Secretion and engineering of unspecific peroxygenases for diverse terpene oxyfunctionalization

Abstract

Unspecific peroxygenases (UPOs) are versatile enzymes for valuable oxyfunctionalization reactions, but their application is limited by poor stability, activity, and selectivity. This thesis addresses these challenges through combined computational and experimental enzyme engineering. Computational stability design combined with secretion engineering enabled functional expression of previously inaccessible UPOs, including the first non-fungal UPOs. Using MthUPO as a model, structure-guided directed evolution supported by MD simulations improved activity and enantioselectivity in terpene hydroxylation, yielding enantiodivergent products. In addition, algorithm-based design generated a UPO library that enabled optimization across multiple substrates, resulting in large activity improvements and altered selectivity. Overall, the work shows how combinations of computational tools and experimental validation can systematically advance UPOs toward robust and versatile biocatalysts.