Improving enzyme activity of glucosamine-6-phosphate synthase by semi-rational design strategy and computer analysis

Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement

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.

An update on the review of microbial synthesis of glucosamine and N-acetylglucosamine

Glucosamine (GlcN) is a natural amino monosaccharide in which a hydroxyl group of glucose is substituted by an amino group. It belongs to functional amino sugar compounds. In the traditional preparation process, GlcN and GlcNAc are obtained by hydrolyzing the cell wall of shrimp and crab. There are many potential problems with this method, such as geographical and seasonal restrictions on the supply of raw materials, serious environmental pollution and potential allergic reactions. Microbial fermentation has the advantages of mild conditions, low environmental pollution, high production intensity, and product safety. It can effectively solve the problem of shrimp and crab hydrolysis process, attracting many researchers to participate in the research of microbial fermentation production of GlcN. This paper mainly summarizes the research on strain construction method, metabolic pathway design and fermentation condition optimization in microbial fermentation, which has certain guiding significance for the further production, research and production of glucosamine.

Novel Detection Method for Evaluating the Activity of an Alkaline Serine Protease from Bacillus clausii

Until now, the detection methods for serine proteases have been quite time-consuming or cannot indicate the "real" protease activity. Here, a rapid and simple method for determining the "real" activity of serine proteases toward AAPX (a kind of mixed polypeptide substrates, with X representing 20 standard amino acids) was developed. This AAPX method has high reliability, sensitivity, and repeatability and can be used for detecting the serine protease activity spectrophotometrically. Additionally, the site-directed saturation mutagenesis library of alkaline serine protease PRO (BcPRO) from Bacillus clausii was screened with this AAPX method. Three beneficial mutants S99R, S99H, and S99W were identified, and S99W displayed the highest activity. In comparison to wild-type BcPRO, S99W exhibited enhanced catalytic performance toward eight AAPX monomers, and the molecular dynamics simulation revealed the mechanism responsible for its improved activity toward AAPM. Consequently, this work provides an efficient method for detecting, characterizing, mining, and high-throughput screening of serine proteases.