Many-body effect in ion binding to RNA

Abstract

Ion-mediated electrostatic interactions play an important role in RNA folding stability. For a RNA in a solution with higher Mg(2+) ion concentration, more counterions in the solution can bind to the RNA, causing a strong many-body coupling between the bound ions. The many-body effect can change the effective potential of mean force between the tightly bound ions. This effect tends to dampen ion binding and lower RNA folding stability. Neglecting the many-body effect leads to a systematic error (over-estimation) of RNA folding stability at high Mg(2+) ion concentrations. Using the tightly bound ion model combined with a conformational ensemble model, we investigate the influence of the many-body effect on the ion-dependent RNA folding stability. Comparisons with the experimental data indicate that including the many-body effect led to much improved predictions for RNA folding stability at high Mg(2+) ion concentrations. The results suggest that the many-body effect can be important for RNA folding in high concentrations of multivalent ions. Further investigation showed that the many-body effect can influence the spatial distribution of the tightly bound ions and the effect is more pronounced for compact RNA structures and structures prone to the formation of local clustering of ions.

Predicting Ion Effects in an RNA Conformational Equilibrium

“…28, 52, 56–61 The key idea of the TBI model is to explicitly enumerate the many-body, correlated ion distributions for the TB ions while using a mean-field description (NLPB) for the DB ions. In this way, the model takes into account both the correlation and the fluctuation effects.…”

“…Extensive comparisons between TBI predictions and experimental results support the validity and accuracy of the TBI model for predicting ion binding properties and ion-mediated nucleic acids stability for simple helices, pseudoknots, kissing complexes, and other more complex tertiary folds. 28, 52, 56–61 However, because the original model is based on the explicit enumeration of ion distributions, which is time-consuming, the model is computationally inefficient. The low computational efficiency renders applications to medium (100–200 nts) and large RNA structures ( > 200 nts) impractical.…”

“…In such a CG charge model, each phosphate carries an electronic charge − e and the rest atoms are assumed to be neutral. 52, 60, 61 The coarse-grained charge distribution has the advantage to reduce the computational time. However, the over-simplification of the charge distribution prevents the model from applications to many biologically important problems which require information about realistic and detailed charge distributions on RNA.…”

Monte Carlo Tightly Bound Ion Model: Predicting Ion-Binding Properties of RNA with Ion Correlations and Fluctuations

“…Extensive comparisons between TBI predictions and experimental results support the conclusion that the TBI model offers improved predictions for ion binding properties and ion-mediated nucleic acids stability for simple helices, pseudoknots, kissing complexes, and more complex tertiary folds. 54, 59–65 Since the major difference between the TBI model and the NLPB is the inclusion of the ion correlation effect, a comparison between the TBI and NLPB predictions can may show the contribution from the correlation. Indeed, extensive TBI-NLPB comparisons have been made and the theoretical predictions have been tested against experimental data for the number of excess bound ions 10, 14 and the ion-dependence of the free energies.…”

A New Method to Predict Ion Effects in RNA Folding

“…Since the model was first reported in 2005, it has been under continuous development in the past decade for better accuracy and efficiency. 11, 30, 35–40 …”