BioClaw query-alphafold
Query AlphaFold protein structure predictions. Use when user asks about protein structure, 3D structure, protein folding, or structure prediction. Triggers on "alphafold", "protein structure", "3D structure", "folding", "pLDDT", "structure prediction".
install
source · Clone the upstream repo
git clone https://github.com/Runchuan-BU/BioClaw
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/Runchuan-BU/BioClaw "$T" && mkdir -p ~/.claude/skills && cp -r "$T/container/skills/query-alphafold" ~/.claude/skills/runchuan-bu-bioclaw-query-alphafold && rm -rf "$T"
manifest:
container/skills/query-alphafold/SKILL.mdsource content
AlphaFold Structure Database Query
Query the AlphaFold EBI API for predicted protein structures.
When to Use
- User asks about a protein's predicted 3D structure
- User wants to download PDB/CIF structure files
- User asks about structure confidence (pLDDT scores)
- User wants to visualize protein structure
How to Execute
import requests import json BASE_URL = "https://alphafold.ebi.ac.uk/api" # 1. Get prediction info def get_alphafold_prediction(uniprot_id): url = f"{BASE_URL}/prediction/{uniprot_id}" r = requests.get(url) r.raise_for_status() return r.json() # 2. Download structure file def download_structure(uniprot_id, output_dir="/workspace/group", fmt="pdb", version="v4"): filename = f"AF-{uniprot_id}-F1-model_{version}.{fmt}" url = f"https://alphafold.ebi.ac.uk/files/{filename}" r = requests.get(url) r.raise_for_status() filepath = f"{output_dir}/{filename}" with open(filepath, 'wb') as f: f.write(r.content) return filepath # 3. Get per-residue confidence (pLDDT) def get_plddt(uniprot_id): url = f"{BASE_URL}/prediction/{uniprot_id}" r = requests.get(url) data = r.json() if isinstance(data, list) and data: cif_url = data[0].get("cifUrl", "") plddt_url = data[0].get("paeImageUrl", "") return {"cifUrl": cif_url, "paeImageUrl": plddt_url, "data": data[0]} return data # Example data = get_alphafold_prediction("P04637") # TP53 if isinstance(data, list) and data: entry = data[0] print(f"UniProt: {entry.get('uniprotAccession')}") print(f"Gene: {entry.get('gene', 'N/A')}") print(f"Organism: {entry.get('organismScientificName', 'N/A')}") print(f"Model confidence: {entry.get('globalMetricValue', 'N/A')}") print(f"PDB URL: {entry.get('pdbUrl', 'N/A')}") print(f"CIF URL: {entry.get('cifUrl', 'N/A')}")
Endpoints
| Endpoint | URL | Use |
|---|---|---|
| Prediction | | Get model info & download URLs |
| Summary | | Brief summary |
| Annotations | | Per-residue annotations |
Download Formats
- PDB:
AF-{UNIPROT_ID}-F1-model_v4.pdb - CIF:
AF-{UNIPROT_ID}-F1-model_v4.cif - PAE image: Available from prediction endpoint
Follow-up Suggestions
- "Want me to analyze the structure confidence by region?"
- "Should I compare this to the experimental PDB structure?"
- "Want me to identify disordered regions?"