alphafold-database-access

AlphaFold Database Access

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 predictions via BioPython or REST API, download coordinate files in multiple formats, analyze confidence metrics, and retrieve bulk proteome datasets via Google Cloud.

When to Use

• Retrieving AI-predicted protein structures by UniProt accession
• Downloading PDB/mmCIF coordinate files for structural analysis or docking
• Analyzing prediction confidence (pLDDT per-residue, PAE domain-level)
• Bulk-downloading entire proteome predictions via Google Cloud
• Comparing predicted structures with experimental PDB structures
• Building structural models for proteins lacking experimental data
• Identifying high-confidence binding sites for drug discovery
• For experimental structures only → use PDB directly
• For running AlphaFold predictions → use ColabFold or local AlphaFold

Prerequisites

# Core (BioPython for structure access)
pip install biopython requests numpy matplotlib

# Optional: Google Cloud for bulk access
pip install google-cloud-bigquery google-cloud-storage

Quick Start

from Bio.PDB import alphafold_db, MMCIFParser
import requests, numpy as np

# 1. Get prediction for a protein
uniprot_id = "P00520"  # ABL1 kinase
predictions = list(alphafold_db.get_predictions(uniprot_id))
af_id = predictions[0]['entryId']  # AF-P00520-F1

# 2. Download structure
cif_file = alphafold_db.download_cif_for(predictions[0], directory="./structures")

# 3. Check confidence
conf = requests.get(f"https://alphafold.ebi.ac.uk/files/{af_id}-confidence_v4.json").json()
scores = conf['confidenceScore']
print(f"Mean pLDDT: {np.mean(scores):.1f}, High-conf residues: {sum(1 for s in scores if s > 90)}/{len(scores)}")

Core API

1. Prediction Retrieval

BioPython (recommended for single proteins):

from Bio.PDB import alphafold_db

# Get prediction metadata
predictions = list(alphafold_db.get_predictions("P00520"))
pred = predictions[0]
print(f"AlphaFold ID: {pred['entryId']}")
print(f"Gene: {pred['gene']}, Species: {pred['organismScientificName']}")

# Get Structure objects directly
structures = list(alphafold_db.get_structural_models_for("P00520"))

REST API (for metadata or integration):

import requests

uniprot_id = "P00520"
url = f"https://alphafold.ebi.ac.uk/api/prediction/{uniprot_id}"
response = requests.get(url)
data = response.json()

# Response includes download URLs for all file types
pred = data[0]
print(f"CIF: {pred['cifUrl']}")
print(f"PDB: {pred['pdbUrl']}")
print(f"PAE: {pred['paeDocUrl']}")

3D-Beacons federated API (query multiple structure providers):

url = f"https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/api/uniprot/summary/{uniprot_id}.json"
data = requests.get(url).json()
af_structures = [s for s in data['structures'] if s['provider'] == 'AlphaFold DB']

2. Structure File Download

import requests

af_id = "AF-P00520-F1"
version = "v4"
base = "https://alphafold.ebi.ac.uk/files"

# mmCIF (recommended — full metadata, supports large structures)
cif = requests.get(f"{base}/{af_id}-model_{version}.cif")
with open(f"{af_id}.cif", "w") as f:
    f.write(cif.text)

# PDB format (legacy — limited to 99,999 atoms)
pdb = requests.get(f"{base}/{af_id}-model_{version}.pdb")
with open(f"{af_id}.pdb", "wb") as f:
    f.write(pdb.content)

# Confidence JSON (per-residue pLDDT scores)
conf = requests.get(f"{base}/{af_id}-confidence_{version}.json").json()

# PAE matrix JSON (inter-residue confidence)
pae = requests.get(f"{base}/{af_id}-predicted_aligned_error_{version}.json").json()

3. Confidence Metrics Analysis

pLDDT (per-residue confidence, 0–100):

import numpy as np

conf_url = f"https://alphafold.ebi.ac.uk/files/{af_id}-confidence_v4.json"
conf = requests.get(conf_url).json()
scores = conf['confidenceScore']

