The hallmark reaction of P450 monooxygenase involves activation of C–H bond and the production of a hydroxyl. P450s tailoring natural product could further oxidize the hydroxyl to carboxylic acid. However, heterogeneously expressed plant P450s display poor chemo- and regioselectivity in microbes, restricting the efficient biosynthesis of related natural products. CYP72A63 is a P450 enzyme previously used for the biosynthesis of glycyrrhetinic acid, and its poor selectivity resulted in oxidation of 11-oxo-β-amyrin to a mixture of rare licorice triterpenoids (glycyrrhetol, glycyrrhetaldehyde, glycyrrhetinic acid, and 29-OH-11-oxo-β-amyrin). In this study, we have identified key residues, which influence the enzyme–substrate hydrophobic interaction, in controlling the chemo- and regioselectivity of the enzyme and engineered the enzyme toward selectivity oxidation to hydroxyl and carboxylic acid. Moreover, tuning the redox partner of the P450 leads to selective production of glycyrrhetaldehyde, a good starting point for further modification. In this study, controlling the catalytic property of plant P450s prove to be of great use in the synthesis of desired licorice triterpenoids, which can be used in biosynthesis of other terpenoid natural products.

No abstract

No abstract

User interest modeling is critical for personalized news recommendation. Existing news recommendation methods usually learn a single user embedding for each user from their previous behaviors to represent their overall interest. However, user interest is usually diverse and multi-grained, which is difficult to be accurately modeled by a single user embedding. In this paper, we propose a news recommendation method with hierarchical user interest modeling, named HieRec. Instead of a single user embedding, in our method each user is represented in a hierarchical interest tree to better capture their diverse and multi-grained interest in news. We use a three-level hierarchy to represent 1) overall user interest; 2) user interest in coarse-grained topics like sports; and 3) user interest in fine-grained topics like football. Moreover, we propose a hierarchical user interest matching framework to match candidate news with different levels of user interest for more accurate user interest targeting. Extensive experiments on two real-world datasets validate our method can effectively improve the performance of user modeling for personalized news recommendation.

No abstract

No abstract

No abstract

No abstract