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.
Highlights
Protein S-nitrosylation, the covalent attachment of a nitric oxide (NO) moiety to cysteine residues of proteins resulting in the formation of S-nitrosothiols (SNO), is a typical redox-dependent posttranslational modification that is associated with redox-based cellular signaling [1,2,3]
Protein S-nitrosylation products were significantly increased compared with normal levels in diabetes, tuberculosis and other diseases; while protein S-nitrosylation products were significantly decreased compared with normal levels in asthma, neonatal oxygen deficiency, emphysema, and other diseases
We propose a computational model iSNO-ANBPB based on an adapted normal distribution bi-profile Bayes (ANBPB) feature extraction model and Chou’s pseudo amino acid compositions for protein
Summary
Protein S-nitrosylation, the covalent attachment of a nitric oxide (NO) moiety to cysteine residues of proteins resulting in the formation of S-nitrosothiols (SNO), is a typical redox-dependent posttranslational modification that is associated with redox-based cellular signaling [1,2,3]. S-nitrosylated proteins exhibit abnormal increases or decreases in a variety of diseases [6]. Protein S-nitrosylation products were significantly increased compared with normal levels in diabetes, tuberculosis and other diseases; while protein S-nitrosylation products were significantly decreased compared with normal levels in asthma, neonatal oxygen deficiency, emphysema, and other diseases. The regulation of protein S-nitrosylation modification may be a new and effective way for health protection. The increasing prominence of the roles of S-nitrosylation in diseases indicates a need for improved analytical methods to identify and quantify S-nitrosylated proteins under various physiological and pathophysiological conditions for investigative studies and clinical diagnosis [1,6,7]
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