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bindcraft-reference

BindCraft protein binder design reference. Use when working with AF2 backpropagation, binder design, interface optimization, PyRosetta metrics, design filtering, or comparing with RFdiffusion approaches.

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Installation

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.

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Skill instructions

BindCraft Reference Skill

Full documentation: docs/plans/2026-01-15-bindcraft-comprehensive-skill.md

Quick Reference

Pipeline Overview
Target PDB → AF2 Hallucination → MPNN Sequences → AF2 Validation → PyRosetta Relax → Filtering
Key Difference from RFdiffusion
  • BindCraft: AF2 backpropagation (sequence+structure simultaneously)
  • RFdiffusion: Diffusion model (backbone first, then MPNN)
Design Algorithms
Algorithm Method Speed
4stage Logits→Softmax→One-hot→PSSM (default) Medium
3stage Logits→Softmax→One-hot Faster
2stage Logits→PSSM semi-greedy Fastest
greedy Random loss-reducing mutations Slowest
Target Settings (JSON)
{
    "design_path": "/path/to/output/",
    "binder_name": "MyBinder",
    "starting_pdb": "/path/to/target.pdb",
    "chains": "A",
    "target_hotspot_residues": "56,58,62",
    "lengths": [65, 150],
    "number_of_final_designs": 100
}
Key Loss Weights
Weight Effect
weights_con_inter Interface contacts (higher = more)
weights_plddt Confidence optimization
weights_helicity Negative = favor β-sheets
weights_rg Compactness (radius of gyration)
weights_iptm Interface pTM
Key Filters
Filter Good Value Meaning
pLDDT ≥0.8 Confidence
i_pTM ≥0.5 Interface confidence
dG ≤0 Binding energy (negative = good)
ShapeComplementarity ≥0.6 Surface fit
n_InterfaceHbonds ≥3 Hydrogen bonds
PyRosetta Interface Metrics
  • dG: Binding free energy
  • dSASA: Buried surface area
  • ShapeComplementarity: Geometric fit
  • PackStat: Packing quality
  • InterfaceHbonds: H-bond count
  • InterfaceUnsatHbonds: Buried polar without H-bonds
Presets
Preset Use Case
default_4stage_multimer Standard binders
betasheet_4stage_multimer β-sheet rich
peptide_3stage_multimer Short peptides
*_hardtarget Difficult targets
*_flexible Flexible interfaces
Concepts for Banta Lab
  1. Multi-stage optimization: Soft → temp → hard → greedy
  2. Combined loss functions: Multiple objectives
  3. Integrated AF2 validation: After MPNN design
  4. PyRosetta interface scoring: Add to validation pipeline
  5. Filter-based selection: Multi-metric thresholds
  6. Acceptance rate monitoring: Auto-terminate poor campaigns
When to Use BindCraft vs RFdiffusion
Use BindCraft Use RFdiffusion
Need high AF2 confidence Need structural diversity
Integrated validation Large-scale screening
Well-defined binding site Novel fold exploration
Quality over quantity Memory constrained

Examples

Read the usage examples in the source repository.

View source ↗

Source history

Indexed on
2026-01-20
Index updated
2026-01-26
Branch
main

Index and sync dates describe this directory. View the source repository for code history.

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