The functional performances are encoded by protein structures, and modified structure-based strategies for customizing food proteins have major implications for the food industry. The glycation reaction that typically occurs between food components is a promising strategy for protein modification due to its mild reaction conditions and natural occurrence during processing. However, the complexity and dynamic nature of glycation reactions hinder precise control, and there is a large imbalance between abundant structural data and function information. Artificial intelligence (AI), with its capacity for large-scale data integration and predictive modeling, offers transformative potential for elucidating glycation-structure-function relationships. This review therefore aims to (1) summarize advances in analytical strategies for glycated proteins, highlighting techniques for site localization, conformational analysis, and multi-source data mining; (2) elucidate how glycation-induced structural modifications alter protein functional performance, providing mechanistic insights into physicochemical properties and biological activities; and (3) discuss emerging AI-driven approaches, including deep learning and inverse design, for predicting and optimizing glycation patterns. These insights provide a systematic framework to accelerate rational development of functional proteins and promote innovative applications in the food industry.
Chen et al. (Thu,) studied this question.