Tooluniverse Protein Structure Retrieval logo

Tooluniverse Protein Structure Retrieval

CommunityPopular
wu-yc
tooluniverse-protein-structure-retrieval

Retrieves protein structure data from RCSB PDB, PDBe, and AlphaFold with protein disambiguation, quality assessment, and comprehensive structural profiles. Creates detailed structure reports with experimental metadata, ligand information, and download links. Use when users need protein structures, 3D models, crystallography data, or mention PDB IDs (4-character codes like 1ABC) or UniProt accessions.

Overview

Publisherwu-yc
RepositoryLabClaw
Skill nametooluniverse-protein-structure-retrieval
Stars
1.1K
Forks
161
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by wu-yc on GitHub. Read the source before you install it.

Installation

Install the Tooluniverse Protein Structure Retrieval AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/wu-yc/LabClaw/tree/main/skills/bio/tooluniverse-protein-structure-retrieval
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/wu-yc/LabClaw.git /tmp/LabClaw
mkdir -p .claude/skills
cp -r /tmp/LabClaw/skills/bio/tooluniverse-protein-structure-retrieval .claude/skills/tooluniverse-protein-structure-retrieval
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Tooluniverse Protein Structure Retrieval in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Tooluniverse Protein Structure Retrieval on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Tooluniverse Protein Structure Retrieval is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Protein Structure Data Retrieval

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

IMPORTANT: Always use English terms in tool calls (protein names, organism names), even if the user writes in another language. Only try original-language terms as a fallback if English returns no results. Respond in the user's language.

Workflow Overview

Phase 0: Clarify (if needed)
    ↓
Phase 1: Disambiguate Protein Identity
    ↓
Phase 2: Retrieve Structures (Internal)
    ↓
Phase 3: Report Structure Profile

Phase 0: Clarification (When Needed)

Ask the user ONLY if:

  • Protein name matches multiple genes/families (e.g., "kinase" → which kinase?)
  • Organism not specified for conserved proteins
  • Intent unclear: need experimental structure vs AlphaFold prediction?

Skip clarification for:

  • Specific PDB IDs (4-character codes)
  • UniProt accessions
  • Unambiguous protein names with organism

Phase 1: Protein Disambiguation

1.1 Resolve Protein Identity

python
from tooluniverse import ToolUniverse
tu = ToolUniverse()
tu.load_tools()

# Strategy depends on input type
if user_provided_pdb_id:
    # Direct structure retrieval
    pdb_id = user_provided_pdb_id.upper()
    
elif user_provided_uniprot:
    # Get UniProt info, then search structures
    uniprot_id = user_provided_uniprot
    # Can also get AlphaFold structure
    af_structure = tu.tools.alphafold_get_structure_by_uniprot(
        uniprot_id=uniprot_id
    )
    
elif user_provided_protein_name:
    # Search by name
    result = tu.tools.search_structures_by_protein_name(
        protein_name=protein_name
    )

1.2 Identity Resolution Checklist

  • Protein name/gene identified
  • Organism confirmed
  • UniProt accession (if available)
  • Isoform/variant specified (if relevant)

1.3 Handle Naming Collisions

Common ambiguous terms:

TermAmbiguityResolution
"kinase"Hundreds of kinasesAsk which kinase (EGFR, CDK2, etc.)
"receptor"Many receptor typesSpecify receptor family
"protease"Multiple familiesAsk serine/cysteine/metallo/etc.
"hemoglobin"ClearProceed (α/β chain specified if needed)
"insulin"ClearProceed

Phase 2: Data Retrieval (Internal)

Retrieve all data silently. Do NOT narrate the search process.

2.1 Search Structures

python
# Search by protein name
result = tu.tools.search_structures_by_protein_name(
    protein_name=protein_name
)

# Filter results by quality
high_res = [
    entry for entry in result["data"]
    if entry.get("resolution") and entry["resolution"] < 2.5
]

2.2 Get Structure Details

For each relevant structure:

python
pdb_id = "4INS"

# Basic metadata
metadata = tu.tools.get_protein_metadata_by_pdb_id(pdb_id=pdb_id)

# Experimental details
exp_details = tu.tools.get_protein_experimental_details_by_pdb_id(
    pdb_id=pdb_id
)

# Resolution (if X-ray)
resolution = tu.tools.get_protein_resolution_by_pdb_id(pdb_id=pdb_id)

# Bound ligands
ligands = tu.tools.get_protein_ligands_by_pdb_id(pdb_id=pdb_id)

# Similar structures
similar = tu.tools.get_similar_structures_by_pdb_id(
    pdb_id=pdb_id,
    cutoff=2.0
)

2.3 PDBe Additional Data

python
# Entry summary
summary = tu.tools.pdbe_get_entry_summary(pdb_id=pdb_id)

