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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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A flowchart of the KGSrna sampling algorithm. KGSrna takes as inputs an initial conformation and a file of hydrogen bonds A(N3)–U(H3) and G(H1)–C(N3) as distance constraints. Next, a pool of conformations is initialized with the input structure and then grown by repeatedly perturbing a randomly selected conformation from the pool with a rebuild or -space perturbation at a 10/90 rate. If no clashes between atoms were introduced in the perturbed conformation, it is added to the pool. The procedure is repeated until a desired number nstruct of conformations is obtained.
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Figure 2: A flowchart of the KGSrna sampling algorithm. KGSrna takes as inputs an initial conformation and a file of hydrogen bonds A(N3)–U(H3) and G(H1)–C(N3) as distance constraints. Next, a pool of conformations is initialized with the input structure and then grown by repeatedly perturbing a randomly selected conformation from the pool with a rebuild or -space perturbation at a 10/90 rate. If no clashes between atoms were introduced in the perturbed conformation, it is added to the pool. The procedure is repeated until a desired number nstruct of conformations is obtained.

Mentions: An overview of the sampling procedure is shown in Figure 2. KGSrna takes as input an initial conformation , an exploration radius rinit and a set of canonical WC pairs to identify hydrogen bonds A(N3)–U(H3) and G(H1)–C(N3) as distance constraints. WC pairs are obtained from the RNAView program (37). Next, it grows a pool of conformations by repeatedly perturbing either or a previously generated seed conformation, , in the pool that is within rinit C4′ root mean square deviation (RMSD) of . The seed conformation is selected by first generating a completely random conformation . Next, the conformation closest to from all previously generated conformations that are within a spherical shell of random radius from and width rinit/100 is selected as , and then is discarded. This guarantees that samples in sparsely populated regions within the exploration sphere are more likely to be chosen as seeds and that the sample population will distribute widely. A rebuild perturbation of two free nucleotides or a -space perturbation is then performed at a 10/90 rate. To characterize the apical loop of HIV-1 TAR, see below, the C2′-endo peak was up-shifted by 60° to oversample non-helical ribose conformations. A -space perturbation can start from a seed generated by a rebuild perturbation or vice versa, allowing detailed exploration of remote parts of conformation space. The trial-vector is scaled down to ensure no torsional change exceeds 0.1 radians = 5.7°. If no clashes between atoms were introduced in generating a new sample, , it is accepted in the conformation pool. An efficient grid-indexing method is used for clash detection by overlapping van der Waals radii (38). The van der Waals radii were scaled by a factor 0.5.


Characterizing RNA ensembles from NMR data with kinematic models.

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

A flowchart of the KGSrna sampling algorithm. KGSrna takes as inputs an initial conformation and a file of hydrogen bonds A(N3)–U(H3) and G(H1)–C(N3) as distance constraints. Next, a pool of conformations is initialized with the input structure and then grown by repeatedly perturbing a randomly selected conformation from the pool with a rebuild or -space perturbation at a 10/90 rate. If no clashes between atoms were introduced in the perturbed conformation, it is added to the pool. The procedure is repeated until a desired number nstruct of conformations is obtained.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4150802&req=5

Figure 2: A flowchart of the KGSrna sampling algorithm. KGSrna takes as inputs an initial conformation and a file of hydrogen bonds A(N3)–U(H3) and G(H1)–C(N3) as distance constraints. Next, a pool of conformations is initialized with the input structure and then grown by repeatedly perturbing a randomly selected conformation from the pool with a rebuild or -space perturbation at a 10/90 rate. If no clashes between atoms were introduced in the perturbed conformation, it is added to the pool. The procedure is repeated until a desired number nstruct of conformations is obtained.
Mentions: An overview of the sampling procedure is shown in Figure 2. KGSrna takes as input an initial conformation , an exploration radius rinit and a set of canonical WC pairs to identify hydrogen bonds A(N3)–U(H3) and G(H1)–C(N3) as distance constraints. WC pairs are obtained from the RNAView program (37). Next, it grows a pool of conformations by repeatedly perturbing either or a previously generated seed conformation, , in the pool that is within rinit C4′ root mean square deviation (RMSD) of . The seed conformation is selected by first generating a completely random conformation . Next, the conformation closest to from all previously generated conformations that are within a spherical shell of random radius from and width rinit/100 is selected as , and then is discarded. This guarantees that samples in sparsely populated regions within the exploration sphere are more likely to be chosen as seeds and that the sample population will distribute widely. A rebuild perturbation of two free nucleotides or a -space perturbation is then performed at a 10/90 rate. To characterize the apical loop of HIV-1 TAR, see below, the C2′-endo peak was up-shifted by 60° to oversample non-helical ribose conformations. A -space perturbation can start from a seed generated by a rebuild perturbation or vice versa, allowing detailed exploration of remote parts of conformation space. The trial-vector is scaled down to ensure no torsional change exceeds 0.1 radians = 5.7°. If no clashes between atoms were introduced in generating a new sample, , it is accepted in the conformation pool. An efficient grid-indexing method is used for clash detection by overlapping van der Waals radii (38). The van der Waals radii were scaled by a factor 0.5.

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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