The identification of novel RNA structural motifs using COMPADRES: an automated approach to structural discovery

Wadley, Leven M.

doi:10.1093/nar/gkh1002

Cited by 65 publications

(83 citation statements)

References 49 publications

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“…A web server providing a sophisticated annotation of secondary structures and some tertiary interactions has been presented by Major and co-workers (Gendron et al 2001). Conformational analysis of the RNA backbone using reduced representations has also been used to characterize known motifs (Duarte & Pyle, 1998 ;Duarte et al 2003 ;Hershkovitz et al 2003 ;Murray et al 2003) and even identify new motifs (Wadley & Pyle, 2004). Cluster analysis of root-mean-square deviations between pairs of RNA fragments has been used as the basis for cluster analysis of RNA loop structures to group tetraloops into various families (Huang et al 2005).…”

Section: Tools For Identifying and Classifying Elements And Motifsmentioning

confidence: 99%

RNA structural motifs: building blocks of a modular biomolecule

Hendrix

Brenner

Holbrook

2005

Quart. Rev. Biophys.

197

141

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Abstract. RNAs are modular biomolecules, composed largely of conserved structural subunits, or motifs. These structural motifs comprise the secondary structure of RNA and are knit together via tertiary interactions into a compact, functional, three-dimensional structure and are to be distinguished from motifs defined by sequence or function. A relatively small number of structural motifs are found repeatedly in RNA hairpin and internal loops, and are observed to be composed of a limited number of common 'structural elements '. In addition to secondary and tertiary structure motifs, there are functional motifs specific for certain biological roles and binding motifs that serve to complex metals or other ligands. Research is continuing into the identification and classification of RNA structural motifs and is being initiated to predict motifs from sequence, to trace their phylogenetic relationships and to use them as building blocks in RNA engineering.

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Section: Tools For Identifying and Classifying Elements And Motifsmentioning

confidence: 99%

RNA structural motifs: building blocks of a modular biomolecule

Hendrix

Brenner

Holbrook

2005

Quart. Rev. Biophys.

197

141

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“…Even after the revolution in information content provided by the ribosome structures (Ban et al 2000;Schluenzen et al 2000;Wimberley et al 2000), most RNA backbone analyses have depended on some form of simplification: on plots for pairs of dihedrals (Kim et al 1973;Murthy et al 1999), on reduced dimensionality methods (Sims and Kim 2003), or on use of few parameters insensitive to the most common errors, such as the h,u virtual-angle system (Malathi and Yathindra 1980;Olson 1980;Duarte and Pyle 1998;Wadley and Pyle 2004), which is still the method most often used for identifying or comparing backbone motifs in an unfiltered general database.…”

Section: Introductionmentioning

confidence: 99%

RNA backbone: Consensus all-angle conformers and modular string nomenclature (an RNA Ontology Consortium contribution)

Richardson

Schneider²,

Murray³

et al. 2008

RNA

238

438

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A consensus classification and nomenclature are defined for RNA backbone structure using all of the backbone torsion angles. By a consensus of several independent analysis methods, 46 discrete conformers are identified as suitably clustered in a qualityfiltered, multidimensional dihedral angle distribution. Most of these conformers represent identifiable features or roles within RNA structures. The conformers are given two-character names that reflect the seven-angle dezabgd combinations empirically found favorable for the sugar-to-sugar ''suite'' unit within which the angle correlations are strongest (e.g., 1a for A-form, 5z for the start of S-motifs). Since the half-nucleotides are specified by a number for dez and a lowercase letter for abgd, this modular system can also be parsed to describe traditional nucleotide units (e.g., a1) or the dinucleotides (e.g., a1a1) that are especially useful at the level of crystallographic map fitting. This nomenclature can also be written as a string with two-character suite names between the uppercase letters of the base sequence (N1aG1gN1aR1aA1cN1a for a GNRA tetraloop), facilitating bioinformatic comparisons. Cluster means, standard deviations, coordinates, and examples are made available, as well as the Suitename software that assigns suite conformer names and conformer match quality (suiteness) from atomic coordinates. The RNA Ontology Consortium will combine this new backbone system with others that define base pairs, base-stacking, and hydrogen-bond relationships to provide a full description of RNA structural motifs.

