AlphaFold Database API Reference

AlphaFold Database API Reference

This document provides comprehensive technical documentation for programmatic access to the AlphaFold Protein Structure Database.

Table of Contents

1. REST API Endpoints
2. File Access Patterns
3. Data Schemas
4. Google Cloud Access
5. BigQuery Schema
6. Best Practices
7. Error Handling
8. Rate Limiting

REST API Endpoints

Base URL

https://alphafold.ebi.ac.uk/api/

1. Get Prediction by UniProt Accession

Endpoint: /prediction/{uniprot_id}

Method: GET

Description: Retrieve AlphaFold prediction metadata for a given UniProt accession.

Parameters:

• uniprot_id (required): UniProt accession (e.g., "P00520")

Example Request:

curl https://alphafold.ebi.ac.uk/api/prediction/P00520

Example Response:

[
  {
    "entryId": "AF-P00520-F1",
    "gene": "ABL1",
    "uniprotAccession": "P00520",
    "uniprotId": "ABL1_HUMAN",
    "uniprotDescription": "Tyrosine-protein kinase ABL1",
    "taxId": 9606,
    "organismScientificName": "Homo sapiens",
    "uniprotStart": 1,
    "uniprotEnd": 1130,
    "uniprotSequence": "MLEICLKLVGCKSKKGLSSSSSCYLEEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR",
    "modelCreatedDate": "2021-07-01",
    "latestVersion": 4,
    "allVersions": [1, 2, 3, 4],
    "cifUrl": "https://alphafold.ebi.ac.uk/files/AF-P00520-F1-model_v4.cif",
    "bcifUrl": "https://alphafold.ebi.ac.uk/files/AF-P00520-F1-model_v4.bcif",
    "pdbUrl": "https://alphafold.ebi.ac.uk/files/AF-P00520-F1-model_v4.pdb",
    "paeImageUrl": "https://alphafold.ebi.ac.uk/files/AF-P00520-F1-predicted_aligned_error_v4.png",
    "paeDocUrl": "https://alphafold.ebi.ac.uk/files/AF-P00520-F1-predicted_aligned_error_v4.json"
  }
]

Response Fields:

• entryId: AlphaFold internal identifier (format: AF-{uniprot}-F{fragment})
• gene: Gene symbol
• uniprotAccession: UniProt accession
• uniprotId: UniProt entry name
• uniprotDescription: Protein description
• taxId: NCBI taxonomy identifier
• organismScientificName: Species scientific name
• uniprotStart/uniprotEnd: Residue range covered
• uniprotSequence: Full protein sequence
• modelCreatedDate: Initial prediction date
• latestVersion: Current model version number
• allVersions: List of available versions
• cifUrl/bcifUrl/pdbUrl: Structure file download URLs
• paeImageUrl: PAE visualization image URL
• paeDocUrl: PAE data JSON URL

2. 3D-Beacons Integration

AlphaFold is integrated into the 3D-Beacons network for federated structure access.

Endpoint: https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/api/uniprot/summary/{uniprot_id}.json

Example:

import requests

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
alphafold_structures = [
    s for s in data['structures']
    if s['provider'] == 'AlphaFold DB'
]

File Access Patterns

Direct File Downloads

All AlphaFold files are accessible via direct URLs without authentication.

URL Pattern:

https://alphafold.ebi.ac.uk/files/{alphafold_id}-{file_type}_{version}.{extension}

Components:

• {alphafold_id}: Entry identifier (e.g., "AF-P00520-F1")
• {file_type}: Type of file (see below)
• {version}: Database version (e.g., "v4")
• {extension}: File format extension

Available File Types

1. Model Coordinates

mmCIF Format (Recommended):

https://alphafold.ebi.ac.uk/files/AF-P00520-F1-model_v4.cif

• Standard crystallographic format
• Contains full metadata
• Supports large structures
• File size: Variable (100KB - 10MB typical)

Binary CIF Format:

https://alphafold.ebi.ac.uk/files/AF-P00520-F1-model_v4.bcif

• Compressed binary version of mmCIF
• Smaller file size (~70% reduction)
• Faster parsing
• Requires specialized parser

PDB Format (Legacy):

https://alphafold.ebi.ac.uk/files/AF-P00520-F1-model_v4.pdb

• Traditional PDB text format
• Limited to 99,999 atoms
• Widely supported by older tools
• File size: Similar to mmCIF

2. Confidence Metrics

Per-Residue Confidence (JSON):

https://alphafold.ebi.ac.uk/files/AF-P00520-F1-confidence_v4.json

Structure:

{
  "confidenceScore": [87.5, 91.2, 93.8, ...],
  "confidenceCategory": ["high", "very_high", "very_high", ...]
}

Fields:

• confidenceScore: Array of pLDDT values (0-100) for each residue
• confidenceCategory: Categorical classification (very_low, low, high, very_high)

3. Predicted Aligned Error (JSON)

https://alphafold.ebi.ac.uk/files/AF-P00520-F1-predicted_aligned_error_v4.json

Structure:

{
  "distance": [[0, 2.3, 4.5, ...], [2.3, 0, 3.1, ...], ...],
  "max_predicted_aligned_error": 31.75
}

Fields:

• distance: N×N matrix of PAE values in Ångströms
• max_predicted_aligned_error: Maximum PAE value in the matrix

4. PAE Visualization (PNG)

https://alphafold.ebi.ac.uk/files/AF-P00520-F1-predicted_aligned_error_v4.png

• Pre-rendered PAE heatmap
• Useful for quick visual assessment
• Resolution: Variable based on protein size

Batch Download Strategy

For downloading multiple files efficiently, use concurrent downloads with proper error handling and rate limiting to respect server resources.

