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
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:
        entry = data[0]
        # AlphaFold EBI API sunset the old camelCase fields on 2026-06-25;
        # prefer the new snake_case fields, fall back to the old names in
        # case a cached/mirrored endpoint still serves the pre-sunset schema.
        cif_url = entry.get("cif_url") or entry.get("cifUrl", "")
        pae_url = entry.get("pae_url") or entry.get("paeDocUrl", "")
        return {"cif_url": cif_url, "pae_url": pae_url, "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('uniprot_accession') or entry.get('uniprotAccession')}")
    print(f"Gene: {entry.get('gene', 'N/A')}")
    print(f"Organism: {entry.get('organism_scientific_name') or entry.get('organismScientificName', 'N/A')}")
    mean_plddt = entry.get('mean_plddt')
    if mean_plddt is None:
        mean_plddt = entry.get('globalMetricValue', 'N/A')
    print(f"Model confidence (mean pLDDT): {mean_plddt}")
    print(f"PDB URL: {entry.get('pdb_url') or entry.get('pdbUrl', 'N/A')}")
    print(f"CIF URL: {entry.get('cif_url') or 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?"