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

Related Skills

• structure-prediction: If the protein is not in AlphaFold DB, predict its structure with ESMFold.
• esm: For protein embeddings, generative design, or ESM3 capabilities beyond structure retrieval.
• protein-diagram: For publication-quality protein diagrams from existing structures.

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: