Mini-review: Recent advances in post-translational modification site prediction based on deep learning

Abstract

Post-translational modifications (PTMs) are closely linked to numerous diseases, playing a significant role in regulating protein structures, activities, and functions. Therefore, the identification of PTMs is crucial for understanding the mechanisms of cell biology and diseases therapy. Compared to traditional machine learning methods, the deep learning approaches for PTM prediction provide accurate and rapid screening, guiding the downstream wet experiments to leverage the screened information for focused studies. In this paper, we reviewed the recent works in deep learning to identify phosphorylation, acetylation, ubiquitination, and other PTM types. In addition, we summarized PTM databases and discussed future directions with critical insights.

Ke Cheng

Postdoctoral Researcher

My research interests lie at the surface of chemistry and biology, where I am deeply passionate about applying innovative chemistry to advance fields such as chemoproteomics, drug discovery, nanomedicine, and theranostics. My aim is to provide robust methodologies for mapping biological interactomes, accelerating drug development, and expanding therapeutic opportunities.