文献类型：期刊文献

英文题名：Crius: A novel fragment-based algorithm of de novo substrate prediction for enzymes

作者：Yao, Zhiqiang[1];Jiang, Shuiqin[1];Zhang, Lujia[1,2,3];Gao, Bei[1];He, Xiao[2,3];Zhang, John Z. H.[2,3];Wei, Dongzhi[1]

机构：[1]East China Univ Sci & Technol, State Key Lab Bioreactor Engn, Meilong Rd 130th, Shanghai, Peoples R China;[2]East China Normal Univ, Sch Mol Engn, Dept Chem, Shanghai Engn Res Ctr Mol Therapeut & New Drug De, Shanghai 200062, Peoples R China;[3]NYU Shanghai, NYU ECNU Ctr Computat Chem, Shanghai 200062, Peoples R China

年份：2018

卷号：27

期号：8

起止页码：1526

外文期刊名：PROTEIN SCIENCE

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000446175200015)】；

基金：Grant sponsor: National Key R&D Program of China; Grant number: 2016YFA0501701; Grant sponsor: National Natural Science Foundation of China; Grant numbers: 31571786 and 31772007; Grant sponsor: Natural Science Foundation of Shanghai; Grant number: 16ZR1449500; Grant sponsor: Innovation Program of Shanghai Municipal Education Commission; Grant number: 201701070005E00020; Grant sponsor: Open Funding Project of the State Key Laboratory of Bioreactor Engineering and the Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase; Grant number: U1501501.

语种：英文

外文关键词：substrate prediction; enzyme selectivity; substrate specificity; fragment-based algorithm

摘要：The study of enzyme substrate specificity is vital for developing potential applications of enzymes. However, the routine experimental procedures require lot of resources in the discovery of novel substrates. This article reports an in silico structure-based algorithm called Crius, which predicts substrates for enzyme. The results of this fragment-based algorithm show good agreements between the simulated and experimental substrate specificities, using a lipase from Candida antarctica (CALB), a nitrilase from Cyanobacterium syechocystis sp. PCC6803 (Nit6803), and an aldo-keto reductase from Gluconobacter oxydans (Gox0644). This opens new prospects of developing computer algorithms that can effectively predict substrates for an enzyme.