liorz/protein-design-skills (bindcraft)

Run BindCraft — a hallucination-based de novo binder design pipeline that couples **AF2 backpropagation** (ColabDesign), **ProteinMPNN** redesign, **AF2 reprediction**, and a full **PyRosetta** interface-score filter pass against a single target PDB. One `bindcraft.py` (or `sbatch bindcraft.slurm`) call runs an endless trajectory loop until the requested number of designs that pass every filter is reached. Use this skill when: (1) De novo designing **mini-protein binders** (~65–150 aa) against a protein target — the canonical BindCraft use case, (2) Designing **peptide binders** (≤ ~25 aa) using the dedicated `peptide_3stage_multimer` advanced preset + `peptide_filters`, (3) Designing for a **β-sheet-rich** target or a target where the binder *should* be β-sheet (`betasheet_4stage_multimer` preset), (4) Selecting a **hotspot patch** (`target_hotspot_residues`: `"56"`, `"A1-10,B1-20"`, `"A"`, or `null`) to focus AF2 on a specific epitope, (5) Producing AF2-validated designs ranked by **interface ipTM / pLDDT / pAE / Rosetta dG / ShapeComplementarity / unsat-Hbonds** with per-model and average-across-models filters, (6) Choosing the right **modifier preset** for the campaign: `_mpnn` (let MPNN redesign the interface), `_flexible` (mask target template seq → flexible target backbone), `_hardtarget` (warm-start reprediction from binder atom positions → rescues failures on rigid targets), (7) **Relaxing filters** for hard targets via `relaxed_filters.json` / `peptide_relaxed_filters.json` or going `no_filters.json` to see what AF2 is producing before tightening, (8) Tuning **design weights** (`weights_plddt`, `weights_pae_inter`, `weights_con_inter`, `weights_iptm`, `weights_helicity`, `weights_rg`, `weights_termini_loss`) and **design iterations** (soft / temporary / hard / greedy) for difficult targets, (9) Diagnosing **trajectory failure modes** (low confidence, clashes, wrong hotspot, deformed binder, lo

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Source:

github.com/liorz/protein-design-skills

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