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

Area of Science:

• Biochemistry
• Computational Biology
• Genomics

Background:

• Post-translational modifications (PTMs) are critical regulators of protein function and are implicated in various diseases.
• Identifying PTMs is essential for understanding cellular mechanisms and developing therapeutic strategies.
• Traditional methods for PTM identification are often time-consuming and less efficient.

Purpose of the Study:

• To review recent advancements in deep learning for identifying various types of PTMs.
• To provide an overview of existing PTM databases.
• To discuss future research directions and challenges in the field of PTM prediction.

Main Methods:

• Literature review of deep learning applications in PTM prediction.
• Categorization of PTM types, including phosphorylation, acetylation, and ubiquitination.
• Analysis of PTM databases and their utility.

Main Results:

• Deep learning methods offer accurate and rapid screening for PTMs compared to traditional approaches.
• Recent studies demonstrate the effectiveness of deep learning in identifying diverse PTMs.
• A summary of key PTM databases is presented.

Conclusions:

• Deep learning is a powerful tool for PTM identification, accelerating biological research.
• The integration of deep learning with experimental validation is crucial for advancing PTM studies.
• Further development in deep learning models and databases will enhance our understanding of PTMs in health and disease.