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structural-biology

Compare experimental and AlphaFold protein structures with per-residue RMSD analysis.

13|Updated Dec 16, 2025
Search Tags:#validation#structure#confidence#alphafold#protein#discrepancies#rmsd
One-click install
npx skills add https://github.com/justaddcoffee/open-science-skills --skill structural-biology
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: structural-biology
Source: https://github.com/justaddcoffee/open-science-skills/tree/main/structural-biology
Command: npx skills add https://github.com/justaddcoffee/open-science-skills --skill structural-biology

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Structural-biology team members and AI-assisted workflows often struggle to reconcile experimental structures with AlphaFold predictions; this skill enables systematic comparison, validation, and interpretation of structural differences to guide experiments and modeling decisions.

Core Features & Use Cases

  • Per-residue deviation analysis between experimental and predicted structures.
  • AlphaFold confidence interpretation (pLDDT, PAE) integrated with structural comparisons.
  • Guided workflows for identifying conformational changes, domain orientations, and modeling limitations.

Quick Start

Ask it to compare your AlphaFold model against an experimental structure to identify confident regions and discordant areas.

Frequently Asked Questions about structural-biology

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

FAQPage Schema
How do I compare an experimental protein structure with an AlphaFold model?▼

Per-residue deviation analysis compares experimental and predicted protein structures by calculating RMSD and integrating AlphaFold confidence scores like pLDDT and PAE to flag discordant regions.

What does AlphaFold pLDDT and PAE confidence tell me about protein structure validation?▼

AlphaFold pLDDT and PAE confidence metrics indicate local prediction accuracy and domain positioning, guiding protein structure validation by highlighting confident regions and modeling limitations.

Can I analyze conformational changes in multi-domain proteins using AlphaFold predictions?▼

Yes, guided workflows analyze conformational changes and domain orientations in multi-domain proteins by identifying structural discrepancies between experimental and AlphaFold predicted models.

How do I identify regions of high RMSD between predicted and experimental protein structures?▼

Per-residue deviation analysis identifies high RMSD regions between predicted and experimental protein structures, flagging specific residues for targeted quality-control reviews and structural investigation.

What are the limitations of using AlphaFold models for protein structure validation?▼

Limitations include modeling discrepancies in multi-domain orientations and conformational changes, where high RMSD and low pLDDT scores indicate areas requiring experimental structures for accurate validation.

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