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Title: Structure-based enzyme engineering improves donor-substrate recognition of Arabidopsis thaliana Glycosyltransferases
Authors: Akere, A
Chen, SH
Liu, X
Chen, Y
Dantu, SC
Pandini, A
Bhowmik, D
Haider, S
Keywords: Deep-learning;UDP-dependent glycosyltransferase;Molecular dynamics simulations;GAR screen;Mass spectrometry
Issue Date: 13-Jul-2020
Publisher: Portland Press
Citation: ishat Akere, Serena H Chen, Xiaohan Liu, Yanger Chen, Sarath Chandra Dantu, Alessandro Pandini, Debsindhu Bhowmik, Shozeb Haider; Structure-based enzyme engineering improves donor-substrate recognition of Arabidopsis thaliana Glycosyltransferases . Biochem J BCJ20200477.
Abstract: Glycosylation of secondary metabolites involves plant UDP-dependent glycosyltransferases (UGTs). UGTs have shown promise as catalysts in the synthesis of glycosides for medical treatment. However, limited understanding at the molecular level due to insufficient biochemical and structural information has hindered potential applications of most of these UGTs. In the absence of experimental crystal structures, we employed advanced molecular modelling and simulations in conjunction with biochemical characterisation to design a workflow to study five Group H Arabidopsis thaliana (76E1, 76E2, 76E4, 76E5, 76D1) UGTs. Based on our rational structural manipulation and analysis, we identified key amino acids (P129 in 76D1; D374 in 76E2; K275 in 76E4), which when mutated improved donor-substrate recognition than wildtype UGTs. Molecular dynamics simulations and deep learning analysis identified structural differences, which drive substrate preferences. The design of these UGTs with broader substrate specificity may play important role in biotechnological and industrial applications. These findings can also serve as basis to study other plant UGTs and thereby advancing UGT enzyme engineering.
ISSN: 0264-6021
Appears in Collections:Dept of Computer Science Research Papers

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