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PLoS Comput Biol. 2015 May 20;11(5):e1004126. doi: 10.1371/journal.pcbi.1004126. eCollection 2015 May.

Model-Free RNA Sequence and Structure Alignment Informed by SHAPE Probing Reveals a Conserved Alternate Secondary Structure for 16S rRNA.

Author information

1
Department of Chemistry, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina, United States of America.
2
Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria; Bioinformatics Group, Department of Computer Science, University of Leipzig, Leipzig, Germany.
3
Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina, United States of America.
4
Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria; Bioinformatics and Computational Biology, University of Vienna, Vienna, Austria.

Abstract

Discovery and characterization of functional RNA structures remains challenging due to deficiencies in de novo secondary structure modeling. Here we describe a dynamic programming approach for model-free sequence comparison that incorporates high-throughput chemical probing data. Based on SHAPE probing data alone, ribosomal RNAs (rRNAs) from three diverse organisms--the eubacteria E. coli and C. difficile and the archeon H. volcanii--could be aligned with accuracies comparable to alignments based on actual sequence identity. When both base sequence identity and chemical probing reactivities were considered together, accuracies improved further. Derived sequence alignments and chemical probing data from protein-free RNAs were then used as pseudo-free energy constraints to model consensus secondary structures for the 16S and 23S rRNAs. There are critical differences between these experimentally-informed models and currently accepted models, including in the functionally important neck and decoding regions of the 16S rRNA. We infer that the 16S rRNA has evolved to undergo large-scale changes in base pairing as part of ribosome function. As high-quality RNA probing data become widely available, structurally-informed sequence alignment will become broadly useful for de novo motif and function discovery.

PMID:
25992778
PMCID:
PMC4438973
DOI:
10.1371/journal.pcbi.1004126
[Indexed for MEDLINE]
Free PMC Article

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