Atomic-level description of protein-lipid interactions using an accelerated membrane model

Javier L. Baylon, Josh V. Vermaas, Melanie P. Muller, Mark J. Arcario, Taras V. Pogorelov, Emad Tajkhorshid

Research output: Contribution to journalArticlepeer-review

40 Scopus citations

Abstract

Peripheral membrane proteins are structurally diverse proteins that are involved in fundamental cellular processes. Their activity of these proteins is frequently modulated through their interaction with cellular membranes, and as a result techniques to study the interfacial interaction between peripheral proteins and the membrane are in high demand. Due to the fluid nature of the membrane and the reversibility of protein-membrane interactions, the experimental study of these systems remains a challenging task. Molecular dynamics simulations offer a suitable approach to study protein-lipid interactions; however, the slow dynamics of the lipids often prevents sufficient sampling of specific membrane-protein interactions in atomistic simulations. To increase lipid dynamics while preserving the atomistic detail of protein-lipid interactions, in the highly mobile membrane-mimetic (HMMM) model the membrane core is replaced by an organic solvent, while short-tailed lipids provide a nearly complete representation of natural lipids at the organic solvent/water interface. Here, we present a brief introduction and a summary of recent applications of the HMMM to study different membrane proteins, complementing the experimental characterization of the presented systems, and we offer a perspective of future applications of the HMMM to study other classes of membrane proteins. This article is part of a Special Issue entitled: Membrane proteins edited by J.C. Gumbart and Sergei Noskov.

Original languageEnglish
Pages (from-to)1573-1583
Number of pages11
JournalBiochimica et Biophysica Acta - Biomembranes
Volume1858
Issue number7
DOIs
StatePublished - Jul 1 2016

Keywords

  • Lipids
  • Membrane
  • Molecular dynamics
  • Peripheral proteins

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