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.

The introduction of a halogen atom into a small molecule can effectively modulate its properties, yielding bioactive substances of agrochemical and pharmaceutical interest. Consequently, the development of selective halogenation strategies is of high technological value. Besides chemical methodologies, enzymatic halogenations have received increased interest as they allow the selective installation of halogen atoms in molecular scaffolds of varying complexity under mild reaction conditions. Today, a comprehensive library of aromatic halogenases exists, and enzyme as well as reaction engineering approaches are being explored to broaden this enzyme family’s biocatalytic application range. In this review, we highlight recent developments in the sourcing, engineering, and application of flavin-dependent halogenases with a special focus on chemoenzymatic and coupled biosynthetic approaches.

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In 2017, two companies, Novartis AG and Syngenta AG, joined forces with the group of Prof. Buller, head of the Competence Center for Biocatalysis (CCBIO), to tackle the challenge of enzymatic late‐stage halogenation. This biocatalytic method was considered to provide a more sustainable approach to late‐stage halogenation of complex molecules than traditional synthetic approaches. Using machine‐learning guided protein engineering, α‐ketoglutarate dependent halogenases were evolved into versatile catalysts capable of selectively chlorinating inactivated C−H bonds. Structurally diverse molecules, namely an analogue of martinelline as well as two members of the soraphen natural product family, were enzymatically chlorinated at two distinct positions in a regio‐ and stereoselective manner, thus demonstrating the synthetic usefulness of such a strategy. As part of our three‐year collaboration, flavin‐dependent halogenases were also studied.