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/struct-predictor

Protein structure prediction with Boltz-2. Accepts YAML inputs (single protein or multi-chain complex), runs

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clawbio
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Install
$ npx -y skills add ClawBio/ClawBio --skill struct-predictor --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition โ†’
  • You can call itInvoke it directly when you want it.
  • Slash command/struct-predictor

Context preview

The summary Claude sees to decide when to auto-load this skill.

Protein structure prediction with Boltz-2. Accepts YAML inputs (single protein or multi-chain complex), runs

SKILL.md

struct-predictor.SKILL.md
name: struct-predictor
description: Protein structure prediction with Boltz-2. Accepts YAML inputs (single protein or multi-chain complex), runs
  boltz predict, extracts per-residue pLDDT and PAE confidence, and writes a markdown report with figures.
license: MIT
metadata:
  version: 0.2.0
  openclaw:
    requires:
      bins:
      - python3
      anyBins:
      - boltz
    always: false
    emoji: ๐Ÿงฑ
    homepage: https://github.com/ClawBio/ClawBio
    os:
    - darwin
    - linux
    install:
    - kind: uv
      package: boltz
      bins:
      - boltz
      comment: 'GPU: uv pip install ''boltz[cuda]'' -U'
    - kind: uv
      package: numpy
    - kind: uv
      package: matplotlib
    - kind: uv
      package: pyyaml

Struct Predictor

You are the **Struct Predictor**, a specialised agent for protein structure prediction using Boltz-2.

Core Capabilities

1. **Structure Prediction**: Run Boltz-2 locally on a YAML input 2. **Confidence Extraction**: Per-residue pLDDT (from CIF B-factors) and PAE matrix (from confidence JSON) 3. **Report Generation**: Markdown with pLDDT line plot, PAE heatmap, band breakdown, and reproducibility bundle 4. **Demo Mode**: Trp-cage miniprotein (20 residues, PDB 1L2Y) โ€” runs immediately, no input required

CLI Reference

# Single protein or multi-chain complex (YAML)
python skills/struct-predictor/struct_predictor.py \
  --input complex.yaml --output /tmp/struct_out

# Demo (Trp-cage miniprotein, PDB 1L2Y โ€” no input needed)
python skills/struct-predictor/struct_predictor.py \
  --demo --output /tmp/struct_demo

Plain Text Examples

Predict the structure of a single protein from a YAML file:

python skills/struct-predictor/struct_predictor.py --input my_protein.yaml --output /tmp/struct_out

Run the built-in Trp-cage demo (no input file needed):

python skills/struct-predictor/struct_predictor.py --demo --output /tmp/struct_demo

Predict a two-chain complex:

python skills/struct-predictor/struct_predictor.py --input complex_ab.yaml --output /tmp/complex_out

Output Structure

output_dir/
  boltz_results_[name]/                    # Boltz native output
    lightning_logs/                        # training/eval logs
    predictions/
      [name]/
        [name]_model_0.cif                 # predicted structure (pLDDT in B-factors)
        confidence_[name]_model_0.json     # confidence scores (ptm, iptm, pae, plddt)
    processed/                             # Boltz intermediate files
  report.md                                # primary markdown report
  viewer.html                              # self-contained 3Dmol.js 3D viewer (open in browser)
  result.json                              # machine-readable summary
  figures/
    plddt.png                              # per-residue pLDDT confidence plot
    pae.png                                # PAE inter-residue error heatmap
  reproducibility/
    commands.sh                            # exact boltz predict command used
    environment.txt                        # boltz version snapshot

YAML Complex Format

version: 1
sequences:
  - protein:
      id: A
      sequence: ACDEFGHIKLMNPQRSTVWY
      msa: empty        # runs offline; replace with a path to a .a3m file for MSA-guided prediction
  - protein:
      id: B
      sequence: NPQRSTVWYLSDEDFKAVFG
      msa: empty

MSA Options

| `msa` value | Behaviour | |---|---| | `msa: empty` | No MSA โ€” fast, fully offline, suitable for short/designed sequences | | `msa: /path/to/file.a3m` | Pre-computed MSA โ€” best accuracy for natural proteins | | *(omit field)* | Boltz errors unless `--use_msa_server` is passed at predict time |

pLDDT Confidence Bands

| Band | pLDDT Range | Interpretation | |------|------------|----------------| | Very high | โ‰ฅ 90 | Backbone accurate to ~0.5 ร… | | High | 70โ€“90 | Generally reliable | | Low | 50โ€“70 | Disordered or uncertain | | Very low | < 50 | Likely intrinsically disordered |

Demo Data

| Item | Value | |------|-------| | File | `skills/struct-predictor/demo_data/trpcage.yaml` | | Sequence | `NLYIQWLKDGGPSSGRPPPS` | | Name | Trp-cage miniprotein | | Length | 20 residues | | PDB reference | 1L2Y |

Dependencies

uv pip install boltz -U          # CPU
uv pip install "boltz[cuda]" -U  # GPU (recommended)
uv pip install numpy matplotlib pyyaml

Citations

  • Passaro S et al. (2025) *Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction*. bioRxiv. doi:10.1101/2025.06.14.659707. PMID: 40667369; PMCID: PMC12262699.
  • Wohlwend J et al. (2024) *Boltz-1: Democratizing Biomolecular Interaction Modeling*. bioRxiv. doi:10.1101/2024.11.19.624167
  • Jumper J et al. (2021) *AlphaFold2 pLDDT definition*. Nature. doi:10.1038/s41586-021-03819-2
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