Literature DB >> 18020664

Analysis of integral expressions for effective Born radii.

John Mongan1, W Andreas Svrcek-Seiler, Alexey Onufriev.   

Abstract

Generalized Born (GB) models provide a computationally efficient means of representing the electrostatic effects of solvent and are widely used, especially in molecular dynamics (MD). Accurate and facile computation of the effective Born radii is a key for the performance of GB models. Here, we examine a simple integral prescription, R6, based on the exact solution of the Poisson-Boltzmann (PB) equation for a perfect sphere. Numerical tests on 22 molecules representing a variety of structural classes show that R6 may be more accurate than the more complex integral-based approaches such as GBMV2. At the same time, R6 is computationally less demanding. Fundamental limitations of current integration-based methods for calculating effective radii, including R6, are explored and the deviations from the numerical PB results are correlated with specific topological and geometrical features of the molecular surface. A small systematic bias observed in the R6-based radii can be removed with a single, transferable constant offset; when the resulting effective radii are used in the "classical" (Still et al.'s) GB formula to compute the electrostatic solvation free energy, the average deviation from the PB reference is no greater than when the "perfect" (PB-based) effective radii are used. This deviation is also appreciably smaller than the uncertainty of the PB reference itself, as estimated by comparison to explicit solvent.

Entities:  

Mesh:

Substances:

Year:  2007        PMID: 18020664     DOI: 10.1063/1.2783847

Source DB:  PubMed          Journal:  J Chem Phys        ISSN: 0021-9606            Impact factor:   3.488


  15 in total

1.  An analytical approach to computing biomolecular electrostatic potential. II. Validation and applications.

Authors:  John C Gordon; Andrew T Fenley; Alexey Onufriev
Journal:  J Chem Phys       Date:  2008-08-21       Impact factor: 3.488

2.  Explicit ions/implicit water generalized Born model for nucleic acids.

Authors:  Igor S Tolokh; Dennis G Thomas; Alexey V Onufriev
Journal:  J Chem Phys       Date:  2018-05-21       Impact factor: 3.488

3.  Improved Generalized Born Solvent Model Parameters for Protein Simulations.

Authors:  Hai Nguyen; Daniel R Roe; Carlos Simmerling
Journal:  J Chem Theory Comput       Date:  2013-04-09       Impact factor: 6.006

Review 4.  Generalized Born Implicit Solvent Models for Biomolecules.

Authors:  Alexey V Onufriev; David A Case
Journal:  Annu Rev Biophys       Date:  2019-03-11       Impact factor: 12.981

5.  A new FFT-based algorithm to compute Born radii in the generalized Born theory of biomolecule solvation.

Authors:  Wei Cai; Zhenli Xu; Andrij Baumketner
Journal:  J Comput Phys       Date:  2008-12-20       Impact factor: 3.553

6.  Combining the polarizable Drude force field with a continuum electrostatic Poisson-Boltzmann implicit solvation model.

Authors:  Alexey Aleksandrov; Fang-Yu Lin; Benoît Roux; Alexander D MacKerell
Journal:  J Comput Chem       Date:  2018-05-08       Impact factor: 3.376

7.  Fast Analytical Methods for Macroscopic Electrostatic Models in Biomolecular Simulations.

Authors:  Zhenli Xu; Wei Cai
Journal:  SIAM Rev Soc Ind Appl Math       Date:  2011-11-07       Impact factor: 10.780

8.  Protein-Ligand Electrostatic Binding Free Energies from Explicit and Implicit Solvation.

Authors:  Saeed Izadi; Boris Aguilar; Alexey V Onufriev
Journal:  J Chem Theory Comput       Date:  2015-08-21       Impact factor: 6.006

9.  Refinement of Generalized Born Implicit Solvation Parameters for Nucleic Acids and Their Complexes with Proteins.

Authors:  Hai Nguyen; Alberto Pérez; Sherry Bermeo; Carlos Simmerling
Journal:  J Chem Theory Comput       Date:  2015-08-11       Impact factor: 6.006

10.  Accuracy of continuum electrostatic calculations based on three common dielectric boundary definitions.

Authors:  Alexey V Onufriev; Boris Aguilar
Journal:  J Theor Comput Chem       Date:  2014-05       Impact factor: 0.939

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.