Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics

Chapter 8

Prediction of RNA Binding Sites in Proteins

ZHI-PING LIU and LUONAN CHEN

8.1 Introduction

Protein–RNA interactions play a key role in a number of biological processes in DNA packaging and replication, mRNA processing, protein synthesis, assembly, and function of ribosomes and eukaryotic spliceosomes. A reliable identification of RNA binding sites in proteins is important for functional annotation and site-directed mutagenesis. However, it is time-consuming and labor-intensive to detect the interaction sites in proteins by traditional experimental methods. There are some computational methods that have been proposed to address this challenge. Generally, prediction of RNA binding sites is based on the sequence and structure features identified from protein and its RNA partner. The residue properties as well as various element features are detected and combined together into description vectors to represent the interacting events. For the encoding scheme, numerous methods have been proposed to describe the interacting preferences of protein residue and its RNA partners. In this chapter, we provide an introduction for the prediction of RNA binding sites in proteins by machine learning algorithms, such as neural network, naive Bayes, support vector machines, and random forest. On the basis of these classification methods, we can identify the RNA binding sites of proteins by various features underlying the interaction.

8.2 Background

It is crucial to decipher the mechanism ...