title: Identifying protein phosphorylation sites with kinase substrate specificity on human viruses.
abstract: Viruses infect humans and progress inside the body leading to various diseases and complications. The phosphorylation of viral proteins catalyzed by host kinases plays crucial regulatory roles in enhancing replication and inhibition of normal host-cell functions. Due to its biological importance, there is a desire to identify the protein phosphorylation sites on human viruses. However, the use of mass spectrometry-based experiments is proven to be expensive and labor-intensive. Furthermore, previous studies which have identified phosphorylation sites in human viruses do not include the investigation of the responsible kinases. Thus, we are motivated to propose a new method to identify protein phosphorylation sites with its kinase substrate specificity on human viruses. The experimentally verified phosphorylation data were extracted from virPTM--a database containing 301 experimentally verified phosphorylation data on 104 human kinase-phosphorylated virus proteins. In an attempt to investigate kinase substrate specificities in viral protein phosphorylation sites, maximal dependence decomposition (MDD) is employed to cluster a large set of phosphorylation data into subgroups containing significantly conserved motifs. The experimental human phosphorylation sites are collected from Phospho.ELM, grouped according to its kinase annotation, and compared with the virus MDD clusters. This investigation identifies human kinases such as CK2, PKB, CDK, and MAPK as potential kinases for catalyzing virus protein substrates as confirmed by published literature. Profile hidden Markov model is then applied to learn a predictive model for each subgroup. A five-fold cross validation evaluation on the MDD-clustered HMMs yields an average accuracy of 84.93% for Serine, and 78.05% for Threonine. Furthermore, an independent testing data collected from UniProtKB and Phospho.ELM is used to make a comparison of predictive performance on three popular kinase-specific phosphorylation site prediction tools. In the independent testing, the high sensitivity and specificity of the proposed method demonstrate the predictive effectiveness of the identified substrate motifs and the importance of investigating potential kinases for viral protein phosphorylation sites.
authors: Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan.
authors: Neil Arvin
authors: NA
authors: Bretaña
authors: Cheng-Tsung
authors: CT
authors: Lu
authors: Chiu-Yun
authors: CY
authors: Chiang
authors: Min-Gang
authors: MG
authors: Su
authors: Kai-Yao
authors: KY
authors: Huang
authors: Tzong-Yi
authors: TY
authors: Lee
authors: Shun-Long
authors: SL
authors: Weng
mesh_terms: D020816:Amino Acid Motifs; D001665:Binding Sites; D019295:Computational Biology; D006801:Humans; D010766:Phosphorylation; D011494:Protein Kinases / Q000378:metabolism*; D015203:Reproducibility of Results; D013379:Substrate Specificity; D014764:Viral Proteins / Q000737:chemistry* / Q000378:metabolism*
chemical_list: D014764:Viral Proteins; D011494:Protein Kinases