Dataset Information

Problems of robustness in Poisson-Boltzmann binding free energies.

ABSTRACT: Although models based on the Poisson–Boltzmann (PB) equation have been fairly successful at predicting some experimental quantities, such as solvation free energies (?G), these models have not been consistently successful at predicting binding free energies (??G). Here we found that ranking a set of protein–protein complexes by the electrostatic component (??Gel) of ??G was more difficult than ranking the same molecules by the electrostatic component (?Gel) of ?G. This finding was unexpected because ??Gel can be calculated by combining estimates of ?Gel for the complex and its components with estimates of the ??Gel in vacuum. One might therefore expect that if a theory gave reliable estimates of ?Gel, then its estimates of ??Gel would be reliable. However, ??Gel for these complexes were orders of magnitude smaller than ?Gel, so although estimates of ?Gel obtained with different force fields and surface definitions were highly correlated, similar estimates of ??Gel were often not correlated.

SUBMITTER: Harris RC

PROVIDER: S-EPMC4610304 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Problems of robustness in Poisson-Boltzmann binding free energies.

Harris Robert C RC Mackoy Travis T Fenley Marcia O MO

Journal of chemical theory and computation 20150201 2

Although models based on the Poisson–Boltzmann (PB) equation have been fairly successful at predicting some experimental quantities, such as solvation free energies (ΔG), these models have not been consistently successful at predicting binding free energies (ΔΔG). Here we found that ranking a set of protein–protein complexes by the electrostatic component (ΔΔGel) of ΔΔG was more difficult than ranking the same molecules by the electrostatic component (ΔGel) of ΔG. This finding was unexpected bec ...[more]

PMID: 26528091

Similar Datasets

Project description:The Poisson-Nernst-Planck (PNP) model is based on a mean-field approximation of ion interactions and continuum descriptions of concentration and electrostatic potential. It provides qualitative explanation and increasingly quantitative predictions of experimental measurements for the ion transport problems in many areas such as semiconductor devices, nanofluidic systems, and biological systems, despite many limitations. While the PNP model gives a good prediction of the ion transport phenomenon for chemical, physical, and biological systems, the number of equations to be solved and the number of diffusion coefficient profiles to be determined for the calculation directly depend on the number of ion species in the system, since each ion species corresponds to one Nernst-Planck equation and one position-dependent diffusion coefficient profile. In a complex system with multiple ion species, the PNP can be computationally expensive and parameter demanding, as experimental measurements of diffusion coefficient profiles are generally quite limited for most confined regions such as ion channels, nanostructures and nanopores. We propose an alternative model to reduce number of Nernst-Planck equations to be solved in complex chemical and biological systems with multiple ion species by substituting Nernst-Planck equations with Boltzmann distributions of ion concentrations. As such, we solve the coupled Poisson-Boltzmann and Nernst-Planck (PBNP) equations, instead of the PNP equations. The proposed PBNP equations are derived from a total energy functional by using the variational principle. We design a number of computational techniques, including the Dirichlet to Neumann mapping, the matched interface and boundary, and relaxation based iterative procedure, to ensure efficient solution of the proposed PBNP equations. Two protein molecules, cytochrome c551 and Gramicidin A, are employed to validate the proposed model under a wide range of bulk ion concentrations and external voltages. Extensive numerical experiments show that there is an excellent consistency between the results predicted from the present PBNP model and those obtained from the PNP model in terms of the electrostatic potentials, ion concentration profiles, and current-voltage (I-V) curves. The present PBNP model is further validated by a comparison with experimental measurements of I-V curves under various ion bulk concentrations. Numerical experiments indicate that the proposed PBNP model is more efficient than the original PNP model in terms of simulation time.

Dataset Information

Problems of robustness in Poisson-Boltzmann binding free energies.

Publications

Problems of robustness in Poisson-Boltzmann binding free energies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets