Algorithm-aided engineering of aliphatic halogenase WelO5* for the asymmetric late-stage functionalization of soraphens

Johannes Büchler, Sumire Honda Malca, David Patsch, Moritz Voss, Nicholas J. Turner, Uwe T. Bornscheuer, Oliver Allemann, Camille Chapelain, Alexandre Lumbroso, Olivier Loiseleur () and Rebecca Buller ()
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Johannes Büchler: Competence Center for Biocatalysis, Institute of Chemistry and Biotechnology, Zurich University of Applied Sciences
Sumire Honda Malca: Competence Center for Biocatalysis, Institute of Chemistry and Biotechnology, Zurich University of Applied Sciences
David Patsch: Competence Center for Biocatalysis, Institute of Chemistry and Biotechnology, Zurich University of Applied Sciences
Moritz Voss: Competence Center for Biocatalysis, Institute of Chemistry and Biotechnology, Zurich University of Applied Sciences
Nicholas J. Turner: School of Chemistry, The University of Manchester, Manchester Institute of Biotechnology
Uwe T. Bornscheuer: Institute of Biochemistry, Dept. of Biotechnology & Enzyme Catalysis, Greifswald University
Oliver Allemann: Syngenta Crop Protection AG
Camille Chapelain: Syngenta Crop Protection AG
Alexandre Lumbroso: Syngenta Crop Protection AG
Olivier Loiseleur: Syngenta Crop Protection AG
Rebecca Buller: Competence Center for Biocatalysis, Institute of Chemistry and Biotechnology, Zurich University of Applied Sciences

Nature Communications, 2022, vol. 13, issue 1, 1-11

Abstract: Abstract Late-stage functionalization of natural products offers an elegant route to create novel entities in a relevant biological target space. In this context, enzymes capable of halogenating sp3 carbons with high stereo- and regiocontrol under benign conditions have attracted particular attention. Enabled by a combination of smart library design and machine learning, we engineer the iron/α-ketoglutarate dependent halogenase WelO5* for the late-stage functionalization of the complex and chemically difficult to derivatize macrolides soraphen A and C, potent anti-fungal agents. While the wild type enzyme WelO5* does not accept the macrolide substrates, our engineering strategy leads to active halogenase variants and improves upon their apparent kcat and total turnover number by more than 90-fold and 300-fold, respectively. Notably, our machine-learning guided engineering approach is capable of predicting more active variants and allows us to switch the regio-selectivity of the halogenases facilitating the targeted analysis of the derivatized macrolides’ structure-function activity in biological assays.

Date: 2022
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DOI: 10.1038/s41467-022-27999-1

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