title: Using support vector machines to identify protein phosphorylation sites in viruses.
abstract: Phosphorylation of viral proteins plays important roles in enhancing replication and inhibition of normal host-cell functions. Given its importance in biology, a unique opportunity has arisen to identify viral protein phosphorylation sites. However, experimental methods for identifying phosphorylation sites are resource intensive. Hence, there is significant interest in developing computational methods for reliable prediction of viral phosphorylation sites from amino acid sequences. In this study, a new method based on support vector machine is proposed to identify protein phosphorylation sites in viruses. We apply an encoding scheme based on attribute grouping and position weight amino acid composition to extract physicochemical properties and sequence information of viral proteins around phosphorylation sites. By 10-fold cross-validation, the prediction accuracies for phosphoserine, phosphothreonine and phosphotyrosine with window size of 23 are 88.8%, 95.2% and 97.1%, respectively. Furthermore, compared with the existing methods of Musite and MDD-clustered HMMs, the high sensitivity and accuracy of our presented method demonstrate the predictive effectiveness of the identified phosphorylation sites for viral proteins.
authors.affiliation: Department of Chemical Engineering, Pingxiang College, Pingxiang 337055, China.
authors.forename: Shu-Yun
authors.initials: SY
authors.lastname: Huang
authors.affiliation: Department of Chemistry, Nanchang University, Nanchang 330031, China; Department of Mathematics, Nanchang University, Nanchang 330031, China.
authors.forename: Shao-Ping
authors.initials: SP
authors.lastname: Shi
authors.affiliation: Department of Chemical Engineering, Pingxiang College, Pingxiang 337055, China; Department of Chemistry, Nanchang University, Nanchang 330031, China. Electronic address: jdqiu@ncu.edu.cn.
authors.forename: Jian-Ding
authors.initials: JD
authors.lastname: Qiu
authors.affiliation: Department of Chemical Engineering, Pingxiang College, Pingxiang 337055, China.
authors.forename: Ming-Chu
authors.initials: MC
authors.lastname: Liu
mesh_terms: D000595:Amino Acid Sequence; D030562:Databases, Protein; D008969:Molecular Sequence Data; D010750:Phosphoproteins / Q000737:chemistry* / Q000378:metabolism; D010766:Phosphorylation; D017434:Protein Structure, Tertiary; D012694:Serine / Q000737:chemistry* / Q000378:metabolism; D060388:Support Vector Machine; D013912:Threonine / Q000737:chemistry* / Q000378:metabolism; D014443:Tyrosine / Q000737:chemistry* / Q000378:metabolism; D014764:Viral Proteins / Q000737:chemistry* / Q000378:metabolism
chemical_list: D010750:Phosphoproteins; D014764:Viral Proteins; D013912:Threonine; D014443:Tyrosine; D012694:Serine
keywords: Encoding scheme based on attribute grouping; Phosphorylation site; Position weight amino acid composition; Support vector machine; Virus proteins