title: Optimizing electrostatic field calculations with the adaptive Poisson-Boltzmann Solver to predict electric fields at protein-protein interfaces. I. Sampling and focusing.
abstract: Continuum electrostatics methods are commonly used to calculate electrostatic potentials in proteins and at protein-protein interfaces to aid many types of biophysical studies. Despite their ubiquity throughout the biophysical literature, these calculations are difficult to test against experimental data to determine their accuracy and validity. To address this, we have calculated the Boltzmann-weighted electrostatic field at the midpoint of a nitrile bond placed at a variety of locations on the surface of the protein RalGDS, both in its monomeric form as well as when docked to four different constructs of the protein Rap, and compared the computation results to vibrational absorption energy measurements of the nitrile oscillator. This was done by generating a statistical ensemble of protein structures using enhanced molecular dynamics sampling with the Amber03 force field, followed by solving the linear Poisson-Boltzmann equation for each structure using the Applied Poisson-Boltzmann Solver (APBS) software package. Using a two-stage focusing strategy, we examined numerous second stage box dimensions, grid point densities, box locations, and compared the numerical result to the result obtained from the sum of the numeric reaction field and the analytic Coulomb field. It was found that the reaction field method yielded higher correlation with experiment for the absolute calculation of fields, while the numeric solutions yielded higher correlation with experiment for the relative field calculations. Finer grid spacing typically improved the calculation, although this effect was less pronounced in the reaction field method. These sorts of calculations were also very sensitive to the box location, particularly for the numeric calculations of absolute fields using a 10(3) Å(3) box.
authors.affiliation: Department of Chemistry, Center for Nano- and Molecular Science and Technology, and Institute for Cell and Molecular Biology, The University of Texas at Austin , 1 University Station, A5300, Austin, Texas 78712, United States.
authors.forename: Andrew W
authors.initials: AW
authors.lastname: Ritchie
authors.forename: Lauren J
authors.initials: LJ
authors.lastname: Webb
mesh_terms: D000465:Algorithms; D016014:Linear Models; D008958:Models, Molecular; D062105:Molecular Docking Simulation; D056004:Molecular Dynamics Simulation; D009570:Nitriles / Q000737:chemistry; D011336:Probability; D011487:Protein Conformation; D012984:Software; D055672:Static Electricity; D013861:Thiocyanates / Q000737:chemistry; D014732:Vibration; D020726:ral Guanine Nucleotide Exchange Factor / Q000737:chemistry*; D020668:rap GTP-Binding Proteins / Q000737:chemistry*
chemical_list: D009570:Nitriles; D013861:Thiocyanates; D020726:ral Guanine Nucleotide Exchange Factor; D020668:rap GTP-Binding Proteins; C031760:thiocyanate