# De novo design of protein structure and function with RFdiffusion

Source: https://onco.cc/key-papers/paper-watson-nature/  
OnCo record `paper-watson-nature` (Key paper). Data CC BY-NC 4.0, attribute "Data from OnCo (onco.cc)"; commercial use needs a licence.

## TL;DR

Paper cited by one technology page, indexed on Europe PMC as PubMed record 37433327 and published in Nature; the citing page links this DOI, which is how the record was matched.

## Summary

There has been considerable recent progress in designing new proteins using deep-learning methods 1-9. Despite this progress, a general deep-learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher-order symmetric architectures, has yet to be described. Diffusion models 10,11 have had considerable success in image and language generative modelling but limited success when applied to protein modelling, probably due to the complexity of protein backbone geometry and sequence-structure relationships. Here we show that by fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of designed symmetric assemblies, metal-binding proteins and protein binders. The accuracy of RFdiffusion is confirmed by the cryogenic electron microscopy structure of a designed binder in complex with influenza haemagglutinin that is nearly identical to the design model. In a manner analogous to networks that produce images from user-specified inputs, RFdiffusion enables the design of diverse functional proteins from simple molecular specifications.

Indexed on Europe PMC as PubMed record 37433327 (DOI 10.1038/s41586-023-06415-8). Matched by DOI alone: one technology page cites this DOI among its external links (the pages are listed under Related), and this page was written so that the citation resolves inside OnCo. No figure has been checked by an editor.

## Fields

- Kind: Key paper
- Last checked: 2026-09-22
- Tags: europepmc-ingest
- Journal: Nature
- Year: 2023
- DOI: 10.1038/s41586-023-06415-8
- Authors: Watson JL, Juergens D, Bennett NR, et al.
- What it means: One technology page on OnCo cites this paper by its DOI; this record gives the citation a page of its own so a reader can follow it without leaving OnCo. Read the abstract above alongside the citing page listed under Related; the record was created automatically from the Europe PMC entry and its figures have not been checked by hand.
- Caveats: Matched to the citing OnCo records by DOI alone; the summary reproduces the Europe PMC abstract and no figure has been verified against the full paper.

## Sources

- Nature 2023: https://doi.org/10.1038/s41586-023-06415-8
- PubMed: https://pubmed.ncbi.nlm.nih.gov/37433327/
- Europe PMC: https://europepmc.org/article/MED/37433327

## Connected records

- technologies: [RFdiffusion / RFdiffusion2 and ProteinMPNN (Baker Lab)](https://onco.cc/technologies/rfdiffusion/)
- journals: [Nature](https://onco.cc/journals/nature/)

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