title: ViralPhos: incorporating a recursively statistical method to predict phosphorylation sites on virus proteins.
abstract: BACKGROUND
  The phosphorylation of virus proteins by host kinases is linked to viral replication. This leads to an inhibition of normal host-cell functions. Further elucidation of phosphorylation in virus proteins is required in order to aid in drug design and treatment. However, only a few studies have investigated substrate motifs in identifying virus phosphorylation sites. Additionally, existing bioinformatics tool do not consider potential host kinases that may initiate the phosphorylation of a virus protein.
  
  
  RESULTS
  329 experimentally verified phosphorylation fragments on 111 virus proteins were collected from virPTM. These were clustered into subgroups of significantly conserved motifs using a recursively statistical method. Two-layered Support Vector Machines (SVMs) were then applied to train a predictive model for the identified substrate motifs. The SVM models were evaluated using a five-fold cross validation which yields an average accuracy of 0.86 for serine, and 0.81 for threonine. Furthermore, the proposed method is shown to perform at par with three other phosphorylation site prediction tools: PPSP, KinasePhos 2.0 and GPS 2.1.
  
  
  CONCLUSIONS
  In this study, we propose a computational method, ViralPhos, which aims to investigate virus substrate site motifs and identify potential phosphorylation sites on virus proteins. We identified informative substrate motifs that matched with several well-studied kinase groups as potential catalytic kinases for virus protein substrates. The identified substrate motifs were further exploited to identify potential virus phosphorylation sites. The proposed method is shown to be capable of predicting virus phosphorylation sites and has been implemented as a web server http://csb.cse.yzu.edu.tw/ViralPhos/.
authors.forename: Kai-Yao
authors.initials: KY
authors.lastname: Huang
authors.forename: Cheng-Tsung
authors.initials: CT
authors.lastname: Lu
authors.forename: Neil
authors.initials: N
authors.lastname: Bretaña
authors.forename: Tzong-Yi
authors.initials: TY
authors.lastname: Lee
authors.forename: Tzu-Hao
authors.initials: TH
authors.lastname: Chang
mesh_terms: D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D020407:Internet; D015233:Models, Statistical; D010766:Phosphorylation; D010770:Phosphotransferases / Q000737:chemistry; D017434:Protein Structure, Tertiary; D015203:Reproducibility of Results; D060388:Support Vector Machine; D014764:Viral Proteins / Q000737:chemistry*
chemical_list: D014764:Viral Proteins; D010770:Phosphotransferases