Characterizing RNA ensembles from NMR data with kinematic models

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Characterizing RNA ensembles from NMR data with kinematic models. / Fonseca, Rasmus; Pachov, Dimitar V.; Bernauer, Julie; van den Bedem, Henry.

In: Nucleic Acids Research, Vol. 42, No. 15, 2014, p. 9562-9572.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Fonseca, R, Pachov, DV, Bernauer, J & van den Bedem, H 2014, 'Characterizing RNA ensembles from NMR data with kinematic models', Nucleic Acids Research, vol. 42, no. 15, pp. 9562-9572. https://doi.org/10.1093/nar/gku707

APA

Fonseca, R., Pachov, D. V., Bernauer, J., & van den Bedem, H. (2014). Characterizing RNA ensembles from NMR data with kinematic models. Nucleic Acids Research, 42(15), 9562-9572. https://doi.org/10.1093/nar/gku707

Vancouver

Fonseca R, Pachov DV, Bernauer J, van den Bedem H. Characterizing RNA ensembles from NMR data with kinematic models. Nucleic Acids Research. 2014;42(15):9562-9572. https://doi.org/10.1093/nar/gku707

Author

Fonseca, Rasmus ; Pachov, Dimitar V. ; Bernauer, Julie ; van den Bedem, Henry. / Characterizing RNA ensembles from NMR data with kinematic models. In: Nucleic Acids Research. 2014 ; Vol. 42, No. 15. pp. 9562-9572.

Bibtex

@article{66fbca6dde204e899c5065ee550cac71,

title = "Characterizing RNA ensembles from NMR data with kinematic models",

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.",

author = "Rasmus Fonseca and Pachov, {Dimitar V.} and Julie Bernauer and {van den Bedem}, Henry",

note = "{\textcopyright} The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.",

year = "2014",

doi = "10.1093/nar/gku707",

language = "English",

volume = "42",

pages = "9562--9572",

journal = "Nucleic Acids Research",

issn = "0305-1048",

publisher = "Oxford University Press",

number = "15",

}

RIS

TY - JOUR

T1 - Characterizing RNA ensembles from NMR data with kinematic models

AU - Fonseca, Rasmus

AU - Pachov, Dimitar V.

AU - Bernauer, Julie

AU - van den Bedem, Henry

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

U2 - 10.1093/nar/gku707

DO - 10.1093/nar/gku707

M3 - Journal article

C2 - 25114056

VL - 42

SP - 9562

EP - 9572

JO - Nucleic Acids Research

JF - Nucleic Acids Research

SN - 0305-1048

IS - 15

ER -

ID: 122656878

Department of Computer Science