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- W3005206756 abstract "Macromolecular processes are governed by their free energy landscape and thus often depend on a delicate balance of enthalpy and entropy. The hydrophobic effect, which is crucial for protein folding, is largely governed differences in rotational solvent entropy. It is therefore essential to have a quantitative understanding of the thermodynamics of solvation, and in particular the associated rotational entropies. To quantify the entropic effects of local features, such as individual amino acids, spatial resolution is required. Whereas several methods yield solute entropies from atomistic simulations, spatially resolved solvation shell entropies are notoriously difficult to obtain. Here we present a new method, which employs a mutual information expansion to compute spatially resolved rotational solvent entropies. Mutual information terms are calculated using a non-parametric k-nearest-neighbor density estimator, which we adapted for SO(3)n, the group of rotations and its product spaces. We assessed and applied our method to atomistic simulations of pure water and small proteins, where we quantified pronounced topological effects and entropy losses due to correlated dynamics at hydrophobic interfaces." @default.
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- W3005206756 date "2020-02-01" @default.
- W3005206756 modified "2023-09-27" @default.
- W3005206756 title "Computing Spatially Resolved Rotational Hydration-Shell Entropies from MD Simulations using an Orientational K-Nearest-Neighbor Density Estimator" @default.
- W3005206756 doi "https://doi.org/10.1016/j.bpj.2019.11.893" @default.
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