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

Area of Science:

• Biotechnology
• Enzyme Engineering
• Biocatalysis

Background:

• Glucose oxidase (GOD) is a key oxidoreductase with broad applications in food, agriculture, and biosensors.
• Native GOD enzymes exhibit limitations in stability and efficiency, hindering their industrial deployment.
• Molecular engineering offers strategies to overcome these limitations for enhanced enzyme performance.

Purpose of the Study:

• To review recent advancements in molecular engineering of glucose oxidase.
• To explore the integration of artificial intelligence and machine learning in enzyme optimization.
• To propose a framework for developing tailored GOD variants for industrial needs.

Main Methods:

• Systematic review of rational design and directed evolution techniques for GOD.
• Highlighting the role of AI and ML in predicting enzyme properties and guiding mutation strategies.
• Integrating computational biology with experimental validation for enzyme development.

Main Results:

• Molecular engineering strategies significantly improve GOD's functional robustness and adaptability.
• AI and ML show promise in overcoming the activity-stability trade-off in enzymes.
• Development of data-driven approaches for predicting enzyme mutation hotspots.

Conclusions:

• Tailored GOD variants can be developed to meet specific industrial requirements.
• AI and ML integration accelerates the design of high-performance enzymes.
• This research provides a roadmap for advancing enzyme engineering in the bioeconomy and sustainable biotechnology.