RBind: computational network method to predict RNA binding sites

被引:40
|
作者
Wang, Kaili [1 ,2 ]
Jian, Yiren [3 ]
Wang, Huiwen [1 ,2 ]
Zeng, Chen [1 ,2 ,3 ]
Zhao, Yunjie [1 ,2 ]
机构
[1] Cent China Normal Univ, Inst Biophys, Wuhan 430079, Hubei, Peoples R China
[2] Cent China Normal Univ, Dept Phys, Wuhan 430079, Hubei, Peoples R China
[3] George Washington Univ, Dept Phys, Washington, DC 20052 USA
基金
中国国家自然科学基金;
关键词
DIRECT-COUPLING ANALYSIS; NONCODING RNAS; PROTEIN STRUCTURES; TERTIARY STRUCTURE; FUNCTIONAL SITES; COEVOLUTION; SERVER; DNA; MOLECULES; SEQUENCES;
D O I
10.1093/bioinformatics/bty345
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Motivation: Non-coding RNA molecules play essential roles by interacting with other molecules to perform various biological functions. However, it is difficult to determine RNA structures due to their flexibility. At present, the number of experimentally solved RNA-ligand and RNA-protein structures is still insufficient. Therefore, binding sites prediction of non-coding RNA is required to understand their functions. Results: Current RNA binding site prediction algorithms produce many false positive nucleotides that are distance away from the binding sites. Here, we present a network approach, RBind, to predict the RNA binding sites. We benchmarked RBind in RNA-ligand and RNA-protein datasets. The average accuracy of 0.82 in RNA-ligand and 0.63 in RNA-protein testing showed that this network strategy has a reliable accuracy for binding sites prediction.
引用
收藏
页码:3131 / 3136
页数:6
相关论文
共 50 条
  • [41] Benchmarking a computational design method for the incorporation of metal ion-binding sites at symmetric protein interfaces
    Hansen, William A.
    Khare, Sagar D.
    PROTEIN SCIENCE, 2017, 26 (08) : 1584 - 1594
  • [42] A Systematic Computational Method to Predict and Enhance Antibody-Antigen Binding in the Absence of Antibody Crystal Structures
    Xu, Jianqing
    Miklos, Aleksandr E.
    Hughes, Randy
    Kuhlman, Brian
    Georgiou, George
    Ellington, Andrew D.
    Gray, Jeffrey J.
    BIOPHYSICAL JOURNAL, 2012, 102 (03) : 621A - 621A
  • [43] Computational identification of protein binding sites on RNAs using high-throughput RNA structure-probing data
    Hu, Xihao
    Wong, Thomas K. F.
    Lu, Zhi John
    Chan, Ting Fung
    Lau, Terrence Chi Kong
    Yiu, Siu Ming
    Yip, Kevin Y.
    BIOINFORMATICS, 2014, 30 (08) : 1049 - 1055
  • [44] Predict and enhance antibody-antigen binding in the absence of antibody crystal structures: A systematic computational method
    Xu, Jianqing
    Miklos, Aleksandr E.
    Hughes, Randy
    Kuhlman, Brian
    Georgiou, George
    Ellington, Andrew D.
    Gray, Jeffrey J.
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2012, 243
  • [45] DeepPN: a deep parallel neural network based on convolutional neural network and graph convolutional network for predicting RNA-protein binding sites
    Jidong Zhang
    Bo Liu
    Zhihan Wang
    Klaus Lehnert
    Mark Gahegan
    BMC Bioinformatics, 23
  • [46] DeepPN: a deep parallel neural network based on convolutional neural network and graph convolutional network for predicting RNA-protein binding sites
    Zhang, Jidong
    Liu, Bo
    Wang, Zhihan
    Lehnert, Klaus
    Gahegan, Mark
    BMC BIOINFORMATICS, 2022, 23 (01)
  • [47] Finding the target sites of RNA-binding proteins
    Li, Xiao
    Kazan, Hilal
    Lipshitz, Howard D.
    Morris, Quaid D.
    WILEY INTERDISCIPLINARY REVIEWS-RNA, 2014, 5 (01) : 111 - 130
  • [48] MCSS-based predictions of RNA binding sites
    Fabrice Leclerc
    Martin Karplus
    Theoretical Chemistry Accounts, 1999, 101 : 131 - 137
  • [49] Microenvironment analysis and identification of magnesium binding sites in RNA
    Banatao, DR
    Altman, RB
    Klein, TE
    NUCLEIC ACIDS RESEARCH, 2003, 31 (15) : 4450 - 4460
  • [50] MCSS-based predictions of RNA binding sites
    Leclerc, F
    Karplus, M
    THEORETICAL CHEMISTRY ACCOUNTS, 1999, 101 (1-3) : 131 - 137