Role of Machine and Deep Learning in Predicting Protein Modification Sites: Review and Future Directions

Post-translational modifications (PTMs) of proteins are essential for cellular function. Owing to the high cost and time demands of high-throughput sequencing, machine learning and deep learning methods are being rapidly developed for predicting PTM sites. This manuscript presents a comprehensive review of the current research on the application of intelligent algorithms for predicting PTM sites. It outlines the key steps for identifying modified sites based on intelligent algorithms, including data pre-processing, feature extraction, dimension reduction, and classifier development. This review also discusses potential future research directions in this field, providing valuable insights for advancing the state-of-the-art PTM site prediction. Collectively, this review provides comprehensive knowledge on PTM identification and contributes to the development of advanced predictors in the future.