title: An efficient support vector machine approach for identifying protein S-nitrosylation sites.
abstract: Protein S-nitrosylation plays a key and specific role in many cellular processes. Detecting possible S-nitrosylated 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.
authors.affiliation: College of Science, China Agricultural University, Beijing 100083, China.
authors.forename: Yu-Xin
authors.initials: YX
authors.lastname: Li
authors.forename: Yuan-Hai
authors.initials: YH
authors.lastname: Shao
authors.forename: Ling
authors.initials: L
authors.lastname: Jing
authors.forename: Nai-Yang
authors.initials: NY
authors.lastname: Deng
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D001665:Binding Sites; D006801:Humans; D020407:Internet; D051379:Mice; D058977:Molecular Sequence Annotation; D009589:Nitrogen Oxides / Q000378:metabolism*; D010363:Pattern Recognition, Automated; D011499:Protein Processing, Post-Translational; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D012372:ROC Curve
chemical_list: D009589:Nitrogen Oxides; D011506:Proteins