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How to generate new protein backbones from scratch with RFdiffusion

Run unconditional diffusion to design monomers of a chosen length.

Short answer: Clone the repo with git clone https://github.com/RosettaCommons/RFdiffusion.git and download the weights into models as the README lists; then 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. All steps.

platform. The README ships only a CUDA 11.1 environment file and says each user must adapt it to their GPU and drivers.

Who it is forA protein designer who wants fresh backbones to screen.

The problemDe novo backbone generation used to need bespoke code and expertise.

The outcomeA set of designed backbone PDB files in your output folder.

What you need first

  • 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

Steps

  1. Clone the repo with git clone https://github.com/RosettaCommons/RFdiffusion.git and download the weights into models as the README lists
  2. 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
  3. Run ./scripts/run_inference.py 'contigmap.contigs=[150-150]' inference.output_prefix=test_outputs/test inference.num_designs=10
  4. Look at the .pdb outputs and the .trb metadata file for each design

Prompt for your agent

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.

Caveat. Every designed residue comes out as glycine because only the backbone is designed, with no sidechains.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for RosettaCommons/RFdiffusion and "Generate new protein backbones from scratch"

Add Deduped to Claude Code once

claude mcp add --transport http deduped https://dedupedai.com/mcp

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