Characterizing RNA ensembles from NMR data with kinematic models

Rasmus Fonseca 1, 2, 3 Dimitar V Pachov 4 Julie Bernauer 1, 2 Henry Van den Bedem 5
1 AMIB - Algorithms and Models for Integrative Biology
CNRS - Centre National de la Recherche Scientifique : UMR8623, X - École polytechnique, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique, LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau]
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.
Type de document :
Article dans une revue
Nucleic Acids Research, Oxford University Press, 2014, 42 (15), pp.9562-72. 〈10.1093/nar/gku707〉
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Contributeur : Julie Bernauer <>
Soumis le : jeudi 28 août 2014 - 20:17:11
Dernière modification le : dimanche 17 juin 2018 - 18:26:01

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Rasmus Fonseca, Dimitar V Pachov, Julie Bernauer, Henry Van den Bedem. Characterizing RNA ensembles from NMR data with kinematic models. Nucleic Acids Research, Oxford University Press, 2014, 42 (15), pp.9562-72. 〈10.1093/nar/gku707〉. 〈hal-01058971〉



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