Entropy-Driven Folding of an RNA Helical Junction:  An Isothermal Titration Calorimetric Analysis of the Hammerhead Ribozyme

Abstract

Helical junctions are extremely common motifs in naturally occurring RNAs, but little is known about the thermodynamics that drive their folding. Studies of junction folding face several challenges: non-two-state folding behavior, superposition of secondary and tertiary structural energetics, and drastically opposing enthalpic and entropic contributions to folding. Here we describe a thermodynamic dissection of the folding of the hammerhead ribozyme, a three-way RNA helical junction, by using isothermal titration calorimetry of bimolecular RNA constructs. By using this method, we show that tertiary folding of the hammerhead core occurs with a highly unfavorable enthalpy change, and is therefore entropically driven. Furthermore, the enthalpies and heat capacities of core folding are the same whether supported by monovalent or divalent ions. These properties appear to be general to the core sequence of bimolecular hammerhead constructs. We present a model for the ion-induced folding of the hammerhead core that is similar to those advanced for the folding of much larger RNAs, involving ion-induced collapse to a structured, non-native state accompanied by rearrangement of core residues to produce the native fold. In agreement with previous enzymological and structural studies, our thermodynamic data suggest that the hammerhead structure is stabilized in vitro predominantly by diffusely bound ions. Our approach addresses several significant challenges that accompany the study of junction folding, and should prove useful in defining the thermodynamic determinants of stability in these important RNA motifs.RNA helical junctions, single-stranded loops flanked by multiple double helices, are pervasive in known and predicted RNA structures. Helical junctions appear in a broad range of functional RNAs (1,2), including small and large ribozymes, mRNA untranslated regions, riboregulatory RNAs, snRNAs, and rRNAs. Alone or in complex with proteins, junctions may serve as the major tertiary structural elements of small RNAs or as critical elements in organizing much larger architectures. Despite their prominence, little is known about the forces that drive the folding of RNA helical junctions. Pairwise coaxial stacking and ion-dependent folding have emerged as common themes in junction architecture, but the exact role of central, looped regions and the energetics of the folding process are still unclear. As a result, there are currently no general principles of helical junction folding dependable enough for use in predicting RNA tertiary structure, much less in predicting the impact of solution conditions or bound ligands on junction stabilities. † This work was supported by IU, the IU Department of Chemistry, grants from the NIH (GM-065430 to A.L.F. and T32-GM07757 to IU/P.J.M.), the NSF (CHE-9909407 to A.L.F.) and an HHMI/Capstone award to J.C.T. Andrew Feig is a Cottrell Scholar of Research Corporation.*To whom correspondence should be addressed:Andrew L. Feig, Department of Chemistry, Indiana University, 800 E. Kirk...

Visualizing large RNA molecules in solution

“…Helices at a 2-way junction can therefore remain collinear even though no significant electrostatic repulsions exist between them. Moreover, at a 3-helix junction, the collinearity of two helices due to coaxial stacking implicitly means that a plane is formed with the third (Kim et al 2002;Mikulecky et al 2004;Mohanty et al 2005;Ouellet et al 2010). Some 4-helix junctions have also been shown to have relatively planar structures (Lilley 1998(Lilley , 2000.…”

Escherichia coli Hfq has distinct interaction surfaces for DsrA, rpoS and poly(A) RNAs

“…Data were corrected by subtraction of a baseline defined by the terminal 10-15 injection points after saturation of the binding event. Data were analyzed using Origin 7.0 (MicroCal) as described 52 .…”

Discrete state model and accurate estimation of loop entropy of RNA secondary structures

“…5), the large discrepancy for long loops is reflected by the small slope of the curve of the estimated values. A fitting of data between 10<n<50 using an empirical model gives (4) where c=1.55 and −ΔS(10) =9.5k B . The coefficient c of the second term is smaller than the value 1.75 in the model of Jacobson and Stockmayer, suggesting that as the length of internal loop increases, the entropy decrease slower than what would be expected from the JacksonStockmayer model.…”

“…7-9 However, little is known about the entropic cost of the loop regions, especially when loops are long and when multibranched loops form. 4 An analysis of a database containing 246 RNA structures shows that instead of engaged in base pairing, 46% of the nucleotides remain as single strands forming different types of loops. 10 Therefore, substantial improvements in RNA structure prediction is likely to require that the entropic cost of forming such loops be calculated or measured accurately.…”