struct-predictor

Predict protein structures locally and compare them to PDB references.

1.1k|238|Updated Feb 25, 2026
One-click install
npx skills add https://github.com/ClawBio/ClawBio --skill struct-predictor
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: struct-predictor
Source: https://github.com/ClawBio/ClawBio/tree/main/skills/struct-predictor
Command: npx skills add https://github.com/ClawBio/ClawBio --skill struct-predictor

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Many researchers and clinicians need accurate, reproducible 3D protein models and comparisons to experimental structures without sending sensitive data to the cloud; this Skill enables local prediction, retrieval, and quantitative comparison of protein structures to support variant interpretation, model validation, and visualization.

Core Features & Use Cases

  • Structure Prediction: Run local predictors such as AlphaFold (ColabFold), Boltz, or Chai to generate 3D models from amino-acid sequences.
  • PDB Retrieval & Comparison: Fetch experimental structures from the PDB and compute RMSD and TM-score against predictions.
  • Confidence Mapping & Visualization: Produce pLDDT and PAE confidence maps and generate 3D renders for reports or interactive inspection.
  • Report Generation: Compile reproducible markdown reports containing visualizations, confidence plots, and comparison tables for publication or review.

Quick Start

Predict the 3D structure of the protein sequence MVLSPADKTNVKAA using ColabFold locally and compare the result to PDB ID 1A3N.

Frequently Asked Questions about struct-predictor

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

FAQPage Schema
How do I predict protein structures locally and compare them to PDB references?

Predict protein structures locally by providing amino-acid sequences to predictors like ColabFold, Boltz, or Chai, then fetch experimental PDB structures to compute RMSD and TM-score for quantitative comparison.

Can I compute RMSD and TM-score between AlphaFold predictions and experimental PDB structures?

Yes, you can compute RMSD and TM-score by fetching experimental PDB structures and comparing them against local AlphaFold predictions, enabling quantitative model validation and variant interpretation.

Do I need to install ColabFold or Boltz locally to run batch protein structure predictions?

Yes, local installation of at least one predictor such as colabfold_batch, boltz, or chai is required, along with Python libraries like biopython and py3Dmol for PDB parsing and visualization.

What is the best way to visualize pLDDT and PAE confidence maps for predicted protein structures?

Visualize pLDDT and PAE confidence maps by generating 3D renders and confidence plots from local prediction outputs, which can be compiled into reproducible markdown reports for interactive inspection or publication.

Does this approach support sensitive protein variant interpretation workflows without sending data to the cloud?

Yes, running local batch predictions using AlphaFold, Boltz, or Chai ensures sensitive amino-acid sequences remain on-premise, supporting reproducible variant interpretation and model validation without cloud data transfer.

How do I generate reproducible reports containing protein structure visualizations and comparison tables?

Generate reproducible markdown reports by compiling 3D visualizations, pLDDT and PAE confidence plots, and RMSD comparison tables from local protein structure prediction and analysis outputs.