Study On Links Prediction Based On Phosphorylation Network

Reversible protein phosphorylation is an important post-translational modification, which regulates various biological cellar processes. Therefore, it is important to identify the protein substrate-protein kinase interactions, which are helpful for us to understand regulation processes and the phosphorylation mechanism. In this study, protein substrate-protein kinase interactions are links in phosphorylation network, therefore the interactions prediction are links prediction. Experiment method is the common method, which is used to identify interaction between protein substrate and protein kinase. Experiment method has high accuracy, however, it has low efficiency. In order to overcome the disadvantage of it, Machine learning method is used to predict the interactions between protein substrate and protein kinase. Machine learning method uses mass data to predict the potential interactions between protein substrate and protein kinase. Then, experiment-based method is used to verify those protein substrate-protein kinase interactions which have high probability. This procedure can reduce identification time and improve working efficiency.In this work, known interactions between protein substrate and protein kinase are used to construct phosphorylation network. Based on phosphorylation network, protein substrate sequence information and protein kinase sequence information are included to predict the potential interaction links between protein substrate and protein kinase in phosphorylation network. First, protein substrate sequence information and phosphorylation network information are used to calculate protein substrate similarity matrix, which is used as feature data for substrate-kinase interaction prediction. And this feature data is used as input data for Support Vector Machine and then an algorithm SVM-Net was developed. SVM-Net is used to predict interaction links between protein substrate and protein kinase in phosphorylation network.Except for SVM-Net algorithm, we also developed a new network-based algorithm LapRLS-AWN, which is developed from Laplacian Regularized Least Squares algorithm. LapRLS-AWN algorithm uses protein substrate sequence information, protein kinase information and phosphorylation network information to predict interaction links between protein substrate and protein kinase in phosphorylation network. Furthermore, AWN method is also used to solve the isolated protein substrate node in phosphorylation network.In this study, we first use different information as input data for algorithms, and compare the performance of these algorithms. The result shows that phosphorylation network between protein substrate and protein kinase interaction and protein kinase sequence information are helpful for improving the prediction performance. Second, we compare the prediction performance of SVM-Net and LapRLS-AWN with BDT and SVM, the result shows that the prediction performance of SVM-Net and LapRLS-AWN is better than SVM and BDT. Finally, we compared the prediction performace of SVM-Net with LapRLS-AWN, the result shows that treatment of isolated nodes and protein substrate sequence information can improve the prediction result.