期刊
BMC BIOINFORMATICS
卷 18, 期 -, 页码 -出版社
BMC
DOI: 10.1186/s12859-016-1410-1
关键词
RNA-binding pocket; Local structure classification; Structural alignment; Network clustering; Structure motif
类别
资金
- Strategic Priority Research Program of the Chinese Academy of Sciences [XDB13040600]
- National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences
- National Natural Science Foundation of China (NSFC) [61533011, 61572287, 31100949, 11131009, 11631014, 91330114]
- Shandong Provincial Natural Science Foundation of China [ZR2015FQ001]
- Fundamental Research Funds of Shandong University [2014 TB006, 2015QY001, 2016JC007]
- Scientific Research Foundation for Returned Overseas Chinese Scholars, Ministry of Education of China
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.
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