Robust deep learning-based protein sequence design using ProteinMPNN.

Journal: Science (New York, N.Y.)
Published Date: Oct 7, 2022

Abstract

Although deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here, we describe a deep learning-based protein sequence design method, ProteinMPNN, that has outstanding performance in both in silico and experimental tests. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4% compared with 32.9% for Rosetta. The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges. We demonstrate the broad utility and high accuracy of ProteinMPNN using x-ray crystallography, cryo-electron microscopy, and functional studies by rescuing previously failed designs, which were made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target-binding proteins.

Authors

  • J Dauparas
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • I Anishchenko
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • N Bennett
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • H Bai
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • R J Ragotte
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • L F Milles
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • B I M Wicky
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • A Courbet
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • R J de Haas
    Department of Physical Chemistry and Soft Matter, Wageningen University and Research, Wageningen, Netherlands.
  • N Bethel
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • P J Y Leung
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • T F Huddy
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • S Pellock
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • D Tischer
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • F Chan
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • B Koepnick
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • H Nguyen
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • A Kang
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • B Sankaran
    Berkeley Center for Structural Biology, Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley Laboratory, Berkeley, CA, USA.
  • A K Bera
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • N P King
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • D Baker
    Department of Biochemistry, University of Washington, Seattle, WA, USA.

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