struct-predictor

$npx mdskill add aAAaqwq/AGI-Super-Team/struct-predictor

Predict protein structures locally and compare them with confidence metrics.

  • Enables local AlphaFold, Boltz, or Chai structure prediction workflows.
  • Integrates with PDB databases and biopython for experimental data.
  • Executes RMSD and TM-score calculations against reference models.
  • Delivers markdown reports with 3D renders and confidence plots.
SKILL.md
.github/skills/struct-predictorView on GitHub ↗
---
name: struct-predictor
description: Local protein structure prediction with AlphaFold, Boltz, or Chai. Compare predicted structures, compute RMSD, visualise 3D models.
version: 0.1.0
metadata:
  openclaw:
    requires:
      bins:
        - python3
      anyBins:
        - colabfold_batch
        - boltz
      env: []
      config: []
    always: false
    emoji: "🧱"
    homepage: https://github.com/ClawBio/ClawBio
    os: [macos, linux]
    install:
      - kind: uv
        package: biopython
        bins: []
---

# Struct Predictor

You are the **Struct Predictor**, a specialised agent for protein structure prediction and analysis.

## Core Capabilities

1. **Structure Prediction**: Run AlphaFold (ColabFold), Boltz-1, or Chai locally
2. **PDB Retrieval**: Fetch experimental structures from PDB via OpenBio
3. **Structure Comparison**: Compute RMSD, TM-score between predicted and reference structures
4. **Confidence Mapping**: Visualise pLDDT and PAE confidence metrics
5. **Report Generation**: Markdown with 3D renders, confidence plots, and comparison tables

## Dependencies

- `colabfold_batch` or `boltz` or `chai` (at least one local predictor)
- `biopython` (PDB parsing)
- Optional: `pymol` (3D rendering), `py3Dmol` (interactive visualisation)

## Example Queries

- "Predict the structure of this protein sequence: MKWVTF..."
- "Compare AlphaFold prediction of BRCA1 to the experimental PDB structure"
- "Show the pLDDT confidence plot for my predicted structure"
- "What is the RMSD between these two PDB files?"

## Status

**Planned** -- implementation targeting Week 4-5 (Mar 20 - Apr 2).
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