title: RNA-binding residues prediction using structural features.
abstract: BACKGROUND
  RNA-protein complexes play an essential role in many biological processes. To explore potential functions of RNA-protein complexes, it's important to identify RNA-binding residues in proteins.
  
  
  RESULTS
  In this work, we propose a set of new structural features for RNA-binding residue prediction. A set of template patches are first extracted from RNA-binding interfaces. To construct structural features for a residue, we compare its surrounding patches with each template patch and use the accumulated distances as its structural features. These new features provide sufficient structural information of surrounding surface of a residue and they can be used to measure the structural similarity between the surface surrounding two residues. The new structural features, together with other sequence features, are used to predict RNA-binding residues using ensemble learning technique.
  
  
  CONCLUSIONS
  The experimental results reveal the effectiveness of the proposed structural features. In addition, the clustering results on template patches exhibit distinct structural patterns of RNA-binding sites, although the sequences of template patches in the same cluster are not conserved. We speculate that RNAs may have structure preferences when binding with proteins.
authors.affiliation: 2011 Collaborative Innovation Center of Tianjin for Medical Epigenetics, Key Laboratory of Hormones and Development (Ministry of Health), Metabolic Diseases Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300070, China. huizhuren@gmail.com.
authors.forename: Huizhu
authors.initials: H
authors.lastname: Ren
authors.affiliation: School of Software Engineering, Tongji University, Shanghai, 201804, China. yingshen@tongji.edu.cn.|Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information, Ministry of Education, Nanjing University of Science and Technology, Nanjing, 210094, P.R. China. yingshen@tongji.edu.cn.
authors.forename: Ying
authors.initials: Y
authors.lastname: Shen
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods; D006801:Humans; D008958:Models, Molecular; D012313:RNA / Q000737:chemistry* / Q000378:metabolism*; D016601:RNA-Binding Proteins / Q000737:chemistry* / Q000378:metabolism*; D012984:Software
chemical_list: D016601:RNA-Binding Proteins; D012313:RNA