Chapter title |
Elucidating Mechanisms of Molecular Recognition Between Human Argonaute and miRNA Using Computational Approaches.
|
---|---|
Chapter number | 18 |
Book title |
Drug Target miRNA
|
Published in |
Methods in molecular biology, January 2017
|
DOI | 10.1007/978-1-4939-6563-2_18 |
Pubmed ID | |
Book ISBNs |
978-1-4939-6561-8, 978-1-4939-6563-2
|
Authors |
Hanlun Jiang, Lizhe Zhu, Amélie Héliou, Xin Gao, Julie Bernauer, Xuhui Huang |
Editors |
Marco F. Schmidt |
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. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 11 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 4 | 36% |
Professor > Associate Professor | 2 | 18% |
Student > Bachelor | 1 | 9% |
Student > Doctoral Student | 1 | 9% |
Unknown | 3 | 27% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 3 | 27% |
Biochemistry, Genetics and Molecular Biology | 2 | 18% |
Agricultural and Biological Sciences | 2 | 18% |
Chemistry | 1 | 9% |
Unknown | 3 | 27% |