A Stochastic Solver of the Generalized Born Model

Robert C. Harris; Travis Mackoy; Marcia O. Fenley

Molecular Based Mathematical Biology (2013)

  • Volume: 1, page 63-74
  • ISSN: 2299-3266

Abstract

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A stochastic generalized Born (GB) solver is presented which can give predictions of energies arbitrarily close to those that would be given by exact effective GB radii, and, unlike analytical GB solvers, these errors are Gaussian with estimates that can be easily obtained from the algorithm. This method was tested by computing the electrostatic solvation energies (ΔGsolv) and the electrostatic binding energies (ΔGbind) of a set of DNA-drug complexes, a set of protein-drug complexes, a set of protein-protein complexes, and a set of RNA-peptide complexes. Its predictions of ΔGsolv agree with those of the linearized Poisson-Boltzmann equation, but it does not predict ΔGbind well, although these predictions of ΔGbind may be marginally better than those of traditional analytical GB solvers. Apparently, the GB model itself must be improved before accurate estimates of ΔGbind can be obtained.

How to cite

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Robert C. Harris, Travis Mackoy, and Marcia O. Fenley. "A Stochastic Solver of the Generalized Born Model." Molecular Based Mathematical Biology 1 (2013): 63-74. <http://eudml.org/doc/267081>.

@article{RobertC2013,
abstract = {A stochastic generalized Born (GB) solver is presented which can give predictions of energies arbitrarily close to those that would be given by exact effective GB radii, and, unlike analytical GB solvers, these errors are Gaussian with estimates that can be easily obtained from the algorithm. This method was tested by computing the electrostatic solvation energies (ΔGsolv) and the electrostatic binding energies (ΔGbind) of a set of DNA-drug complexes, a set of protein-drug complexes, a set of protein-protein complexes, and a set of RNA-peptide complexes. Its predictions of ΔGsolv agree with those of the linearized Poisson-Boltzmann equation, but it does not predict ΔGbind well, although these predictions of ΔGbind may be marginally better than those of traditional analytical GB solvers. Apparently, the GB model itself must be improved before accurate estimates of ΔGbind can be obtained.},
author = {Robert C. Harris, Travis Mackoy, Marcia O. Fenley},
journal = {Molecular Based Mathematical Biology},
keywords = {generalized Born; Poisson-Boltzmann; electrostatics; stochastic; solvation; binding; implicit solvent model},
language = {eng},
pages = {63-74},
title = {A Stochastic Solver of the Generalized Born Model},
url = {http://eudml.org/doc/267081},
volume = {1},
year = {2013},
}

TY - JOUR
AU - Robert C. Harris
AU - Travis Mackoy
AU - Marcia O. Fenley
TI - A Stochastic Solver of the Generalized Born Model
JO - Molecular Based Mathematical Biology
PY - 2013
VL - 1
SP - 63
EP - 74
AB - A stochastic generalized Born (GB) solver is presented which can give predictions of energies arbitrarily close to those that would be given by exact effective GB radii, and, unlike analytical GB solvers, these errors are Gaussian with estimates that can be easily obtained from the algorithm. This method was tested by computing the electrostatic solvation energies (ΔGsolv) and the electrostatic binding energies (ΔGbind) of a set of DNA-drug complexes, a set of protein-drug complexes, a set of protein-protein complexes, and a set of RNA-peptide complexes. Its predictions of ΔGsolv agree with those of the linearized Poisson-Boltzmann equation, but it does not predict ΔGbind well, although these predictions of ΔGbind may be marginally better than those of traditional analytical GB solvers. Apparently, the GB model itself must be improved before accurate estimates of ΔGbind can be obtained.
LA - eng
KW - generalized Born; Poisson-Boltzmann; electrostatics; stochastic; solvation; binding; implicit solvent model
UR - http://eudml.org/doc/267081
ER -

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