Progress in the Application of Enzyme Improvement Design Technology
DOI:
https://doi.org/10.61173/9aq1fr25Keywords:
-Enzyme engineering, rational design of en-zymes, random design of enzymes, semi-rational design of enzymesAbstract
At present, enzyme improvement is becoming more and more indispensable in production and scientific research, but there is no accurate improvement method recommendation for enzymes of different types and structures. In the current research, the author has analyzed several enzyme improvement methods and given suggestions for improvement methods for different types of enzymes. However, with the development of biotechnology, enzyme improvement technology is also constantly updated and iterated. This article analyzes the most commonly used mainstream methods. This article compares and studies three methods of enzyme improvement design, namely rational design, semi-rational design and random design, and conducts a detailed study and analysis of these three improvement methods, and obtains the most suitable improvement methods for different types of enzymes. It can provide a reference for the selection of enzyme improvement methods in future research, but the screening of methods when the enzyme meets the conditions of multiple improvement methods at the same time has not been solved. Future research can focus on the screening and prediction of improvement methods.
References
[1] H. Minagawa, Y. Yoshida, N. Kenmochi, M. Furuichi, J. Shimada, and H. Kaneko, “Improving the thermal stability of lactate oxidase by directed evolution,” Cellular and Molecular Life Sciences, vol. 64, pp. 77–81, 2007.
[2] J. Bi, S. Chen, X. Zhao, Y. Nie, and Y. Xu, “Computationaided engineering of starch-debranching pullulanase from Bacillus thermoleovorans for enhanced thermostability,” Applied Microbiology and Biotechnology, vol. 104, pp. 7551–7562, Dean&Francis ISSN 2959-409X 2020.
[3] A. E. Wentz and E. V. Shusta, “A novel high-throughput screen reveals yeast genes that increase secretion of heterologous proteins,” Applied and Environmental Microbiology, vol. 73, no. 4, pp. 1189–1198, 2007.
[4] L. Li, X. Liu, Y. Bai, B. Yao, H. Luo, and T. Tu, “Highthroughput screening techniques for the selection of thermostable enzymes,” Journal of Agricultural and Food Chemistry, vol. 72, no. 8, pp. 3833–3845, 2024.
[5] L.-S. Gu et al., “ARTP/EMS-combined multiple mutagenesis efficiently improved production of raw starch-degrading enzymes in Penicillium oxalicum and characterization of the enzyme-hyperproducing mutant,” Biotechnology for Biofuels, vol. 13, pp. 1–12, 2020.
[6] L. I. Gutierrez-Rus, M. Alcalde, V. A. Risso, and J. M. Sanchez-Ruiz, “Efficient base-catalyzed Kemp elimination in an engineered ancestral enzyme,” International Journal of Molecular Sciences, vol. 23, no. 16, p. 8934, 2022.
[7] A. Li, Q. Wang, X. Song, X. Zhang, J. W. Huang, C. C. Chen et al., “Engineering of a P450-based Kemp eliminase with a new mechanism,” Chinese Journal of Catalysis, vol. 47, pp. 191–199, 2023.
[8] C. Paul, U. Hanefeld, F. Hollmann, G. Qu, B. Yuan, and Z. Sun, “Enzyme engineering for biocatalysis,” Molecular Catalysis, vol. 555, p. 113874, 2024.
[9] Y. Sürmeli and G. Şanlı‐Mohamed, “Engineering of xylanases for the development of biotechnologically important characteristics,” Biotechnology and Bioengineering, vol. 120, no. 5, pp. 1171–1188, 2023.
[10] K. Ding, M. Chin, Y. Zhao, W. Huang, B. K. Mai, H. Wang et al., “Machine learning-guided co-optimization of fitness and diversity facilitates combinatorial library design in enzyme engineering,” Nature Communications, vol. 15, no. 1, p. 6392, 2024.
[11] Z. Wu, S. J. Kan, R. D. Lewis, B. J. Wittmann, and F. H. Arnold, “Machine learning-assisted directed protein evolution with combinatorial libraries,” Proceedings of the National Academy of Sciences, vol. 116, no. 18, pp. 8852–8858, 2019.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 by the authors.
This work is licensed under a Creative Commons Attribution 4.0 International License.
