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

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
```
.
Backbones (
```
rfdiffusion-nim
```
) — binder contig +
```
hotspot_res
```
; N backbones.
Sequences (
```
proteinmpnn-nim
```
) — k sequences per backbone; drop the native/WT row from
```
mfasta
```
.
Co-fold + score (
```
boltz2-nim
```
/
```
openfold3-nim
```
) — co-fold binder+target; collect interface confidence (ipTM) and binder pLDDT.
Self-consistency — CA-RMSD between the RFdiffusion backbone and the predicted binder (
```
scripts/metrics.py
```
).
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)

RFdiffusion
```
output_pdb
```
→ ProteinMPNN
```
input_pdb
```
(inline PDB text).
ProteinMPNN
```
mfasta
```
→ Boltz2 binder polymer
```
sequence
```
(exclude the native/WT row; pair scores only with designed rows).
Epitope author residue numbers → 1-based sequence indices: remap with
```
scripts/pdb_utils.py:remap_to_seq_index
```
. RFdiffusion
```
hotspot_res
```
uses chain+author strings like
```
"A50"
```
; Boltz2 pocket/contacts use 1-based indices.
Boltz2 complex
```
.cif
```
→ binder chain → self-consistency RMSD vs the backbone.

Run manifest (reproducibility backbone)

Every campaign writes

manifest.json

candidates.csv

) under a run dir via

scripts/manifest.py

. It records lineage, params, scores, artifacts, filter status, and controls — enabling ranking, resumability, validation, and the final report. Schema and usage:

references/manifest.md

Filters (defaults)

ipTM ≥ 0.8, binder pLDDT ≥ 80, self-consistency RMSD ≤ 2.0 Å.
Override per campaign and record overrides in the manifest
```
filters
```
.

Validation

Always run controls and report a success rate, not just top scores. Negative controls via

scripts/controls.py

(scrambled sequences); positive controls = published binders re-scored through the same pipeline. Benchmark targets live in

assets/targets.json

(

scripts/registry.py

). Methodology and metric definitions:

references/validation.md

Human-in-the-loop + cost

Confirm target, epitope/hotspots, binder length range, and hosted-vs-local with the user before generating backbones (AskUserQuestion).
Co-folding is the expensive stage: co-fold a capped shortlist, review, then expand. State hosted vs local once and reuse it across all NIM calls.

Responsible use

De novo binder design is dual-use. Decline requests aimed at enhancing pathogen fitness, toxin potency, or bioweapon function; keep designs to legitimate research and therapeutic intent.

Configuration (NIM access)

Each composed NIM is reached over HTTP; choose hosted or local once and reuse it for every call:

Hosted (managed): base URL
```
https://health.api.nvidia.com/v1/...
```
per NIM at build.nvidia.com; set
```
NVIDIA_API_KEY
```
(sent as
```
Authorization: Bearer
```
). Read keys from the env — never hardcode them.
Local (self-hosted NGC containers): point each NIM at its local URL (e.g.
```
http://localhost:8000/...
```
); local NIMs need no auth header. To launch the NIMs yourself (docker run per NIM, persistent caches, health checks, and the GPU profile‑selection gotcha — some NIMs (e.g. Boltz2) need
```
NIM_MODEL_PROFILE
```
pinned on GPUs that have no bundled profile, while others (RFdiffusion/ProteinMPNN) auto‑select by compute capability): see references/local-nim-setup.md
.

Per-NIM paths, request/response schemas, and worked

curl

/Python examples live in

references/pipeline.md

Scripts

```
scripts/manifest.py
```
— campaign manifest (create / load / score / filter / rank / CSV).
```
scripts/pdb_utils.py
```
— PDB parse, chain extract, sequence, residue remap, CA coords.
```
scripts/metrics.py
```
— Kabsch CA-RMSD for self-consistency.
```
scripts/controls.py
```
— scrambled negative controls.
```
scripts/registry.py
```
+
```
assets/targets.json
```
— example benchmark target registry (illustrative epitopes — verify against the cited structure before a real campaign). Replace with your own targets.

protein-binder-design

NPX Install

Tags

SKILL.md Content