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DC Field | Value | Language |
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dc.contributor.author | Akere, A | - |
dc.contributor.author | Chen, SH | - |
dc.contributor.author | Liu, X | - |
dc.contributor.author | Chen, Y | - |
dc.contributor.author | Dantu, SC | - |
dc.contributor.author | Pandini, A | - |
dc.contributor.author | Bhowmik, D | - |
dc.contributor.author | Haider, S | - |
dc.date.accessioned | 2020-07-17T16:15:47Z | - |
dc.date.available | 2020-07-17T16:15:47Z | - |
dc.date.issued | 2020-07-13 | - |
dc.identifier | ORCiD: Serena H. Chen https://orcid.org/0000-0002-6535-6812 | - |
dc.identifier | ORCiD: Sarath Chandra Dantu https://orcid.org/0000-0003-2019-5311 | - |
dc.identifier | ORCiD: Alessandro Pandini https://orcid.org/0000-0002-4158-233X | - |
dc.identifier | ORCiD: Shozeb Haider https://orcid.org/0000-0003-2650-2925 | - |
dc.identifier.citation | Akere, A. et al. (2020) 'Structure-based enzyme engineering improves donor-substrate recognition of Arabidopsis thaliana Glycosyltransferases', Biochemical Journal, 477 (15), pp. 2791-2805. doi: 10.1042/bcj20200477. | en_US |
dc.identifier.issn | 0264-6021 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/21235 | - |
dc.description.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. | en_US |
dc.description.sponsorship | Federal Scholarship Board/Presidential Special Scholarship Scheme for Innovation and Development (PRESSID), Nigeria; Sichuan Science and Technology Program. 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 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. | en_US |
dc.language | English | - |
dc.language.iso | en | en_US |
dc.publisher | Portland Press on behalf of the Biochemical Society | en_US |
dc.rights | Copyright © 2020 The Author(s). This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY). | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | deep-learning | en_US |
dc.subject | UDP-dependent glycosyltransferase | en_US |
dc.subject | molecular dynamics simulations | en_US |
dc.subject | GAR screen | en_US |
dc.subject | mass spectrometry | en_US |
dc.title | Structure-based enzyme engineering improves donor-substrate recognition of Arabidopsis thaliana Glycosyltransferases | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1042/bcj20200477 | - |
dc.relation.isPartOf | Biochemical Journal | - |
pubs.publication-status | Published | - |
dc.identifier.eissn | 1470-8728 | - |
dc.rights.license | https://creativecommons.org/licenses/by/4.0/legalcode.en | - |
dc.rights.holder | The Author(s) | - |
Appears in Collections: | Dept of Computer Science Research Papers |
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FullText.pdf | Copyright © 2020 The Author(s). This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY). | 15.33 MB | Adobe PDF | View/Open |
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