Ion Pairing and Dielectric Decrement in Glycosaminoglycan Brushes

Abstract

Molecular dynamics simulations of hyaluronic acid and heparin brushes are presented that show important effects of ion-pairing, water dielectric decrease, and co-ion exclusion. As in prior studies of macromolecular crowding under physiologically-relevant salt concentrations, our results show equilibria with electroneutrality attained through screening and pairing of brush anionic charges by cations. Most surprising is the reversal of the Donnan potential that would be expected based on electrostatic Boltzmann partitioning alone. Water dielectric decrement within the brush domain is also associated with Born hydration-driven cation exclusion from the brush. We observe that the primary partition energy attracting cations to attain brush electroneutrality is the ion-pairing or salt-bridge energy associated with cation-sulfate and cation-carboxylate solvent-separated and contact ion pairs. Potassium and sodium pairing to glycosaminoglycan carboxylates and sulfates consistently show similar abundance of contact-pairing and solvent-separated pairing. In these crowded macromolecular brushes, ion-pairing, Born-hydration, and electrostatic potential energies all contribute to attain electroneutrality and should therefore contribute in mean-field models to accurately represent brush electrostatics.

The Influences of Sulphation, Salt Type, and Salt Concentration on the Structural Heterogeneity of Glycosaminoglycans

“…The second difference between the different systems is that the carboxylate group interacted more strongly with either cation than the sulphate group did; this agrees well with previous findings [ 26 , 29 , 59 ]. Finally, in the absence of Cl ions, i.e., in the 0 mM KCl and NaCl systems, the interactions between either cation and either of COO and OSO appeared stronger than in the presence of Cl ions (i.e., in the 150 mM KCl and NaCl systems).…”

“…Given the negative charge of the GAGs, there are no attractive interactions with the Cl ions that needed to be considered. As shown by others, cations prefer to localize near the OSO and COO groups of the GAGs (see Figure S7 for the atom naming of these groups), leading to the formation of contact ion pairs and solvent-separated ion pairs [ 59 ]. These pairs can be identified from the radial distribution functions ( ), which are shown in Figure 7 for COO and in Figure S8 for OSO .…”

“…In order to understand the prevalence of solvent-separated ion pairs involving Na , we performed a literature search for comparable studies and performed an in-depth visual inspection of the different trajectories. Sterling and coworkers explained this finding by the fact that a cation is often shared between two neighboring anions, which due to ion size and GAG rigidity limitations, can in some cases be better realized via solvent-separated ion pairs [ 59 ]. Here, we found that the smaller size of Na (ionic radius of 0.116 nm) compared to K (ionic radius of 0.152 nm) plays a role in that the former is more often found in solvent-separated ion pairs, as the comparison of typical binding modes found for H6S in Figure S10 demonstrated.…”

“…NBFIX is a possibility to adjust pair-specific LJ parameters without modifying solute–water interactions. In a previous simulation study of GAGs interacting with Na and K , the same adjusted LJ parameters were employed [ 59 ]; they were listed in Table S1b in the Supporting Information of that previous work.…”

Mathematical Modeling of Microscale Biology: Ion Pairing, Dielectric Decrement, and Born Energy in Glycosaminoglycan Brushes

“…We then compare our models to a previous model [12], which does not incorporate dielectric decrement or ion pairing, showing how electrostatic potential and ion concentration profiles match in the limit and we compare results from both models. We then consider molecular simulation data [13], which show that dielectric decrement and ion pairing lead to Born energy and a binding energy, respectively. We show how to relate the binding energy to our dissociation constant and compare predictions from our model to the simulation data.…”