Computationally designed peroxygenases that exhibit diverse and selective terpene oxyfunctionalization

Abstract

The selective oxyfunctionalization of terpenes remains a major challenge in chemical synthesis and is of significant industrial importance. This study presents a computational enzyme design approach based on an AlphaFold2 model of an unspecific peroxygenase (MthUPO). Using the FuncLib algorithm, only 50 variants were required, and they exhibit remarkable advancements. All 50 designs retained 100% measurable activity across the tested substrate panel, with each design showing activity on at least one substrate. Among the terpene substrates, improvements in activity varied considerably: while some substrates had only a single design exhibiting a ≥2-fold increase in activity, the top-performing substrate had 26 such designs. The most active design per terpene substrate showed enhancements ranging from 2.2-fold to 7.1-fold relative to the wild type. In addition to increased activity, many designs also demonstrated useful and dramatic shifts in regio-, chemo-, and stereoselectivity. Regioselectivity for the energetically less favored 3-hydroxy-β-damascone increased from 3 to 46%. Particularly striking is the dramatic improvement in chemoselectivity for the oxidation of geraniol and nerol to citral A (>99%) and citral B (89%), respectively. While wild-type MthUPO exhibited only a moderate selectivity of 40% for citral A and 72% for citral B, our computationally designed variants displayed significantly enhanced product preference and up to a 4.5-fold increase in activity. Additionally, further products not found with the wild-type enzyme, such as isopiperitenol from limonene and epoxides from geraniol and nerol, were synthesized. For the hydroxylation of β-ionone, the enantioselectivity was inverted to a ratio of 1:99 from (R)- to (S)-4-hydroxy-β-ionone. FuncLib-enabled active-site remodeling allowed us to generate a small yet highly diverse enzyme panel that significantly outperformed the wild type across multiple synthetic challenges. The best-performing variants, such as design 4 and design 11 (both 4 mutations), exhibit improvements that result from epistatic effects. MD simulations demonstrated that these mutations collectively reshape the active site, allowing for regio- and chemoselectivities that are difficult to achieve by single-point mutations. Herein, we demonstrate the potential of in silico-guided approaches to rapidly develop highly selective biocatalysts for synthetic applications.