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Numerical Optimization of a Walk-on-Spheres Solver for the Linear Poisson-Boltzmann Equation

Published online by Cambridge University Press:  03 June 2015

Travis Mackoy ,
Robert C. Harris ,
Jesse Johnson ,
Michael Mascagni  and
Marcia O. Fenley
Travis Mackoy*
Affiliation:
Institute of Molecular Biophysics, Florida State University, Tallahassee, FL 32306, USA
Robert C. Harris*
Affiliation:
Institute of Molecular Biophysics, Florida State University, Tallahassee, FL 32306, USA Department of Physics, Florida State University, Tallahassee, FL 32306, USA
Jesse Johnson*
Affiliation:
Institute of Molecular Biophysics, Florida State University, Tallahassee, FL 32306, USA Department of Physics, Florida State University, Tallahassee, FL 32306, USA
Michael Mascagni*
Affiliation:
Departments of Computer Science, Mathematics and Scientific Computing, Florida State University, Tallahassee, FL 32306, USA
Marcia O. Fenley*
Affiliation:
Institute of Molecular Biophysics, Florida State University, Tallahassee, FL 32306, USA
*
Email:mackoy@cs.fsu.edu
Email:rch02c@fsu.edu
Email:johnson@biomaps.rutgers.edu
Email:mascagni@fsu.edu
Corresponding author.Email:mfenley@sb.fsu.edu
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Abstract

Stochastic walk-on-spheres (WOS) algorithms for solving the linearized Poisson-Boltzmann equation (LPBE) provide several attractive features not available in traditional deterministic solvers: Gaussian error bars can be computed easily, the algorithm is readily parallelized and requires minimal memory and multiple solvent environments can be accounted for by reweighting trajectories. However, previously-reported computational times of these Monte Carlo methods were not competitive with existing deterministic numerical methods. The present paper demonstrates a series of numerical optimizations that collectively make the computational time of these Monte Carlo LPBE solvers competitive with deterministic methods. The optimization techniques used are to ensure that each atom’s contribution to the variance of the electrostatic solvation free energy is the same, to optimize the bias-generating parameters in the algorithm and to use an epsilon-approximate rather than exact nearest-neighbor search when determining the size of the next step in the Brownian motion when outside the molecule.

Keywords

02.70.Uu41.20Cv05.40.FbPoisson-Boltzmann equationwalk-on-spheressolvation
Type
Research Article
Information
Communications in Computational Physics , Volume 13 , Issue 1 , January 2013 , pp. 195 - 206
Copyright
Copyright © Global Science Press Limited 2013

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