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    Elucidating Mechanisms of Molecular Recognition Between Human Argonaute and miRNA Using Computational Approaches

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    Type
    Book Chapter
    Authors
    Jiang, Hanlun
    Zhu, Lizhe cc
    Héliou, Amélie
    Gao, Xin cc
    Bernauer, Julie
    Huang, Xuhui cc
    KAUST Department
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2016-12-07
    Online Publication Date
    2016-12-07
    Print Publication Date
    2017
    Permanent link to this record
    http://hdl.handle.net/10754/622141
    
    Metadata
    Show full item record
    Abstract
    MicroRNA (miRNA) and Argonaute (AGO) protein together form the RNA-induced silencing complex (RISC) that plays an essential role in the regulation of gene expression. Elucidating the underlying mechanism of AGO-miRNA recognition is thus of great importance not only for the in-depth understanding of miRNA function but also for inspiring new drugs targeting miRNAs. In this chapter we introduce a combined computational approach of molecular dynamics (MD) simulations, Markov state models (MSMs), and protein-RNA docking to investigate AGO-miRNA recognition. Constructed from MD simulations, MSMs can elucidate the conformational dynamics of AGO at biologically relevant timescales. Protein-RNA docking can then efficiently identify the AGO conformations that are geometrically accessible to miRNA. Using our recent work on human AGO2 as an example, we explain the rationale and the workflow of our method in details. This combined approach holds great promise to complement experiments in unraveling the mechanisms of molecular recognition between large, flexible, and complex biomolecules.
    Citation
    Jiang H, Zhu L, Héliou A, Gao X, Bernauer J, et al. (2016) Elucidating Mechanisms of Molecular Recognition Between Human Argonaute and miRNA Using Computational Approaches. Drug Target miRNA: 251–275. Available: http://dx.doi.org/10.1007/978-1-4939-6563-2_18.
    Sponsors
    This work is supported by the Hong Kong Research Grant Council [grant numbers 16302214, 609813, HKUST C6009-15G, AoE/ M-09/12, M-HKUST601/13, and T13-607/12R to X.H.] and the National Science Foundation of China [grant number 21273188 to X.H.]. The work is also supported by a grant from the PROCOREFrance/ Hong Kong Joint Research Scheme sponsored by the Research Grants Council and the Consulate General of France in Hong Kong (F-HK29/11T) (X.H. and J.B.). X.G. was supported by funding from King Abdullah University of Science and Technology. This research made use of the resources of the Supercomputing Laboratory at King Abdullah University of Science and Technology.
    Publisher
    Springer Nature
    Journal
    Methods in Molecular Biology
    DOI
    10.1007/978-1-4939-6563-2_18
    Additional Links
    http://link.springer.com/protocol/10.1007%2F978-1-4939-6563-2_18
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-1-4939-6563-2_18
    Scopus Count
    Collections
    Computer Science Program; Computational Bioscience Research Center (CBRC); Book Chapters; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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