ViralPhos: Incorporating a recursively statistical method to predict phosphorylation sites on virus proteins

Kai Yao Huang, Cheng Tsung Lu, Neil A. Bretaña, Tzong Yi Lee, Tzu Hao Chang

研究成果: 雜誌貢獻文章同行評審

13 引文 斯高帕斯(Scopus)


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.Conclusion: 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

期刊BMC Bioinformatics
出版狀態已發佈 - 10月 22 2013

ASJC Scopus subject areas

  • 結構生物學
  • 生物化學
  • 分子生物學
  • 電腦科學應用
  • 應用數學


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