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SNAREs pp 15-31 | Cite as

Mesoscale Computational Modeling of Protein-Membrane Interactions Based on Continuum Mean-Field Theory

  • George Khelashvili
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1860)

Abstract

Quantitative computational modeling of protein-membrane interactions is of great importance as it aids in the interpretation of experimental results and enables design and exploration of new experimental systems. This review describes one such computational approach conceived specifically to treat electrostatically driven interactions between a lipid membrane and a protein (or protein domains) adsorbing onto the membrane. The methodology is based on self-consistent minimization of the governing free energy functional which is expressed in the mean-field approximation and has contributions from electrostatic interactions as well as from mixing entropy of lipids in the membrane and ions in the solution. The method enables calculation of the free energy of the binding process and quantification of the steady-state lipid distribution around the adsorbing protein. The extension of the method to include membrane deformation degrees of freedom further allows calculation of the equilibrium bilayer shape upon the protein binding.

Key words

Poisson-Boltzmann theory Cahn-Hilliard equation Coarse-grained theory Lipid mixing Electrostatic interactions PIP2 lipids Lipid diffusion and segregation Cell signaling 

Notes

Acknowledgments

GK is grateful to Profs. Harel Weinstein and Daniel Harries for their guidance and support during the development of the computational methodology described in this work. GK is also thankful to Nathan Baker for his advice on modifying APBS and his valuable feedback on the mean-field model. GK is supported by the HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute of Computational Biomedicine at Weill Cornell Medical College through gratefully acknowledged support from the 1923 Fund. 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Physiology and BiophysicsWeill Cornell Medical College of Cornell UniversityNew YorkUSA
  2. 2.Institute for Computational BiomedicineWeill Cornell Medical College of Cornell UniversityNew YorkUSA

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