Journal
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
Volume 20, Issue -, Pages 3522-3532Publisher
ELSEVIER
DOI: 10.1016/j.csbj.2022.06.045
Keywords
Post -translational modification; Machine learning; Deep learning; Prediction; Mass spectrometry
Funding
- National Natural Science Foundation of China [32170654, 32122003, 32000464]
- Hong Kong Research Grants Council [11102719, 11302320]
- Health and Medical Research Fund
- Food and Health Bureau
- Government of the Hong Kong Special Administrative Region [07181426]
- City University of Hong Kong [CityU 11202219, CityU 11203520, CityU 11203221]
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This paper reviews the recent works in deep learning for identifying various types of PTMs and discusses PTM databases and future research directions.
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.(c) 2022 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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