FridrichMethod Boltz Structure Prediction
Prerequisites
| Requirement | Minimum | Recommended |
|---|---|---|
| Python | 3.10+ | 3.11 |
| CUDA | 12.0+ | 12.1+ |
| GPU VRAM | 24GB | 48GB (L40S) |
| RAM | 32GB | 64GB |
How to run
First time? See Getting started to set up Modal and biomodals.
Option 1: Modal
cd biomodals
modal run modal_boltz.py \
--input-faa complex.fasta \
--out-dir predictions/
GPU: L40S (48GB) | Timeout: 1800s default
Option 2: Local installation
pip install boltz
boltz predict \
--fasta complex.fasta \
--output predictions/
Key parameters
| Parameter | Default | Range | Description |
|---|---|---|---|
--recycling_steps |
3 | 1-10 | Recycling iterations |
--sampling_steps |
200 | 50-500 | Diffusion steps |
--use_msa_server |
true | bool | Use MSA server |
FASTA Format
>protein_A
MKTAYIAKQRQISFVK...
>protein_B
MVLSPADKTNVKAAWG...
Output format
predictions/
├── model_0.cif # Best model (CIF format)
├── confidence.json # pLDDT, pTM, ipTM
└── pae.npy # PAE matrix
Note: Boltz outputs CIF format. Convert to PDB if needed:
from Bio.PDB import MMCIFParser, PDBIO
parser = MMCIFParser()
structure = parser.get_structure("model", "model_0.cif")
io = PDBIO()
io.set_structure(structure)
io.save("model_0.pdb")
Comparison
| Feature | Boltz-1 | Boltz-2 | AF2-Multimer |
|---|---|---|---|
| MSA-free mode | Yes | Yes | No |
| Diffusion | Yes | Yes | No |
| Speed | Fast | Faster | Slower |
| Open source | Yes | Yes | Yes |
Sample output
Successful run
$ boltz predict --fasta complex.fasta --output predictions/
[INFO] Loading Boltz-1 weights...
[INFO] Predicting structure...
[INFO] Saved model to predictions/model_0.cif
predictions/confidence.json:
{
"ptm": 0.78,
"iptm": 0.65,
"plddt": 0.81
}
What good output looks like:
- pTM: > 0.7 (confident global structure)
- ipTM: > 0.5 (confident interface)
- pLDDT: > 0.7 (confident per-residue)
- CIF file: ~100-500 KB for typical complex
Decision tree
Should I use Boltz?
│
├─ What are you predicting?
│ ├─ Protein-protein complex → Boltz ✓ or Chai or ColabFold
│ ├─ Protein + ligand → Boltz ✓ or Chai
│ └─ Single protein → Use ESMFold (faster)
│
├─ Need MSA?
│ ├─ No / want speed → Boltz ✓
│ └─ Yes / maximum accuracy → ColabFold
│
└─ Why Boltz over Chai?
├─ Open weights preference → Boltz ✓
├─ Boltz-2 speed → Boltz ✓
└─ DNA/RNA support → Consider Chai
Typical performance
| Campaign Size | Time (L40S) | Cost (Modal) | Notes |
|---|---|---|---|
| 100 complexes | 30-45 min | ~$8 | Standard validation |
| 500 complexes | 2-3h | ~$35 | Large campaign |
| 1000 complexes | 4-6h | ~$70 | Comprehensive |
Per-complex: ~15-30s for typical binder-target complex.
Verify
find predictions -name "*.cif" | wc -l # Should match input count
Troubleshooting
Low confidence: Increase recycling_steps OOM errors: Use MSA-free mode or A100-80GB Slow prediction: Reduce sampling_steps
Error interpretation
| Error | Cause | Fix |
|---|---|---|
RuntimeError: CUDA out of memory |
Complex too large | Use --use_msa_server false or larger GPU |
KeyError: 'iptm' |
Single chain only | Ensure FASTA has 2+ chains |
FileNotFoundError: weights |
Missing model | Run boltz download first |
ValueError: invalid residue |
Non-standard AA | Check for modified residues in sequence |
Boltz-1 vs Boltz-2
| Aspect | Boltz-1 | Boltz-2 |
|---|---|---|
| Speed | Fast | Faster |
| Accuracy | Good | Improved, notably antibody-antigen |
| Ligands | Basic | Better support |
| Affinity prediction | No | Yes (small-molecule binding) |
| Release | 2024 | 2025 |
Boltz-2 is the current default. Boltz-1 is still used where a design pipeline inverts the v1 model.
Affinity prediction (Boltz-2)
Boltz-2 adds an affinity-prediction module that approaches free-energy-perturbation accuracy at a fraction of the cost. It is trained on small-molecule binding data, so use it for protein-ligand and small-molecule work. It does not predict protein-protein binding affinity; for protein binders, rely on interface confidence (ipTM, ipSAE) instead.
Next: protein-qc for filtering and ranking.