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 Le Chapelain, Alexandre Lumbroso, Olivier Loiseleur, Rebecca Buller

Nature communications 2022 Jan 18

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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. © 2022. The Author(s).

Citation

Johannes Büchler, Sumire Honda Malca, David Patsch, Moritz Voss, Nicholas J Turner, Uwe T Bornscheuer, Oliver Allemann, Camille Le Chapelain, Alexandre Lumbroso, Olivier Loiseleur, Rebecca Buller. Algorithm-aided engineering of aliphatic halogenase WelO5* for the asymmetric late-stage functionalization of soraphens. Nature communications. 2022 Jan 18;13(1):371