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name: bindcraft description: "Guide CUDA-enabled BindCraft protein-binder design from target PDB preparation through AF2/MPNN/PyRosetta execution, filtering, output analysis, and conservative troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

BindCraft

BindCraft is a GPU-first de-novo protein-binder design pipeline that combines AlphaFold2 backpropagation, ProteinMPNN sequence redesign, AF2 complex/monomer validation, and PyRosetta relaxation and interface scoring. Use this skill to plan and operate a reproducible campaign; do not treat it as a generic CPU protein-design library.

Route the request

• Prepare or repair a target PDB and target JSON: read target-preparation. It covers chain selection, hotspot syntax, binder lengths, and safe validation.
• Install prerequisites, choose presets, build a launch, run or resume a campaign: read design-pipeline. It owns CUDA/JAX, AF2-weight, MPNN, PyRosetta, direct, and SLURM decisions.
• Inspect results, rejection causes, scores, and ranked binders: read results-analysis. It owns output reconciliation and conservative metric interpretation.

Before using any route, read installation for external prerequisites and licensing, configuration for the three JSON families, and troubleshooting for cross-cutting failures. Read repo-provenance when checking whether this graph matches a repository revision.

Minimal operating contract

1. Work on Linux with Python 3.10 and an NVIDIA GPU. The main design path requires a CUDA-visible JAX runtime; a CPU import is not a valid substitute.
2. Obtain the AlphaFold2 parameter bundle, configure its directory, and ensure DSSP and DAlphaBall are readable/executable. BindCraft's installer downloads large external artifacts; review and run setup commands manually rather than blindly executing a bundled installer.
3. Install ColabDesign and PyRosetta in a private, compatible environment. PyRosetta may have commercial-license restrictions; resolve those before a production run.
4. Validate the target JSON and PDB, then use the design route's command builder to print a direct or SLURM command. The builder never executes or submits.
5. Use a distinct writable design_path per campaign. Expect hundreds or thousands of target-dependent trajectories for difficult targets; monitor GPU memory, disk use, failure_csv.csv, and acceptance rate.
6. Rank and select candidates from recorded artifacts, not from confidence metrics alone. BindCraft documents Average_i_pTM as a useful binding binary/ranking signal, not an affinity measurement.

Safe helpers

• From the generated skill root, python scripts/check_bindcraft_env.py reports import/backend/asset readiness and never installs, downloads, or launches a design.
• From the generated skill root, python scripts/validate_bindcraft_config.py checks the target, filter, and advanced JSON contracts without editing them.
• The focused routes link additional read-only target and results helpers. All paths in examples are placeholders that must be replaced on the launch host.

Non-goals and stop conditions

This graph does not download AF2 weights, submit SLURM jobs, run a full design campaign, promise a binder, or infer experimental affinity. Stop and repair the specific prerequisite when CUDA/JAX, ColabDesign, PyRosetta, AF2 weights, DSSP, DAlphaBall, PDB chains, settings, output permissions, or disk/VRAM capacity are not verified. A successful JSON/PDB check or generated command is not evidence that the GPU design loop will complete.