Mul-SNO: A Novel Prediction Tool for S-Nitrosylation Sites Based on Deep Learning Methods.

Journal: IEEE journal of biomedical and health informatics
PMID: 34762593

Abstract

Protein s-nitrosylation (SNO) is one of the most important post-translational modifications and is formed by the covalent modification of nitric oxide and cysteine residues. Extensive studies have shown that SNO plays a pivotal role in the plant immune response and treating various major human diseases. In recent years, SNO sites have become a hot research topic. Traditional biochemical methods for SNO site identification are time-consuming and costly. In this study, we developed an economical and efficient SNO site prediction tool named Mul-SNO. Mul-SNO ensembled current popular and powerful deep learning model bidirectional long short-term memory (BiLSTM) and bidirectional encoder representations from Transformers (BERT). Compared with existing state-of-the-art methods, Mul-SNO obtained better ACC of 0.911 and 0.796 based on 10-fold cross-validation and independent data sets, respectively.

Authors

  • Qian Zhao
    Key Lab of Cell Differentiation and Apoptosis of Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Jiaqi Ma
  • Yu Wang
    Clinical and Technical Support, Philips Healthcare, Shanghai, China.
  • Fang Xie
    Shandong Luoxin Pharmaceutical Group Stock Co. Ltd, Linyi, Shandong, China.
  • Zhibin Lv
    Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, P. R. China.
  • Yaoqun Xu
  • Hua Shi
    School of Optoelectronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China.
  • Ke Han
    School of Computer and Information Engineering, Harbin University of Commerce, Harbin 150040, China. hanke@hrbcu.edu.cn.

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