Prediction of RNA Binding Sites in Proteins

Abstract

In this chapter, the authors provide an introduction for the prediction of RNA binding sites in proteins by machine learning algorithms, such as neural network (NN), naive Bayes (NB), support vector machines (SVM), and random forest (RF). On the basis of these classification methods, one can identify the RNA binding sites of proteins by various features underlying the interaction. Here, the authors mainly describe the protocols of predicting RNA binding sites in proteins by feature based machine learning methods. The available protein-RNA complexes from the Protein Data Bank (PDB) are selected to build the data source and define the binding sites in proteins. Here, the authors also compare these features and those of the existing methods. In particular, they identify the importance of each feature in determining the specificity of protein-RNA interaction, as well as the contribution of various hybrid features in the prediction.