Data Schemas

Coordinate File (mmCIF) Schema

AlphaFold mmCIF files contain:

Key Data Categories:

• _entry: Entry-level metadata
• _struct: Structure title and description
• _entity: Molecular entity information
• _atom_site: Atomic coordinates and properties
• _pdbx_struct_assembly: Biological assembly info

Important Fields in _atom_site:

• group_PDB: "ATOM" for all records
• id: Atom serial number
• label_atom_id: Atom name (e.g., "CA", "N", "C")
• label_comp_id: Residue name (e.g., "ALA", "GLY")
• label_seq_id: Residue sequence number
• Cartn_x/y/z: Cartesian coordinates (Ångströms)
• B_iso_or_equiv: B-factor (contains pLDDT score)

pLDDT in B-factor Column: AlphaFold stores per-residue confidence (pLDDT) in the B-factor field. This allows standard structure viewers to color by confidence automatically.

Confidence JSON Schema

{
  "confidenceScore": [
    87.5,   // Residue 1 pLDDT
    91.2,   // Residue 2 pLDDT
    93.8    // Residue 3 pLDDT
    // ... one value per residue
  ],
  "confidenceCategory": [
    "high",      // Residue 1 category
    "very_high", // Residue 2 category
    "very_high"  // Residue 3 category
    // ... one category per residue
  ]
}

Confidence Categories:

• very_high: pLDDT > 90
• high: 70 < pLDDT ≤ 90
• low: 50 < pLDDT ≤ 70
• very_low: pLDDT ≤ 50

PAE JSON Schema

{
  "distance": [
    [0.0, 2.3, 4.5, ...],     // PAE from residue 1 to all residues
    [2.3, 0.0, 3.1, ...],     // PAE from residue 2 to all residues
    [4.5, 3.1, 0.0, ...]      // PAE from residue 3 to all residues
    // ... N×N matrix for N residues
  ],
  "max_predicted_aligned_error": 31.75
}

Interpretation:

• distance[i][j]: Expected position error (Ångströms) of residue j if the predicted and true structures were aligned on residue i
• Lower values indicate more confident relative positioning
• Diagonal is always 0 (residue aligned to itself)
• Matrix is not symmetric: distance[i][j] ≠ distance[j][i]

Google Cloud Access

AlphaFold DB is hosted on Google Cloud Platform for bulk access.

Cloud Storage Bucket

Bucket: gs://public-datasets-deepmind-alphafold-v4

Directory Structure:

gs://public-datasets-deepmind-alphafold-v4/
├── accession_ids.csv              # Index of all entries (13.5 GB)
├── sequences.fasta                # All protein sequences (16.5 GB)
└── proteomes/                     # Grouped by species (1M+ archives)

Installing gsutil

# Using pip
pip install gsutil

# Or install Google Cloud SDK
curl https://sdk.cloud.google.com | bash

Downloading Proteomes

By Taxonomy ID:

# Download all archives for a species
TAX_ID=9606  # Human
gsutil -m cp gs://public-datasets-deepmind-alphafold-v4/proteomes/proteome-tax_id-${TAX_ID}-*_v4.tar .

BigQuery Schema

AlphaFold metadata is available in BigQuery for SQL-based queries.

Dataset: bigquery-public-data.deepmind_alphafold Table: metadata

Key Fields

Example Query

SELECT
  entryId,
  uniprotAccession,
  gene,
  fractionPlddtVeryHigh
FROM `bigquery-public-data.deepmind_alphafold.metadata`
WHERE
  taxId = 9606  -- Homo sapiens
  AND fractionPlddtVeryHigh > 0.8
  AND isReviewed = TRUE
ORDER BY fractionPlddtVeryHigh DESC
LIMIT 100;

Best Practices

1. Caching Strategy

Always cache downloaded files locally to avoid repeated downloads.

2. Error Handling

Implement robust error handling for API requests with retry logic for transient failures.

3. Bulk Processing

For processing many proteins, use concurrent downloads with appropriate rate limiting.

4. Version Management

Always specify and track database versions in your code (current: v4).

Error Handling

Common HTTP Status Codes

Rate Limiting

Recommendations

• Limit to 10 concurrent requests maximum
• Add 100-200ms delay between sequential requests
• Use Google Cloud for bulk downloads instead of REST API
• Cache all downloaded data locally

Additional Resources

• AlphaFold GitHub: https://github.com/google-deepmind/alphafold
• Google Cloud Documentation: https://cloud.google.com/datasets/alphafold
• 3D-Beacons Documentation: https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/docs
• Biopython Tutorial: https://biopython.org/wiki/AlphaFold

Version History

• v1 (2021): Initial release with ~350K structures
• v2 (2022): Expanded to 200M+ structures
• v3 (2023): Updated models and expanded coverage
• v4 (2024): Current version with improved confidence metrics

Citation

When using AlphaFold DB in publications, cite:

1. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
2. Varadi, M. et al. AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences. Nucleic Acids Res. 52, D368–D375 (2024).