Artificial intelligence-driven insights into the modulation of enzyme activity toward accelerated industrial...

Artificial Intelligence (AI) has emerged as a transformative tool in industrial enzyme engineering, shifting from empirical optimization to data-driven, rational modulation of enzyme activity. Recent AI methodologies extend beyond traditional structure-guided strategies, enabling the prediction of functional residues, estimation of mutational effects, and design of enzyme sequences tailored for enhanced catalytic properties. This review systematically explores the latest advances in AI-driven approaches for enzyme activity modulation, focusing on industrial applications. It first outlines major enzyme classes and their associated regulatory challenges, then delves into AI methods such as structure prediction, mutation effect prediction, and generative design frameworks. Selected case studies from industries like food processing, biomedicine, and biotechnology demonstrate the practical impacts of AI in enzyme engineering. Finally, the review addresses key challenges, including data scarcity, model interpretability, and the gap between computational predictions and real-world industrial implementation. The integration of AI with experimental systems offers a promising path forward, aiming for robust, scalable enzyme design and industrial applications.