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name: rfdiffusion description: > Generate de novo protein backbones with RFdiffusion for protein-target binders, hotspot-conditioned interfaces, motif scaffolding, partial diffusion, or symmetric assemblies. Use this skill when a design workflow needs reproducible RFdiffusion contigs, residue mappings, checkpoints, seeds, batch execution, and handoff to ProteinMPNN plus independent structure validation. RFdiffusion generates backbones; it does not validate folding or binding. license: MIT origin: openai4s category: biomodels requirements: [gpu] capabilities: network: mode: raw_required domains: [] metadata: display-name: RFdiffusion third_party: - kind: recipe name: Adaptyv protein-design-skills RFdiffusion recipe license: MIT terms_url: https://github.com/adaptyvbio/protein-design-skills/blob/b231232025e3db41e9c637abc1e8082e915f6cb1/LICENSE - kind: code-and-weights name: RFdiffusion license: BSD-3-Clause terms_url: https://github.com/RosettaCommons/RFdiffusion/blob/main/LICENSE

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RFdiffusion

Use RFdiffusion for the backbone-generation stage of a protein-design workflow. Treat its output as a structural proposal, not as evidence that a sequence folds or binds. Design sequences afterward with proteinmpnn (or ligandmpnn when non-protein atoms must be visible), then validate both the isolated design and the target–design complex with an independent predictor such as boltz, chai1, or alphafold2.

The adapted recipe is MIT-licensed. The upstream RFdiffusion code and the model weights referenced by its README are BSD-3-Clause; record the exact upstream commit and checkpoint digest used by each run.

Preflight: freeze the design contract

Before inference:

1. Preserve the original chain IDs and residue numbers. Write an explicit map if the input is renumbered, cropped, or has insertion codes.
2. Audit missing residues/atoms, alternate locations, non-standard residues, and biological assembly choice. Do not silently remove cofactors or chains.
3. For binder design, confirm proposed hotspot residues are surface-accessible and belong to the intended target chain. A typical pilot uses 3–6 spatially coherent hotspots, but the biological interface determines the final set.
4. Freeze the target PDB digest, RFdiffusion commit, checkpoint digest, contig string, hotspot list, seed policy, number of designs, and output prefix in a machine-readable manifest.
5. Keep target-only coordinates separate from any withheld reference binder. Do not leak a reference complex into generation or validation.

Do not trim a target merely to make inference cheaper unless the retained construct is biologically justified and the residue map is preserved.

Install and invoke the official runner

RFdiffusion is a source repository rather than an OpenAI4S sidecar. Prepare a pinned GPU environment, clone a fixed revision of the official repository, install it there, and download the documented checkpoint. Verify every download before starting a campaign. Follow the upstream CUDA/PyTorch/DGL compatibility instructions for the selected revision; do not improvise a version matrix from this recipe.

Do not install RFdiffusion into OpenAI4S's shared struct environment. That environment intentionally targets portable Python 3.13 with a CPU PyTorch build, whereas upstream RFdiffusion publishes a Python 3.9, CUDA-specific PyTorch/DGL stack. Use a pinned dedicated conda environment or the official RFdiffusion Docker image pinned by digest. openai4s setup --profile full creates the shared struct environment; it does not provision RFdiffusion, GPU drivers, model weights, or a container image.

Run scripts from the RFdiffusion repository root. The official inference entry point is scripts/run_inference.py:

./scripts/run_inference.py \
  inference.input_pdb=target.pdb \
  'contigmap.contigs=[A1-150/0 70-100]' \
  'ppi.hotspot_res=[A45,A67,A89]' \
  inference.output_prefix=results/backbones/design \
  inference.num_designs=48

This example fixes target chain A residues 1–150, inserts a chain break, and generates a 70–100-residue binder chain. The output is backbone-only: designed residues are represented as glycine by default. That is expected and is the handoff point to inverse folding.

