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Sequence- and Structure-Based Mining of Thermostable D-Allulose 3-Epimerase and Computer-Guided Protein Engineering To Improve Enzyme Activity

文献类型: 外文期刊

作者: Qi, Hongbin 1 ; Wang, Tong 1 ; Li, Huimin 1 ; Li, Chao 1 ; Guan, Lijun 2 ; Liu, Weidong 3 ; Wang, Jianwen 4 ; Lu, Fuping 1 ; Mao, Shuhong 1 ; Qin, Hui-Min 1 ;

作者机构: 1.Tianjin Univ Sci & Technol, Coll Biotechnol, Key Lab Ind Fermentat Microbiol, Minist Educ,Natl Engn Lab Ind Enzymes,Tianjin Key, Tianjin 300457, Peoples R China

2.Heilongjiang Acad Agr Sci, Inst Food Proc, Harbin 150086, Peoples R China

3.Chinese Acad Sci, Tianjin Inst Ind Biotechnol, Ind Enzymes Natl Engn Lab, Tianjin 300308, Peoples R China

4.Univ Tokyo, Grad Sch Agr & Life Sci, Tokyo 1138657, Japan

关键词: D-allulose 3-epimerase; big data mining; semirational design; in silico screening; thermostability

期刊名称:JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY ( 影响因子:6.1; 五年影响因子:6.3 )

ISSN: 0021-8561

年卷期: 2023 年 71 卷 47 期

页码:

收录情况: SCI

摘要: D-Allulose, a functional sweetener, can be synthesized from fructose using D-allulose 3-epimerase (DAEase). Nevertheless, a majority of the reported DAEases have inadequate stability under harsh industrial reaction conditions, which greatly limits their practical applications. In this study, big data mining combined with a computer-guided free energy calculation strategy was employed to discover a novel DAEase with excellent thermostability. Consensus sequence analysis of flexible regions and comparison of binding energies after substrate docking were performed using phylogeny-guided big data analyses. TtDAE from Thermogutta terrifontis was the most thermostable among 358 candidate enzymes, with a half-life of 32 h at 70 degrees C. Subsequently, structure-guided virtual screening and a customized strategy based on a combinatorial active-site saturation test/iterative saturation mutagenesis were utilized to engineer TtDAE. Finally, the catalytic activity of the M4 variant (P105A/L14C/T63G/I65A) was increased by 5.12-fold. Steered molecular dynamics simulations indicated that M4 had an enlarged substrate-binding pocket, which enhanced the fit between the enzyme and the substrate. The approach presented here, combining DAEases mining with further rational modification, provides guidance for obtaining promising catalysts for industrial-scale production.

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