ClawBio struct-predictor

Name: struct-predictor
Author: ClawBio

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.

install

source · Clone the upstream repo

git clone https://github.com/ClawBio/ClawBio

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/ClawBio/ClawBio "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/struct-predictor" ~/.claude/skills/clawbio-clawbio-struct-predictor && rm -rf "$T"

manifest: skills/struct-predictor/SKILL.md

source content

Struct Predictor

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

Core Capabilities

Structure Prediction: Run Boltz-2 locally on a YAML input
Confidence Extraction: Per-residue pLDDT (from CIF B-factors) and PAE matrix (from confidence JSON)
Report Generation: Markdown with pLDDT line plot, PAE heatmap, band breakdown, and reproducibility bundle
Demo Mode: Trp-cage miniprotein (20 residues, PDB 1L2Y) — 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 — 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 — fast, fully offline, suitable for short/designed sequences
`msa: /path/to/file.a3m`	Pre-computed MSA — 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	≥ 90	Backbone accurate to ~0.5 Å
High	70–90	Generally reliable
Low	50–70	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