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.

Biosensors have emerged as a valuable tool with high specificity and sensitivity for fast and reliable detection of hazardous substances in drinking water. Numerous substances have been addressed using synthetic biology approaches. However, many proposed biosensors are based on living, genetically modified organisms and are therefore limited in shelf life, usability and biosafety. We addressed these issues by the construction of an extensible, cell-free biosensor. Storage is possible through freeze drying on paper. Following the addition of an aqueous sample, a highly efficient cell-free protein synthesis (CFPS) reaction is initiated. Specific allosteric transcription factors modulate the expression of ‘superfolder’ green fluorescent protein (sfGFP) depending on the presence of the substance of interest. The resulting fluorescence intensities are analyzed with a conventional smartphone accompanied by simple and cheap light filters. An ordinary differential equitation (ODE) model of the biosensors was developed, which enabled prediction and optimization of performance. With an optimized cell-free biosensor based on the Shigella flexneri MerR transcriptional activator, detection of 6 μg/L Hg(II) ions in water was achieved. Furthermore, a completely new biosensor for the detection of gamma-hydroxybutyrate (GHB), a substance used as date-rape drug, was established by employing the naturally occurring transcriptional repressor BlcR from Agrobacterium tumefaciens .

Enzyme engineering has made impressive progress in the past decades, paving the way for the widespread use of enzymes for various purposes. In contrast to “classical” enzyme engineering, which focuses on optimizing specific properties of natural enzymes, a more recent trend towards the creation of artificial enzymes that catalyze fundamentally distinct, new‐to‐nature reactions is observable. While approaches for creating such enzymes differ significantly, they share the common goal of enabling biocatalytic novelty to broaden the range of applications for enzymes. Although most artificial enzymes reported to date are only moderately active and barely function in vivo, they have the potential to endow cells with capabilities that were previously out of reach and thus herald a new wave of “functional xenobiology”. Herein, we highlight recent developments in the field of artificial enzymes with a particular focus on challenges and opportunities for their use in xenobiology.

Artificial metalloenzymes (ArMs) catalyze new-to-nature reactions under mild conditions and could therefore play an important role in the transition to a sustainable, circular economy. While ArMs have been created for a variety of reactions, their activity for most biorthogonal transformations has remained modest and attempts at optimizing them by means of enzyme engineering have been case-specific and unsystematic. To realize the great potential of ArMs for biocatalysis and synthetic biology, there is a need for methods that enable the rapid discovery of highly active ArM variants for any reaction of interest. Here, we present a broadly applicable, automation-compatible ArM engineering platform. It relies on periplasmic compartmentalization of the ArM in Escherichia coli to rapidly and reliably identify improved variants based on the biotin-streptavidin technology. We assess 400 sequence-verified ArM mutants for five bio-orthogonal transformations involving different metal cofactors, reaction mechanisms and substrate-product pairs, including novel ArMs for gold-catalyzed hydroamination and hydroarylation. The achieved activity enhancements of six-to fifteen-fold highlight the potential of the proposed systematic approach to ArM engineering. We further capitalize on the sequence-activity data to suggest and validate smart strategies for future screening campaigns. This systematic, multi-reaction study has important implications for the development of highly active ArMs for novel applications in biocatalysis and synthetic biology.