AlphaFold DB (by DeepMind and EMBL-EBI) provides predicted 3D structures for over 200M proteins. Structures are predicted by AlphaFold2 and stored with per-residue confidence scores (pLDDT).

API Access

import requests

# Fetch prediction for a UniProt accession
uniprot_id = "P04637"
r = requests.get(f"https://alphafold.ebi.ac.uk/api/prediction/{uniprot_id}")
prediction = r.json()[0]

# Get model URLs
cif_url = prediction["cifUrl"]        # mmCIF format
pdb_url = prediction["pdbUrl"]        # PDB format
pae_url = prediction["paeImageUrl"]   # PAE plot image

Download Structure

# Direct download
pdb_url = f"https://alphafold.ebi.ac.uk/files/AF-{uniprot_id}-F1-model_v4.pdb"
cif_url = f"https://alphafold.ebi.ac.uk/files/AF-{uniprot_id}-F1-model_v4.cif"
pae_url = f"https://alphafold.ebi.ac.uk/files/AF-{uniprot_id}-F1-predicted_aligned_error_v4.json"

pLDDT Confidence Score

Stored in the B-factor column of PDB files.
>90: High confidence (blue). Reliable backbone and side-chain.
70-90: Confident (cyan). Good backbone prediction.
50-70: Low confidence (yellow). Caution with interpretation.
<50: Very low (orange). Likely disordered or uncertain.

Predicted Aligned Error (PAE)

Matrix of expected position error between all residue pairs.
Low PAE between domains indicates confident relative orientation.
High PAE between domains means they may be flexible or uncertain.

Key Details

One model per UniProt accession (longest isoform, up to 2700 residues).
Structures lack ligands, cofactors, and post-translational modifications.
Use pLDDT to filter reliable regions before docking or analysis.
For custom sequences not in the DB, run AlphaFold2 or use ESMFold.