Is AlphaFold DB data free to use for research?

Yes, AlphaFold DB data is provided by DeepMind and EMBL-EBI under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Can I download entire species proteomes with this skill?

Yes, the skill includes instructions for accessing bulk proteome datasets via Google Cloud Storage using taxonomy IDs and the gsutil command-line tool.

What Python libraries are required for the AlphaFold skill?

For full functionality, it is recommended to install Biopython, requests, and optionally the Google Cloud BigQuery and gsutil packages for bulk data access.

How do I retrieve a protein structure using this skill?

You can use the provided Biopython patterns or direct REST API calls to query the AlphaFold DB by UniProt accession ID and download mmCIF or PDB coordinate files.

What are pLDDT and PAE metrics in AlphaFold?

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

AlphaFold Database Access

Name: AlphaFold Database Access
Author: pur3v4d3r

bypur3v4d3r

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Data Science & ML

Accesses and analyzes over 200 million AI-predicted protein structures for drug discovery and structural biology research.

About

This skill integrates AlphaFold DB's massive repository of AI-predicted 3D protein structures directly into computational workflows. It enables developers and researchers to retrieve protein structures using UniProt IDs, download coordinate files in PDB or mmCIF formats, and interpret critical confidence metrics like pLDDT and PAE. Whether performing single-protein analysis or large-scale proteome studies via Google Cloud, this skill provides the implementation patterns needed to bridge the gap between AI-driven structural predictions and practical biological research applications.

Key Features

Integrate structural data into Biopython and bioinformatics pipelines
Analyze prediction reliability using pLDDT and PAE confidence metrics
Download model coordinates in high-fidelity mmCIF and PDB formats
1 GitHub stars
Access bulk proteome datasets via Google Cloud Storage and BigQuery
Retrieve 3D protein structures via UniProt IDs and protein names

Use Cases

Large-scale structural proteomics and proteome-wide data analysis
Protein engineering and mutation modeling in high-confidence regions
Structure-based drug discovery and target protein docking studies

About

Key Features

Integrate structural data into Biopython and bioinformatics pipelines
Analyze prediction reliability using pLDDT and PAE confidence metrics
Download model coordinates in high-fidelity mmCIF and PDB formats
1 GitHub stars
Access bulk proteome datasets via Google Cloud Storage and BigQuery
Retrieve 3D protein structures via UniProt IDs and protein names

Use Cases

Large-scale structural proteomics and proteome-wide data analysis
Protein engineering and mutation modeling in high-confidence regions
Structure-based drug discovery and target protein docking studies