struct-predictor

name: struct-predictordescription: Protein structure prediction with Boltz-2 (default) or OpenFold3. Accepts YAML inputs (single protein or multi-chain complex), runs  the chosen backend offline, extracts per-residue pLDDT and PAE confidence, and writes a markdown report with figures.license: MITmetadata:   version: 0.3.0  openclaw:     requires:       bins:       - python3      anyBins:       - boltz      - run_openfold    always: false    emoji: 🧱    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: openfold3      bins:       - run_openfold      comment: 'optional, for --backend openfold3: run setup_openfold once to download weights; needs a CUDA GPU'    - 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 (default) or OpenFold3.

Core Capabilities

1. Structure Prediction: Run Boltz-2 (default) or OpenFold3 (--backend openfold3, CUDA GPU required) 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) — 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

# Same input with OpenFold3 instead of the Boltz-2 default
python skills/struct-predictor/struct_predictor.py \
  --input complex.yaml --output /tmp/struct_out --backend openfold3

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

Both backends run offline (no MSA server, no templates). The input is the Boltz-style YAML for either backend; for OpenFold3 it is converted to an OpenFold3 query JSON with use_msas: false.

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/
  predictions/[name]/                      # Boltz native output (default backend)
    [name]_model_0.cif                     # predicted structure (pLDDT in B-factors)
    confidence_[name]_model_0.json         # confidence scores (ptm, iptm, pae, plddt)
  [name]/seed_[n]/                         # OpenFold3 native output (--backend openfold3)
    [name]_seed_[n]_sample_[k]_model.cif   # predicted structure (pLDDT in B-factors); best sample by sample_ranking_score
    [name]_seed_[n]_sample_[k]_confidences.json             # per-atom plddt, pae, pde
    [name]_seed_[n]_sample_[k]_confidences_aggregated.json  # avg_plddt, ptm, iptm, sample_ranking_score
  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 struct_predictor.py command used
    environment.txt                        # backend package 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

The msa field is ignored by the OpenFold3 backend, which always runs with use_msas: false.

pLDDT Confidence Bands

Demo Data

Gotchas

• The model will want to pip install openfold3 and run it. Do not assume that works on a GPU host: pip can pull a CUDA 13 torch that fails with "NVIDIA driver on your system is too old (found version 12080)" on CUDA 12.8 drivers. Check nvidia-smi, then pin a matching build, e.g. pip install "torch==2.11.0+cu128" --index-url https://download.pytorch.org/whl/cu128 --extra-index-url https://pypi.org/simple.
• The model will want to call run_openfold predict directly. Do not. Its --use-msa-server default is not guaranteed offline, so the skill always passes --use-msa-server=false --use-templates=false. Dropping those flags can send sequences to the ColabFold server.
• The model will want to say OpenFold3 works anywhere. Do not. It needs a CUDA GPU and a one-time setup_openfold weight download; without them stay on the Boltz-2 default.
• The model will want to treat both engines as equally licensed. Do not. Boltz code and weights are MIT. OpenFold3 code is Apache-2.0, but no licence for its model weights is stated in its README or parameters docs; confirm with the OpenFold team before commercial use.

Dependencies

uv pip install boltz -U          # CPU
uv pip install "boltz[cuda]" -U  # GPU (recommended)
uv pip install openfold3         # optional, --backend openfold3; CUDA GPU required
setup_openfold                   # once: downloads OpenFold3 weights (~2 GB)
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
• OpenFold Consortium. OpenFold3. https://github.com/aqlaboratory/openfold-3
• Abramson J et al. (2024) Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature. doi:10.1038/s41586-024-07487-w
• Jumper J et al. (2021) AlphaFold2 pLDDT definition. Nature. doi:10.1038/s41586-021-03819-2