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

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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    Cited by:

    1. Jie Ni & Jingyuan Zhuang & Yiming Shi & Ying-Chih Chiang & Gui-Juan Cheng, 2024. "Discovery and substrate specificity engineering of nucleotide halogenases," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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