Molecular Dynamics Simulations of Membrane Proteins

Part of the Methods in Molecular Biology book series (MIMB, volume 1033)


Molecular dynamics simulations are a powerful tool for complementing experimental studies, providing insights in biological processes at the molecular and atomistic level, at timescales from picoseconds to microseconds. Simulations are useful for testing hypotheses and can provide explanations for experimental observations as well as suggestions for further experiments. This does require that the simulation setup allows assessment of the question addressed. For example, it is evident that for simulation of a protein in its functional state the protein model and the environment have to mimic the biological situation as close as possible. In this chapter, a general strategy is presented for setting up and running simulations of membrane proteins of known structure in biological membranes of diverse composition and size.

Key words

Membrane proteins Lipid bilayers Molecular dynamics simulation All-atom force field Coarse-grained force field MARTINI Backmapping 



This work was supported by a grant from the Deutsche Forschungsgemeinschaft (BO 2963/2-1) to RAB.


  1. 1.
    Jensen MO, Jogini V, Borhani DW, Leffler AE, Dror RO, Shaw DE (2012) Mechanism of voltage gating in potassium channels. Science 336:229–233PubMedCrossRefGoogle Scholar
  2. 2.
    Tieleman DP (2012) Computer simulation of membrane dynamics. In: Comprehensive biophysics, vol 5. ElsevierGoogle Scholar
  3. 3.
    Wolf MG, Hoefling M, Aponte-Santamaría C, Grubmüller H, Groenhof G (2010) g_membed: efficient insertion of a membrane protein into an equilibrated lipid bilayer with minimal perturbation. J Comput Chem 31:2169–2174PubMedCrossRefGoogle Scholar
  4. 4.
    Kandt C, Ash WL, Tieleman DP (2007) Setting up and running molecular dynamics simulations of membrane proteins. Methods 41:475–488PubMedCrossRefGoogle Scholar
  5. 5.
    Marrink SJ, Lindahl E, Edholm O (2001) Simulation of the spontaneous aggregation of phospholipids into bilayers. J Am Chem Soc 123:8638–8639PubMedCrossRefGoogle Scholar
  6. 6.
    Böckmann RA, Caflisch A (2005) Formation of detergent micelles around the outer membrane protein OmpX. Biophys J 88: 3191–3204PubMedCrossRefGoogle Scholar
  7. 7.
    Marrink SJ, Risselada HJ, Yefimov S, Tieleman DP, de Vries AH (2007) The MARTINI forcefield: coarse grained model for biomolecular simulations. J Phys Chem B 111:7812–7824PubMedCrossRefGoogle Scholar
  8. 8.
    Monticelli L, Kandasamy SK, Periole X, Larson RG, Tieleman DP, Marrink SJ (2008) The MARTINI coarse grained forcefield: extension to proteins. J Chem Theory Comput 4: 819–834CrossRefGoogle Scholar
  9. 9.
    Hess B, Kutzner K, van der Spoel D, Lindahl E (2008) GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput 4:435–447CrossRefGoogle Scholar
  10. 10.
    Sali A, Potterton L, Yuan F, van Vlijmen H, Karplus M (1995) Evaluation of comparative protein modelling by MODELLER. Proteins 23:318–326PubMedCrossRefGoogle Scholar
  11. 11.
    Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22:2577–2637PubMedCrossRefGoogle Scholar
  12. 12.
    de Jong DH, Gurpreet S, Bennett WFD, Arnarez C, Wassenaar TA, Schäfer LV, Periole X, Tieleman DP, Marrink SJ (2013) Improved parameters for the martini coarse-grained protein force field. J Chem Theory Comput 9: 687–697CrossRefGoogle Scholar
  13. 13.
    Wassenaar TA, Sengupta D, Tieleman DP, Marrink SJ (in preparation) INSANE: fast and versatile generation of custom membranes for molecular simulationsGoogle Scholar
  14. 14.
    Wassenaar TA, Pluhackova K, Böckmann RA, Marrink SJ, Tieleman DP (2013) Going backward: A flexible geometric approach to reverse transformation from coarse grained to atomistic models. (in preparation)Google Scholar
  15. 15.
    Humphrey W, Dalke A, Schulten K (1996) VMD—visual molecular dynamics. J Mol Graph 14:33–38PubMedCrossRefGoogle Scholar
  16. 16.
    Guex N, Peitsch MC (1997) SWISS-MODEL and the Swiss-PdbViewer: an environment for comparative protein modeling. Electrophoresis 18:2714–2723PubMedCrossRefGoogle Scholar
  17. 17.
    The PyMOL molecular graphics system, Version Schrödinger, LLCGoogle Scholar
  18. 18.
    Klauda JB et al (2010) Update of the CHARMM all-atom additive force field for lipids: validation on six lipid types. J Phys Chem B 114:7830–7843PubMedCrossRefGoogle Scholar
  19. 19.
    Hornak V, Abel R, Okur A, Strockbine B, Roitberg A, Simmerling C (2006) Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins 65:712–725PubMedCrossRefGoogle Scholar
  20. 20.
    Jämbec JPM, Lyubartsev AP (2012) An extension and further validation of an all-atomistic force field for biological membranes. J Chem Theory Comput 8:2938–2948CrossRefGoogle Scholar
  21. 21.
    Schuler LD, Daura X, van Gunsteren WF (2001) An improved GROMOS96 force field for aliphatic hydrocarbons in the condensed phase. J Comput Chem 22:1205–1218CrossRefGoogle Scholar
  22. 22.
    Poger D, Mark AE (2010) On the validation of molecular dynamics simulations of saturated and cis-mono unsaturated phosphatidylcholine lipid bilayers: A comparison with experiment. J Chem Theory Comput 6:325–336CrossRefGoogle Scholar
  23. 23.
    Darden T, York D, Pedersen L (1993) Particle mesh Ewald: an N-log(N) method for Ewald sums in large systems. J Chem Phys 98: 10089–10092CrossRefGoogle Scholar
  24. 24.
    Allen WJ, Lemkul JA, Bevan DR (2009) GridMAT-MD: a grid-based membrane analysis tool for use with molecular dynamics. J Comput Chem 30:1952–1958PubMedCrossRefGoogle Scholar
  25. 25.
    Georgescu RE, Alexov EG, Gunner MR (2002) Combining conformational flexibility and continuum electrostatics for calculating pKa’s in proteins. Biophys J 83:1731–1748PubMedCrossRefGoogle Scholar
  26. 26.
    Alexov E, Gunner MR (1997) Incorporating protein conformational flexibility into pH-titration calculations: results on T4 lysozyme. Biophys J 74:2075–2093CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  1. 1.Computational BiologyUniversity of Erlangen-NürnbergErlangenGermany
  2. 2.Department of Biological Sciences and Institute for Biocomplexity and InformaticsUniversity of CalgaryCalgaryCanada

Personalised recommendations