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<p>Glucose oxidase (GOD), an oxidoreductase (EC 1.1.3.4), catalyzes the oxidation of β-D-glucose to gluconic acid using molecular oxygen as the electron acceptor, with concomitant generation of hydrogen peroxide. Owing to its versatile catalytic properties, GOD has garnered significant attention across diverse fields, including food and beverage manufacture, agriculture, biosensors and biotechnology. However, the inherent limitations of native enzymes, including susceptibility to inactivation under harsh conditions and insufficient catalytic efficiency, restrict their practical utility in advanced industry. This review systematically summarizes recent advances in molecular engineering strategies for GOD optimization, focusing on rational design and directed evolution approaches to improve its functional robustness and application adaptability in the bioeconomy. Furthermore, we highlight the prospective role of artificial intelligence (AI) and machine learning (ML) in addressing the classical activity-stability trade-off, enabling data-driven prediction of mutation hotspots and dynamic regulation of enzymatic properties. By integrating computational biology with experimental validation, this work proposes a theoretical framework and technical roadmap for developing “tailored” GOD variants that meet precise industrial requirements. The insights presented herein aim to bridge the gap between fundamental enzyme research and scalable biomanufacturing, fostering innovation in sustainable biotechnology.</p>

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Modification and applications of glucose oxidase: optimization strategies and high-throughput screening technologies

  • 期刊论文

  • 发表日期：

    2025年7月12日

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  • Zeyang Li,
  • Yong Chen,
  • Xihua Chen,
  • Zhongjian Guo,
  • Guoqiang Guan,
  • Yong Feng,
  • Huayou Chen

摘要

Glucose oxidase (GOD), an oxidoreductase (EC 1.1.3.4), catalyzes the oxidation of β-D-glucose to gluconic acid using molecular oxygen as the electron acceptor, with concomitant generation of hydrogen peroxide. Owing to its versatile catalytic properties, GOD has garnered significant attention across diverse fields, including food and beverage manufacture, agriculture, biosensors and biotechnology. However, the inherent limitations of native enzymes, including susceptibility to inactivation under harsh conditions and insufficient catalytic efficiency, restrict their practical utility in advanced industry. This review systematically summarizes recent advances in molecular engineering strategies for GOD optimization, focusing on rational design and directed evolution approaches to improve its functional robustness and application adaptability in the bioeconomy. Furthermore, we highlight the prospective role of artificial intelligence (AI) and machine learning (ML) in addressing the classical activity-stability trade-off, enabling data-driven prediction of mutation hotspots and dynamic regulation of enzymatic properties. By integrating computational biology with experimental validation, this work proposes a theoretical framework and technical roadmap for developing “tailored” GOD variants that meet precise industrial requirements. The insights presented herein aim to bridge the gap between fundamental enzyme research and scalable biomanufacturing, fostering innovation in sustainable biotechnology.