End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization, (5) Want high experimental success rate. For backbone-only generation, use rfdiffusion. For QC thresholds, use protein-qc. For tool selection guidance, use binder-design.
| Requirement | Minimum | Recommended |
|---|---|---|
| Python | 3.9+ | 3.10 |
| CUDA | 11.7+ | 12.0+ |
| GPU VRAM | 32GB | 48GB (L40S) |
| RAM | 32GB | 64GB |
First time? See Installation Guide to set up Modal and biomodals.
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
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
| 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 |
| Protocol | Speed | Quality | Use Case |
|---|---|---|---|
| fast | Fast | Lower | Initial screening |
| default | Medium | Good | Standard campaigns |
| slow | Slow | High | Final production |
output/
├── design_0/
│ ├── binder.pdb # Final design
│ ├── complex.pdb # Binder + target
│ ├── metrics.json # QC scores
│ └── trajectory/ # Optimization trajectory
├── design_1/
│ └── ...
└── summary.csv # All metrics
{
"plddt": 0.89,
"ptm": 0.78,
"iptm": 0.62,
"pae": 8.5,
"rmsd": 1.2,
"sequence": "MKTAYIAK..."
}
$ 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:
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
| 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).
find output -name "binder.pdb" | wc -l # Should match num_designs
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 | 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.