Abstract
Despite its prominent contribution to the free energy of solvated macromolecules such as proteins or DNA, and although principally contained within molecular dynamics simulations, the entropy of the solvation shell is inaccessible to straightforward application of established entropy estimation methods. The complication is twofold. First, the configurational space density of such systems is too complex for a sufficiently accurate fit. Second, and in contrast to the internal macromolecular dynamics, the configurational space volume explored by the diffusive motion of the solvent molecules is too large to be exhaustively sampled by current simulation techniques. Here, we develop a method to overcome the second problem and to significantly alleviate the first one. We propose to exploit the permutation symmetry of the solvent by transforming the trajectory in a way that renders established estimation methods applicable, such as the quasiharmonic approximation or principal component analysis. Our permutation-reduced approach involves a combinatorial problem, which is solved through its equivalence with the linear assignment problem, for which O(N3) methods exist. From test simulations of dense Lennard-Jones gases, enhanced convergence and improved entropy estimates are obtained. Moreover, our approach renders diffusive systems accessible to improved fit functions.
Highlights
Biomolecular processes are driven by molecular free energy changes
We have presented a entropy estimation method tailored for diffusive systems, such as the solvent contribution to the entropy of solvated macromolecules, e.g., proteins
We have applied this method to a gas of 100 Lennard-Jones particles and to the solution of a strongly interacting Lennard-Jones particle in this gas
Summary
Biomolecular processes are driven by molecular free energy changes. Many physico-chemical phenomena, such as the hydrophobic effect, emerge from a fine-tuned competition of the two free energy components, entropy and enthalpy. E.g., molecular interactions compete with the associated huge decrease of conformational entropy of the protein.[1] Hydrophobic forces, which drive many biological phenomena such as membrane association and protein folding, are governed by entropy changes, but mainly of the solvent.[2]. As a fully atomistic description, molecular dynamicsMDsimulations should capture this enthalpy-entropy competition. That this is the case has become evident by the successful first-principle MD folding of peptides and proteins of increasing size,[3,4] where the obtained native structure is sensitive to the enthalpy-entropy balance. MD has provided in-depth insights of many other complex biomolecular processes.[5–9]
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