Quantitative Studies of an RNA Duplex Electrostatics by Ion Counting

Abstract

RNAs are one of the most charged polyelectrolytes in nature, and understanding their electrostatics is fundamental to their structure and biological functions. An effective way to characterize the electrostatic field generated by nucleic acids is to quantify interactions between nucleic acids and ions that surround the molecules. These ions form a loosely associated cloud referred to as an ion atmosphere. Although theoretical and computational studies can describe the ion atmosphere around RNAs, benchmarks are needed to guide the development of these approaches, and experiments to date that read out RNA-ion interactions are limited. Here, we present ion counting studies to quantify the number of ions surrounding well-defined model systems of RNA and DNA duplexes. We observe that the RNA duplex attracts more cations and expels fewer anions compared to the DNA duplex, and the RNA duplex interacts significantly stronger with the divalent cation Mg 2þ , despite their identical total charge. These experimental results suggest that the RNA duplex generates a stronger electrostatic field than DNA, as is predicted based on the structural differences between their helices. Theoretical calculations using a nonlinear Poisson-Boltzmann equation give excellent agreement with experiments for monovalent ions but underestimate Mg 2þ-DNA and Mg 2þ-RNA interactions by 20%. These studies provide needed stringent benchmarks to use against other all-atom theoretical models of RNA-ion interactions, interactions that likely must be accurately accounted for in structural, dynamic, and energetic terms to confidently model RNA structure, interactions, and function.

The structural plasticity of nucleic acid duplexes revealed by WAXS and MD

“…As a result, c (ρ) shows only a single dominant peak around the major groove. The magnitude of the peak reflects the strength of attractive forces between positively charged cations and negatively charged RNA surface and reveals a stronger electrostatic potential for dsRNA than for dsDNA, supporting results of recent ion-counting experiments ( 42 ). The 3D ion density profiles visually highlight the ion-binding preferential locations ( Fig.…”

“…Past studies on DNA show the importance of this type of information in deciphering the sequence variability of dsNA molecules, an area that needs further development (35). However, a promising line of study integrates experimental and computational approaches to study RNA conformations (36)(37)(38)(39)(40)(41)(42)(43). Simulations provide atomic-level spatial resolution for macromolecular structure and dynamics while closely correlated experiments can be used to finely tune the simulation parameters (36,38,44) or guide the efficient exploration of conformational space sampled (37,40,43).…”

Quantifying and visualizing weak interactions between anions and proteins

“…Compared to nucleic acids, the proteins appear to have a weaker ability to accumulate counterions per charge. Whereas Δ N ia (cations)/| n | is 0.80–0.90 for DNA and RNA duplexes at ionic strength < 100 mM ( 8 , 12 ), our NMR data show that Δ N ia (anions)/| n | is 0.56 for the Antp homeodomain and 0.68 for BPTI. The weaker ability to attract counterions is presumably due to the smaller charge density on the molecular surfaces.…”

“…For nucleic acids, Δ N ia (anions) and Δ N ia (cations) have been well studied. Herschlag and coworkers studied the ion atmosphere around various DNA and RNA molecules using ion-counting methods ( 7 , 8 , 12 ). They showed that the experimental Δ N ia (anions) and Δ N ia (cations) data agreed well with those predicted by the Poisson–Boltzmann equation-based theory.…”

De novo determination of near-surface electrostatic potentials by NMR

“…As mentioned in the introduction, it has been suggested that the Poisson-Boltzmann theory may inaccurately predict electrostatic potentials for highly charged systems (12). Previous studies showed that the Poisson-Boltzmann equation-based approaches can accurately predict the total number of ions accumulated around highly charged proteins (32) and nucleic acids (33)(34)(35). However, the total number of accumulated ions is more relevant to long-range electrostatic potentials.…”