ArticleNucleic acids research2010
Arrangement of 3D structural motifs in ribosomal RNA.
Article in Nucleic acids research, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- GINClus: RNA structural motif clustering using graph isomorphism network.NAR genomics and bioinformatics · 2025Article
- The Unpaved Road of Non-Coding RNA Structure-Function Relationships: Current Knowledge, Available Methodologies, and Future Trends.Non-coding RNA · 2025Review
- On the predictibility of A-minor motifs from their local contexts.RNA biology · 2022Article
- Finding recurrent RNA structural networks with fast maximal common subgraphs of edge-colored graphs.PLoS computational biology · 2021Article
- Mining for recurrent long-range interactions in RNA structures reveals embedded hierarchies in network families.Nucleic acids research · 2018Article
- RNA structural motif recognition based on least-squares distance.RNA (New York, N.Y.) · 2013Article
- RNAMotifScan: automatic identification of RNA structural motifs using secondary structural alignment.Nucleic acids research · 2010Article
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Authors and funding
2 authors.
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Abstract
Structural 3D motifs in RNA play an important role in the RNA stability and function. Previous studies have focused on the characterization and discovery of 3D motifs in RNA secondary and tertiary structures. However, statistical analyses of the distribution of 3D motifs along the RNA appear to be lacking. Herein, we present a novel strategy for evaluating the distribution of 3D motifs along the RNA chain and those motifs whose distributions are significantly non-random are identified. By applying it to the X-ray structure of the large ribosomal subunit from Haloarcula marismortui, helical motifs were found to cluster together along the chain and in the 3D structure, whereas the known tetraloops tend to be sequentially and spatially dispersed. That the distribution of key structural motifs such as tetraloops differ significantly from a random one suggests that our method could also be used to detect novel 3D motifs of any size in sufficiently long/large RNA structures. The motif distribution type can help in the prediction and design of 3D structures of large RNA molecules.
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