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

文献类型：期刊论文

作者Wang, Xun1,2; Zhang, Zhiyuan1; Zhang, Chaogang1; Meng, Xiangyu1; Shi, Xin1; Qu, Peng1
刊名INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
出版日期2022-04-01
卷号23期号:8页码:17
关键词phosphorylation site prediction transformer post-translational modifications
DOI10.3390/ijms23084263
英文摘要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.
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
语种英语
WOS记录号WOS:000786074500001
出版者MDPI
源URL[http://119.78.100.204/handle/2XEOYT63/18890]  
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.

入库方式： OAI收割

来源：计算技术研究所

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