bindcraft-binder-design

name	bindcraft-binder-design
description	Run a pinned BindCraft campaign for de novo protein binders using AlphaFold2 optimization, ProteinMPNN, PyRosetta, and configurable filters. Use only when the target structure, hotspot strategy, candidate budget, dependency licenses, and experimental handoff are explicit.
license	MIT

BindCraft Binder Design

Use $cx-modeling-problem-execution for a concrete target and continue through pilot, campaign, filtering, orthogonal validation, and review.

License and gate

Ask once before running the installer, using conda/mamba, downloading AlphaFold2 weights, accepting PyRosetta terms, and launching GPU/Slurm compute. Pin BindCraft release/commit and every dependency; state target, candidate/final-design budget, wall time, storage, and experimental non-goals.

Workflow

1. Audit target structure/assembly/chains, missing regions, ligands, hotspot residues and numbering, binder length, off-targets, and desired specificity.
2. Pin settings, filter and advanced JSON, algorithm, seeds, AF2/MPNN/PyRosetta versions, stop criteria, ranking rule, and compute resources.
3. Smoke-test an official example and a small target-specific pilot. If viable, run the approved campaign into artifacts/<run-id>/bindcraft/; preserve all trajectories, sequences, structures, metrics, configs, logs, and rejects.
4. Report the full funnel, diversity, novelty, interface geometry, clashes, predicted confidence, filter sensitivity, and failure reasons. Re-score with an orthogonal structure model and evaluate off-target/specificity hypotheses.
5. Record with $science-provenance; run $science-review before candidate selection.

Boundaries

• ipTM and in-silico filters are not affinity or experimental validation.
• Do not auto-order or synthesize candidates. PyRosetta and model assets have separate terms; this skill does not grant commercial rights.