Computational Prediction of RNA-Binding Proteins and Binding Sites.

Jingna Si,Rongling Wu,Jing Cui,Jin Cheng

doi:10.3390/ijms161125952

Jingna Si, Rongling Wu + Show 2 more

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https://doi.org/10.3390/ijms161125952

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Abstract

Proteins and RNA interaction have vital roles in many cellular processes such as protein synthesis, sequence encoding, RNA transfer, and gene regulation at the transcriptional and post-transcriptional levels. Approximately 6%–8% of all proteins are RNA-binding proteins (RBPs). Distinguishing these RBPs or their binding residues is a major aim of structural biology. Previously, a number of experimental methods were developed for the determination of protein–RNA interactions. However, these experimental methods are expensive, time-consuming, and labor-intensive. Alternatively, researchers have developed many computational approaches to predict RBPs and protein–RNA binding sites, by combining various machine learning methods and abundant sequence and/or structural features. There are three kinds of computational approaches, which are prediction from protein sequence, prediction from protein structure, and protein-RNA docking. In this paper, we review all existing studies of predictions of RNA-binding sites and RBPs and complexes, including data sets used in different approaches, sequence and structural features used in several predictors, prediction method classifications, performance comparisons, evaluation methods, and future directions.

Highlights

6%–8% of proteins are RNA-binding proteins (RBPs)
The prediction approaches that use templates are more effective than those using machine learning methods for distinguishing RBPs from DNA-binding proteins
The second important issue is that which vectors contribute more and which ones offer less to the mature predictor in machine learning methods remains unclear

Summary

Introduction

6%–8% of proteins are RNA-binding proteins (RBPs). These RBPs play an important part in gene expression and regulation. Only a few types of RBPs have been identified such as HuR, AUF1, TTP, TIA1, and CUGBP2. These RBPs perform essential roles in various biological processes such as mRNA stability [1], stress responses [2], cell cycle, tumor differentiation [3], apoptosis, and gene regulation at the transcriptional and post-transcriptional levels [4]. Determining the three-dimensional (3D) structures of protein–RNA complexes facilitates the identification of physiochemical properties and biological interactions

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