bindcraft

Others

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.

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How to use this skill

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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.