# Classify residues by confidence
very_high = sum(1 for s in scores if s > 90)
high = sum(1 for s in scores if 70 < s <= 90)
low = sum(1 for s in scores if 50 < s <= 70)
very_low = sum(1 for s in scores if s <= 50)
print(f"Very high (>90): {very_high}, High (70-90): {high}, Low (50-70): {low}, Very low (<50): {very_low}")

PAE (Predicted Aligned Error) visualization:

import matplotlib.pyplot as plt

pae_url = f"https://alphafold.ebi.ac.uk/files/{af_id}-predicted_aligned_error_v4.json"
pae = requests.get(pae_url).json()
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: {af_id}')
plt.xlabel('Residue')
plt.ylabel('Residue')
plt.savefig(f'{af_id}_pae.png', dpi=300, bbox_inches='tight')
# Low PAE (<5 Å) = confident relative positioning; >15 Å = uncertain domain arrangement

4. Bulk Data Access (Google Cloud)

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

# Download entire proteome by taxonomy ID
gsutil -m cp gs://public-datasets-deepmind-alphafold-v4/proteomes/proteome-tax_id-9606-*_v4.tar .

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

BigQuery metadata queries:

from google.cloud import bigquery

client = bigquery.Client()
query = """
SELECT entryId, uniprotAccession, gene, organismScientificName,
       globalMetricValue, fractionPlddtVeryHigh
FROM `bigquery-public-data.deepmind_alphafold.metadata`
WHERE organismScientificName = 'Homo sapiens'
  AND fractionPlddtVeryHigh > 0.8
  AND isReviewed = TRUE
LIMIT 100
"""
df = client.query(query).to_dataframe()
print(f"Found {len(df)} high-confidence human proteins")

5. Structure Parsing & Analysis

from Bio.PDB import MMCIFParser
import numpy as np
from scipy.spatial.distance import pdist, squareform

parser = MMCIFParser(QUIET=True)
structure = parser.get_structure("protein", f"{af_id}-model_v4.cif")

# Extract alpha-carbon coordinates
coords, plddt_scores = [], []
for model in structure:
    for chain in model:
        for residue in chain:
            if 'CA' in residue:
                coords.append(residue['CA'].get_coord())
                plddt_scores.append(residue['CA'].get_bfactor())  # pLDDT stored as B-factor

coords = np.array(coords)
print(f"Residues: {len(coords)}, Mean pLDDT: {np.mean(plddt_scores):.1f}")

# Contact map (Cα-Cα < 8 Å)
dist_matrix = squareform(pdist(coords))
contacts = np.where((dist_matrix > 0) & (dist_matrix < 8))
print(f"Contacts: {len(contacts[0]) // 2}")

Key Concepts

Confidence Interpretation

| Metric | Range | Interpretation | Suitable For | |--------|-------|---------------|--------------| | pLDDT >90 | Very high | Backbone + side-chain reliable | Detailed analysis, docking | | pLDDT 70–90 | High | Backbone generally reliable | Fold analysis, domain ID | | pLDDT 50–70 | Low | Use with caution | May be flexible/disordered | | pLDDT <50 | Very low | Likely disordered | Exclude from analysis | | PAE <5 Å | Confident | Reliable relative domain positions | Multi-domain assembly | | PAE 5–10 Å | Moderate | Uncertain arrangement | Treat domains independently | | PAE >15 Å | Uncertain | Domains may be mobile | Do not trust orientation |

AlphaFold ID Format

Format: AF-{UniProt_accession}-F{fragment_number} (e.g., AF-P00520-F1). Large proteins may be split into fragments (F1, F2, ...). Current database version: v4 — include version suffix in all file URLs.

File Types

| File | URL Suffix | Format | Use | |------|-----------|--------|-----| | Model coordinates | -model_v4.cif | mmCIF | Structural analysis (recommended) | | Model coordinates | -model_v4.pdb | PDB | Legacy tools (<99,999 atoms) | | Model coordinates | -model_v4.bcif | Binary CIF | Compressed (~70% smaller) | | Confidence | -confidence_v4.json | JSON | Per-residue