query-alphafold

name	query-alphafold
description	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".

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
# take the URL from the API: AlphaFold DB serves only the latest version per entry,
# so a filename built from a pinned version number 404s after every release
def download_structure(uniprot_id, output_dir="/workspace/group", fmt="pdb"):
    entry = get_alphafold_prediction(uniprot_id)[0]
    url = entry[{"pdb": "pdbUrl", "cif": "cifUrl", "bcif": "bcifUrl"}[fmt]]
    r = requests.get(url)
    r.raise_for_status()
    filepath = f"{output_dir}/{url.rsplit('/', 1)[-1]}"
    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:
        entry = data[0]
        return {"cif_url": entry.get("cifUrl", ""), "pae_url": entry.get("paeDocUrl", ""), "data": entry}
    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 (mean pLDDT): {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}.json?type=MUTAGEN	Per-residue AlphaMissense annotations. Both the .json suffix and type are required; MUTAGEN is the only value the schema accepts

Download Formats

Read pdbUrl, cifUrl, or bcifUrl off the prediction response rather than building a filename. AlphaFold DB serves only the latest version per entry -- AF-{UNIPROT_ID}-F1-model_v6.* today, with v4 and v5 both 404 -- so any pinned version breaks at the next release.

• PAE image: paeImageUrl; PAE matrix JSON: paeDocUrl

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?"