Rational design of enzyme activity and enantioselectivity

The titer of the microbial fermentation products can be increased by enzyme engineering. L-Sorbosone dehydrogenase (SNDH) is a key enzyme in the production of 2-keto-L-gulonic acid (2-KLG), which is the precursor of vitamin C. Enhancing the activity of SNDH may have a positive impact on 2-KLG production. In this study, a computer-aided semirational design of SNDH was conducted. Based on the analysis of SNDH's substrate pocket and multiple sequence alignment, three modification strategies were established: (1) expanding the entrance of SNDH's substrate pocket, (2) engineering the residues within the substrate pocket, and (3) enhancing the electron transfer of SNDH. Finally, mutants S453A, L460V, and E471D were obtained, whose specific activity was increased by 20, 100, and 10%, respectively. In addition, the ability of Gluconobacter oxidans WSH-004 to synthesize 2-KLG was improved by eliminating H 2 O 2 . This study provides mutant enzymes and metabolic engineering strategies for the microbial-fermentation-based production of 2-KLG.

Section: Resultsmentioning

confidence: 99%

Computer-Aided Semi-Rational Design to Enhance the Activity of l-Sorbosone Dehydrogenase from Gluconobacter oxidans WSH-004

Li,

Wang,

Huo

et al. 2024

“…The strategy for rational enzyme engineering is to predict potential mutants based on knowledge of the link between protein function and structure and then introduce mutations using site-directed mutagenesis. 156 In 1952, Cram and his collaborators proposed a rule to predict the enantioselectivity induced by carbonyl asymmetry based on the least hindered conformation in noncatalytic reactions. 157 The prediction of enzyme stereochemistry is still a major challenge in computational chemistry because it targets very small energy gaps in highly complex macromolecular systems.…”

Section: Application Of Ai Technology For Lipase Enantioselectivitymentioning

confidence: 99%

Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement

Ge,

Chen,

Qian

et al. 2023

With the development of artificial intelligence (AI), tailoring methods for enzyme engineering have been widely expanded. Additional protocols based on optimized network models have been used to predict and optimize lipase production as well as properties, namely, catalytic activity, stability, and substrate specificity. Here, different network models and algorithms for the prediction and reforming of lipase, focusing on its modification methods and cases based on AI, are reviewed in terms of both their advantages and disadvantages. Different neural networks coupled with various algorithms are usually applied to predict the maximum yield of lipase by optimizing the external cultivations for lipase production, while one part is used to predict the molecule variations affecting the properties of lipase. However, few studies have directly utilized AI to engineer lipase by affecting the structure of the enzyme, and a set of research gaps needs to be explored. Additionally, future perspectives of AI application in enzymes, including lipase engineering, are deduced to help the redesign of enzymes and the reform of new functional biocatalysts. This review provides a new horizon for developing effective and innovative AI tools for lipase production and engineering and facilitating lipase applications in the food industry and biomass conversion.

“…114 First, regarding the rational design strategy, the catalytic properties or functions can be predicted when inputting certain amino acid replacements. 115 It largely depends on prior knowledge regarding catalytic mechanisms and physicochemical properties of the protein. Second, through directed evolution of protein stability and activity, improved enzymes can be obtained directly.…”

Section: Prospectivesmentioning

confidence: 99%

Progress and Prospects of Natural Glycoside Sweetener Biosynthesis: A Review

Qu,

Liu,

et al. 2023

To achieve an adequate sense of sweetness with a healthy low-sugar diet, it is necessary to explore and produce sugar alternatives. Recently, glycoside sweeteners and their biosynthetic approaches have attracted the attention of researchers. In this review, we first outlined the synthetic pathways of glycoside sweeteners, including the key enzymes and rate-limiting steps. Next, we reviewed the progress in engineered microorganisms producing glycoside sweeteners, including de novo synthesis, whole-cell catalysis synthesis, and in vitro synthesis. The applications of metabolic engineering strategies, such as cofactor engineering and enzyme modification, in the optimization of glycoside sweetener biosynthesis were summarized. Finally, the prospects of combining enzyme engineering and machine learning strategies to enhance the production of glycoside sweeteners were discussed. This review provides a perspective on synthesizing glycoside sweeteners in microbial cells, theoretically guiding the bioproduction of glycoside sweeteners.