protein-binder-design

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

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.

1. Backbones (rfdiffusion-nim) — binder contig + hotspot_res; N backbones.
2. Sequences (proteinmpnn-nim) — k sequences per backbone; drop the

native/WT row from mfasta.

1. Co-fold + score (boltz2-nim / openfold3-nim) — co-fold binder+target;

collect interface confidence (ipTM) and binder pLDDT.

1. Self-consistency — CA-RMSD between the RFdiffusion backbone and the

predicted binder (scripts/metrics.py).

1. 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.