alphafold-database

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:

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

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