BindCraft Binder Design
Prerequisites
| Requirement | Minimum | Recommended |
|---|---|---|
| Python | 3.9+ | 3.10 |
| CUDA | 11.7+ | 12.0+ |
| GPU VRAM | 32GB | 48GB (L40S) |
| RAM | 32GB | 64GB |
How to run
First time? See Installation Guide to set up Modal and biomodals.
Option 1: Modal (recommended)
bashcd biomodals modal run modal_bindcraft.py \ --target-pdb target.pdb \ --target-chain A \ --binder-lengths 70-100 \ --hotspots "A45,A67,A89" \ --num-designs 50
GPU: L40S (48GB) | Timeout: 3600s default
Option 2: Local installation
bashgit clone https://github.com/martinpacesa/BindCraft.git cd BindCraft pip install -r requirements.txt python bindcraft.py \ --target target.pdb \ --target_chains A \ --binder_lengths 70-100 \ --hotspots A45,A67,A89 \ --num_designs 50
Key parameters
| Parameter | Default | Range | Description |
|---|---|---|---|
--target-pdb | required | path | Target structure |
--target-chain | required | A-Z | Target chain(s) |
--binder-lengths | 70-100 | 40-150 | Length range |
--hotspots | None | residues | Target hotspots |
--num-designs | 50 | 1-500 | Number of designs |
--protocol | default | fast/default/slow | Quality vs speed |
Protocols
| Protocol | Speed | Quality | Use Case |
|---|---|---|---|
| fast | Fast | Lower | Initial screening |
| default | Medium | Good | Standard campaigns |
| slow | Slow | High | Final production |
Output format
output/ ├── design_0/ │ ├── binder.pdb # Final design │ ├── complex.pdb # Binder + target │ ├── metrics.json # QC scores │ └── trajectory/ # Optimization trajectory ├── design_1/ │ └── ... └── summary.csv # All metrics
Metrics Output
json{ "plddt": 0.89, "ptm": 0.78, "iptm": 0.62, "pae": 8.5, "rmsd": 1.2, "sequence": "MKTAYIAK..." }
Sample output
Successful run
$ modal run modal_bindcraft.py --target-pdb target.pdb --num-designs 50 [INFO] Loading BindCraft model... [INFO] Target: target.pdb (chain A) [INFO] Hotspots: A45, A67, A89 [INFO] Protocol: default [INFO] Generating 50 designs... Design 1/50: Length: 78 AA pLDDT: 0.89, ipTM: 0.62 Saved: output/design_0/ Design 50/50: Length: 85 AA pLDDT: 0.86, ipTM: 0.58 Saved: output/design_49/ [INFO] Campaign complete. Summary: output/summary.csv Pass rate: 32/50 (64%) with ipTM > 0.5
What good output looks like:
- pLDDT: > 0.85 for most designs
- ipTM: > 0.5 for passing designs
- Pass rate: 30-70% depending on target
- Diverse sequences across designs
Decision tree
Should I use BindCraft? │ ├─ What type of design? │ ├─ Production-quality binders → BindCraft ✓ │ ├─ High diversity exploration → RFdiffusion │ └─ All-atom precision → BoltzGen │ ├─ What matters most? │ ├─ Experimental success rate → BindCraft ✓ │ ├─ Speed / diversity → RFdiffusion + ProteinMPNN │ ├─ AF2 gradient optimization → ColabDesign │ └─ All-atom control → BoltzGen │ └─ Compute resources? ├─ Have L40S/A100 → BindCraft ✓ └─ Only A10G → RFdiffusion + ProteinMPNN
Typical performance
| Campaign Size | Time (L40S) | Cost (Modal) | Notes |
|---|---|---|---|
| 50 designs | 2-4h | ~$15 | Quick campaign |
| 100 designs | 4-8h | ~$30 | Standard |
| 200 designs | 8-16h | ~$60 | Large campaign |
Expected pass rate: 30-70% with ipTM > 0.5 (target-dependent).
Verify
bashfind output -name "binder.pdb" | wc -l # Should match num_designs
Troubleshooting
Low ipTM scores: Check hotspot selection, increase designs Slow convergence: Use fast protocol for screening OOM errors: Reduce num_models, use L40S GPU Poor diversity: Lower sampling_temp, run multiple seeds
Error interpretation
| Error | Cause | Fix |
|---|---|---|
RuntimeError: CUDA out of memory | Large target or long binder | Use L40S/A100, reduce binder length |
ValueError: no hotspots | Hotspots not found | Check residue numbering |
TimeoutError | Design taking too long | Use fast protocol |
Next: Rank by ipsae → experimental validation.
