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alphafold-validation

Analyze AlphaFold/ESMFold predictions and visualize pLDDT and pAE confidence metrics in PyMOL.

3|Updated Jan 28, 2026
Search Tags:#pae#plddt#alphafold#protein-design#structure-prediction#rmsd#pymol
One-click install
npx skills add https://github.com/ANaka/claudemol --skill alphafold-validation
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: alphafold-validation
Source: https://github.com/ANaka/claudemol/tree/main/claude-plugin/skills/alphafold-validation
Command: npx skills add https://github.com/ANaka/claudemol --skill alphafold-validation

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps researchers validate designed proteins by inspecting AlphaFold/ESMFold predictions, visualizing per-residue confidence (pLDDT), inter- and intra-mol confidence (pAE), and assessing self-consistency RMSD within PyMOL.

Core Features & Use Cases

  • pLDDT coloring to assess per-residue confidence in structure predictions.
  • pAE interpretation for relative positioning confidence between domains or chains.
  • Self-consistency RMSD calculations to verify designed structures fold as intended.
  • AF2-Multimer validation and interface analysis for binder designs.
  • Batch screening support across multiple predictions to rank candidates.
  • ESMFold comparisons to speed up early design passes.

Quick Start

Load your AlphaFold / ESMFold prediction files into PyMOL and begin analyzing with the recommended pLDDT/pAE coloring and RMSD workflow.

Frequently Asked Questions about alphafold-validation

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

FAQPage Schema
How do I validate protein designs using AlphaFold predictions and PyMOL?▼

To validate protein designs in PyMOL, you analyze AlphaFold predictions by applying per-residue pLDDT coloring, interpreting pAE for domain positioning, and calculating self-consistency RMSD to verify if designed structures fold as intended.

What does pLDDT coloring tell me about my AlphaFold structure prediction?▼

pLDDT coloring in AlphaFold structure predictions assesses per-residue confidence, allowing you to visually identify regions of your protein design that are structurally reliable versus those with low prediction confidence.

How do I calculate self-consistency RMSD for AlphaFold designed proteins?▼

Self-consistency RMSD calculations verify whether designed proteins fold as intended by comparing predicted structures, ensuring your AlphaFold designs maintain structural integrity across different prediction methods or design iterations.

Can I use this approach for AF2-Multimer interface analysis and binder screening?▼

Yes, AlphaFold validation supports AF2-Multimer interface analysis for binder designs and includes batch screening capabilities to rank multiple prediction candidates based on confidence metrics across structural models.

Does ESMFold comparison work for early protein design validation passes?▼

ESMFold comparisons speed up early protein design passes by providing faster structure predictions, which you can then cross-check against AlphaFold predictions using pLDDT, pAE, and RMSD validation workflows.

What is the best way to interpret pAE for multi-domain protein designs?▼

Interpreting pAE for multi-domain protein designs involves analyzing inter- and intra-molecular confidence values to assess the relative positioning accuracy between distinct domains or interacting chains within your predicted structure.

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