Nucleic Acids Research Advance Access originally published online on April 27, 2007
Nucleic Acids Research 2007 35(10):3339-3354; doi:10.1093/nar/gkm101
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Nucleic Acids Research, 2007, Vol. 35, No. 10 3339-3354
© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Computational Biology |
Evolutionary rates vary among rRNA structural elements
Department of Chemistry and Biochemistry, University of Colorado, Boulder, CO 80309
*To whom correspondence should be addressed. Tel: 303-492-1984; Fax: 303-492-7744; Email: rob{at}spot.colorado.edu
Received December 8, 2006. Revised February 2, 2007. Accepted February 5, 2007.
Understanding patterns of rRNA evolution is critical for a number of fields, including structure prediction and phylogeny. The standard model of RNA evolution is that compensatory mutations in stems make up the bulk of the changes between homologous sequences, while unpaired regions are relatively homogeneous. We show that considerable heterogeneity exists in the relative rates of evolution of different secondary structure categories (stems, loops, bulges, etc.) within the rRNA, and that in eukaryotes, loops actually evolve much faster than stems. Both rates of evolution and abundance of different structural categories vary with distance from functionally important parts of the ribosome such as the tRNA path and the peptidyl transferase center. For example, fast-evolving residues are mainly found at the surface; stems are enriched at the subunit interface, and junctions near the peptidyl transferase center. However, different secondary structure categories evolve at different rates even when these effects are accounted for. The results demonstrate that relative rates and patterns of evolution are lineage specific, suggesting that phylogenetically and structurally specific models will improve evolutionary and structural predictions.
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