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- Title
ViralPhos: incorporating a recursively statistical method to predict phosphorylation sites on virus proteins.
- Authors
Kai-Yao Huang; Cheng-Tsung Lu; Arvin Bretaña, Neil; Tzong-Yi Lee; Tzu-Hao Chang
- 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. Twolayered 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.
- Publication
BMC Bioinformatics, 2013, Vol 14, Issue Suppl 16, p1
- ISSN
1471-2105
- Publication type
Article
- DOI
10.1186/1471-2105-14-S16-S10