The Journal of Membrane Biology

, Volume 248, Issue 3, pp 563–582 | Cite as

Efficient Exploration of Membrane-Associated Phenomena at Atomic Resolution

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


Biological membranes constitute a critical component in all living cells. In addition to providing a conducive environment to a wide range of cellular processes, including transport and signaling, mounting evidence has established active participation of specific lipids in modulating membrane protein function through various mechanisms. Understanding lipid–protein interactions underlying these mechanisms at a sufficiently high resolution has proven extremely challenging, partly due to the semi-fluid nature of the membrane. In order to address this challenge computationally, multiple methods have been developed, including an alternative membrane representation termed highly mobile membrane mimetic (HMMM) in which lateral lipid diffusion has been significantly enhanced without compromising atomic details. The model allows for efficient sampling of lipid–protein interactions at atomic resolution, thereby significantly enhancing the effectiveness of molecular dynamics simulations in capturing membrane-associated phenomena. In this review, after providing an overview of HMMM model development, we will describe briefly successful application of the model to study a variety of membrane processes, including lipid-dependent binding and insertion of peripheral proteins, the mechanism of phospholipid insertion into lipid bilayers, and characterization of optimal tilt angle of transmembrane helices. We conclude with practical recommendations for proper usage of the model in simulation studies of membrane processes.


Cellular membrane Lipid bilayer Membrane proteins Peripheral proteins Molecular dynamics simulation 



This work was supported in part by the National Institutes of Health (Grants R01-GM101048, R01-GM086749, U54-GM087519, and P41-GM104601 to E.T.) and XSEDE compute resources (Grant TG-MCA06N060 to E.T. and Grant TG-MCB130112 to T.V.P.). J.V.V. acknowledges support from the Sandia National Laboratories Campus Executive Program, which is funded by the Laboratory Directed Research and Development (LDRD) Program. Sandia is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the US Department of Energy’s National Nuclear Security Administration under Contract No. DE-AC04-94AL85000, and previous support from the DOE CSGF Fellowship (DE-FG02-97ER25308). M.J.A. acknowledges past support from the NSF GRF Program. Z.W. acknowledges support from the NSF-funded Center of Physics in Living Cell (NSF PHY1430124). T.V.P. is grateful for the support from the Illinois Campus Research Board. T.V.P. was a Faculty Fellow of the National Center for Supercomputing Applications when this work was completed.

Conflicts of interest

The authors declare no conflicts of interest.

Compliance with Ethical Standards

This is a review of prior work funded publicly, and appropriate figures have been reused or adapted with permission from the original authors and publishers. The nature of the presented work is purely computational and does not include human or animal subjects.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Josh V. Vermaas
    • 1
  • Javier L. Baylon
    • 1
  • Mark J. Arcario
    • 1
  • Melanie P. Muller
    • 1
  • Zhe Wu
    • 1
  • Taras V. Pogorelov
    • 1
  • Emad Tajkhorshid
    • 1
  1. 1.Department of Biochemistry, and Center for Biophysics and Computational Biology, Beckman InstituteUniversity of Illinois at Urbana-ChampaignUrbanaUSA

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