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alphafold-database

Retrieves AlphaFold protein structures and confidence metrics by UniProt IDs.

15|2|Updated Dec 17, 2025
Search Tags:#bioinformatics#pae#plddt#alphafold#uniprot#protein-structures
One-click install
npx skills add https://github.com/rubensliv/k-dense-ai --skill alphafold-database-rubensliv
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: alphafold-database
Source: https://github.com/rubensliv/k-dense-ai/tree/main/scientific-skills/alphafold-database
Command: npx skills add https://github.com/rubensliv/k-dense-ai --skill alphafold-database-rubensliv

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

AlphaFold DB provides access to AI-predicted protein structures and associated metrics, enabling researchers to incorporate structural data into analyses, design, and discovery workflows without manual scraping.

Core Features & Use Cases

  • Retrieve AlphaFold predictions by UniProt accession to obtain structure coordinates, PAE, and pLDDT metrics
  • Download model files in mmCIF, PDB, or BCIF formats for downstream analysis in modeling, docking, or visualization
  • Access bulk datasets via Google Cloud and BigQuery for large-scale proteome studies
  • Validate predicted structures against experimental data and integrate into computational pipelines
  • Use cases include structure-based drug design, protein engineering, and structural bioinformatics research

Quick Start

Retrieve the AlphaFold prediction for a given UniProt ID and download the corresponding structure file.

Frequently Asked Questions about alphafold-database

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

FAQPage Schema
How do I retrieve AlphaFold protein structures using a UniProt ID?▼

To retrieve AlphaFold protein structures, query the database by UniProt accession to obtain predicted coordinates, pLDDT, and PAE confidence metrics. This allows you to download model files in mmCIF, PDB, or BCIF formats for downstream computational analysis.

Can I access AlphaFold predictions in bulk for large-scale proteome studies?▼

Yes, you can access AlphaFold bulk datasets via Google Cloud and BigQuery. This enables large-scale proteome studies by allowing you to query and integrate massive volumes of predicted protein structures and metadata into computational pipelines.

What confidence metrics are available when downloading AlphaFold models?▼

AlphaFold models include pLDDT (predicted local distance difference test) and PAE (predicted aligned error) confidence metrics. These metrics help validate predicted structures against experimental data and assess local and global model reliability.

What file formats are supported for AlphaFold structure downloads?▼

AlphaFold structure downloads support mmCIF, PDB, and BCIF file formats. These formats provide the 3D coordinates necessary for downstream analysis in molecular modeling, protein docking, and structural visualization workflows.

Does this AlphaFold database integration work with Google Cloud and BigQuery?▼

Yes, the AlphaFold database integration supports Google Cloud and BigQuery. This allows researchers to programmatically access bulk structure predictions and metadata, enabling scalable bioinformatics workflows and large-scale structural comparisons.

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