Query alphafold
Skill BioTender-max/awesome-bio-agent-skills/skills/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".From its SKILL.md
npx -y skills add BioTender-max/awesome-bio-agent-skills --skill query-alphafoldAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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SKILL.md
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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 | /api/prediction/{uniprot_id} | Get model info & download URLs |
| Summary | /api/uniprot/summary/{uniprot_id}.json | Brief summary |
| Annotations | /api/annotations/{uniprot_id} | 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?"
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.