Systematic engineering of artificial metalloenzymes for new-to-nature reactions

Artificial metalloenzymes (ArMs) catalyzing new-to-nature reactions could play an important role in transitioning toward a sustainable economy. While ArMs have been created for various transformations, attempts at their genetic optimization have been case specific and resulted mostly in modest improvements. To realize their full potential, methods to rapidly discover active ArM variants for ideally any reaction of interest are required. Here, we introduce a reaction-independent, automation-compatible platform, which relies on periplasmic compartmentalization in Escherichia coli to rapidly and reliably engineer ArMs based on the biotin-streptavidin technology. We systematically assess 400 ArM mutants for five bioorthogonal transformations involving different metals, reaction mechanisms, and reactants, which include novel ArMs for gold-catalyzed hydroamination and hydroarylation. Activity enhancements up to 15-fold highlight the potential of the systematic approach. Furthermore, we suggest smart screening strategies and build machine learning models that accurately predict ArM activity from sequence, which has crucial implications for future ArM development.

Automated summary

Artificial metalloenzymes (ArMs) could play a crucial role in transitioning toward a sustainable economy, but methods for rapidly discovering active ArM variants are needed. A reaction-independent, automation-compatible platform based on biotin-streptavidin technology was introduced to engineer ArMs in Escherichia coli, resulting in up to 15-fold activity enhancements. Smart screening strategies and machine learning models were proposed to accurately predict ArM activity, which has significant implications for future ArM development.