# Molecular entities
molecules = tu.tools.pdbe_get_molecules(pdb_id=pdb_id)

# Binding sites
binding_sites = tu.tools.pdbe_get_binding_sites(pdb_id=pdb_id)

2.4 AlphaFold Predictions

python
# When no experimental structure exists, or for comparison
if uniprot_id:
    af_structure = tu.tools.alphafold_get_structure_by_uniprot(
        uniprot_id=uniprot_id
    )

Fallback Chains

PrimaryFallbackNotes
RCSB searchPDBe searchRegional availability
get_protein_metadatapdbe_get_entry_summaryAlternative source
Experimental structureAlphaFold predictionNo experimental structure
get_protein_ligandspdbe_get_binding_sitesLigand info unavailable

Phase 3: Report Structure Profile

Output Structure

Present as a Structure Profile Report. Hide search process.

markdown
# Protein Structure Profile: [Protein Name]

**Search Summary**
- Query: [protein name/PDB ID]
- Organism: [species]
- Structures Found: [N] experimental, [M] AlphaFold

---

## Best Available Structure

### [PDB ID]: [Title]

| Attribute | Value |
|-----------|-------|
| **PDB ID** | [pdb_id] |
| **UniProt** | [uniprot_id] |
| **Organism** | [species] |
| **Method** | X-ray / Cryo-EM / NMR |
| **Resolution** | [X.XX] Å |
| **Release Date** | [date] |

**Quality Assessment**: ●●● High / ●●○ Medium / ●○○ Low

### Experimental Details
| Parameter | Value |
|-----------|-------|
| **Method** | [X-ray crystallography] |
| **Resolution** | [1.9 Å] |
| **R-factor** | [0.18] |
| **R-free** | [0.21] |
| **Space Group** | [P 21 21 21] |

### Structure Composition
| Component | Count | Details |
|-----------|-------|---------|
| **Chains** | [N] | [A (enzyme), B (inhibitor)] |
| **Residues** | [N] | [coverage %] |
| **Ligands** | [N] | [list ligand names] |
| **Waters** | [N] | |
| **Metals** | [N] | [Zn, Mg, etc.] |

### Bound Ligands
| Ligand ID | Name | Type | Binding Site |
|-----------|------|------|--------------|
| [ATP] | Adenosine triphosphate | Substrate | Active site |
| [MG] | Magnesium ion | Cofactor | Catalytic |

### Binding Site Details
For drug discovery applications:

**Site 1: Active Site**
- Location: Chain A, residues 45-89
- Key residues: Asp45, Glu67, His89
- Pocket volume: [X] ų
- Druggability: High/Medium/Low

---

## Alternative Structures

Ranked by quality and relevance:

| Rank | PDB ID | Resolution | Method | Ligands | Notes |
|------|--------|------------|--------|---------|-------|
| 1 | [4INS] | 1.9 Å | X-ray | Zn | Best resolution |
| 2 | [3I40] | 2.1 Å | X-ray | Zn, phenol | With inhibitor |
| 3 | [1TRZ] | 2.3 Å | X-ray | None | Porcine |

---

## AlphaFold Prediction

### AF-[UniProt]-F1

| Attribute | Value |
|-----------|-------|
| **UniProt** | [uniprot_id] |
| **Model Version** | [v4] |
| **Confidence (pLDDT)** | [average score] |

**Confidence Distribution**:
- Very High (>90): [X]% of residues
- High (70-90): [X]% of residues
- Low (50-70): [X]% of residues
- Very Low (<50): [X]% of residues

**Use Cases**:
- ✓ Overall fold reliable
- ✓ Core domain structure
- ⚠ Loop regions uncertain
- ✗ Not suitable for binding site analysis

---

## Structure Comparison

| Property | [PDB_1] | [PDB_2] | AlphaFold |
|----------|---------|---------|-----------|
| Resolution | 1.9 Å | 2.5 Å | N/A (predicted) |
| Completeness | 98% | 85% | 100% |
| Ligands | Yes | No | No |
| Confidence | Experimental | Experimental | High (85 avg) |

