A Systematic Review of Computational Methods for Protein Post-Translational Modification Site Prediction

Abstract Protein post-translational modifications (PTMs) are critical for regulating protein function and are closely linked to disease mechanisms. In-depth research and precise prediction of PTMs are vital for understanding life mechanisms, screening disease biomarkers, and identifying drug targets. Artificial intelligence (AI) approaches for PTM site prediction offer complementary advantages to traditional experimental methods, providing high-throughput and cost-effective screening that can prioritize candidate sites for further validation. This paper reviews advances in PTM site prediction since 2012, focusing on machine learning and deep learning techniques. It analyzes more than 500 relevant studies and categorizes 36 types of PTMs. Additionally, the paper briefly outlines core contents such as database resources related to PTMs, commonly used feature extraction methods, and major classification algorithms. In addition, 36 representative recent studies on PTMs have been carefully selected for in-depth analysis. The findings indicate that current machine learning-based PTM research employs multivariate feature extraction and construct composite models to enhance prediction performance. Finally, keyword visualization using CiteSpace identifies emerging research hotspots and future directions for PTM site prediction.