De novo design of protein structure and function with RFdiffusion.

Journal: Nature
PMID: 37433327

Abstract

There has been considerable recent progress in designing new proteins using deep-learning methods. 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 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.

Authors

  • Joseph L Watson
    Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.
  • David Juergens
    Department of Biochemistry, University of Washington, Seattle, WA 98105.
  • Nathaniel R Bennett
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • Brian L Trippe
    Institute for Protein Design, University of Washington, Seattle, WA, USA.
  • Jason Yim
    DeepMind, London, UK.
  • Helen E Eisenach
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • Woody Ahern
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • Andrew J Borst
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • Robert J Ragotte
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • Lukas F Milles
    Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.
  • Basile I M Wicky
    Department of Biochemistry, University of Washington, Seattle, WA 98105.
  • Nikita Hanikel
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • Samuel J Pellock
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • Alexis Courbet
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • William Sheffler
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • Jue Wang
    State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Taipa, Macau SAR, China.
  • Preetham Venkatesh
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • Isaac Sappington
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • Susana Vázquez Torres
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • Anna Lauko
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • Valentin De Bortoli
    National Centre for Scientific Research, École Normale Supérieure rue d'Ulm, Paris, France.
  • Emile Mathieu
    Department of Engineering, University of Cambridge, Cambridge, UK.
  • Sergey Ovchinnikov
    Center for Systems Biology, Harvard University, Cambridge, MA 02138, United States.
  • Regina Barzilay
    Computer Science and Artificial Intelligence Laboratory , Massachusetts Institute of Technology , 77 Massachusetts Avenue , Cambridge , MA 02139 , USA . Email: regina@csail.mit.edu.
  • Tommi S Jaakkola
    Computer Science and Artificial Intelligence Laboratory , Massachusetts Institute of Technology , 77 Massachusetts Avenue , Cambridge , MA 02139 , USA . Email: regina@csail.mit.edu.
  • Frank DiMaio
    Department of Biochemistry, University of Washington, Seattle, WA 98195, USA.
  • Minkyung Baek
    Department of Biochemistry and Institute for Protein Design, University of Washington, Washington, WA, USA.
  • David Baker
    Department of Biochemistry, University of Washington, Seattle, Washington.

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