2. Academic Validation
  3. De novo design of luciferases using deep learning

De novo design of luciferases using deep learning

  • Nature. 2023 Feb;614(7949):774-780. doi: 10.1038/s41586-023-05696-3.
Andy Hsien-Wei Yeh # 1 2 3,  Christoffer Norn # 4 5,  Yakov Kipnis 4 5 6,  Doug Tischer 4 5,  Samuel J Pellock 4 5,  Declan Evans 7,  Pengchen Ma 7 8,  Gyu Rie Lee 4 5,  Jason Z Zhang 4 5,  Ivan Anishchenko 4 5,  Brian Coventry 4 5 6,  Longxing Cao 4 5,  Justas Dauparas 4 5,  Samer Halabiya 5,  Michelle DeWitt 5,  Lauren Carter 5,  K N Houk 7,  David Baker 9 10 11
Affiliations

Affiliations

  • 1 Department of Biochemistry, University of Washington, Seattle, WA, USA. hsyeh@ucsc.edu.
  • 2 Institute for Protein Design, University of Washington, Seattle, WA, USA. hsyeh@ucsc.edu.
  • 3 Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA. hsyeh@ucsc.edu.
  • 4 Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • 5 Institute for Protein Design, University of Washington, Seattle, WA, USA.
  • 6 Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA.
  • 7 Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, USA.
  • 8 School of Chemistry, Xi'an Key Laboratory of Sustainable Energy Materials Chemistry, MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, Xi'an Jiaotong University, Xi'an, China.
  • 9 Department of Biochemistry, University of Washington, Seattle, WA, USA. dabaker@uw.edu.
  • 10 Institute for Protein Design, University of Washington, Seattle, WA, USA. dabaker@uw.edu.
  • 11 Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA. dabaker@uw.edu.
  • # Contributed equally.
PMID: 36813896 DOI: 10.1038/s41586-023-05696-3
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.

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