A novel network-based computational method to predict protein phosphorylation on tyrosine sites.

Phosphorylation plays a great role in regulating a variety of cellular processes and the identification of tyrosine phosphorylation sites is fundamental for understanding the post-translational modification (PTM) regulation processes. Although a lot of computational methods have been developed, most of them only concern local sequence information and few studies focus on the tyrosine sites with in situ PTM information, which refers to different types of PTM occurring on the same modification site. In this study, by constructing the site-modification network that efficiently incorporates in situ PTM information, we introduce a novel network-based computational method, site-modification network-based inference (SMNBI) to predict tyrosine phosphorylation. In order to verify the effectiveness of the proposed method, we compare it with other network-based computational methods. The results clearly show the superior performance of SMNBI. Besides, we extensively compare SMNBI with other sequence-based methods including SVM and Bayesian decision theory. The evaluation demonstrates the power of site-modification network in predicting tyrosine phosphorylation. The proposed method is freely available at http://bioinformatics.ustc.edu.cn/smnbi/.