P085. SNOSID, a proteomic method for identification of cysteine S-nitrosylation sites in complex protein mixtures

Gross, Steven S.; Hao, Gang; Derakhshan, Behrad; Garceau, M. Carrie; Wille, Pamela C.; Shi, Lei; Compagne, Fabien

doi:10.1016/j.niox.2006.04.148

Cited by 1 publication

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“…It is noticed that S-nitrosylation (SNO) is the covalent interaction of nitric oxide with the thiol group of cysteine residues 1,3 and is well characterized as a major source of NO bioactivity 4 . Many experimental methods have been applied for distinguishing the SNO sites, such as the biotin-switch technique (BST) 5,6 , SNO-Cys site identification (SNOSID) 7–9 , and the resin-associated capture (RAC) 10 . These experimental methods have successfully provided a very effective information in identifying the SNO sites.…”

Section: Introductionmentioning

confidence: 99%

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Identification of S-nitrosylation sites based on multiple features combination

Song

Yin

et al. 2019

Sci Rep

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Protein S-nitrosylation (SNO) is a typical reversible, redox-dependent and post-translational modification that involves covalent modification of cysteine residues with nitric oxide (NO) for the thiol group. Numerous experiments have shown that SNO plays a major role in cell function and pathophysiology. In order to rapidly analysis the big sets of data, the computing methods for identifying the SNO sites are being considered as necessary auxiliary tools. In this study, multiple features including Parallel correlation pseudo amino acid composition (PC-PseAAC), Basic kmer1 (kmer1), Basic kmer2 (kmer2), General parallel correlation pseudo amino acid composition (PC-PseAAC_G), Adapted Normal distribution Bi-Profile Bayes (ANBPB), Double Bi-Profile Bayes (DBPB), Bi-Profile Bayes (BPB), Incorporating Amino Acid Pairwise (IAAPair) and Position-specific Tri-Amino Acid Propensity(PSTAAP) were employed to extract the sequence information. To remove information redundancy, information gain (IG) was applied to evaluate the importance of amino acids, which is the information entropy of class after subtracting the conditional entropy for the given amino acid. The prediction performance of the SNO sites was found to be best by using the cross-validation and independent tests. In addition, we also calculated four commonly used performance measurements, i.e. Sensitivity (Sn), Specificity (Sp), Accuracy (Acc), and the Matthew’s Correlation Coefficient (MCC). For the training dataset, the overall Acc was 83.11%, the MCC was 0.6617. For an independent test dataset, Acc was 73.17%, and MCC was 0.3788. The results indicate that our method is likely to complement the existing prediction methods and is a useful tool for effective identification of the SNO sites.

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Section: Introductionmentioning

confidence: 99%

“…Hao et al . 7 developed a prediction tool for the SNO sites, which was based on the support vector machine (SVM) 17 algorithm, and used a training dataset that consisted of 65 positive SNO sites and 65 non-SNO sites. A few years later, Xue et al .…”

Section: Introductionmentioning

confidence: 99%

Identification of S-nitrosylation sites based on multiple features combination

Song

Yin

et al. 2019

Sci Rep

View full text Add to dashboard Cite

show abstract

P085. SNOSID, a proteomic method for identification of cysteine S-nitrosylation sites in complex protein mixtures

Cited by 1 publication

References 0 publications

Identification of S-nitrosylation sites based on multiple features combination

Identification of S-nitrosylation sites based on multiple features combination

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