Author
Listed:
- Johannes Büchler
(Competence Center for Biocatalysis, Institute of Chemistry and Biotechnology, Zurich University of Applied Sciences
School of Chemistry, The University of Manchester, Manchester Institute of Biotechnology)
- 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
Institute of Biochemistry, Dept. of Biotechnology & Enzyme Catalysis, Greifswald University)
- 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
Idorsia Pharmaceuticals Ltd)
- 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)
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.
Suggested Citation
Johannes Büchler & Sumire Honda Malca & David Patsch & Moritz Voss & Nicholas J. Turner & Uwe T. Bornscheuer & Oliver Allemann & Camille Chapelain & Alexandre Lumbroso & Olivier Loiseleur & Rebecca Bu, 2022.
"Algorithm-aided engineering of aliphatic halogenase WelO5* for the asymmetric late-stage functionalization of soraphens,"
Nature Communications, Nature, vol. 13(1), pages 1-11, December.
Handle:
RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-27999-1
DOI: 10.1038/s41467-022-27999-1
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