Literature DB >> 25018579

On the Modeling of Polar Component of Solvation Energy using Smooth Gaussian-Based Dielectric Function.

Lin Li1, Chuan Li1, Emil Alexov1.   

Abstract

Traditional implicit methods for modeling electrostatics in biomolecules use a two-dielectric approach: a biomolecule is assigned low dielectric constant while the water phase is considered as a high dielectric constant medium. However, such an approach treats the biomolecule-water interface as a sharp dielectric border between two homogeneous dielectric media and does not account for inhomogeneous dielectric properties of the macromolecule as well. Recently we reported a new development, a smooth Gaussian-based dielectric function which treats the entire system, the solute and the water phase, as inhomogeneous dielectric medium (J Chem Theory Comput. 2013 Apr 9; 9(4): 2126-2136.). Here we examine various aspects of the modeling of polar solvation energy in such inhomogeneous systems in terms of the solute-water boundary and the inhomogeneity of the solute in the absence of water surrounding. The smooth Gaussian-based dielectric function is implemented in the DelPhi finite-difference program, and therefore the sensitivity of the results with respect to the grid parameters is investigated, and it is shown that the calculated polar solvation energy is almost grid independent. Furthermore, the results are compared with the standard two-media model and it is demonstrated that on average, the standard method overestimates the magnitude of the polar solvation energy by a factor 2.5. Lastly, the possibility of the solute to have local dielectric constant larger than of a bulk water is investigated in a benchmarking test against experimentally determined set of pKa's and it is speculated that side chain rearrangements could result in local dielectric constant larger than 80.

Entities:  

Keywords:  Poisson-Boltzmann equation; dielectric constant; electrostatics; finite-difference method; protein flexibility

Year:  2014        PMID: 25018579      PMCID: PMC4092036          DOI: 10.1142/S0219633614400021

Source DB:  PubMed          Journal:  J Theor Comput Chem            Impact factor:   0.939


  39 in total

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Authors:  D Bashford; D A Case
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Authors:  N A Baker; D Sept; S Joseph; M J Holst; J A McCammon
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3.  Role of the protein side-chain fluctuations on the strength of pair-wise electrostatic interactions: comparing experimental with computed pK(a)s.

Authors:  Emil Alexov
Journal:  Proteins       Date:  2003-01-01

Review 4.  Force fields for protein simulations.

Authors:  Jay W Ponder; David A Case
Journal:  Adv Protein Chem       Date:  2003

Review 5.  Progress in the prediction of pKa values in proteins.

Authors:  Emil Alexov; Ernest L Mehler; Nathan Baker; António M Baptista; Yong Huang; Francesca Milletti; Jens Erik Nielsen; Damien Farrell; Tommy Carstensen; Mats H M Olsson; Jana K Shen; Jim Warwicker; Sarah Williams; J Michael Word
Journal:  Proteins       Date:  2011-10-15

6.  Modulation of buried ionizable groups in proteins with engineered surface charge.

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Review 7.  On the role of electrostatics in protein-protein interactions.

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Journal:  Phys Biol       Date:  2011-05-13       Impact factor: 2.583

8.  MCCE analysis of the pKas of introduced buried acids and bases in staphylococcal nuclease.

Authors:  M R Gunner; Xuyu Zhu; Max C Klein
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Review 9.  Proteins, lipids, and water in the gas phase.

Authors:  David van der Spoel; Erik G Marklund; Daniel S D Larsson; Carl Caleman
Journal:  Macromol Biosci       Date:  2011-01-10       Impact factor: 4.979

10.  Water clusters in nonpolar cavities.

Authors:  Subramanian Vaitheeswaran; Hao Yin; Jayendran C Rasaiah; Gerhard Hummer
Journal:  Proc Natl Acad Sci U S A       Date:  2004-11-30       Impact factor: 11.205

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  14 in total

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Journal:  J Mol Model       Date:  2015-04-12       Impact factor: 1.810

2.  Predicting protein-DNA binding free energy change upon missense mutations using modified MM/PBSA approach: SAMPDI webserver.

Authors:  Yunhui Peng; Lexuan Sun; Zhe Jia; Lin Li; Emil Alexov
Journal:  Bioinformatics       Date:  2018-03-01       Impact factor: 6.937

3.  DelPhiForce, a tool for electrostatic force calculations: Applications to macromolecular binding.

Authors:  Lin Li; Arghya Chakravorty; Emil Alexov
Journal:  J Comput Chem       Date:  2017-01-28       Impact factor: 3.376

Review 4.  Continuum Electrostatics Approaches to Calculating pKas and Ems in Proteins.

Authors:  M R Gunner; N A Baker
Journal:  Methods Enzymol       Date:  2016-06-20       Impact factor: 1.600

5.  pKa predictions for proteins, RNAs, and DNAs with the Gaussian dielectric function using DelPhi pKa.

Authors:  Lin Wang; Lin Li; Emil Alexov
Journal:  Proteins       Date:  2015-10-16

6.  Electrostatic component of binding energy: Interpreting predictions from poisson-boltzmann equation and modeling protocols.

Authors:  Arghya Chakavorty; Lin Li; Emil Alexov
Journal:  J Comput Chem       Date:  2016-08-21       Impact factor: 3.376

7.  AweGNN: Auto-parametrized weighted element-specific graph neural networks for molecules.

Authors:  Timothy Szocinski; Duc Duy Nguyen; Guo-Wei Wei
Journal:  Comput Biol Med       Date:  2021-05-12       Impact factor: 6.698

Review 8.  On the energy components governing molecular recognition in the framework of continuum approaches.

Authors:  Lin Li; Lin Wang; Emil Alexov
Journal:  Front Mol Biosci       Date:  2015-03-06

9.  Multiscale method for modeling binding phenomena involving large objects: application to kinesin motor domains motion along microtubules.

Authors:  Lin Li; Joshua Alper; Emil Alexov
Journal:  Sci Rep       Date:  2016-03-18       Impact factor: 4.379

10.  Cytoplasmic dynein binding, run length, and velocity are guided by long-range electrostatic interactions.

Authors:  Lin Li; Joshua Alper; Emil Alexov
Journal:  Sci Rep       Date:  2016-08-17       Impact factor: 4.379

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