Deep-learning model guided discovery and characterization of bacterial unspecific peroxygenases

Abstract

Unspecific peroxygenases (UPOs) are capable of catalyzing the selective oxidation of organic substrates under mild conditions, using hydrogen peroxide (H2O2) as the sole oxidant. This makes them one of the most promising biocatalysts for chemical synthesis. However, the major limitation restricting the application of UPOs to date is their difficulty in heterologous expression. Although more than 4,000 putative UPO enzymes have been recorded in databases, only about 50 of them can currently be heterologously expressed. All UPOs discovered so far originate from eukaryotes (mainly basidiomycetes and some ascomycetes), and they rely on the complex expression systems and post-translational modifications of their native hosts. This further exacerbates the challenges associated with heterologous expression of eukaryotic UPOs. In this work, we developed a deep-learning-based enzyme mining strategy and, for the first time, discovered novel UPO enzymes from bacteria, achieving successful heterologous expression in Escherichia coli. Bacterial UPOs differ greatly from fungal UPOs in sequence similarity, displaying completely distinct evolutionary trajectories. The discovery of bacterial UPOs advances our understanding of the catalytic mechanisms and expression characteristics within the UPO family, breaking the long-held assumption that UPOs can only originate from eukaryotes. Their excellent heterologous expression performance and broad catalytic versatility will further expand the application potential of UPOs.