Agent SkillsRunchuan-BU/BioClaw › query-alphafold

query-alphafold

GitHub

查询AlphaFold蛋白质结构预测,获取3D结构、PDB/CIF文件及置信度分数。

container/skills/query-alphafold/SKILL.md Runchuan-BU/BioClaw

Trigger Scenarios

alphafold protein structure 3D structure folding pLDDT structure prediction

Install

npx skills add Runchuan-BU/BioClaw --skill query-alphafold -g -y
More Options

Non-standard path

npx skills add https://github.com/Runchuan-BU/BioClaw/tree/main/container/skills/query-alphafold -g -y

Use without installing

npx skills use Runchuan-BU/BioClaw@query-alphafold

指定 Agent (Claude Code)

npx skills add Runchuan-BU/BioClaw --skill query-alphafold -a claude-code -g -y

安装 repo 全部 skill

npx skills add Runchuan-BU/BioClaw --all -g -y

预览 repo 内 skill

npx skills add Runchuan-BU/BioClaw --list

SKILL.md

Frontmatter
{
    "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:
        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?"

Version History

  • a79b8c4 Current 2026-07-25 11:44

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Metadata

Files
0
Version
a79b8c4
Hash
a9bb2b81
Indexed
2026-07-25 11:44

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