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bindcraft

BindCraft generates de novo protein binder designs targeting specific residues on a protein structure using deep learning. Use it for computational protein engineering tasks requiring custom binding proteins with defined lengths and interaction hotspots, with optional deployment via Modal cloud compute or local GPU installation supporting batch design generation with quality metrics.

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SKILL.md

# 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.
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