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alphafold-database

Retrieve AlphaFold protein structures and analyze pLDDT and PAE confidence metrics.

Updated Mar 15, 2026
Search Tags:#bioinformatics#protein structure#alphafold#uniprot#structural biology#ai prediction
One-click install
npx skills add https://github.com/sagunkayastha/claude_skills_collection --skill alphafold-database-sagunkayastha
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: alphafold-database
Source: https://github.com/sagunkayastha/claude_skills_collection/tree/main/scientific-databases/alphafold-database
Command: npx skills add https://github.com/sagunkayastha/claude_skills_collection --skill alphafold-database-sagunkayastha

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 a vast repository of AI-predicted 3D protein structures, enabling researchers to analyze protein architecture and function for proteins that may lack experimental data.

Core Features & Use Cases

  • Structure Retrieval: Fetch predicted structures by UniProt ID.
  • Confidence Analysis: Evaluate prediction reliability using pLDDT and PAE scores.
  • Data Download: Obtain PDB or mmCIF files for detailed structural analysis.
  • Use Case: A structural biologist needs to investigate the potential binding site of a newly identified protein. They use this Skill to retrieve the AlphaFold prediction, analyze its confidence, and download the structure for further computational modeling.

Quick Start

Retrieve the AlphaFold 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 retrieve AI-predicted protein structures using a UniProt ID?▼

To retrieve AI-predicted protein structures, you can fetch AlphaFold predictions directly by providing a UniProt ID. This allows you to download coordinate files in PDB or mmCIF formats for detailed structural analysis.

How can I evaluate the reliability of AlphaFold protein structure predictions?▼

You can evaluate prediction reliability by analyzing per-residue confidence (pLDDT) and inter-residue confidence (PAE) metrics. These scores help determine the accuracy of specific protein regions and inter-domain interactions.

Can I download bulk AlphaFold protein structures via Google Cloud?▼

Yes, you can access bulk AlphaFold protein structure data via Google Cloud. This feature supports large-scale structural biology, drug discovery, and protein engineering applications by providing programmatic access to millions of proteins.

What is the best way to get 3D protein structures for proteins lacking experimental data?▼

The best way to get 3D protein structures without experimental data is accessing the AlphaFold database. It provides AI-predicted structural insights for millions of proteins to analyze protein architecture and function.

Do I need Biopython to analyze AlphaFold mmCIF and PDB files?▼

Using Biopython is supported for analyzing downloaded AlphaFold mmCIF and PDB coordinate files. The environment also utilizes numpy, scipy, pandas, and matplotlib to facilitate computational modeling and structural analysis.

When should I not rely on AlphaFold database predictions for structural biology?▼

AlphaFold database predictions should not be used as a sole substitute for experimental data when precise atomic coordinates are critical. Always verify prediction reliability using pLDDT and PAE confidence scores before downstream drug discovery applications.

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