‘Well-determined’ regions in RNA secondary structure prediction: analysis of small subunit ribosomal RNA (original) (raw)

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Department of Microbiology, State University of New York

Stony Brook, NY 11794-5222, USA

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06 January 1995

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Michael Zuker, Ann B. Jacobson, ‘Well-determined’ regions in RNA secondary structure prediction: analysis of small subunit ribosomal RNA, Nucleic Acids Research, Volume 23, Issue 14, 25 July 1995, Pages 2791–2798, https://doi.org/10.1093/nar/23.14.2791
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Abstract

Recent structural analyses of genomic RNAs from RNA coliphages suggest that both well-determined base paired helices and well-determined structural domains that are identified by ‘energy dot plot’ analysis using the RNA folding package mfold, are likely to be predicted correctly. To test these observations with another group of large RNAs, we have analyzed 15 ribosomal RNAs. Published secondary structure models that were derived by comparative sequence analysis were used to evaluate the predicted structures. Both the optimal predicted fold and the predicted ‘energy dot plot’ of each sequence were examined. Each prediction was obtained from a single computer run on an entire ribosomal RNA sequence. All predicted base pairs in optimal foldings were examined for agreement with proven base pairs in the comparative models. Our analyses show that the overall correspondence between the predicted and comparative models varies for different RNAs and ranges from a low of 27% to a high of 70%, with a mean value of 49%. The correspondence improves to a mean value of 81% when the analysis is limited to welldetermined helices. In addition to well-determined helices, large well-determined structural domains can be observed in ‘energy dot plots’ of some 16S ribosomal RNAs. The predicted domains correspond closely with structural domains that are found by the comparative method in the same RNAs. Our analyses also show that measuring the agreement between predicted and comparative secondary structure models underestimates the reliability of structural prediction by mfold.

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