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Efficient Calculation of Configurational Entropy from Molecular Simulations by Combining the Mutual-Information Expansion and Nearest-Neighbor Methods

TLDR
The results indicate that the truncation of the MIE at the two‐body level can be an accurate, computationally nondemanding approximation to the configurational entropy of anharmonic internal degrees of freedom.
Abstract
Changes in the configurational entropies of molecules make important contributions to free energies of reaction for processes such as protein-folding, noncovalent association, and conformational change. However, obtaining entropy from molecular simulations represents a long-standing computational challenge. Here, two recently introduced approaches, the nearest-neighbor (NN) method and the mutual-information expansion (MIE), are combined to furnish an efficient and accurate method of extracting the configurational entropy from a molecular simulation to a given order of correlations among the internal degrees of freedom. The resulting method takes advantage of the strengths of each approach. The NN method is entirely nonparametric (i.e., it makes no assumptions about the underlying probability distribution), its estimates are asymptotically unbiased and consistent, and it makes optimum use of a limited number of available data samples. The MIE, a systematic expansion of entropy in mutual information terms of increasing order, provides a well-characterized approximation for lowering the dimensionality of the numerical problem of calculating the entropy of a high-dimensional system. The combination of these two methods enables obtaining well-converged estimations of the configurational entropy that capture many-body correlations of higher order than is possible with the simple histogramming that was used in the MIE method originally. The combined method is tested here on two simple systems: an idealized system represented by an analytical distribution of 6 circular variables, where the full joint entropy and all the MIE terms are exactly known; and the R,S stereoisomer of tartaric acid, a molecule with 7 internal-rotation degrees of freedom for which the full entropy of internal rotation has been already estimated by the NN method. For these two systems, all the expansion terms of the full MIE of the entropy are estimated by the NN method and, for comparison, the MIE approximations up to 3rd order are also estimated by simple histogramming. The results indicate that the truncation of the MIE at the 2-body level can be an accurate, computationally non-demanding approximation to the configurational entropy of anharmonic internal degrees of freedom. If needed, higher-order correlations can be estimated reliably by the NN method without excessive demands on the molecular-simulation sample size and computing time.

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Theory of free energy and entropy in noncovalent binding.

TL;DR: A rigorous but hopefully accessible discussion of the statistical thermodynamics of binding, taking an approach that differs from and elaborates on prior presentations; background material and detailed derivations are provided in Supporting Information.
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Grid inhomogeneous solvation theory: hydration structure and thermodynamics of the miniature receptor cucurbit[7]uril.

TL;DR: The grid inhomogeneous solvation theory (GIST) as mentioned in this paper discretizes the equations of inhomogenous solvation theories onto a three-dimensional grid situated in the region of interest around a solute molecule or complex.
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Quantifying Correlations Between Allosteric Sites in Thermodynamic Ensembles.

TL;DR: A novel method, "MutInf", is presented, to identify statistically significant correlated motions from equilibrium molecular dynamics simulations, which should be a useful tool for finding novel or "orphan" allosteric sites in proteins of biological and therapeutic importance.
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Equilibrium sampling in biomolecular simulations.

TL;DR: Efforts to enhance sampling capability range from the development of new algorithms to parallelization to novel uses of hardware, and special focus is placed on classifying algorithms in order to understand their fundamental strengths and limitations.
Journal ArticleDOI

Molecular Recognition and Ligand Association

TL;DR: The challenges posed by compensating enthalpic and entropic terms, competing solute and solvent contributions, and the relevance of complex configurational ensembles comprising multiple protein, ligand, and solvent intermediate states are highlighted.
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