rfdiffusion-nim

name	rfdiffusion-nim
description	Run RFDiffusion protein backbone design via NVIDIA NIM. Use for de novo protein backbones, motif scaffolding, binder design, hotspot residues, contigs syntax, diffusion steps, hosted NVIDIA API calls, local Docker deployment, and PDB backbone outputs for ProteinMPNN sequence design.
license	Apache-2.0 AND CC-BY-4.0
compatibility	requests>=2.28
allowed-tools	Bash, Read, Write, AskUserQuestion

RFDiffusion NIM

Design protein backbone PDBs for de novo proteins, motif scaffolds, and binders. Use this SKILL.md for first-pass hosted/local usage; load supplemental files only when needed:

• references/api.md: exact endpoints, schemas, Docker flags, response fields.
• references/science.md: design modes, strengths, limits, and handoffs.
• references/parameters.md: contigs, hotspots, steps, and seeds.
• references/validation.md: PDB, contig, and artifact sanity checks.
• references/examples.md: compact hosted/local request patterns.

Choose Mode

Ask only when context is unclear:

Hosted NVIDIA API or local Docker NIM?

• Hosted: https://health.api.nvidia.com/v1/biology/ipd/rfdiffusion/generate
• Local: http://localhost:8000/biology/ipd/rfdiffusion/generate

Local inference paths do not include /v1/. Hosted requests use Authorization: Bearer $NGC_API_KEY. Supported local Docker startup uses NGC_API_KEY (or NVIDIA_API_KEY via the preflight) for registry login, entitlement checks, and first-run model downloads; pass it into the container with -e NGC_API_KEY. Local inference requests use no auth header after readiness. Warm-cache key-free startup varies by image/version and should not be assumed.

Local Docker

For local setup answers, copy the preflight below exactly before docker login, docker run, readiness, and the no-auth local request. Do not replace it with a simple : "${NGC_API_KEY:?Set NGC_API_KEY}" check, do not invent a cache default, and do not drop the NVIDIA_API_KEY fallback. Default setup is single GPU device=0.

set -a
[ -f .env ] && . ./.env
set +a

if [ -z "${NGC_API_KEY:-}" ] && [ -n "${NVIDIA_API_KEY:-}" ]; then
  export NGC_API_KEY="$NVIDIA_API_KEY"
fi
: "${NGC_API_KEY:?Set NGC_API_KEY or NVIDIA_API_KEY}"
: "${LOCAL_NIM_CACHE:?Set LOCAL_NIM_CACHE}"

echo "$NGC_API_KEY" | docker login nvcr.io --username '$oauthtoken' --password-stdin

mkdir -p "${LOCAL_NIM_CACHE}"
chmod 777 "${LOCAL_NIM_CACHE}"

docker run -it \
  --runtime=nvidia \
  --gpus "device=0" \
  -e NGC_API_KEY \
  -v "${LOCAL_NIM_CACHE}:/opt/nim/.cache" \
  -p 8000:8000 \
  nvcr.io/nim/ipd/rfdiffusion:2

Readiness:

until curl -sf http://localhost:8000/v1/health/ready; do sleep 5; done

Contigs DSL

contigs defines what to keep and what to generate.

• "100": generate exactly 100 residues.
• "80-120": generate 80-120 residues.
• "A25-35": keep chain A residues 25-35 from input_pdb.
• "A25-35/0 50-80": keep A25-35, insert chain break /0, generate 50-80.

Design modes:

• De novo: contigs="80-120"; live hosted validation requires a non-empty input_pdb or input_pdb_asset, so inline requests should include the dummy PDB below.
• Motif scaffolding: read target.pdb, pass input_pdb, use a contig like "A25-35/0 50-80".
• Binder design: pass target input_pdb, contig with target and binder segment, and hotspot_res=["A50", "A51", ...] in ChainResidue string format.

DUMMY_PDB = (
    "CRYST1    1.000    1.000    1.000  90.00  90.00  90.00 P 1           1\n"
    "ATOM      1  CA  ALA A   1       0.000   0.000   0.000  1.00  0.00           C\n"
    "END\n"
)

Request Pattern

import os
from pathlib import Path
import requests

HOSTED = True
url = (
    "https://health.api.nvidia.com/v1/biology/ipd/rfdiffusion/generate"
    if HOSTED else "http://localhost:8000/biology/ipd/rfdiffusion/generate"
)
headers = {"Content-Type": "application/json"}
if HOSTED:
    headers["Authorization"] = f"Bearer {os.environ['NGC_API_KEY']}"

payload = {
    "input_pdb": DUMMY_PDB,
    "contigs": "80-120",
    "diffusion_steps": 50,
}
response = requests.post(url, headers=headers, json=payload, timeout=300)
response.raise_for_status()
result = response.json()
Path("designed_backbone.pdb").write_text(result["output_pdb"])

Motif scaffold:

payload = {
    "input_pdb": Path("target.pdb").read_text(),
    "contigs": "A25-35/0 50-80",
    "diffusion_steps": 50,
}

Binder design:

payload = {
    "input_pdb": Path("target.pdb").read_text(),
    "contigs": "A1-100/0 50-100",
    "hotspot_res": ["A50", "A51", "A52", "A53", "A54"],
    "diffusion_steps": 50,
}

Save And Interpret Output

Save result["output_pdb"] as a PDB artifact and report elapsed_ms when present. Generated backbones are not final proteins; feed them to ProteinMPNN for sequence design, then validate sequences/structures with Boltz2 or OpenFold3. For PDB and contig checks, read references/validation.md.

Limits And Troubleshooting

• diffusion_steps: 1-50; 50 is maximum quality, fewer is faster.
• Single GPU; minimum GPU VRAM is about 12 GB.
• hotspot_res uses strings like "A50", not tuples.
• 422 usually means chain IDs in contigs/hotspot_res do not match input_pdb, a malformed contig, or omitted input_pdb for hosted de novo.
• Local URL 404 usually means an accidental /v1/ prefix.