scientific-alphafold-structures

Fetch AlphaFold predictions and analyze pLDDT confidence metrics for UniProt proteins.

3|1|Updated Feb 11, 2026

Search Tags:#bioinformatics #rest-api #pae #plddt #alphafold #protein-structure #uniprot

Ready to run

npx skills add https://github.com/nahisaho/satori --skill scientific-alphafold-structures

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: scientific-alphafold-structures
Source: https://github.com/nahisaho/satori/tree/main/src/.github/skills/scientific-alphafold-structures
Command: npx skills add https://github.com/nahisaho/satori --skill scientific-alphafold-structures

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill enables researchers to fetch AlphaFold predictions for UniProt proteins and assess confidence metrics, providing a streamlined way to gauge structural reliability and prepare for downstream analyses.

Core Features & Use Cases

Retrieve AlphaFold predictions and metadata (entryId, organism, sequence length) via the AlphaFold DB REST API.
Analyze confidence through pLDDT profiles and PAE matrices to identify reliably modeled regions.
Support single-protein queries or batch processing to generate summary datasets and facilitate comparative studies with experimental structures.

Quick Start

Run the alphafold_pipeline with a list of UniProt IDs to fetch predictions and generate pLDDT profiles.

Frequently Asked Questions about scientific-alphafold-structures

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

FAQPage Schema

How do I fetch AlphaFold protein structures and pLDDT scores for UniProt IDs?▼

Fetch AlphaFold predictions by supplying a list of UniProt IDs to the pipeline. It queries the AlphaFold DB REST API, parses CIF/PDB data, extracts pLDDT from B-factors, and outputs structured CSVs containing confidence metrics.

Can I batch process multiple UniProt proteins to compare AlphaFold pLDDT profiles?▼

Yes, batch processing multiple UniProt proteins is supported. The pipeline retrieves AlphaFold predictions, generating summary datasets with pLDDT profiles and PAE insights to facilitate comparative structural analyses across proteins.

How does extracting pLDDT from B-factors help assess AlphaFold model confidence?▼

Extracting pLDDT from B-factors quantifies local structure reliability per residue. Analyzing pLDDT profiles alongside PAE matrices identifies reliably modeled regions within the predicted protein structures.

What is the best way to retrieve AlphaFold PAE matrices and model coverage for downstream analysis?▼

The best way to retrieve AlphaFold PAE matrices and model coverage is using an automated REST API pipeline. It fetches predictions and exports structured CSVs, preparing data directly for downstream comparative structural analyses.

Do I need to manually download CIF or PDB files to analyze AlphaFold confidence metrics?▼

No, manual file downloads are unnecessary. The pipeline automatically fetches AlphaFold predictions via the REST API, parses CIF/PDB structural data, and extracts confidence metrics without manual file handling.

Are there limitations when using the AlphaFold REST API to fetch large batches of protein structures?▼

While batch processing is supported, AlphaFold REST API rate limits may constrain large requests. The pipeline targets retrieving models and confidence metrics for comparative analyses rather than exhaustive database-wide scraping.