Structure-based enzyme engineering improves donor-substrate recognition of Arabidopsis thaliana glycosyltransferases

Aishat Akere; Serena H. Chen; Xiaohan Liu; Yanger Chen; Sarath Chandra Dantu; Alessandro Pandini; Debsindhu Bhowmik; Shozeb Haider

doi:10.1042/BCJ20200477

Structure-based enzyme engineering improves donor-substrate recognition of Arabidopsis thaliana glycosyltransferases

Aishat Akere, Serena H. Chen, Xiaohan Liu, Yanger Chen, Sarath Chandra Dantu, Alessandro Pandini, Debsindhu Bhowmik, Shozeb Haider

Biostatistics and Biomedical Informatics

Research output: Contribution to journal › Article › peer-review

32 Scopus citations

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 modeling and simulations in conjunction with biochemical characterization 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.

Original language	English
Pages (from-to)	2791-2805
Number of pages	15
Journal	Biochemical Journal
Volume	477
Issue number	15
DOIs	https://doi.org/10.1042/BCJ20200477
State	Published - Aug 2020

Funding

sponsored in part by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U.S. Department of Energy. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract no. DE-AC05-00OR22725. AA was funded by the Federal Scholarship Board/Presidential Special Scholarship Scheme for Innovation and Development (PRESSID), Nigeria; SH and YC would like to acknowledge funding from Sichuan Science and Technology Program grant 2018HH0129. This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, under contract number DEAC05-00OR22725. This research is

Funders	Funder number
U.S. Department of Energy
Office of Science
Advanced Scientific Computing Research	DEAC05-00OR22725
Oak Ridge National Laboratory
Sichuan Province Science and Technology Support Program	2018HH0129

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title = "Structure-based enzyme engineering improves donor-substrate recognition of Arabidopsis thaliana glycosyltransferases",

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 modeling and simulations in conjunction with biochemical characterization 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.",

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AU - Pandini, Alessandro

AU - Bhowmik, Debsindhu

AU - Haider, Shozeb

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N2 - 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 modeling and simulations in conjunction with biochemical characterization 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.

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Structure-based enzyme engineering improves donor-substrate recognition of Arabidopsis thaliana glycosyltransferases

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