Next Article in Journal
Next Article in Special Issue
Previous Article in Journal
Previous Article in Special Issue
Int. J. Mol. Sci. 2014, 15(6), 10410-10423; doi:10.3390/ijms150610410
Article

Prediction of Protein S-Nitrosylation Sites Based on Adapted Normal Distribution Bi-Profile Bayes and Chou’s Pseudo Amino Acid Composition

* ,
 and *
Received: 14 February 2014; in revised form: 12 May 2014 / Accepted: 20 May 2014 / Published: 10 June 2014
View Full-Text   |   Download PDF [745 KB, uploaded 19 June 2014]   |   Browse Figure
Abstract: 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.
Keywords: S-nitrosylation; post-translational modification; bi-profile Bayes; amino acid physicochemical properties S-nitrosylation; post-translational modification; bi-profile Bayes; amino acid physicochemical properties
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.

Export to BibTeX |
EndNote


MDPI and ACS Style

Jia, C.; Lin, X.; Wang, Z. Prediction of Protein S-Nitrosylation Sites Based on Adapted Normal Distribution Bi-Profile Bayes and Chou’s Pseudo Amino Acid Composition. Int. J. Mol. Sci. 2014, 15, 10410-10423.

AMA Style

Jia C, Lin X, Wang Z. Prediction of Protein S-Nitrosylation Sites Based on Adapted Normal Distribution Bi-Profile Bayes and Chou’s Pseudo Amino Acid Composition. International Journal of Molecular Sciences. 2014; 15(6):10410-10423.

Chicago/Turabian Style

Jia, Cangzhi; Lin, Xin; Wang, Zhiping. 2014. "Prediction of Protein S-Nitrosylation Sites Based on Adapted Normal Distribution Bi-Profile Bayes and Chou’s Pseudo Amino Acid Composition." Int. J. Mol. Sci. 15, no. 6: 10410-10423.



Int. J. Mol. Sci. EISSN 1422-0067 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert