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
2 LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau]
3 DIKU - Department of Computer Science [Copenhagen]
4 Department of Chemistry [Stanford]
5 JCSG - Joint Center for Structural Genomics

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.

Domaines: Biologie structurale [q-bio.BM]

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