Alphafold Database

1. Searching and Retrieving Predictions

Using Biopython (Recommended):

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

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:

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:

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:

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()

PDB Format (Alternative):

# Download as PDB format instead of mmCIF
pdb_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-model_{version}.pdb"
response = requests.get(pdb_url)
with open(f"{alphafold_id}.pdb", "wb") as f:
    f.write(response.content)

3. Working with Confidence Metrics

AlphaFold predictions include confidence estimates critical for interpretation:

pLDDT (per-residue confidence):

import json
import requests

# Load confidence scores
alphafold_id = "AF-P00520-F1"
confidence_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-confidence_v4.json"
confidence = requests.get(confidence_url).json()

# Extract pLDDT scores
plddt_scores = confidence['confidenceScore']

# Interpret confidence levels
# pLDDT > 90: Very high confidence
# pLDDT 70-90: High confidence
# pLDDT 50-70: Low confidence
# pLDDT < 50: Very low confidence

high_confidence_residues = [i for i, score in enumerate(plddt_scores) if score > 90]
print(f"High confidence residues: {len(high_confidence_residues)}/{len(plddt_scores)}")

PAE (Predicted Aligned Error):

PAE indicates confidence in relative domain positions:

import numpy as np
import matplotlib.pyplot as plt

# Load PAE matrix
pae_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-predicted_aligned_error_v4.json"
pae = requests.get(pae_url).json()

# Visualize PAE matrix
pae_matrix = np.array(pae['distance'])
plt.figure(figsize=(10, 8))
plt.imshow(pae_matrix, cmap='viridis_r', vmin=0, vmax=30)
plt.colorbar(label='PAE (Å)')
plt.title(f'Predicted Aligned Error: {alphafold_id}')
plt.xlabel('Residue')
plt.ylabel('Residue')
plt.savefig(f'{alphafold_id}_pae.png', dpi=300, bbox_inches='tight')

# Low PAE values (<5 Å) indicate confident relative positioning
# High PAE values (>15 Å) suggest uncertain domain arrangements

4. Bulk Data Access via Google Cloud

For large-scale analyses, use Google Cloud datasets:

Google Cloud Storage:

# Install gsutil
uv pip install gsutil

# List available data
gsutil ls gs://public-datasets-deepmind-alphafold-v4/

# Download entire proteomes (by taxonomy ID)
gsutil -m cp gs://public-datasets-deepmind-alphafold-v4/proteomes/proteome-tax_id-9606-*.tar .

# Download specific files
gsutil cp gs://public-datasets-deepmind-alphafold-v4/accession_ids.csv .

BigQuery Metadata Access:

from google.cloud import bigquery

# Initialize client
client = bigquery.Client()

# Query metadata
query = """
SELECT
  entryId,
  uniprotAccession,
  organismScientificName,
  globalMetricValue,
  fractionPlddtVeryHigh
FROM `bigquery-public-data.deepmind_alphafold.metadata`
WHERE organismScientificName = 'Homo sapiens'
  AND fractionPlddtVeryHigh > 0.8
LIMIT 100
"""

results = client.query(query).to_dataframe()
print(f"Found {len(results)} high-confidence human proteins")

Download by Species:

⚠️ Security Note: The example below uses shell=True for simplicity. In production environments, prefer using subprocess.run() with a list of arguments to prevent command injection vulnerabilities. See Python subprocess security.

import subprocess
import shlex

def download_proteome(taxonomy_id, output_dir="./proteomes"):
    """Download all AlphaFold predictions for a species"""
    # Validate taxonomy_id is an integer to prevent injection
    if not isinstance(taxonomy_id, int):
        raise ValueError("taxonomy_id must be an integer")
    
    pattern = f"gs://public-datasets-deepmind-alphafold-v4/proteomes/proteome-tax_id-{taxonomy_id}-*_v4.tar"
    # Use list form instead of shell=True for security
    subprocess.run(["gsutil", "-m", "cp", pattern, f"{output_dir}/"], check=True)

