Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Analytical electrostatics for biomolecules: beyond the generalized Born approximation.

Grigori Sigalov1, Andrew Fenley, Alexey Onufriev

  • 1Department of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, USA.

The Journal of Chemical Physics
|April 8, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Mass medicine vs. personalized medicine: from mathematical methods to regulatory implications.

Frontiers in physiology·2025
Same author

The need to implement FAIR principles in biomolecular simulations.

Nature methods·2025
Same author

Food anaphylaxis diagnostic marker compilation in machine learning design and validation.

PloS one·2023
Same author

Binding of regulatory proteins to nucleosomes is modulated by dynamic histone tails.

Nature communications·2021
Same author

Ten simple rules on how to create open access and reproducible molecular simulations of biological systems.

PLoS computational biology·2019
Same author

A strategy for reducing gross errors in the generalized Born models of implicit solvation.

The Journal of chemical physics·2011

This study introduces the Analytical linearized Poisson-Boltzmann (ALPB) method for faster electrostatic calculations in biomolecular simulations. ALPB offers accurate solvation energy predictions, outperforming generalized Born approximations with similar computational efficiency.

Area of Science:

  • Computational chemistry
  • Biophysics
  • Molecular modeling

Background:

  • Accurate modeling of electrostatic interactions is crucial for simulating macromolecules in solution.
  • Existing analytical approximations, like generalized Born, have limitations in accuracy.
  • Previous work developed a spherical approximation for the linearized Poisson-Boltzmann equation.

Purpose of the Study:

  • To extend the analytical linearized Poisson-Boltzmann (ALPB) method to arbitrary biomolecular shapes.
  • To develop computationally efficient algorithms for parameter estimation in the ALPB model.
  • To validate the ALPB method against numerical Poisson-Boltzmann (NPB) calculations.

Main Methods:

  • Extension of a previously developed analytical solution of the linearized Poisson-Boltzmann equation.

Related Experiment Videos

  • Development of algorithms for estimating model parameters for arbitrary shapes.
  • Testing against 579 proteins, nucleic acids, and peptides using standard numerical Poisson-Boltzmann (NPB) treatment.
  • Comparison with the generalized Born approximation.

    • Main Results:

    • The ALPB method shows systematic deviations of 0.5-3.5 kcal/mol from NPB for solvation energies.
    • ALPB accuracy is 5-50 times better than the conventional generalized Born approximation.
    • The method maintains computational efficiency comparable to generalized Born.
    • ALPB was successfully integrated into the AMBER molecular modeling package.

    Conclusions:

    • The extended ALPB method provides a computationally efficient and accurate approach for electrostatic calculations in biomolecular simulations.
    • ALPB offers a significant improvement over generalized Born approximations for solvation energy prediction.
    • The validated ALPB model is suitable for applications in molecular modeling packages like AMBER.