Abstract
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.
Highlights
Protein post-translational modifications play a very important role in the processing of protein, protein maturation, as well as altering the physical and chemical properties of proteins
The combined features were composed of the PC-PseAAC, kmer[1], kmer[2], PC-PseAAC_G25, ANBPB21, Double Bi-Profile Bayes (DBPB), BPB26, Incorporating Amino Acid Pairwise (IAAPair)[19], and PSTAAP20,27 models and the detailed results are shown in Supplementary Table S1
Combination of features PC-PseAAC + kmer[1] were further incorporated with the component of kmer[2] one by one, and new combined features PC-PseAAC, kmer[1] and kmer[2] reached Acc of 64.89%. This process was terminated at feature combination PC-PseAAC, kmer[1], kmer[2], PC-PseAAC_G, Adapted Normal distribution Bi-Profile Bayes (ANBPB), DBPB, Bi-Profile Bayes (BPB), IAAPair, and Position-specific Tri-Amino Acid Propensity (PSTAAP), which increased the Acc to 74.24% and MCC17,28–30 to 0.4837
Summary
Protein post-translational modifications play a very important role in the processing of protein, protein maturation, as well as altering the physical and chemical properties of proteins. 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)[7,8,9], and the resin-associated capture (RAC)[10]. These experimental methods have successfully provided a very effective information in identifying the SNO sites. In 2009, Foster et al.[16], explored a protein microarray-based approach to screen the SNO sites These methods made great contributions to the development of the prediction of SNOs, to a certain degree, they were considered to be time-consuming and had a relatively low throughput data. There is necessity to discover more efficient methods for the SNO sites identification
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