Computing spatially resolved rotational hydration entropies from atomistic simulations

TL;DR

This paper introduces a novel non-parametric method to compute spatially resolved rotational hydration entropies from atomistic simulations, enhancing understanding of solvation thermodynamics with high accuracy.

Contribution

The paper presents a new approach using k-nearest-neighbor density estimation to calculate solvent entropies with spatial resolution, validated against analytic distributions.

Findings

Achieved better than 9.6% accuracy in entropy calculations.

Enabled spatially resolved hydration entropy analysis in atomistic water simulations.

Provides insights into the hydrophobic effect and solvation thermodynamics.

Abstract

For a first principles understanding of macromolecular processes, a quantitative understanding of the underlying free energy landscape and in particular its entropy contribution is crucial. The stability of biomolecules, such as proteins, is governed by the hydrophobic effect, which arises from competing enthalpic and entropic contributions to the free energy of the solvent shell. While the statistical mechanics of liquids, as well as molecular dynamics simulations have provided much insight, solvation shell entropies remain notoriously difficult to calculate, especially when spatial resolution is required. Here, we present a method that allows for the computation of spatially resolved rotational solvent entropies via a non-parametric k-nearest-neighbor density estimator. We validated our method using analytic test distributions and applied it to atomistic simulations of a water box. With an accuracy of better than 9.6%, the obtained spatial resolution should shed new light on the hydrophobic effect and the thermodynamics of solvation in general.