Protein Binder Design (workflow)

Run a de novo binder design campaign by composing atomic NIM skills. This skill owns orchestration, handoff contracts, filtering, validation, and the run manifest. It does NOT duplicate per-NIM API details, defer those to each atomic skill's SKILL.md.

Composed skills

Step	Skill	Owns
Backbones	rfdiffusion-nim	binder backbone PDBs (contigs + hotspots)
Sequences	proteinmpnn-nim	sequences for each backbone
Co-fold / score	boltz2-nim or openfold3-nim	binder, target complex + confidence / ipTM
MSA (optional)	msa-search-nim	target A3M for higher-quality folding

The atomic NIM skills are recommended companions (one per NIM, from the BioNeMo NIM skill set). They are not required: references/pipeline.md carries the concrete request shape for every NIM call, so an agent with NIM access can follow this skill standalone. For endpoints/auth see Configuration below.

Pipeline

1. Target prep, get the target PDB + epitope; map epitope/hotspot author residue numbers to RFdiffusion hotspot_res strings; optionally build a target MSA with msa-search-nim.
2. Backbones (rfdiffusion-nim), binder contig + hotspot_res; N backbones.
3. Sequences (proteinmpnn-nim), k sequences per backbone; drop the native/WT row from mfasta.
4. Co-fold + score (boltz2-nim / openfold3-nim), co-fold binder+target; collect interface confidence (ipTM) and binder pLDDT.
5. Self-consistency, CA-RMSD between the RFdiffusion backbone and the predicted binder (scripts/metrics.py).
6. Filter + rank, apply thresholds; rank survivors; write manifest + CSV.

Full handoff contracts, branching, and the cost funnel: references/pipeline.md.

Handoff contracts (the fragile glue)