Thermodynamic data for RNA 1 x 2 nucleotide internal loops are lacking. Thermodynamic data that are available for 1 x 2 loops, however, are for loops that rarely occur in nature. In order to identify the most frequently occurring 1 x 2 nucleotide internal loops, a database of 955 RNA secondary structures was compiled and searched. Twenty-four RNA duplexes containing the most common 1 x 2 nucleotide loops were optically melted, and the thermodynamic parameters DeltaH degrees , DeltaS degrees , DeltaG degrees 37, and TM for each duplex were determined. This data set more than doubles the number of 1 x 2 nucleotide loops previously studied. A table of experimental free energy contributions for frequently occurring 1 x 2 nucleotide loops (as opposed to a predictive model) is likely to result in better prediction of RNA secondary structure from sequence. In order to improve free energy calculations for duplexes containing 1 x 2 nucleotide loops that do not have experimental free energy contributions, the data collected here were combined with data from 21 previously studied 1 x 2 loops. Using linear regression, the entire dataset was used to derive nearest neighbor parameters that can be used to predict the thermodynamics of previously unmeasured 1 x 2 nucleotide loops. The DeltaG degrees 37,loop and DeltaH degrees loop nearest neighbor parameters derived here were compared to values that were published previously for 1 x 2 nucleotide loops but were derived from either a significantly smaller dataset of 1 x 2 nucleotide loops or from internal loops of various sizes [Lu, Z. J., Turner, D. H., and Mathews, D. H. (2006) Nucleic Acids Res. 34, 4912-4924]. Most of these values were found to be within experimental error, suggesting that previous approximations and assumptions associated with the derivation of those nearest neighbor parameters were valid. DeltaS degrees loop nearest neighbor parameters are also reported for 1 x 2 nucleotide loops. Both the experimental thermodynamics and the nearest neighbor parameters reported here can be used to improve secondary structure prediction from sequence.

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