DeepNitro: Prediction of Protein Nitration and Nitrosylation Sites by Deep Learning

• Published in: Genomics, proteomics & bioinformatics Vol. 16; no. 4; pp. 294 - 306
• Main Authors: Xie, Yubin, Luo, Xiaotong, Li, Yupeng, Chen, Li, Ma, Wenbin, Huang, Junjiu, Cui, Jun, Zhao, Yong, Xue, Yu, Zuo, Zhixiang, Ren, Jian
• Format: Journal Article
• Language: English
• Published: China Elsevier B.V 01-08-2018; State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, School of Life Sciences, Sun Yat-sen University, Guangzhou 510060, China%Department of Bioinformatics&Systems Biology, MOE Key Laboratory of Molecular Biophysics, College of Life Science and Technology and the Collaborative Innovation Center for Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China; Elsevier; Oxford University Press
• Subjects: Amino Acid Sequence; Amino Acids - metabolism; Deep Learning; Feature extraction; Functional site prediction; Humans; Internet; Neural Networks (Computer); Nitrosation; Protein nitration and nitrosylation; Proteins - chemistry; Proteins - metabolism; Reproducibility of Results; Software; Web Server; Web service; Deep learning; Feature extraction; Protein nitration and nitrosylation; Functional site prediction; Web service; Protein nitration and nitro-sylation
• ISSN: 1672-0229; 2210-3244
• DOI: 10.1016/j.gpb.2018.04.007

Protein nitration and nitrosylation are essential post-translational modifications (PTMs) involved in many fundamental cellular processes. Recent studies have revealed that excessive levels of nitration and nitrosylation in some critical proteins are linked to numerous chronic diseases. Therefore, the identification of substrates that undergo such modifications in a site-specific manner is an important research topic in the community and will provide candidates for targeted therapy. In this study, we aimed to develop a computational tool for predicting nitration and nitrosylation sites in proteins. We first constructed four types of encoding features, including positional amino acid distributions, sequence contextual dependencies, physicochemical properties, and position-specific scoring features, to represent the modified residues. Based on these encoding features, we established a predictor called DeepNitro using deep learning methods for predicting protein nitration and nitrosylation. Using n-fold cross-validation, our evaluation shows great AUC values for DeepNitro, 0.65 for tyrosine nitration, 0.80 for tryptophan nitration, and 0.70 for cysteine nitrosylation, respectively, demonstrating the robustness and reliability of our tool. Also, when tested in the independent dataset, DeepNitro is substantially superior to other similar tools with a 7%−42% improvement in the prediction performance. Taken together, the application of deep learning method and novel encoding schemes, especially the position-specific scoring feature, greatly improves the accuracy of nitration and nitrosylation site prediction and may facilitate the prediction of other PTM sites. DeepNitro is implemented in JAVA and PHP and is freely available for academic research at http://deepnitro.renlab.org.