# Download E. coli proteome (tax ID: 83333)
download_proteome(83333)

# Download human proteome (tax ID: 9606)
download_proteome(9606)

5. Parsing and Analyzing Structures

Work with downloaded AlphaFold structures using BioPython:

from Bio.PDB import MMCIFParser, PDBIO
import numpy as np

# Parse mmCIF file
parser = MMCIFParser(QUIET=True)
structure = parser.get_structure("protein", "AF-P00520-F1-model_v4.cif")

# Extract coordinates
coords = []
for model in structure:
    for chain in model:
        for residue in chain:
            if 'CA' in residue:  # Alpha carbons only
                coords.append(residue['CA'].get_coord())

coords = np.array(coords)
print(f"Structure has {len(coords)} residues")

# Calculate distances
from scipy.spatial.distance import pdist, squareform
distance_matrix = squareform(pdist(coords))

# Identify contacts (< 8 Å)
contacts = np.where((distance_matrix > 0) & (distance_matrix < 8))
print(f"Number of contacts: {len(contacts[0]) // 2}")

Extract B-factors (pLDDT values):

AlphaFold stores pLDDT scores in the B-factor column:

from Bio.PDB import MMCIFParser

parser = MMCIFParser(QUIET=True)
structure = parser.get_structure("protein", "AF-P00520-F1-model_v4.cif")

# Extract pLDDT from B-factors
plddt_scores = []
for model in structure:
    for chain in model:
        for residue in chain:
            if 'CA' in residue:
                plddt_scores.append(residue['CA'].get_bfactor())

# Identify high-confidence regions
high_conf_regions = [(i, score) for i, score in enumerate(plddt_scores, 1) if score > 90]
print(f"High confidence residues: {len(high_conf_regions)}")

6. Batch Processing Multiple Proteins

Process multiple predictions efficiently:

from Bio.PDB import alphafold_db
import pandas as pd

uniprot_ids = ["P00520", "P12931", "P04637"]  # Multiple proteins
results = []

for uniprot_id in uniprot_ids:
    try:
        # Get prediction
        predictions = list(alphafold_db.get_predictions(uniprot_id))

        if predictions:
            pred = predictions[0]

            # Download structure
            cif_file = alphafold_db.download_cif_for(pred, directory="./batch_structures")

            # Get confidence data
            alphafold_id = pred['entryId']
            conf_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-confidence_v4.json"
            conf_data = requests.get(conf_url).json()

            # Calculate statistics
            plddt_scores = conf_data['confidenceScore']
            avg_plddt = np.mean(plddt_scores)
            high_conf_fraction = sum(1 for s in plddt_scores if s > 90) / len(plddt_scores)

            results.append({
                'uniprot_id': uniprot_id,
                'alphafold_id': alphafold_id,
                'avg_plddt': avg_plddt,
                'high_conf_fraction': high_conf_fraction,
                'length': len(plddt_scores)
            })
    except Exception as e:
        print(f"Error processing {uniprot_id}: {e}")

# Create summary DataFrame
df = pd.DataFrame(results)
print(df)

Installation and Setup

Python Libraries

# Install Biopython for structure access
uv pip install biopython

# Install requests for API access
uv pip install requests

# For visualization and analysis
uv pip install numpy matplotlib pandas scipy

# For Google Cloud access (optional)
uv pip install google-cloud-bigquery gsutil

3D-Beacons API Alternative

AlphaFold can also be accessed via the 3D-Beacons federated API:

import requests

# Query via 3D-Beacons
uniprot_id = "P00520"
url = f"https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/api/uniprot/summary/{uniprot_id}.json"
response = requests.get(url)
data = response.json()

# Filter for AlphaFold structures
af_structures = [s for s in data['structures'] if s['provider'] == 'AlphaFold DB']

Common Use Cases

Structural Proteomics

• Download complete proteome predictions for analysis
• Identify high-confidence structural regions across proteins
• Compare predicted structures with experimental data
• Build structural models for protein families

