aminoanalytica AlphaFold Database
Programmatic access to DeepMind's AlphaFold Protein Structure Database (200M+ predicted structures).
Quick Reference
# Fetch structure via Biopython
from Bio.PDB import alphafold_db
predictions = list(alphafold_db.get_predictions("P00520"))
alphafold_db.download_cif_for(predictions[0], directory="./output")
# Direct API call
import requests
resp = requests.get("https://alphafold.ebi.ac.uk/api/prediction/P00520")
entry_id = resp.json()[0]['entryId'] # AF-P00520-F1
# Download structure file
structure_url = f"https://alphafold.ebi.ac.uk/files/{entry_id}-model_v4.cif"
When to Use
- Obtain 3D coordinates for proteins without experimental structures
- Assess prediction quality via pLDDT and PAE metrics
- Download structure files (mmCIF, PDB) for visualization or docking
- Retrieve proteome-scale datasets for computational analysis
Key Concepts
| Term | Description |
|---|---|
| UniProt Accession | Protein identifier (e.g., P00520) used to query |
| AlphaFold ID | Format: AF-{UniProt}-F{fragment} (e.g., AF-P00520-F1) |
| pLDDT | Per-residue confidence (0-100); >90 = reliable, <50 = disordered |
| PAE | Predicted Aligned Error; <5A = high confidence domain positions |
See references/confidence-scores.md for detailed interpretation guidance.
File Types
| File | URL Pattern | Contents |
|---|---|---|
| Coordinates | {id}-model_v4.cif |
Atomic positions (mmCIF) |
| Confidence | {id}-confidence_v4.json |
Per-residue pLDDT array |
| PAE Matrix | {id}-predicted_aligned_error_v4.json |
Inter-residue error |
Base URL: https://alphafold.ebi.ac.uk/files/
Core Operations
Fetch Structure Metadata
import requests
resp = requests.get(f"https://alphafold.ebi.ac.uk/api/prediction/{uniprot_id}")
metadata = resp.json()[0]
af_id = metadata['entryId']
Download All Files
Use scripts/alphafold_utils.py:
from scripts.alphafold_utils import download_alphafold_files
paths = download_alphafold_files("AF-P04637-F1", output_dir="./data")
Analyze Confidence
from scripts.alphafold_utils import get_plddt_scores
stats = get_plddt_scores("AF-P04637-F1")
print(f"Average pLDDT: {stats['mean']:.1f}")
Bulk Proteome Access
# Google Cloud Storage
gsutil ls gs://public-datasets-deepmind-alphafold-v4/
gsutil -m cp "gs://public-datasets-deepmind-alphafold-v4/proteomes/proteome-tax_id-9606-*.tar" ./
See references/bulk-access.md for BigQuery queries and batch processing.
Caveats
- Predictions, not experiments: Verify critical findings experimentally
- Confidence matters: Always check pLDDT before using regions
- Single chains only: No multimers or complexes
- No ligands: Missing cofactors, ions, PTMs
Setup
pip install biopython requests numpy matplotlib pandas scipy
# Optional: pip install google-cloud-bigquery gsutil
Links
- Database: https://alphafold.ebi.ac.uk/
- API Docs: https://alphafold.ebi.ac.uk/api-docs
- Biopython: https://biopython.org/docs/dev/api/Bio.PDB.alphafold_db.html