How do I retrieve a protein structure using this skill?

You can retrieve structures by providing a UniProt ID or protein name, which the skill uses to fetch metadata and coordinate files (PDB/mmCIF) from the AlphaFold DB via Biopython or REST APIs.

What file formats are supported for protein models?

The skill supports downloading protein models in both PDB (Protein Data Bank) and mmCIF (PDBx) formats, ensuring compatibility with most structural biology and visualization software.

Can I download data for an entire species at once?

Yes, the skill provides methods to access bulk proteome datasets via Google Cloud Storage (GCS) using specific taxonomy IDs for large-scale species analysis.

What are pLDDT and PAE scores?

pLDDT measures local confidence (0-100) per residue, while PAE (Predicted Aligned Error) indicates confidence in the relative positions and orientations of different protein domains.

AlphaFold Database Integration

Name: AlphaFold Database Integration
Author: ricable

byricable

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Data Science & ML

Accesses and analyzes over 200 million AI-predicted protein structures from the AlphaFold DB for structural biology and drug discovery.

About

This skill enables researchers and developers to integrate AlphaFold's massive repository of AI-predicted protein structures directly into their computational workflows. It provides specialized tools for retrieving structure coordinates via UniProt IDs, downloading PDB/mmCIF files, and interpreting critical confidence metrics like pLDDT and PAE. Whether you are performing large-scale structural proteomics via Google Cloud BigQuery or conducting targeted structure-based drug discovery, this skill streamlines the acquisition and processing of high-accuracy protein models for proteins that lack experimental structural data.

Key Features

Download coordinates in PDB and mmCIF formats for structural analysis
Access bulk proteome datasets via Google Cloud Platform and BigQuery
1 GitHub stars
Analyze prediction confidence using pLDDT and PAE metrics
Retrieve 3D protein structures by UniProt ID or protein name
Integrate AlphaFold predictions into computational biology pipelines

Use Cases

Large-scale structural proteomics and proteome-wide data analysis
Structure-based drug discovery and target pocket identification
Protein engineering and variant effect prediction based on structural confidence

About

Key Features

Download coordinates in PDB and mmCIF formats for structural analysis
Access bulk proteome datasets via Google Cloud Platform and BigQuery
1 GitHub stars
Analyze prediction confidence using pLDDT and PAE metrics
Retrieve 3D protein structures by UniProt ID or protein name
Integrate AlphaFold predictions into computational biology pipelines

Use Cases

Large-scale structural proteomics and proteome-wide data analysis
Structure-based drug discovery and target pocket identification
Protein engineering and variant effect prediction based on structural confidence