TransPhos: A Deep-Learning Model for General Phosphorylation Site Prediction Based on Transformer-Encoder Architecture

doi:10.3390/ijms23084263

	TransPhos: A Deep-Learning Model for General Phosphorylation Site Prediction Based on Transformer-Encoder Architecture
	Wang, Xun 1,2; Zhang, Zhiyuan 1; Zhang, Chaogang 1; Meng, Xiangyu 1; Shi, Xin 1; Qu, Peng 1
	2022-04-01
发表期刊	INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
卷号	23 期号:8 页码:17
摘要	Protein phosphorylation is one of the most critical post-translational modifications of proteins in eukaryotes, which is essential for a variety of biological processes. Plenty of attempts have been made to improve the performance of computational predictors for phosphorylation site prediction. However, most of them are based on extra domain knowledge or feature selection. In this article, we present a novel deep learning-based predictor, named TransPhos, which is constructed using a transformer encoder and densely connected convolutional neural network blocks, for predicting phosphorylation sites. Data experiments are conducted on the datasets of PPA (version 3.0) and Phospho. ELM. The experimental results show that our TransPhos performs better than several deep learning models, including Convolutional Neural Networks (CNN), Long-term and short-term memory networks (LSTM), Recurrent neural networks (RNN) and Fully connected neural networks (FCNN), and some state-of-the-art deep learning-based prediction tools, including GPS2.1, NetPhos, PPRED, Musite, PhosphoSVM, SKIPHOS, and DeepPhos. Our model achieves a good performance on the training datasets of Serine (S), Threonine (T), and Tyrosine (Y), with AUC values of 0.8579, 0.8335, and 0.6953 using 10-fold cross-validation tests, respectively, and demonstrates that the presented TransPhos tool considerably outperforms competing predictors in general protein phosphorylation site prediction.
关键词	phosphorylation site prediction transformer post-translational modifications
DOI	10.3390/ijms23084263
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61873280] ; National Natural Science Foundation of China[61873281] ; National Natural Science Foundation of China[61972416] ; Natural Science Foundation of Shandong Province[ZR2019MF012]
WOS研究方向	Biochemistry & Molecular Biology ; Chemistry
WOS类目	Biochemistry & Molecular Biology ; Chemistry, Multidisciplinary
WOS记录号	WOS:000786074500001
出版者	MDPI
引用统计	被引频次：12[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/18891
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wang, Xun
作者单位	1.China Univ Petr, Coll Comp Sci & Technol, Qingdao 266555, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Wang, Xun,Zhang, Zhiyuan,Zhang, Chaogang,et al. TransPhos: A Deep-Learning Model for General Phosphorylation Site Prediction Based on Transformer-Encoder Architecture[J]. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES,2022,23(8):17.
APA	Wang, Xun,Zhang, Zhiyuan,Zhang, Chaogang,Meng, Xiangyu,Shi, Xin,&Qu, Peng.(2022).TransPhos: A Deep-Learning Model for General Phosphorylation Site Prediction Based on Transformer-Encoder Architecture.INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES,23(8),17.
MLA	Wang, Xun,et al."TransPhos: A Deep-Learning Model for General Phosphorylation Site Prediction Based on Transformer-Encoder Architecture".INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES 23.8(2022):17.