bindcraft

# 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](../../docs/installation.md) to set up Modal and biomodals. ### Option 1: Modal (recommended) ```bash cd 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 ```bash git 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 ```bash find 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.