This review highlights machine learning applications for predicting post-translational modifications, suggesting future research directions in protein modification site analysis.
Post-translational modifications (PTMs) in proteins are essential for cell function. Due 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 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 preprocessing, feature extraction, dimension reduction and classifier development. The review also discusses potential future research directions in this field, providing valuable insights for advancing the state-of-the-art in PTM site prediction. Collectively, this review provides comprehensive knowledge on PTM identification and contributes to advanced predictors in the future.
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Gong et al. (2025) studied this question.
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