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alphafold-database

Retrieve AlphaFold protein structures by UniProt ID and download PDB/mmCIF files.

1|Updated Mar 12, 2026
Search Tags:#bioinformatics#protein structure#drug discovery#alphafold#structural biology#ai prediction
One-click install
npx skills add https://github.com/yf8578/clawomics --skill alphafold-database-yf8578
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: alphafold-database
Source: https://github.com/yf8578/clawomics/tree/main/skills/alphafold-database
Command: npx skills add https://github.com/yf8578/clawomics --skill alphafold-database-yf8578

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires biopython, requests, numpy, matplotlib, pandas, scipy, google-cloud-bigquery, gsutil, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides access to AlphaFold's vast database of AI-predicted protein structures, enabling researchers to obtain 3D models for proteins that may lack experimental data.

Core Features & Use Cases

  • Retrieve Structures: Get predicted structures by UniProt ID.
  • Download Files: Obtain PDB/mmCIF coordinate files and confidence metrics (pLDDT, PAE).
  • Bulk Access: Utilize Google Cloud for large-scale proteome downloads.
  • Use Case: A structural biologist needs to study the potential binding site of a newly identified protein. They use this Skill to download the AlphaFold prediction, analyze its confidence, and visualize the structure to hypothesize about its function.

Quick Start

Use the alphafold-database skill to download the structure for UniProt ID P00520.

Frequently Asked Questions about alphafold-database

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I download AlphaFold predicted protein structures by UniProt ID?▼

To download AlphaFold predicted protein structures, you can retrieve 3D models by providing a specific UniProt ID. This returns the corresponding coordinate files and confidence metrics for structural biology analysis.

Can I get pLDDT and PAE confidence metrics when retrieving AI-predicted 3D protein structures?▼

Yes, retrieving AI-predicted 3D protein structures includes obtaining confidence metrics like pLDDT and PAE. These metrics help evaluate the reliability of predicted models for drug discovery workflows.

What is the best way to perform bulk downloads of AlphaFold protein structures for an entire proteome?▼

For bulk downloads of AlphaFold protein structures across a proteome, utilize Google Cloud BigQuery and gsutil. This facilitates large-scale access to coordinate files without querying individual entries.

Does this AlphaFold database access method provide PDB and mmCIF coordinate files?▼

Yes, accessing the AlphaFold database provides both PDB and mmCIF coordinate files. These formats are essential for downstream protein engineering and structural visualization tasks.

When do I need to use AI-predicted protein structures instead of experimental data?▼

AI-predicted protein structures are needed for proteins lacking experimental data. They provide computational 3D models to hypothesize about binding sites and protein function in structural biology research.

Do I need Biopython and NumPy to analyze AlphaFold predicted structures?▼

Yes, analyzing AlphaFold predicted structures often requires Biopython and NumPy. These dependencies support processing coordinate files and confidence metrics for structural biology workflows.

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