An Effective Solvation Term Based on Atomic Occupancies for Use in Protein Simulations

Stouten, Pieter F. W.; Frömmel, Cornelius; Nakamura, Haruki; Sander, Chris

doi:10.1080/08927029308022161

Cited by 276 publications

(247 citation statements)

References 49 publications

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“…[23][24][25][26] A widely used and simple class of implicit solvent models are the so-called solvent accessible surface area (SASA) models in which the local solute-solvent interactions are assumed to be proportional to the SASA of the solute atoms. [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41] The SASA term should account for the free energy cost of forming a hardsphere cavity in water. As it has been shown that the cavity creation work depends on both the solvent accessible volume and the SASA, a term proportional to the internal volume, defined as the sum of the volumes of all nonsolvent-exposed atoms, can be added that takes into account interactions between buried atoms and the solvent as well.…”

Section: Implicit Solute Surface Area and Internal Volume Solvation Termmentioning

confidence: 99%

New functionalities in the GROMOS biomolecular simulation software

Kunz¹,

Allison²,

Geerke³

et al. 2011

J Comput Chem

105

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Since the most recent description of the functionalities of the GROMOS software for biomolecular simulation in 2005 many new functions have been implemented. In this article, the new functionalities that involve modified forces in a molecular dynamics (MD) simulation are described: the treatment of electronic polarizability, an implicit surface area and internal volume solvation term to calculate interatomic forces, functions for the GROMOS coarse-grained supramolecular force field, a multiplicative switching function for nonbonded interactions, adiabatic decoupling of a number of degrees of freedom with temperature or force scaling to enhance sampling, and nonequilibrium MD to calculate the dielectric permittivity or viscosity. Examples that illustrate the use of these functionalities are given.

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Section: Implicit Solute Surface Area and Internal Volume Solvation Termmentioning

confidence: 99%

New functionalities in the GROMOS biomolecular simulation software

Kunz¹,

Allison²,

Geerke³

et al. 2011

J Comput Chem

105

View full text Add to dashboard Cite

show abstract

“…An alternative, which is rapid enough to be generally useful, relates solvation free energy to solvent-accessible surface areas or hydration shell volumes through atomic solvation parameters (Chothia & Janin, 1975;Eisenberg & Mclachlan, 1986;Ooi et al, 1987;Colonna-Cesari & Sander, 1990;Cramer & Truhlar, 1992;Wesson & Eisenberg, 1992;Stouten et al, 1993). In this case, the empirical procedure takes account of not just surface area (entropy), but of the types of atomic groups (enthalpy) on the surface.…”

mentioning

confidence: 99%

Prediction of protein complexes using empirical free energy functions

1996

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A long sought goal in the physical chemistry of macromolecular structure, and one directly relevant to understanding the molecular basis of biological recognition, is predicting the geometry of bimolecular complexes from the geometries of their free monomers. Even when the monomers remain relatively unchanged by complex formation, prediction has been difficult because the free energies of alternative conformations of the complex have been difficult to evaluate quickly and accurately. This has forced the use of incomplete target functions, which typically do no better than to provide tens of possible complexes with no way of choosing between them. Here we present a general framework for empirical free energy evaluation and report calculations, based on a relatively complete and easily executable free energy function, that indicate that the structures of complexes can be predicted accurately from the structures of monomers, including close sequence homologues. The calculations also suggest that the binding free energies themselves may be predicted with reasonable accuracy. The method is compared to an alternative formulation that has also been applied recently to the same data set. Both approaches promise to open new opportunities in macromolecular design and specificity modification.Keywords: desolvation free energy; electrostatic interaction energy; rigid body docking; side-chain conformational search Understanding biological function at its most fundamental level, and manipulating it for therapeutic purposes, requires understanding the molecular basis of specificity. The critical determinant of true understanding is the ability to make quantitative predictions. In the case of molecular recognition, this means predicting the geometry of a complex from the free structures of its constituents.Even when conformations remain relatively unperturbed by reaction, thereby restricting the search for potential molecular complexes to a manageable size, achieving the goal of true prediction has proved elusive. Rigid body docking algorithms (Cherfils et al., 1991;Jiang & Kim, 1991;Shoichet & Kuntz, 1991;Bacon & Moult, 1992;Stoddard & Koshland, 1992;Walls & Sternberg, 1992;Helmer-Citterich & Tramontano, 1994;Judson et al., 1995) permit exploration of the orientations of a ligand in a receptor combining site, and effectively select those relatively few that meet prespecified criteria-including surface complementarity, interaction energy, and hydrophobic surface burial.As shown by Shoichet and Kuntz (1991), the methods go far toward reducing hundreds of thousands of possibilities to tens ~

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“…In fact, the mean force potential in the solvent contact or occupancy model (Stouten et al 1993) can be expressed as a sum of two-body terms of the form expi -r^/za 2 ), where r tj is the distance between particles i andj. A slightly different functional form has been proposed…”

Section: Simple Pairwise Solvation Force Modelsmentioning

confidence: 99%