Drug Discovery

• Retrieve target protein structures for docking studies
• Analyze binding site conformations
• Identify druggable pockets in predicted structures
• Compare structures across homologs

Protein Engineering

• Identify stable/unstable regions using pLDDT
• Design mutations in high-confidence regions
• Analyze domain architectures using PAE
• Model protein variants and mutations

Evolutionary Studies

• Compare ortholog structures across species
• Analyze conservation of structural features
• Study domain evolution patterns
• Identify functionally important regions

Key Concepts

UniProt Accession: Primary identifier for proteins (e.g., "P00520"). Required for querying AlphaFold DB.

AlphaFold ID: Internal identifier format: AF-[UniProt accession]-F[fragment number] (e.g., "AF-P00520-F1").

pLDDT (predicted Local Distance Difference Test): Per-residue confidence metric (0-100). Higher values indicate more confident predictions.

PAE (Predicted Aligned Error): Matrix indicating confidence in relative positions between residue pairs. Low values (<5 Å) suggest confident relative positioning.

Database Version: Current version is v4. File URLs include version suffix (e.g., model_v4.cif).

Fragment Number: Large proteins may be split into fragments. Fragment number appears in AlphaFold ID (e.g., F1, F2).

Confidence Interpretation Guidelines

pLDDT Thresholds:

• >90: Very high confidence - suitable for detailed analysis
• 70-90: High confidence - generally reliable backbone structure
• 50-70: Low confidence - use with caution, flexible regions
• <50: Very low confidence - likely disordered or unreliable

PAE Guidelines:

• <5 Å: Confident relative positioning of domains
• 5-10 Å: Moderate confidence in arrangement
• >15 Å: Uncertain relative positions, domains may be mobile

Resources

references/api_reference.md

Comprehensive API documentation covering:

• Complete REST API endpoint specifications
• File format details and data schemas
• Google Cloud dataset structure and access patterns
• Advanced query examples and batch processing strategies
• Rate limiting, caching, and best practices
• Troubleshooting common issues

Consult this reference for detailed API information, bulk download strategies, or when working with large-scale datasets.

Important Notes

Data Usage and Attribution

• AlphaFold DB is freely available under CC-BY-4.0 license
• Cite: Jumper et al. (2021) Nature and Varadi et al. (2022) Nucleic Acids Research
• Predictions are computational models, not experimental structures
• Always assess confidence metrics before downstream analysis

Version Management

• Current database version: v4 (as of 2024-2025)
• File URLs include version suffix (e.g., _v4.cif)
• Check for database updates regularly
• Older versions may be deprecated over time

Data Quality Considerations

• High pLDDT doesn't guarantee functional accuracy
• Low confidence regions may be disordered in vivo
• PAE indicates relative domain confidence, not absolute positioning
• Predictions lack ligands, post-translational modifications, and cofactors
• Multi-chain complexes are not predicted (single chains only)

Performance Tips

• Use Biopython for simple single-protein access
• Use Google Cloud for bulk downloads (much faster than individual files)
• Cache downloaded files locally to avoid repeated downloads
• BigQuery free tier: 1 TB processed data per month
• Consider network bandwidth for large-scale downloads

Additional Resources

• AlphaFold DB Website: https://alphafold.ebi.ac.uk/
• API Documentation: https://alphafold.ebi.ac.uk/api-docs
• Google Cloud Dataset: https://cloud.google.com/blog/products/ai-machine-learning/alphafold-protein-structure-database
• 3D-Beacons API: https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/
• AlphaFold Papers:
  • Nature (2021): https://doi.org/10.1038/s41586-021-03819-2
  • Nucleic Acids Research (2024): https://doi.org/10.1093/nar/gkad1011
• Biopython Documentation: https://biopython.org/docs/dev/api/Bio.PDB.alphafold_db.html
• GitHub Repository: https://github.com/google-deepmind/alphafold