2007
DOI: 10.1093/nar/gkm069
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Modeling RNA tertiary structure motifs by graph-grammars

Abstract: A new approach, graph-grammars, to encode RNA tertiary structure patterns is introduced and exemplified with the classical sarcin–ricin motif. The sarcin–ricin motif is found in the stem of the crucial ribosomal loop E (also referred to as the sarcin–ricin loop), which is sensitive to the α-sarcin and ricin toxins. Here, we generate a graph-grammar for the sarcin-ricin motif and apply it to derive putative sequences that would fold in this motif. The biological relevance of the derived sequences is confirmed b… Show more

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Cited by 24 publications
(16 citation statements)
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“…However, this method is still limited to 50-nt sequences. More recently, Parisien and Major (2008) described a method for modeling 3D structures using energy minimization that builds upon earlier work using predicted cyclic building blocks (St Onge et al 2007). Although this method represents an advance in modeling RNA helical regions, the modeling of nonhelical regions and long-range interactions still requires improvement.…”
Section: Introductionmentioning
confidence: 99%
“…However, this method is still limited to 50-nt sequences. More recently, Parisien and Major (2008) described a method for modeling 3D structures using energy minimization that builds upon earlier work using predicted cyclic building blocks (St Onge et al 2007). Although this method represents an advance in modeling RNA helical regions, the modeling of nonhelical regions and long-range interactions still requires improvement.…”
Section: Introductionmentioning
confidence: 99%
“…Francois Major (University of Montreal) described an approach developed and enhanced over several years to predict RNA secondary and tertiary structures using a geometric approach based on nucleotide cycle building blocks (motifs) (St-Onge et al, 2007;Parisien and Major, 2008). Combined with sequence alignments and low-resolution data, these building block definitions can be incorporated to predict RNA conformation states.…”
Section: Rna Structure Analysismentioning
confidence: 99%
“…MC-Fold predicts a secondary structure; MC-Sym predicts a three dimensional structure from a given secondary structure. The program is based on nucleotide cyclic motifs, the smallest nondivisible unit in a graph grammar description of RNA structure which includes backbone torsion angles, base pairing, and base stacking interactions (56,89). The program also incorporates experimental data from chemical modification, SHAPE, hydroxyl radical footprinting, phylogenetic alignments, and distance restraints from nuclear magnetic resonance spectroscopy.…”
Section: Predicting Three-dimensional Structurementioning
confidence: 99%