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De novo design of luciferases using deep learning

Author

Listed:
  • Andy Hsien-Wei Yeh

    (University of Washington
    University of Washington
    University of California, Santa Cruz)

  • Christoffer Norn

    (University of Washington
    University of Washington)

  • Yakov Kipnis

    (University of Washington
    University of Washington
    University of Washington)

  • Doug Tischer

    (University of Washington
    University of Washington)

  • Samuel J. Pellock

    (University of Washington
    University of Washington)

  • Declan Evans

    (University of California, Los Angeles)

  • Pengchen Ma

    (University of California, Los Angeles
    Xi’an Jiaotong University)

  • Gyu Rie Lee

    (University of Washington
    University of Washington)

  • Jason Z. Zhang

    (University of Washington
    University of Washington)

  • Ivan Anishchenko

    (University of Washington
    University of Washington)

  • Brian Coventry

    (University of Washington
    University of Washington
    University of Washington)

  • Longxing Cao

    (University of Washington
    University of Washington)

  • Justas Dauparas

    (University of Washington
    University of Washington)

  • Samer Halabiya

    (University of Washington)

  • Michelle DeWitt

    (University of Washington)

  • Lauren Carter

    (University of Washington)

  • K. N. Houk

    (University of California, Los Angeles)

  • David Baker

    (University of Washington
    University of Washington
    University of Washington)

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 scaffolds1,2, 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 diphenylterazine3 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 (kcat/Km = 106 M−1 s−1) 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.

Suggested Citation

  • Andy Hsien-Wei Yeh & Christoffer Norn & Yakov Kipnis & Doug Tischer & Samuel J. Pellock & Declan Evans & Pengchen Ma & Gyu Rie Lee & Jason Z. Zhang & Ivan Anishchenko & Brian Coventry & Longxing Cao &, 2023. "De novo design of luciferases using deep learning," Nature, Nature, vol. 614(7949), pages 774-780, February.
  • Handle: RePEc:nat:nature:v:614:y:2023:i:7949:d:10.1038_s41586-023-05696-3
    DOI: 10.1038/s41586-023-05696-3
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    Cited by:

    1. Yue Xu & Shihao Ma & Haotian Cui & Jingan Chen & Shufen Xu & Fanglin Gong & Alex Golubovic & Muye Zhou & Kevin Chang Wang & Andrew Varley & Rick Xing Ze Lu & Bo Wang & Bowen Li, 2024. "AGILE platform: a deep learning powered approach to accelerate LNP development for mRNA delivery," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    2. Lucien F. Krapp & Fernando A. Meireles & Luciano A. Abriata & Jean Devillard & Sarah Vacle & Maria J. Marcaida & Matteo Dal Peraro, 2024. "Context-aware geometric deep learning for protein sequence design," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    3. Enrico Orsi & Lennart Schada von Borzyskowski & Stephan Noack & Pablo I. Nikel & Steffen N. Lindner, 2024. "Automated in vivo enzyme engineering accelerates biocatalyst optimization," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

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