Characterizing Solvent Density Fluctuations in Dynamical Observation Volumes

Abstract

Hydrophobic effects drive diverse aqueous assemblies, such as micelle formation or protein folding, wherein the solvent plays an important role. Consequently, characterizing the free energetics of solvent density fluctuations can lead to important insights into these processes. Although techniques such as the indirect umbrella sampling (INDUS) method (Patel et al. J. Stat. Phys. 2011, 145, 265-275) can be used to characterize solvent fluctuations in static observation volumes of various sizes and shapes, characterizing how the solvent mediates inherently dynamic processes, such as self-assembly or conformational change, remains a challenge. In this work, we generalize the INDUS method to facilitate the enhanced sampling of solvent fluctuations in dynamical observation volumes, whose positions and shapes can evolve. We illustrate the usefulness of this generalization by characterizing water density fluctuations in dynamic volumes pertaining to the hydration of flexible solutes, the assembly of small hydrophobes, and conformational transitions in a model peptide. We also use the method to probe the dynamics of hard spheres.

Identifying hydrophobic protein patches to inform protein interaction interfaces

“…To interrogate whether the most hydrophobic regions of the TS protein are also likely to mediate its interactions, we must first characterize how (un)favorably different protein regions interact with water. To this end, we perform all-atom, explicitsolvent, molecular dynamics simulations, wherein an unfavorable biasing potential, φNv , is applied to systematically disrupt protein-water interactions (44,45); φ represents the potential strength, and Nv is the number of (coarse-grained) waters in the protein hydration shell, v . To ensure that v conforms to the rugged protein surface, we peg spherical subvolumes to every heavy atom on the protein surface and define v to be the union of all such subvolumes (Fig.…”

Understanding Hydrophobic Effects: Insights from Water Density Fluctuations

“…Moreover, an understanding of the role water density fluctuations in hindering assembly (sec. 6), e.g., in protein folding or supramolecular host-guest interactions, could pave the way for engineering assembly pathways and exercising control over the kinetics of assembly [34][35][36]. Finally, an understanding of dewetting pathways in hydrophobic confinement could also facilitate the rational design of textured hydrophobic materials that are capable of dewetting spontaneously, even under hydrostatic pressure, thereby paving the way for their use underwater and in condensation heat transfer [158].…”

“…We then discuss how an understanding hydrophobic hydration and the ability to characterize hydrophobicity inform the thermodynamic forces that drive hydrophobic interactions and assemblies [29,30]. We also discuss how water density fluctuations in confinement between hydrophobic solutes can influence the kinetics and pathways of hydrophobic assembly [31][32][33][34][35][36].…”

A generalized deep learning approach for local structure identification in molecular simulations

“…To showcase the utility of our method beyond applications in crystal structure identification, we use the PointNet to quantify the hydrophobicity of extended surfaces and proteins. Previous work characterizing surface hydrophobicity used local water density fluctuations or solute affinity over different portions of surfaces to create a spatially resolved measure of hydrophobicity 67–70. Recent work found that water orientations near an interface may also be able to predict local surface hydrophobicity 71.…”

“…Multiple methods using water orientations71 to water density fluctuations70,74 have been used to quantify protein surface hydrophobicity. It is difficult to know which method is most correct.…”