---

## Download Links

### Coordinate Files
| Format | PDB ID | Link |
|--------|--------|------|
| PDB | [4INS] | [link] |
| mmCIF | [4INS] | [link] |
| AlphaFold | [UniProt] | [link] |

### Database Links
- RCSB PDB: https://www.rcsb.org/structure/[pdb_id]
- PDBe: https://www.ebi.ac.uk/pdbe/entry/pdb/[pdb_id]
- AlphaFold: https://alphafold.ebi.ac.uk/entry/[uniprot_id]

Retrieved: [date]

Quality Assessment Tiers

Experimental Structures

TierSymbolCriteria
Excellent●●●●X-ray <1.5Å, complete, R-free <0.22
High●●●○X-ray <2.0Å OR Cryo-EM <3.0Å
Good●●○○X-ray 2.0-3.0Å OR Cryo-EM 3.0-4.0Å
Moderate●○○○X-ray >3.0Å OR NMR ensemble
Low○○○○>4.0Å, incomplete, or problematic

Resolution Guide

ResolutionUse Case
<1.5 ÅAtomic detail, H-bond analysis
1.5-2.0 ÅDrug design, mechanism studies
2.0-2.5 ÅStructure-based design
2.5-3.5 ÅOverall architecture, fold
>3.5 ÅDomain arrangement only

AlphaFold Confidence

pLDDT ScoreInterpretation
>90Very high confidence, experimental-like
70-90Good backbone confidence
50-70Uncertain, flexible regions
<50Low confidence, likely disordered

Completeness Checklist

Every structure report MUST include:

For Specific PDB ID (Required)

  • PDB ID and title
  • Experimental method
  • Resolution (or N/A for NMR)
  • Organism
  • Quality assessment
  • Download links

For Protein Name Search (Required)

  • Search summary with result count
  • Top structures with quality ranking
  • Best structure recommendation
  • AlphaFold alternative (if no experimental structure)

Always Include

  • Ligand information (or "No ligands bound")
  • Data sources with links
  • Retrieval date

Common Use Cases

Drug Discovery Target

User: "Get structure for EGFR kinase with inhibitor" → Filter for ligand-bound structures, emphasize binding site

Model Building

User: "Find best template for homology modeling of protein X" → High-resolution structures, note sequence coverage

Structure Comparison

User: "Compare available SARS-CoV-2 main protease structures" → All structures with systematic comparison table

AlphaFold When No Experimental

User: "Structure of protein with UniProt P12345" → Check PDB first, then AlphaFold, note confidence


Error Handling

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

Tool Reference

RCSB PDB (Experimental Structures)

ToolPurpose
search_structures_by_protein_nameName-based search
get_protein_metadata_by_pdb_idBasic info
get_protein_experimental_details_by_pdb_idMethod details
get_protein_resolution_by_pdb_idQuality metric
get_protein_ligands_by_pdb_idBound molecules
download_pdb_structure_fileCoordinate files
get_similar_structures_by_pdb_idHomologs

PDBe (European PDB)

ToolPurpose
pdbe_get_entry_summaryOverview
pdbe_get_moleculesMolecular entities
pdbe_get_experiment_infoExperimental data
pdbe_get_binding_sitesLigand pockets

AlphaFold (Predictions)

ToolPurpose
alphafold_get_structure_by_uniprotGet prediction
alphafold_search_structuresSearch predictions

Frequently asked questions

What does the Tooluniverse Protein Structure Retrieval AI skill do?

Retrieves protein structure data from RCSB PDB, PDBe, and AlphaFold with protein disambiguation, quality assessment, and comprehensive structural profiles. Creates detailed structure reports with experimental metadata, ligand information, and download links. Use when users need protein structures, 3D models, crystallography data, or mention PDB IDs (4-character codes like 1ABC) or UniProt accessions.

Why use Tooluniverse Protein Structure Retrieval on TypingMind?

Because you install it once and use it with any model. Tooluniverse Protein Structure Retrieval is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Tooluniverse Protein Structure Retrieval in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wu-yc/LabClaw/tree/main/skills/bio/tooluniverse-protein-structure-retrieval. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Tooluniverse Protein Structure Retrieval?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Tooluniverse Protein Structure Retrieval?

As many as you like. As long as a model supports skills, you can use Tooluniverse Protein Structure Retrieval with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Tooluniverse Protein Structure Retrieval AI skill free?

It is published on GitHub by wu-yc. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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