Agent skill

bindcraft

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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Install this agent skill to your Project

npx add-skill https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/bindcraft

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 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.

Recommended Agent Skills

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adaptyvbio/protein-design-skills

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ESM2 protein language model for embeddings and sequence scoring. Use this skill when: (1) Computing pseudo-log-likelihood (PLL) scores, (2) Getting protein embeddings for clustering, (3) Filtering designs by sequence plausibility, (4) Zero-shot variant effect prediction, (5) Analyzing sequence-function relationships. For structure prediction, use chai or boltz. For QC thresholds, use protein-qc.

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binding-characterization

Guidance for SPR and BLI binding characterization experiments. Use when: (1) Planning binding kinetics experiments, (2) Troubleshooting poor/no binding signal, (3) Interpreting kinetic data artifacts, (4) Choosing between SPR vs BLI platforms.

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cell-free-expression

Guidance for cell-free protein synthesis (CFPS) optimization. Use when: (1) Planning CFPS experiments, (2) Troubleshooting low yield or aggregation, (3) Optimizing DNA template design for CFPS, (4) Expressing difficult proteins (disulfide-rich, toxic, membrane).

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ligandmpnn

Ligand-aware protein sequence design using LigandMPNN. Use this skill when: (1) Designing sequences around small molecules, (2) Enzyme active site design, (3) Ligand binding pocket optimization, (4) Metal coordination site design, (5) Cofactor binding proteins. For standard protein design, use proteinmpnn. For solubility optimization, use solublempnn.

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