Directed Evolution and Computational Modeling of Galactose Oxidase toward Bulky Benzylic and Alkyl Secondary Alcohols

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Directed Evolution and Computational Modeling of Galactose Oxidase toward Bulky Benzylic and Alkyl Secondary Alcohols
Title:
Directed Evolution and Computational Modeling of Galactose Oxidase toward Bulky Benzylic and Alkyl Secondary Alcohols
Journal Title:
ACS Catalysis
Publication Date:
01 December 2023
Citation:
Yeo, W. L., Tay, D. W. P., Miyajima, J. M. T., Supekar, S., Teh, T. M., Xu, J., Tan, Y. L., See, J. Y., Fan, H., Maurer-Stroh, S., Lim, Y. H., & Ang, E. L. (2023). Directed Evolution and Computational Modeling of Galactose Oxidase toward Bulky Benzylic and Alkyl Secondary Alcohols. ACS Catalysis, 13(24), 16088–16096. https://doi.org/10.1021/acscatal.3c03427
Abstract:
In the field of alcohol oxidation, galactose oxidase (GOase) is one of the most established enzymes capable of this important chemical transformation under benign conditions. However, the applicability of GOase toward more complex molecules such as those frequently found in the pharmaceutical or agrochemical industries remains restricted. Here, by employing a combined approach of directed evolution and computational modeling, we have identified improved GOases with significantly expanded substrate specificity toward both bulky benzylic and alkyl secondary alcohols, showing activity enhancements of up to 2400-fold compared to the reported benchmark M3‑5 mutant. Beneficial mutations conveying relaxed substrate enantioselectivity biases (R/S ratios down to 1.05) and higher thermostabilities (up to 1.6-fold improvement in residual activity versus benchmark) have also been identified. We have applied computational tools YASARA, FoldX, SCWRL, and Glide to show reasonable correlation with features related to GOase structure, protein stability, and catalytic activity. The generated enzyme activity models based on MM/GBSA (r = −0.85) and YASARA (r = −0.89) have successfully predicted the activity trend of a family of related substrates based on the 1-phenyl-1-alkyl alcohol scaffold with varying alkyl chain lengths. Together with curated experimental data sets and further optimization of these in silico models, these approaches can serve as gateway to explore desirable enzyme characteristics, establish enzyme substrate scopes, and accelerate biocatalyst development.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research (A*STAR), Singapore - Advanced Manufacturing and Engineering (AME) SERC Strategic Funds
Grant Reference no. : A1718g0092

This research / project is supported by the Agency for Science, Technology and Research (A*STAR), Singapore - AME Industry Alignment Fund Prepositioning (IAF-PP) PIPS
Grant Reference no. : A19B3a0009

This research / project is supported by the Agency for Science, Technology and Research (A*STAR), Singapore - NA
Grant Reference no. : C211917003

This research / project is supported by the Agency for Science, Technology and Research (A*STAR), Singapore - NA
Grant Reference no. : C211917010

This research / project is supported by the Agency for Science, Technology and Research (A*STAR), Singapore - NA
Grant Reference no. : C233017004

This research / project is supported by the Agency for Science, Technology and Research (A*STAR), Singapore - Manufacturing Trade and Connectivity (MTC) Individual Research Grant (IRG)
Grant Reference no. : M22K2c0086
Description:
This document is the Accepted Manuscript version of a Published Work that appeared in final form in ACS Catalysis, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see doi.org/10.1021/acscatal.3c03427
ISSN:
2155-5435
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