Quote the entire Hydra list override, including brackets. Shell tokenization otherwise splits a contig containing spaces before Hydra sees it. Inside the contig:

• /0 means a chain break: slash, zero, then a space.
• / without the zero joins segments in the same output chain.
• A10-30 selects fixed input coordinates; 20-40 requests a generated segment of variable length.
• The order of contig segments controls the output topology. Never infer output residue identity from the PDB alone; read the mapping in the .trb.
• Hotspots use input chain plus residue number, with no spaces between list items. Quote that whole list override too.

Common malformed variants are an unquoted contig with spaces, a comma between contig segments, a missing /0 at a desired chain break, or hotspot numbers without chain IDs.

Motif scaffolding

For motif scaffolding, retain motif coordinates as an input-coordinate segment and generate flanking residues in the same chain. For example, if the input contains target chain A and a functional motif at B10–B24:

./scripts/run_inference.py \
  inference.input_pdb=target_and_motif.pdb \
  'contigmap.contigs=[A1-150/0 20-40/B10-24/20-40]' \
  'ppi.hotspot_res=[A45,A67,A89]' \
  inference.output_prefix=results/motif_scaffolds/design \
  inference.num_designs=48

Confirm the exact syntax against the pinned upstream revision before spending a large GPU budget. After generation, compute motif backbone RMSD and the motif–target contact geometry using the .trb input/output residue mapping. Do not assume PDB residue numbers survived contig assembly. Preserve motif sequence positions during downstream sequence design.

Batch safely and make runs resumable

One large foreground call can exceed the OpenAI4S cell watchdog. Split a campaign into deterministic batches or use host.exec_background / remote compute. Assign each batch a disjoint output prefix and seed range. Keep an append-only manifest with, at minimum:

{
  "batch_id": "round1_batch03",
  "status": "completed",
  "input_pdb_sha256": "...",
  "rfdiffusion_commit": "...",
  "checkpoint_sha256": "...",
  "contigs": "[A1-150/0 70-100]",
  "hotspots": ["A45", "A67", "A89"],
  "seed_start": 2000,
  "requested": 8,
  "completed": 8,
  "output_prefix": "results/backbones/r1_b03/design"
}

Poll the job rather than repeatedly submitting it. On resume, verify completed PDB/TRB pairs and their digests before scheduling missing design indices. Do not overwrite an earlier round when contigs, hotspots, checkpoints, or target coordinates change; create a new round and record why it changed.

Preserve and interpret every upstream output

For each design, retain:

• the final PDB backbone;
• the .trb, which stores the sampled contig/config plus residue mappings and masks needed to audit the design;
• the denoising trajectory files when produced, or an explicit retention rule if storage policy excludes them;
• stdout/stderr, the resolved Hydra config, the batch manifest, and digests.

Reject or flag outputs that are incomplete, violate requested length/chain layout, lose fixed target or motif coordinates, clash severely, or cannot be mapped back to input residues. Secondary structure visible in a generated PDB is not a sufficient QC result.

Downstream scientific loop

1. Use the .trb mapping to separate fixed target/motif positions from designable binder positions.
2. Generate multiple sequences per accepted backbone with proteinmpnn; preserve fixed motif residues and record temperature, seeds, and model checkpoint.
3. Fold each binder alone and compare it with the intended binder backbone.
4. Re-predict the target–binder complex independently. For benchmark use, do not provide the designed complex as a template or initial guess; this is a stricter anti-self-validation rule than some production pipelines.
5. Compute interface metrics on the independently predicted complex, including hotspot/epitope contacts, non-epitope contacts, buried surface area, clashes, and confidence fields appropriate to the predictor.
6. Apply hard constraints first, then rank and diversity-cluster survivors. Use failure distributions to justify any next RFdiffusion round.

RFdiffusion scores, a generated interface, ProteinMPNN likelihood, monomer confidence, or any one complex-confidence field alone is not proof of binding. Report computational candidates as hypotheses requiring experimental testing.