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Gaussian-Based Smooth Dielectric Function: A Surface-Free Approach for Modeling Macromolecular Binding in Solvents

Conventional modeling techniques to model macromolecular solvation and its effect on binding in the framework of Poisson-Boltzmann based implicit solvent models make use of a geometrically defined surface to depict the separation of macromolecular interior (low dielectric constant) from the solvent...

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Autores principales: Chakravorty, Arghya, Jia, Zhe, Peng, Yunhui, Tajielyato, Nayere, Wang, Lisi, Alexov, Emil
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5881404/
https://www.ncbi.nlm.nih.gov/pubmed/29637074
http://dx.doi.org/10.3389/fmolb.2018.00025
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author Chakravorty, Arghya
Jia, Zhe
Peng, Yunhui
Tajielyato, Nayere
Wang, Lisi
Alexov, Emil
author_facet Chakravorty, Arghya
Jia, Zhe
Peng, Yunhui
Tajielyato, Nayere
Wang, Lisi
Alexov, Emil
author_sort Chakravorty, Arghya
collection PubMed
description Conventional modeling techniques to model macromolecular solvation and its effect on binding in the framework of Poisson-Boltzmann based implicit solvent models make use of a geometrically defined surface to depict the separation of macromolecular interior (low dielectric constant) from the solvent phase (high dielectric constant). Though this simplification saves time and computational resources without significantly compromising the accuracy of free energy calculations, it bypasses some of the key physio-chemical properties of the solute-solvent interface, e.g., the altered flexibility of water molecules and that of side chains at the interface, which results in dielectric properties different from both bulk water and macromolecular interior, respectively. Here we present a Gaussian-based smooth dielectric model, an inhomogeneous dielectric distribution model that mimics the effect of macromolecular flexibility and captures the altered properties of surface bound water molecules. Thus, the model delivers a smooth transition of dielectric properties from the macromolecular interior to the solvent phase, eliminating any unphysical surface separating the two phases. Using various examples of macromolecular binding, we demonstrate its utility and illustrate the comparison with the conventional 2-dielectric model. We also showcase some additional abilities of this model, viz. to account for the effect of electrolytes in the solution and to render the distribution profile of water across a lipid membrane.
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spelling pubmed-58814042018-04-10 Gaussian-Based Smooth Dielectric Function: A Surface-Free Approach for Modeling Macromolecular Binding in Solvents Chakravorty, Arghya Jia, Zhe Peng, Yunhui Tajielyato, Nayere Wang, Lisi Alexov, Emil Front Mol Biosci Molecular Biosciences Conventional modeling techniques to model macromolecular solvation and its effect on binding in the framework of Poisson-Boltzmann based implicit solvent models make use of a geometrically defined surface to depict the separation of macromolecular interior (low dielectric constant) from the solvent phase (high dielectric constant). Though this simplification saves time and computational resources without significantly compromising the accuracy of free energy calculations, it bypasses some of the key physio-chemical properties of the solute-solvent interface, e.g., the altered flexibility of water molecules and that of side chains at the interface, which results in dielectric properties different from both bulk water and macromolecular interior, respectively. Here we present a Gaussian-based smooth dielectric model, an inhomogeneous dielectric distribution model that mimics the effect of macromolecular flexibility and captures the altered properties of surface bound water molecules. Thus, the model delivers a smooth transition of dielectric properties from the macromolecular interior to the solvent phase, eliminating any unphysical surface separating the two phases. Using various examples of macromolecular binding, we demonstrate its utility and illustrate the comparison with the conventional 2-dielectric model. We also showcase some additional abilities of this model, viz. to account for the effect of electrolytes in the solution and to render the distribution profile of water across a lipid membrane. Frontiers Media S.A. 2018-03-27 /pmc/articles/PMC5881404/ /pubmed/29637074 http://dx.doi.org/10.3389/fmolb.2018.00025 Text en Copyright © 2018 Chakravorty, Jia, Peng, Tajielyato, Wang and Alexov. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Molecular Biosciences
Chakravorty, Arghya
Jia, Zhe
Peng, Yunhui
Tajielyato, Nayere
Wang, Lisi
Alexov, Emil
Gaussian-Based Smooth Dielectric Function: A Surface-Free Approach for Modeling Macromolecular Binding in Solvents
title Gaussian-Based Smooth Dielectric Function: A Surface-Free Approach for Modeling Macromolecular Binding in Solvents
title_full Gaussian-Based Smooth Dielectric Function: A Surface-Free Approach for Modeling Macromolecular Binding in Solvents
title_fullStr Gaussian-Based Smooth Dielectric Function: A Surface-Free Approach for Modeling Macromolecular Binding in Solvents
title_full_unstemmed Gaussian-Based Smooth Dielectric Function: A Surface-Free Approach for Modeling Macromolecular Binding in Solvents
title_short Gaussian-Based Smooth Dielectric Function: A Surface-Free Approach for Modeling Macromolecular Binding in Solvents
title_sort gaussian-based smooth dielectric function: a surface-free approach for modeling macromolecular binding in solvents
topic Molecular Biosciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5881404/
https://www.ncbi.nlm.nih.gov/pubmed/29637074
http://dx.doi.org/10.3389/fmolb.2018.00025
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