Computationally Designed Peroxygenases That Exhibit Diverse and Selective Terpene Oxyfunctionalization

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 (<i>Mth</i>UPO). 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 <i>Mth</i>UPO 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 (<i>R</i>)- to (<i>S</i>)-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.