Markov State Models Reveal a Two-Step Mechanism of miRNA Loading into the Human Argonaute Protein: Selective Binding followed by Structural Re-arrangement

Handle URI:
http://hdl.handle.net/10754/561086
Title:
Markov State Models Reveal a Two-Step Mechanism of miRNA Loading into the Human Argonaute Protein: Selective Binding followed by Structural Re-arrangement
Authors:
Jiang, Hanlun; Sheong, Fu Kit; Zhu, Lizhe; Gao, Xin ( 0000-0002-7108-3574 ) ; Bernauer, Julie; Huang, Xuhui
Abstract:
Argonaute (Ago) proteins and microRNAs (miRNAs) are central components in RNA interference, which is a key cellular mechanism for sequence-specific gene silencing. Despite intensive studies, molecular mechanisms of how Ago recognizes miRNA remain largely elusive. In this study, we propose a two-step mechanism for this molecular recognition: selective binding followed by structural re-arrangement. Our model is based on the results of a combination of Markov State Models (MSMs), large-scale protein-RNA docking, and molecular dynamics (MD) simulations. Using MSMs, we identify an open state of apo human Ago-2 in fast equilibrium with partially open and closed states. Conformations in this open state are distinguished by their largely exposed binding grooves that can geometrically accommodate miRNA as indicated in our protein-RNA docking studies. miRNA may then selectively bind to these open conformations. Upon the initial binding, the complex may perform further structural re-arrangement as shown in our MD simulations and eventually reach the stable binary complex structure. Our results provide novel insights in Ago-miRNA recognition mechanisms and our methodology holds great potential to be widely applied in the studies of other important molecular recognition systems.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Markov State Models Reveal a Two-Step Mechanism of miRNA Loading into the Human Argonaute Protein: Selective Binding followed by Structural Re-arrangement 2015, 11 (7):e1004404 PLOS Computational Biology
Publisher:
Public Library of Science (PLoS)
Journal:
PLOS Computational Biology
Issue Date:
16-Jul-2015
DOI:
10.1371/journal.pcbi.1004404
Type:
Article
ISSN:
1553-7358
Additional Links:
http://dx.plos.org/10.1371/journal.pcbi.1004404
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorJiang, Hanlunen
dc.contributor.authorSheong, Fu Kiten
dc.contributor.authorZhu, Lizheen
dc.contributor.authorGao, Xinen
dc.contributor.authorBernauer, Julieen
dc.contributor.authorHuang, Xuhuien
dc.date.accessioned2015-07-27T12:31:14Zen
dc.date.available2015-07-27T12:31:14Zen
dc.date.issued2015-07-16en
dc.identifier.citationMarkov State Models Reveal a Two-Step Mechanism of miRNA Loading into the Human Argonaute Protein: Selective Binding followed by Structural Re-arrangement 2015, 11 (7):e1004404 PLOS Computational Biologyen
dc.identifier.issn1553-7358en
dc.identifier.doi10.1371/journal.pcbi.1004404en
dc.identifier.urihttp://hdl.handle.net/10754/561086en
dc.description.abstractArgonaute (Ago) proteins and microRNAs (miRNAs) are central components in RNA interference, which is a key cellular mechanism for sequence-specific gene silencing. Despite intensive studies, molecular mechanisms of how Ago recognizes miRNA remain largely elusive. In this study, we propose a two-step mechanism for this molecular recognition: selective binding followed by structural re-arrangement. Our model is based on the results of a combination of Markov State Models (MSMs), large-scale protein-RNA docking, and molecular dynamics (MD) simulations. Using MSMs, we identify an open state of apo human Ago-2 in fast equilibrium with partially open and closed states. Conformations in this open state are distinguished by their largely exposed binding grooves that can geometrically accommodate miRNA as indicated in our protein-RNA docking studies. miRNA may then selectively bind to these open conformations. Upon the initial binding, the complex may perform further structural re-arrangement as shown in our MD simulations and eventually reach the stable binary complex structure. Our results provide novel insights in Ago-miRNA recognition mechanisms and our methodology holds great potential to be widely applied in the studies of other important molecular recognition systems.en
dc.publisherPublic Library of Science (PLoS)en
dc.relation.urlhttp://dx.plos.org/10.1371/journal.pcbi.1004404en
dc.rightsThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. http://creativecommons.org/licenses/by/4.0/en
dc.titleMarkov State Models Reveal a Two-Step Mechanism of miRNA Loading into the Human Argonaute Protein: Selective Binding followed by Structural Re-arrangementen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalPLOS Computational Biologyen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionBioengineering Graduate Program, Division of Biomedical Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kongen
dc.contributor.institutionThe HKUST Shenzhen Research Institute, Shenzhen, Chinaen
dc.contributor.institutionDepartment of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kongen
dc.contributor.institutionCenter of Systems Biology and Human Health, School of Science and Institute for Advance Study, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kongen
dc.contributor.institutionInria Saclay-Île de France, Bâtiment Alan Turing, Campus de l’École Polytechnique, Palaiseau, Franceen
dc.contributor.institutionLaboratoire d’Informatique de l’École Polytechnique (LIX), CNRS UMR 7161, École Polytechnique, Palaiseau, Franceen
kaust.authorGao, Xinen
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