A novel thermophilic chitinase directly mined from the marine metagenome using the deep learning tool Preoptem

文献类型： 外文期刊

第一作者： Zhang, Yan

作者： Zhang, Yan;Guan, Feifei;Xu, Guoshun;Liu, Xiaoqing;Zhang, Yuhong;Wu, Ningfeng;Tian, Jian;Zhang, Yan;Sun, Jilu;Xu, Guoshun;Yao, Bin;Huang, Huoqing

作者机构：

关键词： Deep learning; Chitinase; Thermal stability; Chitooligosaccharides

期刊名称：BIORESOURCES AND BIOPROCESSING （ 影响因子：4.983； ）

ISSN：

年卷期： 2022 年 9 卷 1 期

页码：

收录情况： SCI

摘要： Chitin is abundant in nature and its degradation products are highly valuable for numerous applications. Thermophilic chitinases are increasingly appreciated for their capacity to biodegrade chitin at high temperatures and prolonged enzyme stability. Here, using deep learning approaches, we developed a prediction tool, Preoptem, to screen thermophilic proteins. A novel thermophilic chitinase, Chi304, was mined directly from the marine metagenome. Chi304 showed maximum activity at 85 degrees C, its T-m reached 89.65 +/- 0.22 degrees C, and exhibited excellent thermal stability at 80 and 90 degrees C. Chi304 had both endo- and exo-chitinase activities, and the (GlcNAc)(2) was the main hydrolysis product of chitin-related substrates. The product yields of colloidal chitin degradation reached 97% within 80 min, and 20% over 4 days of reaction with crude chitin powder. This study thus provides a method to mine the novel thermophilic chitinase for efficient chitin biodegradation.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序