title: Prediction of protein S-nitrosylation sites based on adapted normal distribution bi-profile Bayes and Chou's pseudo amino acid composition.
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.
authors.affiliation: Department of Mathematics, Dalian Maritime University, Dalian 116026, China. cangzhijia@dlmu.edu.cn.
authors.forename: Cangzhi
authors.initials: C
authors.lastname: Jia
authors.affiliation: Department of Mathematics, Dalian Maritime University, Dalian 116026, China. xilinmath@163.com.
authors.forename: Xin
authors.initials: X
authors.lastname: Lin
authors.affiliation: Department of Mathematics, Dalian Maritime University, Dalian 116026, China. zpwangdlmu@163.com.
authors.forename: Zhiping
authors.initials: Z
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry*; D001499:Bayes Theorem; D003545:Cysteine / Q000737:chemistry / Q000378:metabolism; D009569:Nitric Oxide / Q000737:chemistry; D016011:Normal Distribution; D011499:Protein Processing, Post-Translational; D011506:Proteins / Q000737:chemistry*
chemical_list: D000596:Amino Acids; D011506:Proteins; D009569:Nitric Oxide; D003545:Cysteine