Thermodynamic Characterization of RNA Duplexes Containing Naturally Occurring 1 × 2 Nucleotide Internal Loops
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文摘
Thermodynamic data for RNA 1 × 2 nucleotide internal loops are lacking. Thermodynamicdata that are available for 1 × 2 loops, however, are for loops that rarely occur in nature. In order toidentify the most frequently occurring 1 × 2 nucleotide internal loops, a database of 955 RNA secondarystructures was compiled and searched. Twenty-four RNA duplexes containing the most common 1 × 2nucleotide loops were optically melted, and the thermodynamic parameters H, S, G37, and TM foreach duplex were determined. This data set more than doubles the number of 1 × 2 nucleotide loopspreviously studied. A table of experimental free energy contributions for frequently occurring 1 × 2nucleotide loops (as opposed to a predictive model) is likely to result in better prediction of RNA secondarystructure from sequence. In order to improve free energy calculations for duplexes containing 1 × 2nucleotide loops that do not have experimental free energy contributions, the data collected here werecombined with data from 21 previously studied 1 × 2 loops. Using linear regression, the entire datasetwas used to derive nearest neighbor parameters that can be used to predict the thermodynamics of previouslyunmeasured 1 × 2 nucleotide loops. The G37,loop and Hloop nearest neighbor parameters derived herewere compared to values that were published previously for 1 × 2 nucleotide loops but were derivedfrom either a significantly smaller dataset of 1 × 2 nucleotide loops or from internal loops of varioussizes [Lu, Z. J., Turner, D. H., and Mathews, D. H. (2006) Nucleic Acids Res. 34, 4912-4924]. Most ofthese values were found to be within experimental error, suggesting that previous approximations andassumptions associated with the derivation of those nearest neighbor parameters were valid. Sloop nearestneighbor parameters are also reported for 1 × 2 nucleotide loops. Both the experimental thermodynamicsand the nearest neighbor parameters reported here can be used to improve secondary structure predictionfrom sequence.

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