De novo design of luciferases using deep learning

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, Justas Dauparas, Samer Halabiya, Michelle DeWitt, Lauren Carter, K. N. Houk and David Baker ()
Additional contact information
Andy Hsien-Wei Yeh: University of Washington
Christoffer Norn: University of Washington
Yakov Kipnis: University of Washington
Doug Tischer: University of Washington
Samuel J. Pellock: University of Washington
Declan Evans: University of California, Los Angeles
Pengchen Ma: University of California, Los Angeles
Gyu Rie Lee: University of Washington
Jason Z. Zhang: University of Washington
Ivan Anishchenko: University of Washington
Brian Coventry: University of Washington
Longxing Cao: University of Washington
Justas Dauparas: 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

Nature, 2023, vol. 614, issue 7949, 774-780

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41586-023-05696-3

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