Rational design of a highly active N-glycosyltransferase mutant using fragment replacement approach

Abstract

The modularity of carbohydrate-active enzymes facilitates that enzymes with different functions have similar fragments. However, because of the complex structure of the enzyme active sites and the epistatic effects of various mutations on enzyme activity, it is difficult to design enzymes with multiple mutation sites using conventional methods. In this study, we designed multi-point mutants by fragment replacement in the donor-acceptor binding pocket of Actinobacillus pleuropneumoniae N-glycosyltransferase (ApNGT) to obtain novel properties. Candidate fragments were selected from a customized glycosyltransferase database. The stability and substrate-binding energy of the three fragment replacement mutants were calculated in comparison with wild-type ApNGT, and mutants with top-ranking stability and middle-ranking substrate-binding energy were chosen for priority experimental verification. We found that a mutant called F13, which increased the glycosylation efficiency of the natural substrate by 1.44 times, the relative conversion of UDP-galactose by 14.2 times, and the relative conversion of UDP-xylose from almost 0 to 78.6%. Most importantly, F13 mutant acquired an entirely new property, the ability to utilize UDP-glucuronic acid. On one hand, this work shows that replacing similar fragments in the donor-acceptor binding pocket of the enzyme might provide new ideas for designing mutants with new properties; on the other hand, F13 mutant is expected to play an important role in targeted drug delivery.

Keywords

N-glycosyltransferase / Computational design / Glycoprotein / Fragment replacement / GT41

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Data Availability Statement

All data generated or analyzed during this study are included in this published article (and its Supplementary Materials files).

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

CRediT authorship contribution statement

Jiangyu Yang: Writing - review & editing, Writing - original draft, Software, Conceptualization. Kun Li: Writing - review & editing, Writing - original draft, Conceptualization. Yongheng Rong: Methodology, Investigation. Zhaoxi Liu: Visualization, Resources, Formal analysis. Xiaoyu Liu: Formal analysis, Data curation. Yue Yu: Validation, Methodology. Wenjing Shi: Visualization, Formal analysis. Yun Kong: Software, Resources. Min Chen: Writing - review & editing, Writing - original draft, Project administration, Funding acquisition, Conceptualization.

Acknowledgements

This work was supported by the National Key R&D Program of China (2022YFA1304103), the National Natural Science Foundation of China (Grant numbers 32070921), the Key Research and Develop Program of Shandong Province (grant number 2020CXGC010601) and Taishan industry leading talents project (No. tscy20200221).

We would like to thank Jing Zhu, Jingyao Qu, Guannan Lin and Zhifeng Li from State Key laboratory of Microbial Technology of Shandong University for help and guidance in LC-MS. The AlphaFold2 prediction in this paper have been done on the HPC Cloud Platform of Shandong University.

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