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, PhD

I am a chemical biologist with expertise in organic chemistry, bioconjugation, protein biology, and oncology. My research focuses on developing small molecule probes and drugs, antibody drug conjugates, and radioligand agents for targeted cancer therapy, including the design of cleavable ADC linkers and next generation payloads, as well as tumor activatable probes and chemoproteomics driven target discovery.