The degradation of vancomycin to a series of aglucovancomycin derivatives containing modifications in key functional groups and a study of their thermal atropisomerism are detailed. In all of the cases, selective isomerism of the DE ring system atropisomers was observed under conditions where the CD and AB stereochemistries were unaffected. Competitive retro aldol ring cleavage of the CD and DE ring systems (CD > DE) was observed but could be minimized by the choice of solvent and thermal conditions (DE ring system) or precluded by alcohol protection (CD ring system). Similarly, competitive main chain succinimide formation through the loss of ammonia from the Asn residue could be minimized by the choice of thermal conditions or prevented by carboxamide protection. Resynthesis of natural aglucovancomycin, (M,M,M)-2, and its unnatural DE atropisomer (P,M,M)-2 from 6 are described. The comparative antimicrobial activity of the key derivatives and their unnatural DE ring system P-diastereomers are disclosed.

Beating the superbugs: Diversity‐oriented synthesis using a solid‐supported phosphonate unit to synthesize 242 drug‐like compounds based on 18 natural‐product‐like scaffolds led to the discovery of gemmacin (see scheme). This new structural class of antibiotic is active towards methicillin‐resistant Staphylococcus aureus (MRSA).

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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.

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