An Efficient Support Vector Machine Approach for Identifying Protein S-nitrosylation Sites

Protein S-nitrosylation plays a key and specific role in many cellular processes. Detecting possible Snitrosylated substrates and their corresponding exact sites is crucial for studying the mechanisms of these biological processes. Comparing with the expensive and time-consuming biochemical experiments, the computational methods are attracting considerable attention due to their convenience and fast speed. Although some computational models have been developed to predict S-nitrosylation sites, their accuracy is still low. In this work,we incorporate support vector machine to predict protein S-nitrosylation sites. After a careful evaluation of six encoding schemes, we propose a new efficient predictor, CPR-SNO, using the coupling patterns based encoding scheme. The performance of our CPR-SNO is measured with the area under the ROC curve (AUC) of 0.8289 in 10-fold cross validation experiments, which is significantly better than the existing best method GPS-SNO 1.0's 0.685 performance. In further annotating large-scale potential S-nitrosylated substrates, CPR-SNO also presents an encouraging predictive performance. These results indicate that CPR-SNO can be used as a competitive protein S-nitrosylation sites predictor to the biological community. Our CPR-SNO has been implemented as a web server and is available at http://math.cau.edu.cn/CPR -SNO/CPR-SNO.html.