Computational Tools for Investigating RNA-Protein Interaction Partners

Usha K Muppirala

doi:10.4172/jcsb.1000115

Abstract

RNA-protein interactions are important in a wide variety of cellular and developmental processes. Recently, high-throughput experiments have begun to provide valuable information about RNA partners and binding sites for many RNA-binding proteins (RBPs), but these experiments are expensive and time consuming. Thus, computational methods for predicting RNA-Protein interactions (RPIs) can be valuable tools for identifying potential interaction partners of a given protein or RNA, and for identifying likely interfacial residues in RNA-protein complexes. This review focuses on the “partner prediction” problem and summarizes available computational methods, web servers and databases that are devoted to it. New computational tools for addressing the related “interface prediction” problem are also discussed. Together, these computational methods for investigating RNA-protein interactions provide the basis for new strategies for integrating RNA-protein interactions into existing genetic and developmental regulatory networks, an important goal of future research.

Highlights

In the post-transcriptional regulation of gene expression, RNAbinding proteins (RBPs) interact with target mRNAs and non-coding RNAs to regulate a variety of cellular processes, including RNA splicing, RNA transport and stability, and translation [1,2,3]
Even though the human genome contains more than 400 known or predicted RBPs [7,8], the structures of RNA-protein complexes and the roles of RNA-protein interactions (RPIs) in post-transcriptional regulatory networks [1,9], are much less well characterized than the DNA-protein complexes involved in transcriptional regulation
We focus on existing computational methods and web servers for predicting RNA-protein interaction partners

Summary

Introduction

In the post-transcriptional regulation of gene expression, RNAbinding proteins (RBPs) interact with target mRNAs and non-coding RNAs (ncRNAs) to regulate a variety of cellular processes, including RNA splicing, RNA transport and stability, and translation [1,2,3]. Support Vector Machines (SVMs) and Random Forest (RF) classifiers (Supplementary Text S1), were used to predict the likelihood of interaction between an RBP and its target mRNAs. Input for the classifiers included more than 100 characteristic gene and protein features, but no motifs or experimentally measured binding specificities were used.

Results

Conclusion