{
 "schema_version": 1,
 "generated": "2026-10-31",
 "id": "rosettacommons-rfdiffusion--generate-de-novo-protein-backbones-with-rfdiffusion",
 "repo": "RosettaCommons/RFdiffusion",
 "repo_url": "https://github.com/RosettaCommons/RFdiffusion",
 "repo_slug": "rosettacommons-rfdiffusion",
 "title": "Generate new protein backbones from scratch",
 "page_title": "How to generate new protein backbones from scratch with RFdiffusion",
 "summary": "Run unconditional diffusion to design monomers of a chosen length.",
 "who": "A protein designer who wants fresh backbones to screen.",
 "problem": "De novo backbone generation used to need bespoke code and expertise.",
 "outcome": "A set of designed backbone PDB files in your output folder.",
 "steps": [
  "Clone the repo with `git clone https://github.com/RosettaCommons/RFdiffusion.git` and download the weights into models as the README lists",
  "Create the environment with `conda env create -f env/SE3nv.yml` and `conda activate SE3nv`, then install SE3Transformer and `pip install -e .` from the repo root",
  "Run `./scripts/run_inference.py 'contigmap.contigs=[150-150]' inference.output_prefix=test_outputs/test inference.num_designs=10`",
  "Look at the .pdb outputs and the .trb metadata file for each design"
 ],
 "agent_prompt": "Using RosettaCommons/RFdiffusion, set up the SE3nv environment and run `./scripts/run_inference.py 'contigmap.contigs=[150-150]' inference.output_prefix=test_outputs/test inference.num_designs=10`, then summarize what each output file contains.",
 "agents": [
  "claude-code",
  "codex",
  "cursor",
  "gemini-cli"
 ],
 "difficulty": "Advanced",
 "time": "3 hours",
 "basis": "readme",
 "caveat": "Every designed residue comes out as glycine because only the backbone is designed, with no sidechains.",
 "category": "Security and science",
 "tags": [
  "Security and science"
 ],
 "lang": "",
 "requirements": [
  "A clone of the repo plus the model weights downloaded into its models folder",
  "Anaconda or Miniconda, and NVIDIA's SE(3)-Transformer installed from the env folder",
  "An NVIDIA GPU; the provided environment file targets CUDA 11.1 and may need adapting",
  "Docker as an alternative to the conda setup, using the provided docker/Dockerfile"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [
  {
   "tag": "platform",
   "notice": "The README ships only a CUDA 11.1 environment file and says each user must adapt it to their GPU and drivers."
  }
 ],
 "best": true,
 "score": 1.0,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/rosettacommons-rfdiffusion/generate-de-novo-protein-backbones-with-rfdiffusion/",
 "md": "https://dedupedai.com/use-cases/rosettacommons-rfdiffusion/generate-de-novo-protein-backbones-with-rfdiffusion/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/rosettacommons-rfdiffusion--generate-de-novo-protein-backbones-with-rfdiffusion.json",
 "api_version": "v1"
}