Mul-SNO: A Novel Prediction Tool for S-Nitrosylation Sites Based on Deep Learning Methods
Qian Zhao, Jiaqi Ma, Yu Wang, Fang Xie, Zhibin Lv, Yaoqun Xu, Hua Shi, Ke Han
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.