Data-driven strategies for enzyme engineering: From machine learning to protein language models

Abstract

Enzymes are key biocatalysts that drive diverse biological processes and industrial applications, offering sustainable alternatives to conventional chemical catalysis. However, the enormous enzyme sequence space and the limited functional annotation have created an urgent demand for computational approaches to accelerate enzyme discovery and engineering. Recent advances in computational methods are accelerating enzyme engineering from empirical trial and error toward predictive design. This review summarizes representative computational tools and predictive models in the enzyme engineering workflow, including (1) enzyme data resources, (2) functional enzyme discovery, (3) enzyme property prediction (e.g., kinetics, optimal temperature and PH, solubility, promiscuity, and specificity), (4) de novo enzyme design, and (5) computationally guided enzyme optimization. We further compare current computational strategies, discuss their strengths and limitations in different engineering scenarios, and highlight emerging trends toward foundation models, generative artificial intelligence, multimodal learning, and closed-loop design–build–test–learn platforms.