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Biology & Genetics

De novo design of protein structure and function with RFdiffusion

Joseph L. Watson, David Juergens, David Baker · Collaboration of ~30 authors led from the University of Washington (Baker lab); David Baker is senior/corresponding author. Other authors include Nathaniel R. Bennett, Brian L. Trippe, Jason Yim, Helen E. Eisenach.

Published 11 July 2023 · Nature · Journal article

Summary

This paper introduced RFdiffusion, a generative diffusion model built on the RoseTTAFold network for de novo protein design. It enables a range of design tasks, including unconditional generation, symmetric oligomer design, functional motif scaffolding, and binder design. Many designs were experimentally validated, with solved structures closely matching the intended models.

Key findings

  • Fine-tuning RoseTTAFold as a denoising diffusion model produces a versatile platform for generating novel, designable protein backbones.
  • RFdiffusion succeeded across diverse tasks including symmetric assemblies, enzyme/functional-site scaffolding, and de novo binder design.
  • Experimental characterization, including cryo-EM and crystal structures, confirmed close agreement between designed and realized structures.

Subjects & keywords

Cite this paper

APA

Joseph L. Watson, David Juergens, & David Baker [Collaboration of ~30 authors led from the University of Washington (Baker lab); David Baker is senior/corresponding author. Other authors include Nathaniel R. Bennett, Brian L. Trippe, Jason Yim, Helen E. Eisenach.] (2023). De novo design of protein structure and function with RFdiffusion. Nature. https://doi.org/10.1038/s41586-023-06415-8

BibTeX
@article{watson2023novo,
  author    = {Joseph L. Watson and David Juergens and David Baker and {Collaboration of ~30 authors led from the University of Washington (Baker lab); David Baker is senior/corresponding author. Other authors include Nathaniel R. Bennett, Brian L. Trippe, Jason Yim, Helen E. Eisenach.}},
  title     = {De novo design of protein structure and function with RFdiffusion},
  journal   = {Nature},
  year      = {2023},
  doi       = {10.1038/s41586-023-06415-8},
  url       = {https://doi.org/10.1038/s41586-023-06415-8}
}

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