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Prediction of Protein S-Nitrosylation Sites Based on Adapted Normal Distribution Bi-Profile Bayes and Chou’s Pseudo Amino Acid Composition
Protein S-nitrosylation is a reversible post-translational modification by covalent modification on the thiol group of cysteine residues by nitric oxide. Growing evidence shows that protein S-nitrosylation plays an important role in normal cellular function as well as in various pathophysiologic con...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4100159/ https://www.ncbi.nlm.nih.gov/pubmed/24918295 http://dx.doi.org/10.3390/ijms150610410 |
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author | Jia, Cangzhi Lin, Xin Wang, Zhiping |
author_facet | Jia, Cangzhi Lin, Xin Wang, Zhiping |
author_sort | Jia, Cangzhi |
collection | PubMed |
description | Protein S-nitrosylation is a reversible post-translational modification by covalent modification on the thiol group of cysteine residues by nitric oxide. Growing evidence shows that protein S-nitrosylation plays an important role in normal cellular function as well as in various pathophysiologic conditions. Because of the inherent chemical instability of the S-NO bond and the low abundance of endogenous S-nitrosylated proteins, the unambiguous identification of S-nitrosylation sites by commonly used proteomic approaches remains challenging. Therefore, computational prediction of S-nitrosylation sites has been considered as a powerful auxiliary tool. In this work, we mainly adopted an adapted normal distribution bi-profile Bayes (ANBPB) feature extraction model to characterize the distinction of position-specific amino acids in 784 S-nitrosylated and 1568 non-S-nitrosylated peptide sequences. We developed a support vector machine prediction model, iSNO-ANBPB, by incorporating ANBPB with the Chou’s pseudo amino acid composition. In jackknife cross-validation experiments, iSNO-ANBPB yielded an accuracy of 65.39% and a Matthew’s correlation coefficient (MCC) of 0.3014. When tested on an independent dataset, iSNO-ANBPB achieved an accuracy of 63.41% and a MCC of 0.2984, which are much higher than the values achieved by the existing predictors SNOSite, iSNO-PseAAC, the Li et al. algorithm, and iSNO-AAPair. On another training dataset, iSNO-ANBPB also outperformed GPS-SNO and iSNO-PseAAC in the 10-fold crossvalidation test. |
format | Online Article Text |
id | pubmed-4100159 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-41001592014-07-16 Prediction of Protein S-Nitrosylation Sites Based on Adapted Normal Distribution Bi-Profile Bayes and Chou’s Pseudo Amino Acid Composition Jia, Cangzhi Lin, Xin Wang, Zhiping Int J Mol Sci Article Protein S-nitrosylation is a reversible post-translational modification by covalent modification on the thiol group of cysteine residues by nitric oxide. Growing evidence shows that protein S-nitrosylation plays an important role in normal cellular function as well as in various pathophysiologic conditions. Because of the inherent chemical instability of the S-NO bond and the low abundance of endogenous S-nitrosylated proteins, the unambiguous identification of S-nitrosylation sites by commonly used proteomic approaches remains challenging. Therefore, computational prediction of S-nitrosylation sites has been considered as a powerful auxiliary tool. In this work, we mainly adopted an adapted normal distribution bi-profile Bayes (ANBPB) feature extraction model to characterize the distinction of position-specific amino acids in 784 S-nitrosylated and 1568 non-S-nitrosylated peptide sequences. We developed a support vector machine prediction model, iSNO-ANBPB, by incorporating ANBPB with the Chou’s pseudo amino acid composition. In jackknife cross-validation experiments, iSNO-ANBPB yielded an accuracy of 65.39% and a Matthew’s correlation coefficient (MCC) of 0.3014. When tested on an independent dataset, iSNO-ANBPB achieved an accuracy of 63.41% and a MCC of 0.2984, which are much higher than the values achieved by the existing predictors SNOSite, iSNO-PseAAC, the Li et al. algorithm, and iSNO-AAPair. On another training dataset, iSNO-ANBPB also outperformed GPS-SNO and iSNO-PseAAC in the 10-fold crossvalidation test. MDPI 2014-06-10 /pmc/articles/PMC4100159/ /pubmed/24918295 http://dx.doi.org/10.3390/ijms150610410 Text en © 2014 by the authors; licensee MDPI, Basel, Switzerland. http://creativecommons.org/licenses/by/3.0/ This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/). |
spellingShingle | Article Jia, Cangzhi Lin, Xin Wang, Zhiping Prediction of Protein S-Nitrosylation Sites Based on Adapted Normal Distribution Bi-Profile Bayes and Chou’s Pseudo Amino Acid Composition |
title | Prediction of Protein S-Nitrosylation Sites Based on Adapted Normal Distribution Bi-Profile Bayes and Chou’s Pseudo Amino Acid Composition |
title_full | Prediction of Protein S-Nitrosylation Sites Based on Adapted Normal Distribution Bi-Profile Bayes and Chou’s Pseudo Amino Acid Composition |
title_fullStr | Prediction of Protein S-Nitrosylation Sites Based on Adapted Normal Distribution Bi-Profile Bayes and Chou’s Pseudo Amino Acid Composition |
title_full_unstemmed | Prediction of Protein S-Nitrosylation Sites Based on Adapted Normal Distribution Bi-Profile Bayes and Chou’s Pseudo Amino Acid Composition |
title_short | Prediction of Protein S-Nitrosylation Sites Based on Adapted Normal Distribution Bi-Profile Bayes and Chou’s Pseudo Amino Acid Composition |
title_sort | prediction of protein s-nitrosylation sites based on adapted normal distribution bi-profile bayes and chou’s pseudo amino acid composition |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4100159/ https://www.ncbi.nlm.nih.gov/pubmed/24918295 http://dx.doi.org/10.3390/ijms150610410 |
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