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“…A composite version of this motif occurs in the multi-helix junction in Domain 2 of 23S rRNA (Helices 35, 37, 39, 40 and 45) [31]. Composite motifs are easy to overlook in visual analyses and are generally missed by computational approaches that analyze the conformations of successive nucleotides in the RNA chain [18,43]. Thus, none of the composite kink-turn motifs were identified in the original paper [26].…”

Section: Introductionmentioning

confidence: 99%

“…Several different representations of RNA backbone conformations have been introduced to search for, analyze, and classify recurrent RNA 3D conformations [9,10,18,37,38,41,43]. In general, backbone search methods are relatively fast and can be automated to find new recurrent motifs [43].…”

Section: Introductionmentioning

confidence: 99%

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FR3D: finding local and composite recurrent structural motifs in RNA 3D structures

et al. 2007

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New methods are described for finding recurrent three-dimensional (3D) motifs in RNA atomicresolution structures. Recurrent RNA 3D motifs are sets of RNA nucleotides with similar spatial arrangements. They can be local or composite. Local motifs comprise nucleotides that occur in the same hairpin or internal loop. Composite motifs comprise nucleotides belonging to three or more different RNA strand segments or molecules. We use a base-centered approach to construct efficient, yet exhaustive search procedures using geometric, symbolic, or mixed representations of RNA structure that we implement in a suite of MATLAB programs, "Find RNA 3D" (FR3D). The first modules of FR3D preprocess structure files to classify base-pair and -stacking interactions. Each base is represented geometrically by the position of its glycosidic nitrogen in 3D space and by the rotation matrix that describes its orientation with respect to a common frame. Base-pairing and basestacking interactions are calculated from the base geometries and are represented symbolically according to the Leontis/Westhof basepairing classification, extended to include base-stacking. These data are stored and used to organize motif searches. For geometric searches, the user supplies the 3D structure of a query motif which FR3D uses to find and score geometrically similar candidate motifs, without regard to the sequential position of their nucleotides in the RNA chain or the identity of their bases. To score and rank candidate motifs, FR3D calculates a geometric discrepancy by rigidly rotating candidates to align optimally with the query motif and then comparing the relative orientations of the corresponding bases in the query and candidate motifs. Given the growing size of the RNA structure database, it is impossible to explicitly compute the discrepancy for all conceivable candidate motifs, even for motifs with less than ten nucleotides. The screening algorithm that we describe finds all candidate motifs whose geometric discrepancy with respect to the query motif falls below a user-specified cutoff discrepancy. This technique can be applied to NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript RMSD searches. Candidate motifs identified geometrically may be further screened symbolically to identify those that contain particular basepair types or base-stacking arrangements or that conform to sequence continuity or nucleotide identity constraints. Purely symbolic searches for motifs containing user-defined sequence, continuity and interaction constraints have also been implemented. We demonstrate that FR3D finds all occurrences, both local and composite and with nucleotide substitutions, of sarcin/ricin and kink-turn motifs in the 23S and 5S ribosomal RNA 3D structures of the H. marismortui 50S ribosomal subunit and assigns the lowest discrepancy scores to bona fide examples of these motifs. The search algorithms have been optimized for speed to allow users to search the non-redundant RNA 3D structure database on a personal compute...

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The identification of novel RNA structural motifs using COMPADRES: an automated approach to structural discovery

Cited by 65 publications

References 49 publications

RNA structural motifs: building blocks of a modular biomolecule

RNA structural motifs: building blocks of a modular biomolecule

RNA backbone: Consensus all-angle conformers and modular string nomenclature (an RNA Ontology Consortium contribution)

FR3D: finding local and composite recurrent structural motifs in RNA 3D structures

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