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

BMC Bioinformatics

Research Article

Shutang Liu¹  Zhi-Ping Liu¹  Ruitang Chen²  Xiaopeng Huang³  Ling-Yun Wu³ 

[1] Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, 250061, Jinan, Shandong, China;Department of Computer Science, Stanford University, 94305, Stanford, CA, USA;Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, 100190, Beijing, China;National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, 100190, Beijing, China;University of Chinese Academy of Sciences, 100049, Beijing, China;

关键词: RNA-binding pocket;    Local structure classification;    Structural alignment;    Network clustering;    Structure motif;   

DOI  :  10.1186/s12859-016-1410-1

 received in 2016-07-21, accepted in 2016-12-07,  发布年份 2017

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s). 2017