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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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Sampling properties of 1000 KGSrna samples illustrated with the TYMV pseudoknot. Sampling was started from model one of the NMR bundle with pdb id 1A60, and the sampling-radius was set to 4.6 Å. (a) The evolution of the minimum (red curves) and maximum (blue curves) C4′ RMSD to each structure in the NMR bundle. Bold curves correspond to the starting structure. (b) The backbone of the initial structure indicating varying degrees of ridigity for torsional degrees-of-freedom. A thicker and more red-shifted backbone indicates higher variances for those degrees-of-freedom. The backbones of 25 randomly chosen KGSrna samples are shown in translucent blue to reflect flexibility. (c) Distributions of the τ-angles in the NMR-bundle and the KGSrna samples. Ribose conformations of the 1000 samples are displayed vertically as normalized color-coded histograms with a bin-width of 1.8°. Rebuild perturbations recover the full range of τ-angles in the NMR bundle for free nucleotides (highlighted on the x-axis), as shown by residue 8. The distribution from which τ-angles are sampled is shown on the right. The large peak corresponds to C3′-endo conformations and the smaller one to C2′-endo conformations.
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Figure 3: Sampling properties of 1000 KGSrna samples illustrated with the TYMV pseudoknot. Sampling was started from model one of the NMR bundle with pdb id 1A60, and the sampling-radius was set to 4.6 Å. (a) The evolution of the minimum (red curves) and maximum (blue curves) C4′ RMSD to each structure in the NMR bundle. Bold curves correspond to the starting structure. (b) The backbone of the initial structure indicating varying degrees of ridigity for torsional degrees-of-freedom. A thicker and more red-shifted backbone indicates higher variances for those degrees-of-freedom. The backbones of 25 randomly chosen KGSrna samples are shown in translucent blue to reflect flexibility. (c) Distributions of the τ-angles in the NMR-bundle and the KGSrna samples. Ribose conformations of the 1000 samples are displayed vertically as normalized color-coded histograms with a bin-width of 1.8°. Rebuild perturbations recover the full range of τ-angles in the NMR bundle for free nucleotides (highlighted on the x-axis), as shown by residue 8. The distribution from which τ-angles are sampled is shown on the right. The large peak corresponds to C3′-endo conformations and the smaller one to C2′-endo conformations.

Mentions: Efficient exploration of the native ensemble requires broad and uniform sampling. Sampled conformations need to diffuse away quickly from an initial structure, while simultaneously at least one member of the native ensemble should be found close to any sampled conformation. We first validated these characteristics for KGSrna on a benchmark set of 60 RNA molecules with an average length of 30 nucleotides (nt) determined by NMR spectroscopy from the BMRB (Supplementary Table S1). We view the NMR bundle as structural representatives of a native ensemble, i.e. a ‘synthetic’ ensemble. For each RNA molecule, we created a set of 1000 samples starting from the first model of the NMR bundle. The exploration radius was fixed at the largest pairwise RMSD in each NMR bundle. Creation of 1000 samples took on average 372 s. Figure 3a shows the evolution of the C4′ RMSD between 1000 KGSrna samples and the NMR bundle of the 44 nt pseudoknotted acceptor arm of the transfer RNA-like structure of turnip yellow mosaic virus (TYMV). The procedure quickly expands its sampling neighborhood from the starting model to exceed its preset exploration radius of 4.9 Å (Figure 3a bold blue line). Within ∼300 sampling steps, the distance to the starting model reaches a limiting distance of ∼1.5 Å beyond the exploration radius, a trend that was consistent across our benchmark set (Supplementary Table S1). The maximum RMSD to each member of the NMR bundle of the sample set, represented by the blue lines, ranges from 6.1 to 8.7 Å. These trends indicate that samples diffuse quickly and uniformly through the synthetic ensemble, away from the starting model and consistently equidistant to all members of the NMR bundle.


Characterizing RNA ensembles from NMR data with kinematic models.

