Modeling Loop Composition and Ion Concentration Effects in RNA Hairpin Folding Stability

Abstract

The ability to accurately predict RNA hairpin structure and stability for different loop sequences and salt conditions is important for understanding, modeling, and designing larger RNA folds. However, traditional RNA secondary structure models cannot treat loop-sequence and ionic effects on RNA hairpin folding. Here, we describe a general, three-dimensional (3D) conformation-based computational method for modeling salt concentration-dependent conformational distributions and the detailed 3D structures for a set of three RNA hairpins that contain a variable, 15-nucleotide loop sequence. For a given RNA sequence, the new, to our knowledge, method integrates a Vfold2D two-dimensional structure folding model with IsRNA coarse-grained molecular dynamics 3D folding simulations and Monte Carlo tightly bound ion estimations of ion-mediated electrostatic interactions. The model predicts free-energy landscapes for the different RNA hairpin-forming sequences with variable salt conditions. The theoretically predicted results agree with the experimental fluorescence measurements, validating the strategy. Furthermore, the theoretical model goes beyond the experimental results by enabling in-depth 3D structural analysis, revealing energetic mechanisms for the sequenceand salt-dependent folding stability. Although the computational framework presented here is developed for RNA hairpin systems, the general method may be applied to investigate other RNA systems, such as multiway junctions or pseudoknots in mixed metal ion solutions.

Sodium and Magnesium Ion Location at the Backbone and at the Nucleobase of RNA: Ab Initio Molecular Dynamics in Water Solution

“…Our simulations also suggested that magnesium may not only affect the folding of the ssRNA by interacting simultaneously with two distant phosphate groups as suggested earlier , but also mediate the interaction between nucleobases in these molecules by coordinating simultaneously to both bases via the magnesium ion and one of the water molecules solvating it. This result is consistent with the observation that Mg 2+ ions facilitate RNA folding and participate in hairpin formation . It also corroborates experimental evidence that regions with higher GC content are likely to have a more stable secondary RNA structure. − …”

“…This result is consistent with the observation that Mg 2+ ions facilitate RNA folding and participate in hairpin formation. 72 It also corroborates experimental evidence that regions with higher GC content are likely to have a more stable secondary RNA structure. 73 − 75 …”

Mass Spectrometry of Nucleic Acid Noncovalent Complexes

“…Both the loop size and composition influence hairpin stability. ,,− In RNA, short loops are favored both thermodynamically and kinetically . Stabilizing interactions may occur in the loop itself, such as base stacking, intraloop base pairing, interloop contacts (for longer sequences), and cation binding. , As a result, the hairpin stability can vary dramatically as a function of the loop sequence (for a constant length). , Additionally, naturally occurring hairpins often contain mismatches and bulges (i.e., unpaired nucleotides) that have destabilizing effects . Classically, such structures are favored by higher counterion concentrations (in particular with Mg 2+ ) to overcome the Coulombic repulsion between phosphates. ,, …”

Modeling Noncanonical RNA Base Pairs by a Coarse-Grained IsRNA2 Model

“…27 Subsequently, through MD simulations, a large-scale benchmark test on RNA 3D structure prediction indicated that the updated IsRNA1 (version 1; as compared to version 0 IsRNA) model can provide improved performance for relatively large RNAs of complicated topologies, such as large stem-loop structures and structures containing long-range tertiary interactions. 28 Moreover, combined with experimental data, the IsRNA/IsRNA1 model was able to elucidate the folding pathway of an RNA pseudoknot, 29 to model the loop composition effect in RNA folding stability, 30 and to characterize binding features of an RNA aptamer to its targeted protein. 31 As demonstrated by the RNA-puzzles, 32−35 a critical assessment of protein structure prediction (CASP) 36 34,35 Compared to WC base-pairing interactions, the noncanonical base pairs are more variable and have abundant covariation rules as they also involve the base's sugar and Hoogsteen edges.…”

“…Subsequently, through MD simulations, a large-scale benchmark test on RNA 3D structure prediction indicated that the updated IsRNA1 (version 1; as compared to version 0 IsRNA) model can provide improved performance for relatively large RNAs of complicated topologies, such as large stem-loop structures and structures containing long-range tertiary interactions . Moreover, combined with experimental data, the IsRNA/IsRNA1 model was able to elucidate the folding pathway of an RNA pseudoknot, to model the loop composition effect in RNA folding stability, and to characterize binding features of an RNA aptamer to its targeted protein …”