AI-Assisted rational enzyme engineering: advancing biocatalysis for industrial applications
Abstract
The emergence of artificial intelligence (AI) has initiated a paradigm shift in enzyme engineering. While traditional methods like directed evolution and rational design are proven, they often suffer from limited predictive power, scalability, and efficiency. The integration of AI, particularly machine learning and deep learning, into rational enzyme design allows for accurate modeling of enzyme-substrate interactions, prediction of beneficial mutations, and optimization of catalytic parameters. This review comprehensively examines how AI is enhancing rational enzyme engineering to improve catalytic activity, selectivity, and stability. It outlines the core AI approaches, essential computational tools, and structural databases revolutionizing the field and presents case studies where AI-enhanced enzymes have demonstrated superior performance in biofuel production, pharmaceutical synthesis, and environmental remediation. Examples include generative AI-designed enzymes with enhanced catalytic characteristics, machine learning-assisted engineering of ketoreductases and transaminases for pharmaceutical production, and AI-guided optimization of PETase variants for faster plastic breakdown. Current limitations are critically analyzed, including data scarcity and the interpretability of complex models. Finally, the review proposes future directions, emphasizing the need for collaborative AI-biology ecosystems, explainable AI (XAI), and integrated ethical frameworks. The convergence of AI and enzyme engineering holds significant promise for accelerating the development of customized biocatalysts to enable sustainable and innovative industrial solutions.
