gabrielmoreira 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