AlphaFold Structure Database Query

SkillAI & models

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".

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the AlphaFold Structure Database Query skill

What this skill tells your AI

The instructions your AI receives, as published by biotender-max/awesome-bio-agent-skills in skills/bioclaw/query-alphafold/SKILL.md and read by ahel’s review.

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

EndpointURLUse
Prediction/api/prediction/{uniprot_id}Get model info & download URLs
Summary/api/uniprot/summary/{uniprot_id}.jsonBrief 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?"

Signals

GitHub stars
178
Forks
32
Last commit
Jul 2026
Advanced
Catalog kind
skill
Gateway key
query-alphafold
Source
github.com/biotender-max/awesome-bio-agent-skills