A Complete Conformational Map for RNA

Murthy, Venkatesh L.; Srinivasan, R.; Draper, David E.; Rose, George D.

doi:10.1006/jmbi.1999.2958

Cited by 57 publications

(46 citation statements)

References 46 publications

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“…These could include conformational states that are driven by particular base-base interactions, or they might be driven by electrostatic forces that are not typically accounted for in structural analysis. These ideas are consistent with the patterns of clustering that are observed when the standard torsions are plotted against each other in pairs and the sterically optimal regions of the plot are superimposed 45 . Remarkably, the most sterically optimal regions are not necessarily those that are strongly preferred by actual nucleotides.…”

Section: A Limited Number Of Major Structural Building Blockssupporting

confidence: 80%

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

Wadley

Keating

Duarte

et al. 2007

Journal of Molecular Biology

137

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SummaryQuantitatively describing RNA structure and conformational elements remains a formidable problem. Seven standard torsion angles and the sugar pucker are necessary to completely characterize the conformation of an RNA nucleotide. Progress has been made toward understanding the discrete nature of RNA structure, but classifying simple and ubiquitous structural elements such as helices and motifs remains a difficult task. One approach for describing RNA structure in a simple, mathematically consistent, and computationally accessible manner involves the invocation of two pseudotorsions, η (C4' n-1 , P n , C4' n , P n+1 ) and θ (P n , C4' n , P n+1 , C4' n+1 ), which can be used to describe RNA conformation in much the same way that ϕ and ψ are used to describe backbone configuration of proteins. Here we conduct an exploration and statistical evaluation of pseudotorsional space and of the Ramachandran-like η−θ plot. We show that, through the rigorous quantitative analysis of the η−θ plot, the pseudotorsional descriptors η and θ, together with sugar pucker, are sufficient to describe RNA backbone conformation fully in most cases. These descriptors are also shown to contain considerable information about nucleotide base conformation, revealing a previously uncharacterized interplay between backbone and base orientation. A window function analysis is used to discern statistically relevant regions of density in the η−θ scatter plot and then nucleotides in colocalized clusters in the η−θ plane are shown to have similar three-dimensional structures through RMSD analysis of the RNA structural constituents. We find that major clusters in the η−θ plot are few in number, thereby underscoring the discrete nature of RNA backbone conformation. Like the Ramachandran plot, the η−θ plot is a valuable system for conceptualizing biomolecular conformation, it is a useful tool for analyzing RNA tertiary structures, and it is a vital component of new approaches for solving the three-dimensional structures of large RNA molecules and RNA assemblies.

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Section: A Limited Number Of Major Structural Building Blockssupporting

confidence: 80%

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

Wadley

Keating

Duarte

et al. 2007

Journal of Molecular Biology

137

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“…For example, the first peak βγ I in their β-γ plot and the plateau region in their γ-ε plot shown in Ref. 31 correspond to the central cluster in our θ-η plot. This cluster corresponds to the A-form conformation of RNA, and accounts for a large fraction of the conformations of nucleotides in known RNA structures.…”

Section: B Virtual Bond Representation and Discrete K-state Modelmentioning

confidence: 67%

Discrete state model and accurate estimation of loop entropy of RNA secondary structures

Jian

Lin

Chen

et al. 2008

The Journal of Chemical Physics

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Conformational entropy makes important contribution to the stability and folding of RNA molecule, but it is challenging to either measure or compute conformational entropy associated with long loops. We develop optimized discrete k-state models of RNA backbone based on known RNA structures for computing entropy of loops, which are modeled as self-avoiding walks. To estimate entropy of hairpin, bulge, internal loop, and multibranch loop of long length (up to 50), we develop an efficient sampling method based on the sequential Monte Carlo principle. Our method considers excluded volume effect. It is general and can be applied to calculating entropy of loops with longer length and arbitrary complexity. For loops of short length, our results are in good agreement with a recent theoretical model and experimental measurement. For long loops, our estimated entropy of hairpin loops is in excellent agreement with the Jacobson-Stockmayer extrapolation model. However, for bulge loops and more complex secondary structures such as internal and multibranch loops, we find that the Jacobson-Stockmayer extrapolation model has large errors. Based on estimated entropy, we have developed empirical formulae for accurate calculation of entropy of long loops in different secondary structures. Our study on the effect of asymmetric size of loops suggest that loop entropy of internal loops is largely determined by the total loop length, and is only marginally affected by the asymmetric size of the two loops. Our finding suggests that the significant asymmetric effects of loop length in internal loops measured by experiments are likely to be partially enthalpic. Our method can be applied to develop improved energy parameters important for studying RNA stability and folding, and for predicting RNA secondary and tertiary structures. The discrete model and the program used to calculate loop entropy can be downloaded at

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“…However, the ability of this or any other approach to broadly explore features of a folding landscape may be limited by conformational biases present in the starting unfolded conformation (23)(24)(25). Such biases may be particularly severe for RNA, which typically has considerable secondary structure present in the starting state.…”

mentioning

confidence: 99%

Exploring the folding landscape of a structured RNA

Russell

Zhuang

Babcock

et al. 2001

Proc. Natl. Acad. Sci. U.S.A.

229

241

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Structured RNAs achieve their active states by traversing complex, multidimensional energetic landscapes. Here we probe the folding landscape of the Tetrahymena ribozyme by using a powerful approach: the folding of single ribozyme molecules is followed beginning from distinct regions of the folding landscape. The experiments, combined with small-angle x-ray scattering results, show that the landscape contains discrete folding pathways. These pathways are separated by large free-energy barriers that prevent interconversion between them, indicating that the pathways lie in deep channels in the folding landscape. Chemical protection and mutagenesis experiments are then used to elucidate the structural features that determine which folding pathway is followed. Strikingly, a specific long-range tertiary contact can either help folding or hinder folding, depending on when it is formed during the process. Together these results provide an unprecedented view of the topology of an RNA folding landscape and the RNA structural features that underlie this multidimensional landscape.

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A Complete Conformational Map for RNA

Cited by 57 publications

References 46 publications

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

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

Discrete state model and accurate estimation of loop entropy of RNA secondary structures

Exploring the folding landscape of a structured RNA

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