alphafold-database

Files (2)

• SKILL.md
• api_reference.md · references/

SKILL.md (first lines)

# AlphaFold Database

## Overview

AlphaFold DB is a public repository of AI-predicted 3D protein structures for over 200 million proteins, maintained by DeepMind and EMBL-EBI. Access structure predictions with confidence metrics, download coordinate files, retrieve bulk datasets, and integrate predictions into computational workflows.

## When to Use This Skill

This skill should be used when working with AI-predicted protein structures in scenarios such as:

- Retrieving protein structure predictions by UniProt ID or protein name
- Downloading PDB/mmCIF coordinate files for structural analysis
- Analyzing prediction confidence metrics (pLDDT, PAE) to assess reliability
- Accessing bulk proteome datasets via Google Cloud Platform
- Comparing predicted structures with experimental data
- Performing structure-based drug discovery or protein engineering
- Building structural models for proteins lacking experimental structures
- Integrating AlphaFold predictions into computational pipelines

## Core Capabilities

### 1. Searching and Retrieving Predictions

**Using Biopython (Recommended):**

The Biopython library provides the simplest interface for retrieving AlphaFold structures:

```python
from Bio.PDB import alphafold_db

# Get all predictions for a UniProt accession
predictions = list(alphafold_db.get_predictions("P00520"))

# Download structure file (mmCIF format)
for prediction in predictions:
    cif_file = alphafold_db.download_cif_for(prediction, directory="./structures")
    print(f"Downloaded: {cif_file}")

# Get Structure objects directly
from Bio.PDB import MMCIFParser
structures = list(alphafold_db.get_structural_models_for("P00520"))
```

**Direct API Access:**

Query predictions using REST endpoints:

```python
import requests

# Get prediction metadata for a UniProt accession
uniprot_id = "P00520"
api_url = f"https://alphafold.ebi.ac.uk/api/prediction/{uniprot_id}"
response = requests.get(api_url)
prediction_data = response.json()

# Extract AlphaFold ID
alphafold_id = prediction_data[0]['entryId']
print(f"AlphaFold ID: {alphafold_id}")
```

**Using UniProt to Find Accessions:**

Search UniProt to find protein accessions first:

```python
import urllib.parse, urllib.request

def get_uniprot_ids(query, query_type='PDB_ID'):
    """Query UniProt to get accession IDs"""
    url = 'https://www.uniprot.org/uploadlists/'
    params = {
        'from': query_type,
        'to': 'ACC',
        'format': 'txt',
        'query': query
    }
    data = urllib.parse.urlencode(params).encode('ascii')
    with urllib.request.urlopen(urllib.request.Request(url, data)) as response:
        return response.read().decode('utf-8').splitlines()

# Example: Find UniProt IDs for a protein name
protein_ids = get_uniprot_ids("hemoglobin", query_type="GENE_NAME")
```

### 2. Downloading Structure Files

AlphaFold provides multiple file formats for each prediction:

**File Types Available:**

- **Model coordinates** (`model_v4.cif`): Atomic coordinates in mmCIF/PDBx format
- **Confidence scores** (`confidence_v4.json`): Per-residue pLDDT scores (0-100)
- **Predicted Aligned Error** (`predicted_aligned_error_v4.json`): PAE matrix for residue pair confidence

**Download URLs:**

```python
import requests

alphafold_id = "AF-P00520-F1"
version = "v4"

# Model coordinates (mmCIF)
model_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-model_{version}.cif"
response = requests.get(model_url)
with open(f"{alphafold_id}.cif", "w") as f:
    f.write(response.text)

# Confidence scores (JSON)
confidence_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-confidence_{version}.json"
response = requests.get(confidence_url)
confidence_data = response.json()

# Predicted Aligned Error (JSON)
pae_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-predicted_aligned_error_{version}.json"
response = requests.get(pae_url)
pae_data = response.json()
```

From agent-skills-hub/agent-skills-hub, MIT.

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