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Vo, Lam Tung

Publications and source records attributed to Vo, Lam Tung.

Stereoconvergent Reduction of Activated Alkenes by a Nicotinamide Free Synergistic Photobiocatalytic System

There is a growing interest in developing cooperative chemoenzymatic reactions to harness the reactivity of chemical catalysts and the selectivity of enzymes for the synthesis of nonracemic chiral compounds. However, existing chemoenzymatic systems with more than one chemical reaction and one enzymatic reaction working cooperatively are rare. Moreover, the application of oxidoreductases in cooperative chemoenzymatic reactions is limited by the necessity of using expensive and unstable redox equivalents such as nicotinamide cofactors. Here we report a lightdriven cooperative chemoenzymatic system comprised of a photoinduced electron transfer reaction (PET) and a photosensitized energy transfer reaction (PEnT) with enzymatic reduction in one-pot to synthesize chiral building blocks of bioactive compounds. As a proof of concept, ene-reductase was directly regenerated by PET in the absence of external cofactors. Meanwhile, enzymatic reduction worked cooperatively with photocatalyst-catalyzed energy transfer that continuously replenished the reactive isomer from the less reactive one. The whole system stereoconvergently reduced E/Z mixtures of alkenes to the enantiopure products. Additionally, enantioselective enzymatic reduction worked competitively with photocatalyst-catalyzed racemic background reaction and side reactions to channel the overall electron flow to the single enantiopure product. As a result, such a light-driven cooperative chemoenzymatic system holds great potential for asymmetric synthesis using inexpensive petroleum or biomass derived alkenes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Data from Stereoconvergent Reduction of Activated Alkenes by a Nicotinamide Free Synergistic Photobiocatalytic System

There is a growing interest in developing cooperative chemoenzymatic reactions to harness the reactivity of chemical catalysts and the selectivity of enzymes for the synthesis of nonracemic chiral compounds. However, existing chemoenzymatic systems with more than one chemical reaction and one enzymatic reaction working cooperatively are rare. Moreover, the application of oxidoreductases in cooperative chemoenzymatic reactions is limited by the necessity of using expensive and unstable redox equivalents such as nicotinamide cofactors. Here, we report a light-driven cooperative chemoenzymatic system comprised of a photoinduced electron transfer reaction (PET) and a photosensitized energy transfer reaction (PEnT) with an enzymatic reduction in one-pot to synthesize chiral building blocks of bioactive compounds. As a proof of concept, ene-reductase was directly regenerated by PET in the absence of external cofactors. Meanwhile, enzymatic reduction worked cooperatively with photocatalyst-catalyzed energy transfer that continuously replenished the reactive isomer from the less reactive one. The whole system stereoconvergently reduced E/Z mixtures of alkenes to the enantiopure products. Additionally, enantioselective enzymatic reduction worked competitively with photocatalyst-catalyzed racemic background reaction and side reactions to channel the overall electron flow to the single enantiopure product. Such a light-driven cooperative chemoenzymatic system holds great potential for asymmetric synthesis using inexpensive petroleum or biomass-derived alkenes.

Catalysis↗

Biosystems Design by Machine Learning

Biosystems such as enzymes, pathways, and whole cells have been increasingly explored for biotechnological applications. Yet, the intricate connectivity and complexity of biosystems pose a major hurdle in designing biosystems with desired features. As -omics and other high throughput technologies have been rapidly developed, the promise of applying machine learning (ML) techniques in biosystems design has started to become a reality. ML models enable the identification of patterns within complicated biological data across multiple scales of analysis and can augment biosystems design applications by predicting new candidates for optimized performance. ML is being used at every stage of biosystems design to help find non-obvious engineering solutions with fewer design iterations. In this review, we first describe commonly used models and modeling paradigms within ML. We then discuss some applications of these models that have already shown success in biotechnological applications. Moreover, we discuss successful applications at all scales of biosystems design, including nucleic acids, genetic circuits, proteins, pathways, genomes, and bioprocess. Lastly, we discuss some limitations of these methods and potential solutions as well as prospects of the combination of ML and biosystems design.

59 BASIC BIOLOGICAL SCIENCES↗