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

Sampling properties of 1000 KGSrna samples illustrated with the TYMV pseudoknot. Sampling was started from model one of the NMR bundle with pdb id 1A60, and the sampling-radius was set to 4.6 Å. (a) The evolution of the minimum (red curves) and maximum (blue curves) C4′ RMSD to each structure in the NMR bundle. Bold curves correspond to the starting structure. (b) The backbone of the initial structure indicating varying degrees of ridigity for torsional degrees-of-freedom. A thicker and more red-shifted backbone indicates higher variances for those degrees-of-freedom. The backbones of 25 randomly chosen KGSrna samples are shown in translucent blue to reflect flexibility. (c) Distributions of the τ-angles in the NMR-bundle and the KGSrna samples. Ribose conformations of the 1000 samples are displayed vertically as normalized color-coded histograms with a bin-width of 1.8°. Rebuild perturbations recover the full range of τ-angles in the NMR bundle for free nucleotides (highlighted on the x-axis), as shown by residue 8. The distribution from which τ-angles are sampled is shown on the right. The large peak corresponds to C3′-endo conformations and the smaller one to C2′-endo conformations.
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Related In: Results  -  Collection

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Figure 3: Sampling properties of 1000 KGSrna samples illustrated with the TYMV pseudoknot. Sampling was started from model one of the NMR bundle with pdb id 1A60, and the sampling-radius was set to 4.6 Å. (a) The evolution of the minimum (red curves) and maximum (blue curves) C4′ RMSD to each structure in the NMR bundle. Bold curves correspond to the starting structure. (b) The backbone of the initial structure indicating varying degrees of ridigity for torsional degrees-of-freedom. A thicker and more red-shifted backbone indicates higher variances for those degrees-of-freedom. The backbones of 25 randomly chosen KGSrna samples are shown in translucent blue to reflect flexibility. (c) Distributions of the τ-angles in the NMR-bundle and the KGSrna samples. Ribose conformations of the 1000 samples are displayed vertically as normalized color-coded histograms with a bin-width of 1.8°. Rebuild perturbations recover the full range of τ-angles in the NMR bundle for free nucleotides (highlighted on the x-axis), as shown by residue 8. The distribution from which τ-angles are sampled is shown on the right. The large peak corresponds to C3′-endo conformations and the smaller one to C2′-endo conformations.
Mentions: Efficient exploration of the native ensemble requires broad and uniform sampling. Sampled conformations need to diffuse away quickly from an initial structure, while simultaneously at least one member of the native ensemble should be found close to any sampled conformation. We first validated these characteristics for KGSrna on a benchmark set of 60 RNA molecules with an average length of 30 nucleotides (nt) determined by NMR spectroscopy from the BMRB (Supplementary Table S1). We view the NMR bundle as structural representatives of a native ensemble, i.e. a ‘synthetic’ ensemble. For each RNA molecule, we created a set of 1000 samples starting from the first model of the NMR bundle. The exploration radius was fixed at the largest pairwise RMSD in each NMR bundle. Creation of 1000 samples took on average 372 s. Figure 3a shows the evolution of the C4′ RMSD between 1000 KGSrna samples and the NMR bundle of the 44 nt pseudoknotted acceptor arm of the transfer RNA-like structure of turnip yellow mosaic virus (TYMV). The procedure quickly expands its sampling neighborhood from the starting model to exceed its preset exploration radius of 4.9 Å (Figure 3a bold blue line). Within ∼300 sampling steps, the distance to the starting model reaches a limiting distance of ∼1.5 Å beyond the exploration radius, a trend that was consistent across our benchmark set (Supplementary Table S1). The maximum RMSD to each member of the NMR bundle of the sample set, represented by the blue lines, ranges from 6.1 to 8.7 Å. These trends indicate that samples diffuse quickly and uniformly through the synthetic ensemble, away from the starting model and consistently equidistant to all members of the NMR bundle.

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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