title: Structure alignment-based classification of RNA-binding pockets reveals regional RNA recognition motifs on protein surfaces.
abstract: BACKGROUND
  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. 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.
  
  
  RESULTS
  In 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.
  
  
  CONCLUSIONS
  Large-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.
authors.affiliation: Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China.
authors.forename: Zhi-Ping
authors.initials: ZP
authors.lastname: Liu
authors.affiliation: Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China.
authors.forename: Shutang
authors.initials: S
authors.lastname: Liu
authors.affiliation: Department of Computer Science, Stanford University, Stanford, CA, 94305, USA.
authors.forename: Ruitang
authors.initials: R
authors.lastname: Chen
authors.affiliation: Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China.|National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing, 100190, China.|University of Chinese Academy of Sciences, Beijing, 100049, China.
authors.forename: Xiaopeng
authors.initials: X
authors.lastname: Huang
authors.affiliation: Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China. lywu@amss.ac.cn.|National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing, 100190, China. lywu@amss.ac.cn.|University of Chinese Academy of Sciences, Beijing, 100049, China. lywu@amss.ac.cn.
authors.forename: Ling-Yun
authors.identifier: http://orcid.org/0000-0001-9487-0215
authors.initials: LY
authors.lastname: Wu
mesh_terms: D019295:Computational Biology / Q000379:methods; D008958:Models, Molecular; D008968:Molecular Conformation; D000071377:RNA Recognition Motif; D016601:RNA-Binding Proteins / Q000737:chemistry* / Q000145:classification
chemical_list: D016601:RNA-Binding Proteins
keywords: Local structure classification; Network clustering; RNA-binding pocket; Structural alignment; Structure motif