alphafold-database

What problem does it solve?

Obtaining 3D protein structures for research is often limited by time-consuming experimental methods. This Skill provides automated access to AlphaFold DB's 200M+ AI-predicted structures, accelerating drug discovery, protein engineering, and structural biology studies by providing immediate structural insights.

Core Features & Use Cases

• Structure Retrieval: Get protein structures by UniProt ID in PDB/mmCIF formats, including full atomic coordinates.
• Confidence Analysis: Analyze per-residue pLDDT scores and Predicted Aligned Error (PAE) matrices to rigorously assess prediction reliability and domain arrangements.
• Bulk Data Access: Download entire proteomes via Google Cloud Platform for large-scale structural genomics and comparative analysis.
• Use Case: Automatically download the predicted structure for a target protein, extract its pLDDT scores to identify high-confidence regions, and visualize its PAE matrix to understand domain arrangements, all within minutes for rapid drug target assessment.

Quick Start

Get all predictions for a UniProt accession (e.g., P00520)

from Bio.PDB import alphafold_db predictions = list(alphafold_db.get_predictions("P00520")) print(f"Found {len(predictions)} predictions for P00520.")

Download the first structure file (mmCIF format)

if predictions: cif_file = alphafold_db.download_cif_for(predictions[0], directory="./structures") print(f"Downloaded: {cif_file}")