About this skill

Protein Structure Data Retrieval

Retrieve protein structures with disambiguation, quality assessment, and comprehensive metadata.

IMPORTANT: Always use English terms in tool calls. Respond in the user's language.

LOOK UP DON'T GUESS: Never assume PDB IDs, resolution, or availability. Always query RCSB/PDBe and AlphaFold to confirm.

Domain Reasoning

Not all structures are equal. X-ray <2 A is high-quality for drug design. Cryo-EM 3-4 A is good for fold but not side chains. AlphaFold is excellent for well-folded domains but unreliable for disordered regions. Always check pLDDT (AlphaFold) or resolution (experimental) before drawing conclusions.

Workflow

Phase 0: Clarify (if needed) → Phase 1: Disambiguate Protein → Phase 2: Retrieve Structures → Phase 3: Report

Phase 0: Clarification (When Needed)

Ask ONLY if: protein name ambiguous (e.g., "kinase"), organism not specified, unclear if experimental vs AlphaFold needed. Skip for: specific PDB IDs, UniProt accessions, unambiguous protein+organism.


Phase 1: Protein Disambiguation

# By PDB ID: direct retrieval
# By UniProt: get AlphaFold + search experimental structures
af_structure = tu.tools.alphafold_get_prediction(uniprot_id=uniprot_id)
# By protein name: search
result = tu.tools.PDBeSearch_search_structures(protein_name=protein_name)

Identity Checklist

  • Protein name/gene identified, organism confirmed
  • UniProt accession (if available), isoform/variant specified (if relevant)

Phase 2: Data Retrieval (Internal)

Retrieve silently. Do NOT narrate the process.

pdb_id = "4INS"

# Search, metadata, quality, ligands, similar structures
result = tu.tools.PDBeSearch_search_structures(protein_name=name)
metadata = tu.tools.get_protein_metadata_by_pdb_id(pdb_id=pdb_id)
exp = tu.tools.RCSBData_get_entry(pdb_id=pdb_id)
quality = tu.tools.PDBeValidation_get_quality_scores(pdb_id=pdb_id)
ligands = tu.tools.PDBe_KB_get_ligand_sites(pdb_id=pdb_id)
similar = tu.tools.PDBeSIFTS_get_all_structures(pdb_id=pdb_id, cutoff=2.0)

# PDBe additional data
summary = tu.tools.pdbe_get_entry_summary(pdb_id=pdb_id)
molecules = tu.tools.pdbe_get_entry_molecules(pdb_id=pdb_id)

# AlphaFold (when no experimental structure, or for comparison)
af = tu.tools.alphafold_get_prediction(uniprot_id=uniprot_id)

Fallback Chains

PrimaryFallback
RCSB searchPDBe search
get_protein_metadatapdbe_get_entry_summary
Experimental structureAlphaFold prediction
get_protein_ligandsPDBe_KB_get_ligand_sites

Phase 3: Report Structure Profile

Present as a Structure Profile Report. Hide search process. Include:

  1. Search Summary: query, organism, experimental + AlphaFold structure counts
  2. Best Structure: PDB ID, UniProt, organism, method, resolution, date, quality assessment
  3. Experimental Details: method, resolution, R-factor, R-free, space group
  4. Composition: chains, residues (coverage%), ligands, waters, metals
  5. Bound Ligands: ligand ID, name, type, binding site
  6. Binding Site Details (for drug discovery): location, key residues, druggability
  7. Alternative Structures: ranked by quality with resolution, method, ligands
  8. AlphaFold Prediction: UniProt, model version, pLDDT confidence distribution, use cases
  9. Structure Comparison: resolution, completeness, ligands across structures
  10. Download Links: PDB/mmCIF/AlphaFold formats, database URLs

Quality Assessment

Experimental Structures

TierCriteria
ExcellentX-ray <1.5A, complete, R-free <0.22
HighX-ray <2.0A OR Cryo-EM <3.0A
GoodX-ray 2.0-3.0A OR Cryo-EM 3.0-4.0A
ModerateX-ray >3.0A OR NMR ensemble
Low>4.0A, incomplete, or problematic

Resolution Use Cases

<1.5A: atomic detail, H-bond analysis. 1.5-2.0A: drug design. 2.0-2.5A: structure-based design. 2.5-3.5A: overall architecture. >3.5A: domain arrangement only.

AlphaFold Confidence (pLDDT)

90: very high, experimental-like. 70-90: good backbone. 50-70: uncertain/flexible. <50: likely disordered.


Error Handling

ErrorResponse
"PDB ID not found"Verify 4-char format, check if obsoleted
"No structures"Offer AlphaFold, suggest similar proteins
"Download failed"Retry once, provide alternative link
"Resolution unavailable"Likely NMR/model, note in assessment

Tool Reference

RCSB PDB: PDBeSearch_search_structures (search), get_protein_metadata_by_pdb_id (basic info), RCSBData_get_entry (details), PDBeValidation_get_quality_scores (quality), PDBe_KB_get_ligand_sites (ligands), PDBeSIFTS_get_all_structures (homologs)

PDBe: pdbe_get_entry_summary (overview), pdbe_get_entry_molecules (entities), pdbe_get_entry_experiment (experimental), PDBe_KB_get_ligand_sites (pockets)

AlphaFold: alphafold_get_prediction (get prediction), alphafold_get_summary (search)

When not to use it

  • Analyzing non-protein biological data
  • When structure files are too large for memory

Limitations

  • Does not verify experimental method validity
  • Depends on availability in public databases

How it compares

It enforces strict biological quality checks before data retrieval, preventing the use of low-resolution or inappropriate structures.

Compared to similar skills

tooluniverse-protein-structure-retrieval side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
tooluniverse-protein-structure-retrieval (this skill)12moNo flagsAdvanced
torchdrug37moReviewAdvanced
string-database27moReviewIntermediate
transformer-lens-interpretability27moReviewAdvanced

Try saying

Example prompts that trigger this skill in your AI assistant.

More by mims-harvard

View all by mims-harvard

tooluniverse-drug-research

mims-harvard

Generates comprehensive drug research reports with compound disambiguation, evidence grading, and mandatory completeness sections. Covers identity, chemistry, pharmacology, targets, clinical trials, safety, pharmacogenomics, and ADMET properties. Use when users ask about drugs, medications, therapeutics, or need drug profiling, safety assessment, or clinical development research.

323

tooluniverse-pharmacovigilance

mims-harvard

Analyze drug safety signals from FDA adverse event reports, label warnings, and pharmacogenomic data. Calculates disproportionality measures (PRR, ROR), identifies serious adverse events, assesses pharmacogenomic risk variants. Use when asked about drug safety, adverse events, post-market surveillance, or risk-benefit assessment.

323

tooluniverse-precision-oncology

mims-harvard

Provide actionable treatment recommendations for cancer patients based on molecular profile. Interprets tumor mutations, identifies FDA-approved therapies, finds resistance mechanisms, matches clinical trials. Use when oncologist asks about treatment options for specific mutations (EGFR, KRAS, BRAF, etc.), therapy resistance, or clinical trial eligibility.

321

tooluniverse-expression-data-retrieval

mims-harvard

Retrieves gene expression and omics datasets from ArrayExpress and BioStudies with gene disambiguation, experiment quality assessment, and structured reports. Creates comprehensive dataset profiles with metadata, sample information, and download links. Use when users need expression data, omics datasets, or mention ArrayExpress (E-MTAB, E-GEOD) or BioStudies (S-BSST) accessions.

217

tooluniverse-literature-deep-research

mims-harvard

Conduct comprehensive literature research with target disambiguation, evidence grading, and structured theme extraction. Creates a detailed report with mandatory completeness checklist, biological model synthesis, and testable hypotheses. For biological targets, resolves official IDs (Ensembl/UniProt), synonyms, naming collisions, and gathers expression/pathway context before literature search. Default deliverable is a report file; for single factoid questions, uses a fast verification mode and may include an inline answer. Use when users need thorough literature reviews, target profiles, or to verify specific claims from the literature.

213

tooluniverse-target-research

mims-harvard

Gather comprehensive biological target intelligence from 9 parallel research paths covering protein info, structure, interactions, pathways, expression, variants, drug interactions, and literature. Features collision-aware searches, evidence grading (T1-T4), explicit Open Targets coverage, and mandatory completeness auditing. Use when users ask about drug targets, proteins, genes, or need target validation, druggability assessment, or comprehensive target profiling.

25

You might also like

torchdrug

davila7

Graph-based drug discovery toolkit. Molecular property prediction (ADMET), protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis, GNNs (GIN, GAT, SchNet), 40+ datasets, for PyTorch-based ML on molecules, proteins, and biomedical graphs.

326

string-database

davila7

Query STRING API for protein-protein interactions (59M proteins, 20B interactions). Network analysis, GO/KEGG enrichment, interaction discovery, 5000+ species, for systems biology.

217

transformer-lens-interpretability

davila7

Provides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.

215

denario

davila7

Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.

213

tooluniverse

jimmc414

Use this skill when working with scientific research tools and workflows across bioinformatics, cheminformatics, genomics, structural biology, proteomics, and drug discovery. This skill provides access to 600+ scientific tools including machine learning models, datasets, APIs, and analysis packages. Use when searching for scientific tools, executing computational biology workflows, composing multi-step research pipelines, accessing databases like OpenTargets/PubChem/UniProt/PDB/ChEMBL, performing tool discovery for research tasks, or integrating scientific computational resources into LLM workflows.

36

tooluniverse-binder-discovery

mims-harvard

Discover novel small molecule binders for protein targets using structure-based and ligand-based approaches. Creates actionable reports with candidate compounds, ADMET profiles, and synthesis feasibility. Use when users ask to find small molecules for a target, identify novel binders, perform virtual screening, or need hit-to-lead compound identification.

17

Search skills

Search the agent skills registry