De novo design of luciferases using deep learning.

Journal: Nature
PMID: 36813896

Abstract

De novo enzyme design has sought to introduce active sites and substrate-binding pockets that are predicted to catalyse a reaction of interest into geometrically compatible native scaffolds, but has been limited by a lack of suitable protein structures and the complexity of native protein sequence-structure relationships. Here we describe a deep-learning-based 'family-wide hallucination' approach that generates large numbers of idealized protein structures containing diverse pocket shapes and designed sequences that encode them. We use these scaffolds to design artificial luciferases that selectively catalyse the oxidative chemiluminescence of the synthetic luciferin substrates diphenylterazine and 2-deoxycoelenterazine. The designed active sites position an arginine guanidinium group adjacent to an anion that develops during the reaction in a binding pocket with high shape complementarity. For both luciferin substrates, we obtain designed luciferases with high selectivity; the most active of these is a small (13.9 kDa) and thermostable (with a melting temperature higher than 95 °C) enzyme that has a catalytic efficiency on diphenylterazine (k/K = 10 M s) comparable to that of native luciferases, but a much higher substrate specificity. The creation of highly active and specific biocatalysts from scratch with broad applications in biomedicine is a key milestone for computational enzyme design, and our approach should enable generation of a wide range of luciferases and other enzymes.

Authors

  • Andy Hsien-Wei Yeh
    Department of Biochemistry, University of Washington, Seattle, WA, USA. hsyeh@ucsc.edu.
  • Christoffer Norn
    Department of Biochemistry, University of Washington, Seattle, WA 98105.
  • Yakov Kipnis
    Department of Biochemistry, University of Washington, Seattle, Washington.
  • Doug Tischer
    Department of Biochemistry, University of Washington, Seattle, WA 98105.
  • Samuel J Pellock
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • Declan Evans
    Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, USA.
  • Pengchen Ma
    Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, USA.
  • Gyu Rie Lee
    Department of Chemistry, Seoul National University, Seoul, Republic of Korea.
  • Jason Z Zhang
    Math, Science, and Technology Center, Lexington, KY 40513, United States of America.
  • Ivan Anishchenko
    Computational Biology Program, The University of Kansas, Lawrence, Kansas.
  • Brian Coventry
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • Longxing Cao
    Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, Washington, United States of America.
  • Justas Dauparas
    Department of Biochemistry and Institute for Protein Design, University of Washington, Washington, WA, USA.
  • Samer Halabiya
    Institute for Protein Design, University of Washington, Seattle, WA, USA.
  • Michelle DeWitt
    Institute for Protein Design, University of Washington, Seattle, WA, USA.
  • Lauren Carter
    Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • K N Houk
    Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, USA.
  • David Baker
    Department of Biochemistry, University of Washington, Seattle, Washington.

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