Artificial intelligence-driven protease cleavage site prediction: Advances and challenges

The field of protease cleavage site prediction is undergoing a profound transformation driven by the integration of artificial intelligence (AI) technologies within computational bioinformatics frameworks. Proteolysis, a critical mechanism in regulating protein function and cellular processes, involves complex and variable substrate recognition patterns that pose significant challenges to traditional computational approaches. AI can accurately model the heterogeneity of proteolytic mechanisms and improve the generalizability of protease cleavage site prediction. This review examines how AI leverages feature extraction, multidimensional regulatory information integration and nonlinear relationship modeling to address the limited generalizability of traditional methods. Representative studies demonstrate the successful application of AI in cleavage site prediction across different protease families. We identify major obstacles in current AI applications, focusing on data quality, multimodal feature integration and model interpretability. The content systematically outlines the progress, key challenges and future directions in AI-driven protease cleavage site prediction, aiming to provide theoretical guidance for improving prediction efficiency and promoting broader AI applications in the biomedical field.