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De novo design of luciferases using deep learning

Open access
Yeh, Andy Hsien-Wei, Norn, Christoffer, Kipnis, Yakov, Tischer, Doug, Pellock, Samuel J., Evans, Declan, Ma, Pengchen, Lee, Gyu Rie, Zhang, Jason Z., Anishchenko, Ivan, Coventry, Brian, Cao, Longxing, Dauparas, Justas, Halabiya, Samer, DeWitt, Michelle, Carter, Lauren, Houk, K. N., Baker, David·2023·Nature
DOI 10.1038/s41586-023-05696-3
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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 1,2, 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 diphenylterazine 3 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 = 10 6 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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Show all 7 references

Research works printed but not linked: 35

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Metadata

Authors
Yeh, A. H.-W., Norn, C., Kipnis, Y., Tischer, D., Pellock, S. J., Evans, D., Ma, P., Lee, G. R., Zhang, J. Z., Anishchenko, I., Coventry, B., Cao, L., Dauparas, J., Halabiya, S., DeWitt, M., Carter, L., Houk, K. N., & Baker, D.
Venue
Nature
Publisher
Springer Science and Business Media LLC
Published
2023-02-22
Volume
614
Issue
7949
Pages
774-780
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
DOI
10.1038/s41586-023-05696-3
OpenAlex
W4321480048
Cited by
441
References
7
FWCI
32.95
Type
journal article
Language
en
Open access
hybrid
Licence

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CoChat. (2026). Citation check: De novo design of luciferases using deep learning. https://app.cochat.ai/papers/10.1038/s41586-023-05696-3
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Yeh, A. H.-W., Norn, C., Kipnis, Y., Tischer, D., Pellock, S. J., Evans, D., Ma, P., Lee, G. R., Zhang, J. Z., Anishchenko, I., Coventry, B., Cao, L., Dauparas, J., Halabiya, S., DeWitt, M., Carter, L., Houk, K. N., & Baker, D. (2023). De novo design of luciferases using deep learning. Nature. https://doi.org/10.1038/s41586-023-05696-3

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