Evaluating and Learning from RNA Pseudotorsional Space: Quantitative Validation of a Reduced Representation for RNA Structure

Wadley, Leven M.; Keating, Kevin S.; Duarte, Carlos M.; Pyle, Anna Marie

doi:10.1016/j.jmb.2007.06.058

Cited by 82 publications

(161 citation statements)

References 58 publications

(58 reference statements)

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“…1A; Duarte and Pyle 1998;Wadley et al 2007). A survey of the 142 virtual torsion angles in the L1 ligase (two torsions per each of the 71 nucleotides) reveals that only four show a significant change (>45°): u 18 , u 37 , u 44 , and h 38 (Fig.…”

Section: Four Virtual Torsions Can Be Used To Distinguish Between Thementioning

confidence: 99%

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Identification of dynamical hinge points of the L1 ligase molecular switch

Giambaşu

T²,

Sosa³

et al. 2010

RNA

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The L1 ligase is an in vitro selected ribozyme that uses a noncanonically base-paired ligation site to catalyze regioselectively and regiospecifically the 59 to 39 phosphodiester bond ligation, a reaction relevant to origin of life hypotheses that invoke an RNA world scenario. The L1 ligase crystal structure revealed two different conformational states that were proposed to represent the active and inactive forms. It remains an open question as to what degree these two conformers persist as stable conformational intermediates in solution, and along what pathway are they able to interconvert. To explore these questions, we have performed a series of molecular dynamics simulations in explicit solvent of the inactive-active conformational switch in L1 ligase. Four simulations were performed departing from both conformers in both the reactant and product states, in addition to a simulation where local unfolding in the active state was induced. From these simulations, along with crystallographic data, a set of four virtual torsion angles that span two evolutionarily conserved and restricted regions were identified as dynamical hinge points in the conformational switch transition. The ligation site visits three distinct states characterized by hydrogen bond patterns that are correlated with the formation of specific contacts that may promote catalysis. The insights gained from these simulations contribute to a more detailed understanding of the coupled catalytic/conformational switch mechanism of L1 ligase that may facilitate the design and engineering of new catalytic riboswitches.

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Section: Four Virtual Torsions Can Be Used To Distinguish Between Thementioning

confidence: 99%

“…An alternative twodimensional representation of the RNA backbone conformations can be achieved using a reduced set of so-called virtual torsion angles formed along the virtual bonds between C 49 -P atoms that have been shown to be able to discriminate between major RNA folds (Duarte and Pyle 1998;Wadley et al 2007). …”

Section: Introductionmentioning

confidence: 99%

Identification of dynamical hinge points of the L1 ligase molecular switch

Giambaşu

T²,

Sosa³

et al. 2010

RNA

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“…In the following experiments, we search for five types of motifs in 2QBE, and the proposed LS-RSMR is compared with four state-of-the-art methods: FR3D (Sarver et al 2008), shape histogram used by Apostolico et al (2009), AMIGOS II (Wadley et al 2007), and RNAMotifScan (Zhong et al 2010). The results of FR3D are generated using WebFR3D (Petrov et al 2011) with only the geometric search function used.…”

Section: Methods For Performance Evaluationmentioning

confidence: 99%

“…Then the Ullman algorithm is used to search for RNA motifs on the graph. PRIMOS (Duarte et al 2003), COMPADRES (Wadley and Pyle 2004), and AMIGOS II (Wadley et al 2007) characterize RNA structures and motifs using two pseudotorsion angles of the backbones. The distance between two RNA substructures can be calculated based on the distances between their pseudotorsion angles.…”

Section: Introductionmentioning

confidence: 99%

RNA structural motif recognition based on least-squares distance

Shen

Wong

Zhang

et al. 2013

RNA

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RNA structural motifs are recurrent structural elements occurring in RNA molecules. RNA structural motif recognition aims to find RNA substructures that are similar to a query motif, and it is important for RNA structure analysis and RNA function prediction. In view of this, we propose a new method known as RNA Structural Motif Recognition based on Least-Squares distance (LS-RSMR) to effectively recognize RNA structural motifs. A test set consisting of five types of RNA structural motifs occurring in Escherichia coli ribosomal RNA is compiled by us. Experiments are conducted for recognizing these five types of motifs. The experimental results fully reveal the superiority of the proposed LS-RSMR compared with four other state-of-the-art methods.

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“…As descriptors of local RNA backbone conformation, we chose the h-and u-pseudo-torsion angles using the conformational clustering as proposed in (Wadley et al 2007). In brief, based on a grid cast on the h/u-map containing all individual bases, density (counts per grid cell) thresholds were introduced below which h/u-grid cells were ignored.…”

Section: Structural Parametersmentioning

confidence: 99%

The influence of the local sequence environment on RNA loop structures

Schudoma

Larhlimi

Walther

2011

RNA

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RNA folding is assumed to be a hierarchical process. The secondary structure of an RNA molecule, signified by base-pairing and stacking interactions between the paired bases, is formed first. Subsequently, the RNA molecule adopts an energetically favorable three-dimensional conformation in the structural space determined mainly by the rotational degrees of freedom associated with the backbone of regions of unpaired nucleotides (loops). To what extent the backbone conformation of RNA loops also results from interactions within the local sequence context or rather follows global optimization constraints alone has not been addressed yet. Because the majority of base stacking interactions are exerted locally, a critical influence of local sequence on local structure appears plausible. Thus, local loop structure ought to be predictable, at least in part, from the local sequence context alone. To test this hypothesis, we used Random Forests on a nonredundant data set of unpaired nucleotides extracted from 97 X-ray structures from the Protein Data Bank (PDB) to predict discrete backbone angle conformations given by the discretized h/u-pseudo-torsional space. Predictions on balanced sets with four to six conformational classes using local sequence information yielded average accuracies of up to 55%, thus significantly better than expected by chance (17%-25%). Bases close to the central nucleotide appear to be most tightly linked to its conformation. Our results suggest that RNA loop structure does not only depend on long-range base-pairing interactions; instead, it appears that local sequence context exerts a significant influence on the formation of the local loop structure.

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Evaluating and Learning from RNA Pseudotorsional Space: Quantitative Validation of a Reduced Representation for RNA Structure

Cited by 82 publications

References 58 publications

Identification of dynamical hinge points of the L1 ligase molecular switch

Identification of dynamical hinge points of the L1 ligase molecular switch

RNA structural motif recognition based on least-squares distance

The influence of the local sequence environment on RNA loop structures

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