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Characterizing RNA ensembles from NMR data with kinematic models.

Fonseca R, Pachov DV, Bernauer J, van den Bedem H - Nucleic Acids Res. (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: 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.

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Agreement between measured 1H chemical shifts and those back calculated from KGSrna and NMR 3D structures. (a) Depicted chemical shift values are aggregated by proton type in helical (top) and non-helical regions (bottom). The discrete distributions were smoothed with a Gaussian kernel density estimator (bandwidth n−0.2 where n is number of data points) for easier visualization. Measured values were taken from the BMRB and KGSrna samples and NMR bundle values were back calculated using NUCHEMICS. Marginal distributions are shown as histograms with bin-widths of 0.275 ppm. (b) The symmetric Kullback–Leibler divergence indicates the degree of similarity of two distributions and is calculated for the marginal distributions of measured-to-KGSrna, measured-to-initial and measured-to-NMR. The differences between measured-to-KGSrna, measured-to-initial and measured-to-NMR are shown in the bar plot. A negative value indicates better agreement of the ensemble of KGSrna 3D structures with measured values than its comparison 3D structures. (c) Predicted 1H chemical shifts calculated from the 3D structures from the KGSrna ensemble and the NMR bundle compared to the measured values of the 32nt P2a-J2a/b-P2b (helix-bulge-helix) of human telomerase RNA (pdb id 2L3E). The data points are expected to lie along a 45° line if measured 1H CS are accurately predicted.
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Figure 4: Agreement between measured 1H chemical shifts and those back calculated from KGSrna and NMR 3D structures. (a) Depicted chemical shift values are aggregated by proton type in helical (top) and non-helical regions (bottom). The discrete distributions were smoothed with a Gaussian kernel density estimator (bandwidth n−0.2 where n is number of data points) for easier visualization. Measured values were taken from the BMRB and KGSrna samples and NMR bundle values were back calculated using NUCHEMICS. Marginal distributions are shown as histograms with bin-widths of 0.275 ppm. (b) The symmetric Kullback–Leibler divergence indicates the degree of similarity of two distributions and is calculated for the marginal distributions of measured-to-KGSrna, measured-to-initial and measured-to-NMR. The differences between measured-to-KGSrna, measured-to-initial and measured-to-NMR are shown in the bar plot. A negative value indicates better agreement of the ensemble of KGSrna 3D structures with measured values than its comparison 3D structures. (c) Predicted 1H chemical shifts calculated from the 3D structures from the KGSrna ensemble and the NMR bundle compared to the measured values of the 32nt P2a-J2a/b-P2b (helix-bulge-helix) of human telomerase RNA (pdb id 2L3E). The data points are expected to lie along a 45° line if measured 1H CS are accurately predicted.

Mentions: KGSrna enables broad sampling to identify sparsely populated substates, while maintaining conformational distributions similar to those measured. Figure 4a shows the distribution of measured and predicted 1H CS for helical (top row) and non-helical (bottom row) regions for each proton type over the whole benchmark set. Figure 4b shows the location of the probes. For helix backbone and base protons, the medians of the distributions are virtually identical. This suggests that, on average, our kinematic representation of RNA results in an unbiased exploration of the conformational landscape encoded in the measured proton chemical shifts.


Characterizing RNA ensembles from NMR data with kinematic models.

Fonseca R, Pachov DV, Bernauer J, van den Bedem H - Nucleic Acids Res. (2014)

Agreement between measured 1H chemical shifts and those back calculated from KGSrna and NMR 3D structures. (a) Depicted chemical shift values are aggregated by proton type in helical (top) and non-helical regions (bottom). The discrete distributions were smoothed with a Gaussian kernel density estimator (bandwidth n−0.2 where n is number of data points) for easier visualization. Measured values were taken from the BMRB and KGSrna samples and NMR bundle values were back calculated using NUCHEMICS. Marginal distributions are shown as histograms with bin-widths of 0.275 ppm. (b) The symmetric Kullback–Leibler divergence indicates the degree of similarity of two distributions and is calculated for the marginal distributions of measured-to-KGSrna, measured-to-initial and measured-to-NMR. The differences between measured-to-KGSrna, measured-to-initial and measured-to-NMR are shown in the bar plot. A negative value indicates better agreement of the ensemble of KGSrna 3D structures with measured values than its comparison 3D structures. (c) Predicted 1H chemical shifts calculated from the 3D structures from the KGSrna ensemble and the NMR bundle compared to the measured values of the 32nt P2a-J2a/b-P2b (helix-bulge-helix) of human telomerase RNA (pdb id 2L3E). The data points are expected to lie along a 45° line if measured 1H CS are accurately predicted.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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Figure 4: Agreement between measured 1H chemical shifts and those back calculated from KGSrna and NMR 3D structures. (a) Depicted chemical shift values are aggregated by proton type in helical (top) and non-helical regions (bottom). The discrete distributions were smoothed with a Gaussian kernel density estimator (bandwidth n−0.2 where n is number of data points) for easier visualization. Measured values were taken from the BMRB and KGSrna samples and NMR bundle values were back calculated using NUCHEMICS. Marginal distributions are shown as histograms with bin-widths of 0.275 ppm. (b) The symmetric Kullback–Leibler divergence indicates the degree of similarity of two distributions and is calculated for the marginal distributions of measured-to-KGSrna, measured-to-initial and measured-to-NMR. The differences between measured-to-KGSrna, measured-to-initial and measured-to-NMR are shown in the bar plot. A negative value indicates better agreement of the ensemble of KGSrna 3D structures with measured values than its comparison 3D structures. (c) Predicted 1H chemical shifts calculated from the 3D structures from the KGSrna ensemble and the NMR bundle compared to the measured values of the 32nt P2a-J2a/b-P2b (helix-bulge-helix) of human telomerase RNA (pdb id 2L3E). The data points are expected to lie along a 45° line if measured 1H CS are accurately predicted.
Mentions: KGSrna enables broad sampling to identify sparsely populated substates, while maintaining conformational distributions similar to those measured. Figure 4a shows the distribution of measured and predicted 1H CS for helical (top row) and non-helical (bottom row) regions for each proton type over the whole benchmark set. Figure 4b shows the location of the probes. For helix backbone and base protons, the medians of the distributions are virtually identical. This suggests that, on average, our kinematic representation of RNA results in an unbiased exploration of the conformational landscape encoded in the measured proton chemical shifts.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: 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.

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