Bindcraft Agent Skill

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)

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

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

{
  "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

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.