/struct-predictor

name: struct-predictor
description: 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: MIT
metadata:
  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

| `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 |

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

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 |

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