alphafold-database

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

Updated Mar 10, 2026

Search Tags:#bioinformatics #protein structure #drug discovery #alphafold #uniprot #structural biology

Ready to run

npx skills add https://github.com/Yezez9/Research-Agent --skill alphafold-database-yezez9

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: alphafold-database
Source: https://github.com/Yezez9/Research-Agent/tree/main/scientific-skills/alphafold-database
Command: npx skills add https://github.com/Yezez9/Research-Agent --skill alphafold-database-yezez9

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 repository of AI-predicted 3D protein structures, enabling researchers to analyze protein shapes and functions even when experimental data is unavailable.

Core Features & Use Cases

Retrieve Structures: Get predicted structures by UniProt ID.
Download Files: Download PDB/mmCIF coordinate files and confidence metrics (pLDDT, PAE).
Analyze Confidence: Assess the reliability of predictions using pLDDT and PAE scores.
Bulk Access: Utilize Google Cloud for large-scale downloads of proteomes.
Use Case: A biologist needs to study the potential binding site of a newly identified protein. They use this Skill to download the AlphaFold structure, analyze its predicted confidence, and visualize the 3D model to identify potential drug targets.

Quick Start

Use the alphafold-database skill to retrieve and download the structure file 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 using a UniProt ID?▼

You can download AlphaFold predicted protein structures by querying the database with a specific UniProt ID to retrieve PDB or mmCIF coordinate files. This programmatic access fetches AI-predicted 3D models directly for structural biology analysis.

What is the best way to assess the reliability of AlphaFold predicted structures?▼

Assess the reliability of AlphaFold predicted structures by analyzing the provided confidence metrics. Evaluate pLDDT scores for local backbone confidence and PAE scores for domain arrangement accuracy to determine prediction quality.

Can I use Google Cloud for bulk access to AlphaFold proteomes?▼

Yes, you can use Google Cloud for bulk access to AlphaFold proteomes. This approach supports large-scale downloads of AI-predicted protein structures, facilitating comprehensive drug discovery and protein engineering research.

Does this AlphaFold database skill provide confidence metrics like pLDDT and PAE?▼

Yes, the AlphaFold database skill provides confidence metrics like pLDDT and PAE. These metrics allow you to analyze the reliability of AI-predicted 3D protein structures when experimental data is unavailable.

When do I need to retrieve AI-predicted 3D protein structures for drug discovery?▼

Retrieve AI-predicted 3D protein structures for drug discovery when experimental structural data is unavailable. This allows you to analyze protein shapes, identify potential binding sites, and visualize 3D models to find drug targets.