Recently,the team of Microbial Protein Design and Intelligent Manufacturein the Biotechnology Research Institute, Chinese Academy of Agricultural Sciences (BRI-CAAS) and the Feed Enzyme Engineering Innovation Research Team in the Institute of Animal Science (IAS-CAAS)worked together to develop a new strategy to improve the catalytic activity of glycoside hydrolase based on deep neural networks and molecular evolution analysis. The findings are published in the journal of Science Bulletin.
Glycoside hydrolase is the main enzyme system of polysaccharide degradation, which is widely used in food, feed, agricultural and sideline product processing and waste degradation industry, and has important application value. The market demand for glycoside hydrolase is increasing year by year, but how to improve the catalytic efficiency of glycoside hydrolase and maximize its catalytic potential remain to be a challenging problem.
Fig.1 Computational workflow for enhancing the catalytic efficiency of glycoside hydrolase proteins based on deep neural networks and molecular evolution.
The MECEplatform includes DeepGH, a deep learning model that is able to identify GH families and functionalresidues. This model was developed utilizing 119 GH family protein sequences obtained from theCarbohydrate-Active enZYmes (CAZy) database. After undergoing ten-fold cross-validation, theDeepGH models exhibited a predictive accuracy of 96.73%. The utilization of gradient-weighted classactivation mapping (Grad-CAM) was used to aid us in comprehending the classification features, whichin turn facilitated the creation of enzyme mutants. As a result, the MECE platform was validated with thedevelopment of CHIS1754-MUT7, a mutant that boasts seven amino acid substitutions. The kcat/Km ofCHIS1754-MUT7 was found to be 23.53 times greater than that of the wild type CHIS1754. Due to its highcomputational efficiency and low experimental cost, this method offers significant advantages andpresents a novel approach for the intelligent design of enzyme catalytic efficiency. As a result, it holdsgreat promise for a wide range of applications.
Hanqing Liu, Feifei Guan, Tuoyu Liu and Lixin Yangfrom BRI-CAAS are co-first authors of the paper.Huoqing Huang, Jian Tian, and Feifei Guan are co-corresponding authors of the paper.
The online version available at：https://www.sciencedirect.com/science/article/abs/pii/S2095927323006746 |