Can I access data for an entire species at once?

Yes, the skill provides instructions for accessing bulk proteome datasets via Google Cloud Storage using taxonomy IDs for high-throughput analysis.

What file formats does this skill support?

It supports standard structural formats including mmCIF (.cif) and PDB (.pdb), as well as JSON formats for confidence and error metadata.

How do I download protein structures using this skill?

You can download structures in PDB or mmCIF format by querying the AlphaFold API with a UniProt ID using the provided Biopython or requests-based code snippets.

What confidence metrics are available for AlphaFold models?

The skill allows you to retrieve pLDDT (per-residue confidence) and PAE (Predicted Aligned Error) to evaluate the reliability of specific regions or domain orientations.

AlphaFold Database Access

Name: AlphaFold Database Access
Author: x-cmd

byx-cmd

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Data Science & ML

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

About

This skill provides a comprehensive toolkit for integrating AlphaFold's massive repository of AI-predicted 3D protein structures into computational biology workflows. It enables users to retrieve high-accuracy structural data using UniProt IDs, download atomic coordinate files in mmCIF or PDB formats, and critically evaluate model reliability using pLDDT and PAE confidence metrics. Whether performing large-scale proteome analysis via Google Cloud BigQuery or conducting specific structure-based drug discovery, this skill facilitates seamless access to the DeepMind and EMBL-EBI resource for proteins lacking experimental structures.

Key Features

Download atomic coordinate files in mmCIF and PDB formats
8 GitHub stars
Analyze model reliability using pLDDT and PAE confidence metrics
Access bulk proteome datasets via Google Cloud BigQuery and Storage
Integrate structural predictions directly into Biopython analysis pipelines
Retrieve 3D protein structures using UniProt IDs or protein names

Use Cases

Conducting structure-based drug discovery for targets without experimental data
Performing large-scale structural proteomics across entire species
Analyzing protein domain arrangements and stability using confidence scores

About

Key Features

Download atomic coordinate files in mmCIF and PDB formats
8 GitHub stars
Analyze model reliability using pLDDT and PAE confidence metrics
Access bulk proteome datasets via Google Cloud BigQuery and Storage
Integrate structural predictions directly into Biopython analysis pipelines
Retrieve 3D protein structures using UniProt IDs or protein names

Use Cases

Conducting structure-based drug discovery for targets without experimental data
Performing large-scale structural proteomics across entire species
Analyzing protein domain arrangements and stability using confidence scores