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Nucleic Acids Res. 2014 Sep;42(15):9562-72. doi: 10.1093/nar/gku707. Epub 2014 Aug 11.

Characterizing RNA ensembles from NMR data with kinematic models.

Author information

1
AMIB Project, INRIA Saclay-Île de France, 1 rue Honoré d'Estienne d'Orves, Bâtiment Alan Turing, Campus de l'École Polytechnique, 91120 Palaiseau, France Laboratoire d'Informatique de l'École Polytechnique (LIX), CNRS UMR 7161, École Polytechnique, 91128 Palaiseau, France Department of Computer Science, University of Copenhagen, Nørre Campus, Universitetsparken 5, DK-2100 Copenhagen, Denmark.
2
Department of Chemistry, Stanford University, 333 Campus Dr., Stanford, CA 94305, USA.
3
AMIB Project, INRIA Saclay-Île de France, 1 rue Honoré d'Estienne d'Orves, Bâtiment Alan Turing, Campus de l'École Polytechnique, 91120 Palaiseau, France Laboratoire d'Informatique de l'École Polytechnique (LIX), CNRS UMR 7161, École Polytechnique, 91128 Palaiseau, France.
4
Joint Center for Structural Genomics, Stanford Synchrotron Radiation Lightsource, Stanford University, 2575 Sand Hill Road, Menlo Park, CA 94025, USA vdbedem@stanford.edu.

Abstract

Functional mechanisms of biomolecules often manifest themselves precisely in transient conformational substates. Researchers have long sought to structurally characterize dynamic processes in non-coding RNA, combining experimental data with computer algorithms. However, adequate exploration of conformational space for these highly dynamic molecules, starting from static crystal structures, remains challenging. Here, we report a new conformational sampling procedure, KGSrna, which can efficiently probe the native ensemble of RNA molecules in solution. We found that KGSrna ensembles accurately represent the conformational landscapes of 3D RNA encoded by NMR proton chemical shifts. KGSrna resolves motionally averaged NMR data into structural contributions; when coupled with residual dipolar coupling data, a KGSrna ensemble revealed a previously uncharacterized transient excited state of the HIV-1 trans-activation response element stem-loop. Ensemble-based interpretations of averaged data can aid in formulating and testing dynamic, motion-based hypotheses of functional mechanisms in RNAs with broad implications for RNA engineering and therapeutic intervention.

PMID:
25114056
PMCID:
PMC4150802
DOI:
10.1093/nar/gku707
[Indexed for MEDLINE]
Free PMC Article

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