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Molecules 2018, 23(7), 1667; https://doi.org/10.3390/molecules23071667

NTyroSite: Computational Identification of Protein Nitrotyrosine Sites Using Sequence Evolutionary Features

1
School of Life Sciences and the State Key Lab of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong
2
Laboratory of Bioinformatics, Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh
3
Department of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen 518000, China
*
Author to whom correspondence should be addressed.
Received: 22 May 2018 / Revised: 28 June 2018 / Accepted: 28 June 2018 / Published: 9 July 2018
(This article belongs to the Special Issue Computational Analysis for Protein Structure and Interaction)
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Abstract

Nitrotyrosine is a product of tyrosine nitration mediated by reactive nitrogen species. As an indicator of cell damage and inflammation, protein nitrotyrosine serves to reveal biological change associated with various diseases or oxidative stress. Accurate identification of nitrotyrosine site provides the important foundation for further elucidating the mechanism of protein nitrotyrosination. However, experimental identification of nitrotyrosine sites through traditional methods are laborious and expensive. In silico prediction of nitrotyrosine sites based on protein sequence information are thus highly desired. Here, we report a novel predictor, NTyroSite, for accurate prediction of nitrotyrosine sites using sequence evolutionary information. The generated features were optimized using a Wilcoxon-rank sum test. A random forest classifier was then trained using these features to build the predictor. The final NTyroSite predictor achieved an area under a receiver operating characteristics curve (AUC) score of 0.904 in a 10-fold cross-validation test. It also significantly outperformed other existing implementations in an independent test. Meanwhile, for a better understanding of our prediction model, the predominant rules and informative features were extracted from the NTyroSite model to explain the prediction results. We expect that the NTyroSite predictor may serve as a useful computational resource for high-throughput nitrotyrosine site prediction. The online interface of the software is publicly available at https://biocomputer.bio.cuhk.edu.hk/NTyroSite/. View Full-Text
Keywords: post-translational modification; Wilcoxon-rank sum test; random forest; rule extraction post-translational modification; Wilcoxon-rank sum test; random forest; rule extraction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Hasan, M.M.; Khatun, M.S.; Mollah, M.N.H.; Yong, C.; Dianjing, G. NTyroSite: Computational Identification of Protein Nitrotyrosine Sites Using Sequence Evolutionary Features. Molecules 2018, 23, 1667.

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