The Influence of Different Structure Representations on the Clustering of an RNA Nucleotides Data Set

Reijmers, Theo H.; Wehrens, Ron; Buydens, L.M.C.

doi:10.1021/ci0103626

Cited by 16 publications

(21 citation statements)

References 17 publications

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“…This continuous growth has led to the emergence of computational methods for 3D structural analysis of RNA (Duarte and Pyle 1998;Olson 1999, 2003;Gendron et al 2001;Yang et al 2003). Other methods have been developed for measuring the similarity between equally sized RNA structures with a predefined correspondence, like in the case of different conformations of the same RNA molecule (Reijmers et al 2001;Duarte et al 2003;Huang et al 2005). Additional methods locate small predefined motifs in larger structures and, as such, are useful for finding new occurrences of known motifs (Duarte et al 2003;Harrison et al 2003;Sarver et al 2007).…”

Section: Introductionmentioning

confidence: 99%

Analysis and classification of RNA tertiary structures

Abraham¹,

Dror²,

Nussinov³

et al. 2008

RNA

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There is a fast growing interest in noncoding RNA transcripts. These transcripts are not translated into proteins, but play essential roles in many cellular and pathological processes. Recent efforts toward comprehension of their function has led to a substantial increase in both the number and the size of solved RNA structures. With the aim of addressing questions relating to RNA structural diversity, we examined RNA conservation at three structural levels: primary, secondary, and tertiary structure. Additionally, we developed an automated method for classifying RNA structures based on spatial (three-dimensional [3D]) similarity. Applying the method to all solved RNA structures resulted in a classified database of RNA tertiary structures (DARTS). DARTS embodies 1333 solved RNA structures classified into 94 clusters. The classification is hierarchical, reflecting the structural relationship between and within clusters. We also developed an application for searching DARTS with a new structure. The search is fast and its performance was successfully tested on all solved RNA structures since the creation of DARTS. A user-friendly interface for both the database and the search application is available online. We show intracluster and intercluster similarities in DARTS and demonstrate the usefulness of the search application. The analysis reveals the current structural repertoire of RNA and exposes common global folds and local tertiary motifs. Further study of these conserved substructures may suggest possible RNA domains and building blocks. This should be beneficial for structure prediction and for gaining insights into structure-function relationships.

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Section: Introductionmentioning

confidence: 99%

Analysis and classification of RNA tertiary structures

Abraham¹,

Dror²,

Nussinov³

et al. 2008

RNA

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“…2b, are very difficult to get correct; most are excellent, but clusters of red spikes mark the fairly frequent physically impossible steric clashes where nonpolar atoms overlap by half an angstrom or more. This causes problems at three different levels: fitting and refinement are more difficult and structures less accurate than one would like; conformational analysis of RNA has a very high noise level, so that looking more closely can even make the problem worse (11); and finally, the detailed analyses of geometry and chemistry needed to understand specific drug and protein binding and RNA catalytic mechanisms are compromised. The usual solution is some form of simplification and variable reduction: treating pairs of adjacent angles (12), omitting or binning angles (13), or defining virtual dihedrals (14).…”

mentioning

confidence: 99%

RNA backbone is rotameric

Murray

Arendall

Richardson

et al. 2003

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

187

266

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Despite the importance of local structural detail to a mechanistic understanding of RNA catalysis and binding functions, RNA backbone conformation has been quite recalcitrant to analysis. There are too many variable torsion angles per residue, and their raw empirical distributions are poorly clustered. This study applies quality-filtering techniques (using resolution, crystallographic B factor, and all-atom steric clashes) to the backbone torsion angle distributions from an 8,636-residue RNA database. With noise levels greatly reduced, clear signal appears for the underlying angle preferences. Half-residue torsion angle distributions for ␣-␤-␥ and for ␦--are plotted and contoured in 3D; each shows about a dozen distinct peaks, which can then be combined in pairs to define complete RNA backbone conformers. Traditional nucleic acid residues are defined from phosphate to phosphate, but here we use a base-to-base (or sugar-to-sugar) division into ''suites'' to parse the RNA backbone repeats, both because most backbone steric clashes are within suites and because the relationship of successive bases is both reliably determined and conformationally important. A suite conformer has seven variables, with sugar pucker specified at both ends. Potential suite conformers were omitted if not represented by at least a small cluster of convincing data points after application of quality filters. The final result is a small library of 42 RNA backbone conformers, which should provide valid conformations for nearly all RNA backbone encountered in experimental structures.RNA structure ͉ RNA conformation ͉ backbone conformers ͉ quality filtering ͉ all-atom contacts R NA has long been known to play a central role in the storage, and especially in the communication, of biological information and to be well-suited for specific and regulated molecularbinding interactions. More recently, it has also been shown to perform enzymatic catalysis (1, 2) and is therefore quite likely to have been critical in the first development of living systems (3). The size, complexity, and specific detail of RNA 3D structure are essential to its various functions, and in that respect RNA is more like protein than like DNA. The determination, analysis, and modification of RNA structure have become an important aspect of biology, with major contributions from NMR (4), electron microscopy (5), and crystallography (6). Progress accelerated recently with the tour-de-force x-ray structures of the ribosomal 50S and 30S (7-9) subunits, which expanded the database of known RNA structures enough to make statistical analysis feasible.Despite all the new information, determining and analyzing RNA structure are both still very difficult tasks. Large RNA structures can typically be determined only to resolutions of Ϸ2.5 Å or lower, where the phosphates and base planes can be located quite reliably, but the sugars and especially the rest of the backbone are not seen well at all. As shown in Fig. 1, there are six rotatable torsion angles per residue along the RNA backbon...

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“…Since RNA motifs are largely invariant but slightly different due to local variations, the problem is how to find a representation scheme to preserve their identity and at the same time to have adequate discriminating power. Reijmers et al (2001) argues that Cartesian coordinates is the most basic representation for RNA structures and other representations, such as torsion angles, can be deduced from it. Duarte et al (2003) and Apostolico et al (2009) use backbones as the representative structures of RNA motifs because backbones of motifs are relatively stable and similar within a class.…”

Section: Representative Structure Of An Rna Motifmentioning

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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The Influence of Different Structure Representations on the Clustering of an RNA Nucleotides Data Set

Cited by 16 publications

References 17 publications

Analysis and classification of RNA tertiary structures

Analysis and classification of RNA tertiary structures

RNA backbone is rotameric

RNA structural motif recognition based on least-squares distance

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