Orthogonal glycolytic pathway enables directed evolution of noncanonical cofactor oxidase

Edward King, Sarah Maxel, Yulai Zhang, Karissa C. Kenney, Youtian Cui, Emma Luu, Justin B. Siegel, Gregory A. Weiss, Ray Luo and Han Li ()
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Edward King: University of California Irvine
Sarah Maxel: Department Chemical and Biomolecular Engineering University of California Irvine
Yulai Zhang: Department Chemical and Biomolecular Engineering University of California Irvine
Karissa C. Kenney: University of California Irvine
Youtian Cui: Genome Center, University of California Davis
Emma Luu: Genome Center, University of California Davis
Justin B. Siegel: Genome Center, University of California Davis
Gregory A. Weiss: University of California Irvine
Ray Luo: University of California Irvine
Han Li: Department Chemical and Biomolecular Engineering University of California Irvine

Nature Communications, 2022, vol. 13, issue 1, 1-14

Abstract: Abstract Noncanonical cofactor biomimetics (NCBs) such as nicotinamide mononucleotide (NMN+) provide enhanced scalability for biomanufacturing. However, engineering enzymes to accept NCBs is difficult. Here, we establish a growth selection platform to evolve enzymes to utilize NMN+-based reducing power. This is based on an orthogonal, NMN+-dependent glycolytic pathway in Escherichia coli which can be coupled to any reciprocal enzyme to recycle the ensuing reduced NMN+. With a throughput of >106 variants per iteration, the growth selection discovers a Lactobacillus pentosus NADH oxidase variant with ~10-fold increase in NMNH catalytic efficiency and enhanced activity for other NCBs. Molecular modeling and experimental validation suggest that instead of directly contacting NCBs, the mutations optimize the enzyme’s global conformational dynamics to resemble the WT with the native cofactor bound. Restoring the enzyme’s access to catalytically competent conformation states via deep navigation of protein sequence space with high-throughput evolution provides a universal route to engineer NCB-dependent enzymes.

Date: 2022
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DOI: 10.1038/s41467-022-35021-x

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