Structure alignment-based classification of RNA-binding pockets reveals regional RNA recognition motifs on protein surfaces

Zhi-Ping Liu,Shutang Liu,Ruitang Chen,Ling-Yun Wu,Xiaopeng Huang

doi:10.1186/s12859-016-1410-1

Abstract

BackgroundMany critical biological processes are strongly related to protein-RNA interactions. Revealing the protein structure motifs for RNA-binding will provide valuable information for deciphering protein-RNA recognition mechanisms and benefit complementary structural design in bioengineering. RNA-binding events often take place at pockets on protein surfaces. The structural classification of local binding pockets determines the major patterns of RNA recognition.ResultsIn this work, we provide a novel framework for systematically identifying the structure motifs of protein-RNA binding sites in the form of pockets on regional protein surfaces via a structure alignment-based method. We first construct a similarity network of RNA-binding pockets based on a non-sequential-order structure alignment method for local structure alignment. By using network community decomposition, the RNA-binding pockets on protein surfaces are clustered into groups with structural similarity. With a multiple structure alignment strategy, the consensus RNA-binding pockets in each group are identified. The crucial recognition patterns, as well as the protein-RNA binding motifs, are then identified and analyzed.ConclusionsLarge-scale RNA-binding pockets on protein surfaces are grouped by measuring their structural similarities. This similarity network-based framework provides a convenient method for modeling the structural relationships of functional pockets. The local structural patterns identified serve as structure motifs for the recognition with RNA on protein surfaces.

Highlights

Many critical biological processes are strongly related to protein-Ribonucleic acid (RNA) interactions
RNA-binding pocket groups Based on the pocket similarity network modeling the structural similarities among RNA-binding pockets, a community detection algorithm is employed to decompose the network into smaller groups of similar pockets
Based on the similarity network framework, we identified the major groups of RNAbinding local structures, which illustrates that the RNAbinding domains fold into certain local Three dimensional (3D) structure patterns, and the resulting pockets formed on protein surfaces facilitate the local sites and environments for binding to RNA partners

Summary

Introduction

Many critical biological processes are strongly related to protein-RNA interactions. Revealing the protein structure motifs for RNA-binding will provide valuable information for deciphering protein-RNA recognition mechanisms and benefit complementary structural design in bioengineering. Pockets are one of the local structure patterns on protein surfaces and have proven to be concrete locations and detail-rich environments for many critical biological reactions, such as ligand binding [24, 25]. A protein-RNA binding pocket facilitates the local geometry for RNA packing and constructing protein complexes to perform certain functions [26] Recent bioinformatics studies, such as PRNA [27], RNABindR [28], BindN [29], and PRINTR [30], have made substantial efforts to predict protein-RNA binding residues in proteins, but very few methods are available to identify the structure motifs that underlie the RNAbinding sites [2, 26, 31, 32], especially from the perspective of local protein surface regions. The knowledge of RNA-binding structure motifs in the form of pockets will reveal the local structure groups and the underlying mechanisms involved in the recognition of RNA on protein surfaces. Identifying the structural patterns and physicochemical specificities of these binding pockets will greatly benefit downstream feature studies of protein-RNA interaction

Methods

Results

Conclusion