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.
Installation
Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.
View source ↗GuidesSkill 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
- Multi-stage optimization: Soft → temp → hard → greedy
- Combined loss functions: Multiple objectives
- Integrated AF2 validation: After MPNN design
- PyRosetta interface scoring: Add to validation pipeline
- Filter-based selection: Multi-metric thresholds
- 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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