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Implicit solvent models

  • Universite de Montreal
  • Institut de Génétique et de Biologie Moléculaire et Cellulaire

Research output: Contribution to journalArticlepeer-review

786 Citations (Scopus)

Abstract

Implicit solvent models for biomolecular simulations are reviewed and their underlying statistical mechanical basis is discussed. The fundamental quantity that implicit models seek to approximate is the solute potential of mean force, which determines the statistical weight of solute conformations, and which is obtained by averaging over the solvent degrees of freedom. It is possible to express the total free energy as the reversible work performed in two successive steps. First, the solute is inserted in the solvent with zero atomic partial charges; second, the atomic partial charges of the solute are switched from zero to their full values. Consequently, the total solvation free energy corresponds to a sum of non-polar and electrostatic contributions. These two contributions are often approximated by simple geometrical models (such as solvent exposed area models) and by macroscopic continuum electrostatics, respectively. One powerful route is to approximate the average solvent density distribution around the solute, i.e. the solute-solvent density correlation functions, as in statistical mechanical integral equations. Recent progress with semi-analytical approximations makes continuum electrostatics treatments very efficient. Still more efficient are fully empirical, knowledge-based models, whose relation to explicit solvent treatments is not fully resolved, however. Continuum models that treat both solute and solvent as dielectric continua are also discussed, and the relation between the solute fluctuations and its macroscopic dielectric constant(s) clarified. Copyright (C) 1999 Elsevier Science B.V. All rights reserved.

Original languageEnglish
Pages (from-to)1-20
Number of pages20
JournalBiophysical Chemistry
Volume78
Issue number1-2
DOIs
Publication statusPublished - 5 Apr 1999
Externally publishedYes

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