Thermodynamic Characterization of RNA 2 × 3 Nucleotide Internal Loops

Abstract

In order to better elucidate RNA structure-function relationships and to improve design of pharmaceutical agents that target specific RNA motifs, an understanding of RNA primary, secondary, and tertiary structure is necessary. The prediction of RNA secondary structure from sequence is an intermediate step in predicting RNA three-dimensional structure. RNA secondary structure is typically predicted using a nearest neighbor model based on free energy parameters. The current free energy parameters for 2×3 nucleotide loops are based on a dataset of 23 2×3 loops and internal loops of other sizes. A database of representative RNA secondary structures was searched to identify 2×3 nucleotide loops that occur in nature. Seventeen of the most frequent 2×3 nucleotide loops in this database were studied by optical melting experiments. Fifteen of these loops melted in a two-state manner, and the associated experimental ΔG°37,2×3 values are, on average, 0.6 and 0.7 kcal/mol different from the values predicted for these internal loops using the predictive models proposed by Lu, Turner, and Mathews [Lu, Z. J., Turner, D. H., and Mathews, D. H. (2006) Nucleic Acids Res. 34, 4912–4924] and Chen and Turner [Chen, G. and Turner, D. H. (2006) Biochemistry 45, 4025–4043], respectively. These new ΔG°37,2×3 values can be used to update the current algorithms that predict secondary structure from sequence. In order to improve free energy calculations for duplexes containing 2×3 nucleotide loops that still do not have experimentally-determined free energy contributions, an updated predictive model was derived. This new model resulted from a linear regression analysis of the data reported here combined with 31 previously studied 2×3 nucleotide internal loops. Most of the values for the parameters in this new predictive model are within experimental error of the previous models, suggesting that approximations and assumptions associated with the derivation of the previous nearest neighbor parameters were valid. The updated predictive model predicts free energies of 2×3 nucleotide internal loops within 0.4 kcal/mol, on average, of the experimental free energy values. Both the experimental values and the updated predictive model can be used to improve secondary structure prediction from sequence.

Advancing viral RNA structure prediction: measuring the thermodynamics of pyrimidine-rich internal loops

“…Data from optical melting experiments were fit using the linest function in Excel. The loops at pH 7 from this work were combined with all previous measurements of internal loop stabilities (Santa Lucia et al 1990Peritz et al 1991;Walter et al 1994;Wu et al 1995;Schroeder et al 1996;Xia et al 1997;Turner 2000, 2001;Burkard et al 2001;Chen et al 2004Chen et al , 2005Chen et al , 2009Bourdelat-Parks and Wartell 2005;Badhwar et al 2007;Znosko 2008, 2009;Hausmann and Znosko 2012;Zhong et al 2015). Thermodynamic data were manually curated, and data with nontwo-state behavior, highly probable alternate duplex structures, and three or more consecutive guanine nucleotides without singlestrand optical melting data were not included in the database for analysis.…”

“…All the thermodynamic data incorporated in the 2004 prediction model (Mathews et al 2004) were compiled and updated with new data for 126 RNA loops (Chen et al 2004(Chen et al , 2009Bourdelat-Parks and Wartell 2005;Badhwar et al 2007;Znosko 2008, 2009;Hausmann and Znosko 2012;Zhong et al 2015). A complete spreadsheet of loop thermodynamic parameters is available in the Supplemental Information.…”

“…The RNA secondary structures in the IRES database were experimentally determined from phylogeny and chemical or enzymatic probing. Analysis for RNA internal loops in the thermodynamic database (Santa Lucia et al 1990Peritz et al 1991;Walter et al 1994;Wu et al 1995;Schroeder et al 1996;Xia et al 1997;Turner 2000, 2001;Burkard et al 2001;Chen et al 2004Chen et al , 2005Chen et al , 2009Bourdelat-Parks and Wartell 2005;Badhwar et al 2007;Znosko 2008, 2009;Hausmann and Znosko 2012) (bottom row). Loop sizes are described by the number of nucleotides on each side of a loop.…”

Differential Effects of Strand Asymmetry on the Energetics and Structural Flexibility of DNA Internal Loops

“…Optical melting studies have been an important approach for quantifying the energetics of DNA structural motifs, particularly with regard to the development of nearest neighbor models . The thermodynamic parameters of DNA loop motifs have been reported for single mismatches, , hairpins, bulges, − and tandem mismatches. , DNA internal loops remain largely unexamined, although they have significant use in synthesized functional DNA as toeholds to “fuel” DNA assembly, − linkages in multijunction nanostructures, binding sites for the malaria biomarker Plasmodium lactate dehydrogenase, and metal ion coordination sites for DNAzymes. − Although extensive work has been conducted on RNA internal loops to elucidate their sequence-dependent, thermodynamic parameters and improve nearest neighbor and other predictive models, − DNA internal loops have not deeply studied, with energetic predictions primarily based on entropy-based extrapolations , and the effect of strand asymmetry (i.e., relative distribution of unpaired nucleotides on opposing strands) on loop stability largely unknown.…”

Analysis of RNA nearest neighbor parameters reveals interdependencies and quantifies the uncertainty in RNA secondary structure prediction

“…The current set of RNA folding parameters are enumerated in the nearest neighbor database (NNDB), along with examples of their use (Turner and Mathews 2010). Subsequent to the determination of the latest set of Turner rules, a number of optical melting experiments have demonstrated that improvements in the nearest neighbor parameter models and values are possible (Huynen et al 1997;Znosko et al 2002;Chen et al 2004Chen et al , 2005Chen et al , 2009Chen et al , 2012Vecenie and Serra 2004;Bourdelat-Parks and Wartell 2005;O'Toole et al 2005O'Toole et al , 2006Vecenie et al 2006;Wilkinson et al 2006;Badhwar et al 2007;Blose et al 2007;Davis and Znosko 2007Shankar et al 2007;Carter-O'Connell et al 2008;Znosko 2008, 2009;Clanton-Arrowood et al 2008;Miller et al 2008;Nguyen and Schroeder 2010;Sheehy et al 2010;Thulasi et al 2010;Liu et al 2011;McCann et al 2011;Hausmann and Znosko 2012;Lim et al 2012;Vanegas et al 2012;Kent et al 2014;Murray et al 2014;Kwok et al 2015;Strom et al 2015;Tomcho et al 2015;Crowther et al 2017;Phan et al 2017).…”