Disaccharide phosphorylases: Structure, catalytic mechanisms and directed evolution.

Disaccharide phosphorylases (DSPs) are carbohydrate-active enzymes with outstanding potential for the biocatalytic conversion of common table sugar into products with attractive properties. They are modular enzymes that form active homo-oligomers. From a mechanistic as well as a structural point of view, they are similar to glycoside hydrolases or glycosyltransferases. As the majority of DSPs show strict stereo- and regiospecificities, these enzymes were used to synthesize specific disaccharides. Currently, protein engineering of DSPs is pursued in different laboratories to broaden the donor and acceptor substrate specificities or improve the industrial particularity of naturally existing enzymes, to eventually generate a toolbox of new catalysts for glycoside synthesis. Herein we review the characteristics and classifications of reported DSPs and the glycoside products that they have been used to synthesize.

Construction of an Artificial In Vitro Synthetic Enzymatic Platform for Upgrading Low-Cost Starch to Value-Added Disaccharides.

Disaccharides are valuable oligosaccharides with an increasing demand in the food, cosmetic, and pharmaceutical industries. Disaccharides can be manufactured by extraction from the acid hydrolysate of plant-derived substrates, but this method has several issues, such as the difficulty in accessing natural substrates, laborious product separation processes, and troublesome wastewater treatment. A chemical synthesis using glucose was developed for producing disaccharides, but this approach suffers from a low product yield due to the low specificity and requires tedious protection and deprotection processes. In this study, we adopted an artificial strategy for producing a variety of value-added disaccharides from low-cost starch through the construction of an in vitro synthetic enzymatic platform: two enzymes worked in parallel to convert starch to glucose and glucose 1-phosphate, and these two intermediates were subsequently condensed together to a disaccharide by a disaccharide phosphorylase. Several disaccharides, such as laminaribiose, cellobiose, trehalose, and sophorose, were produced successfully from starch with the yields of more than 80% with the help of kinetic mathematical models to predict the optimal reaction conditions, exhibiting great potential in an industrial scale. This study provided a promising alternative to reform the mode of disaccharide manufacturing.

The Construction of an In Vitro Synthetic Enzymatic Biosystem that Facilitates Laminaribiose Biosynthesis from Maltodextrin and Glucose.

Laminaribiose is a reducing disaccharide linked by a ß-1,3 glycosidic bond; it is also a precursor for building blocks in the pharmaceutical industry, a powerful germinating agent and antiseptic, as well as a potential prebiotic. In this study, an in vitro enzymatic biosystem composed of α-glucan phosphorylase, laminaribiose phosphorylase, isoamylase, and 4-glucanotransferase is designed for the one-pot synthesis of laminaribiose from low-cost maltodextrin and glucose. Through condition optimization, 51 mM laminaribiose is produced from 10 g L-1 maltodextrin (55.5 mM glucose equivalent) and 90 mM glucose. The product yield based on maltodextrin is 91.9%. To investigate the industrial potential of this in vitro enzymatic biosystem, the production of laminaribiose from high concentrations of substrates is also examined, and 179 mM laminaribiose is produced from 50 g L-1 of maltodextrin and 450 mM glucose. This in vitro enzymatic biosystem comprised of thermophilic enzymes can drastically decrease the manufacturing cost of laminaribiose and provide a green method for the production of other disaccharides using phosphorylases.

Pathway Construction in Corynebacterium glutamicum and Strain Engineering To Produce Rare Sugars from Glycerol.

Rare sugars are valuable natural products widely used in pharmaceutical and food industries. In this study, we expected to synthesize rare ketoses from abundant glycerol using dihydroxyacetone phosphate (DHAP)-dependent aldolases. First, a new glycerol assimilation pathway was constructed to synthesize DHAP. The enzymes which convert glycerol to 3-hydroxypropionaldehyde and l-glyceraldehyde were selected, and their corresponding aldehyde synthesis pathways were constructed in vivo. Four aldol pathways based on different aldolases and phosphorylase were gathered. Next, three pathways were assembled and the resulting strains synthesized 5-deoxypsicose, 5-deoxysorbose, and 5-deoxyfructose from glucose and glycerol and produce l-fructose, l-tagatose, l-sorbose, and l-psicose with glycerol as the only carbon source. To achieve higher product titer and yield, the recombinant strains were further engineered and fermentation conditions were optimized. Fed-batch culture of engineered strains obtained 38.1 g/L 5-deoxypsicose with a yield of 0.91 ± 0.04 mol product per mol of glycerol and synthesized 20.8 g/L l-fructose, 10.3 g/L l-tagatose, 1.2 g/L l-sorbose, and 0.95 g/L l-psicose.

Bioinformatic system modeling on hetian uygur natural longevity people.

Longevity and life science are active topics in biomedicine and other subjects. In this research, longevity people from Hetian area in Xinjiang, China are used as an example. The cause of longevity is discussed and a bioinformatic longevity model is established based on the medical findings. Human life is a complex multi-variant natural process. It is complicated yet important to extract expert knowledge that can describe the interactions among different factors and influence of the factors on human life. Artificial intelligent (AI) and information processing techniques are used to efficiently process large amount of collected biomedical data and effectively extract hidden information into the longevity model. The test results show that the established model is able to identify individuals who belong to longevity group with over 90 percent accuracy. This research creates a new approach to explore the cause of formation of human longevity based on comprehensive medical data rather than just from one medical subject. More importantly, this research explores a practical way to model complex bioinformatic systems.