Prediction of S-nitrosylation sites by integrating support vector machines and random forest (original) (raw)
* Corresponding authors
a Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
E-mail: kurata@bio.kyutech.ac.jp
b Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo 102-0083, Japan
c Department of Physiology, Ajou University School of Medicine, Suwon 443380, Korea
d Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
Abstract
Cysteine _S_-nitrosylation is a type of reversible post-translational modification of proteins, which controls diverse biological processes. It is associated with redox-based cellular signaling to protect against oxidative stress. The identification of _S_-nitrosylation sites is an important step to reveal the function of proteins; however, experimental identification of _S_-nitrosylation is expensive and time-consuming work. Hence, sequence-based computational prediction of potential _S_-nitrosylation sites is highly sought before experimentation. Herein, a novel predictor PreSNO has been developed that integrates multiple encoding schemes by the support vector machine and random forest algorithms. The PreSNO achieved an accuracy and Matthews correlation coefficient value of 0.752 and 0.252 respectively in classifying between SNO and non-SNO sites when evaluated on the independent dataset, outperforming the existing methods. The web application of the PreSNO and its associated datasets are freely available at http://kurata14.bio.kyutech.ac.jp/PreSNO/.
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Supplementary files
Article information
DOI
https://doi.org/10.1039/C9MO00098D
Article type
Research Article
Submitted
05 Jun 2019
Accepted
01 Nov 2019
First published
01 Nov 2019
Download Citation
Mol. Omics, 2019,15, 451-458
Permissions
Prediction of _S_-nitrosylation sites by integrating support vector machines and random forest
Md. M. Hasan, B. Manavalan, Mst. S. Khatun and H. Kurata,Mol. Omics, 2019, 15, 451DOI: 10.1039/C9MO00098D
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