Advertisement

Modeling of Cell Membrane Systems

  • Tuğba Arzu Özal İldenizEmail author
Chapter

Abstract

The mechanisms that take place in or through cell membranes are vitally important for all living organisms. The molecules embedded in or associated to membranes, such as transmembrane proteins, behave dynamically to perform their functions. Although experimental techniques have improved considerably in recent decades, when combined with computational means of modeling, they reveal secrets behind the mechanisms related to membrane systems. The resolution of the structures of membrane proteins has become trivial recently using computerized prediction tools. The worldwide accumulation of structural data in databases enables the application of in-silico methodologies. Simulations, together with the various lipid membrane models, provide information through the dynamic exploration of conformational space. In this chapter, the basics of modeling are discussed, with a focus on molecular dynamic modeling methodology. In addition to modeling, visualization and analysis tools are also mentioned.

Keywords

Molecular dynamics Molecular modeling Multiscale modeling Orientation of proteins in membranes Force field Computer-aided drug design Docking Protein data bank Computational 

References

  1. 1.
    R.P. Feynman, The Feynman Lectures on Physics – Vol. III, The Feynman Lectures on Physics (1963)Google Scholar
  2. 2.
    M. Karplus, Y.Q. Gao, J. Ma, A. Van Der Vaart, W. Yang, A.H. Zewail, Protein structural transitions and their functional role. Philos. Trans. Math. Phys. Eng. Sci. Ser. A 363, 331 (2005)CrossRefGoogle Scholar
  3. 3.
    J. Gumbart, Y. Wang, A. Aksimentiev, E. Tajkhorshid, K. Schulten, Molecular dynamics simulations of proteins in lipid bilayers. Curr. Opin. Struct. Biol 15, 423 (2005)CrossRefGoogle Scholar
  4. 4.
    P.C. Biggin, P.J. Bond, Molecular dynamics simulations of membrane proteins. Methods Mol Biol 1215, 91–108 (2015)CrossRefGoogle Scholar
  5. 5.
    E. Lindahl, M.S.P. Sansom, Membrane proteins: molecular dynamics simulations. Curr. Opin. Struct. Biol 18(4), 425–431 (2008)CrossRefGoogle Scholar
  6. 6.
    G.A. Kaminski, R.A. Friesner, J. Tirado-Rives, W.L. Jorgensen, Evaluation and reparametrization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical calculations on peptides. J. Phys. Chem. B 105, 6474 (2001)CrossRefGoogle Scholar
  7. 7.
    A.D. MacKerell et al., All-atom empirical potential for molecular modeling and dynamics studies of proteins. J. Phys. Chem. B 102, 3586 (1998)CrossRefGoogle Scholar
  8. 8.
    C. Oostenbrink, A. Villa, A.E. Mark, W.F. Van Gunsteren, A biomolecular force field based on the free enthalpy of hydration and solvation: The GROMOS force-field parameter sets 53A5 and 53A6. J. Comput. Chem. 25(13), 1656–1676 (2004)CrossRefGoogle Scholar
  9. 9.
    J. Wang, P. Cieplak, P.A. Kollman, How well does a restrained electrostatic potential (RESP) model perform in calculating conformational energies of organic and biological molecules? J. Comput. Chem 21, 1049 (2000)CrossRefGoogle Scholar
  10. 10.
    B.J. Alder, T.E. Wainwright, Phase transition for a hard sphere system. J. Chem. Phys. 27(5), 1208–1209 (1957)CrossRefGoogle Scholar
  11. 11.
    A. Rahman, F.H. Stillinger, Molecular dynamics study of liquid water. J. Chem. Phys 55, 3336 (1971)CrossRefGoogle Scholar
  12. 12.
    J.A. McCammon, B.R. Gelin, M. Karplus, Dynamics of folded proteins. Nature 267(5612), 585–590 (1977)CrossRefGoogle Scholar
  13. 13.
    M.P. Allen, D.J. Tildesley, Computer Simulation of Liquids (Oxford Science Publications) SE - Oxford Science Publications (Oxford University Press, Oxford, 1989)Google Scholar
  14. 14.
    D. Frenkel, B. Smit, Understanding molecular simulation: from algorithms to applications. Comput. Sci. Ser. 2nd (ed). Academic Press (2002)Google Scholar
  15. 15.
    W.W. Garvin, Introduction to Linear Programming (1st (ed), McGraw-Hill, London, 1960)Google Scholar
  16. 16.
    H.B. Callen, Thermodynamics and an Introduction to Thermostatistics (Wiley, New York, 1985)Google Scholar
  17. 17.
    D. Chandler, Introduction to Modern Statistical Mechanics (Oxford University Press, New York, 1987)Google Scholar
  18. 18.
    K.A. Dill, Molecular driving forces: Statistical thermodynamics in chemistry and biology. By K. A. Dill, S. Bromberg. Macromol. Chem. Phys. 204(14), 1800–1800 (2003)CrossRefGoogle Scholar
  19. 19.
    W. Sun, Y. Yuan, Optimization Theory and Methods : Nonlinear Programming (Springer, New York, 2006)Google Scholar
  20. 20.
    E. Schrödinger, Statistical Thermodynamics (Cambridge University Press, London, 1948), p. 95Google Scholar
  21. 21.
    A. Leach, Molecular modelling: principles and applications. Computers 2nd (ed.), Pearson Education Limited (2001)Google Scholar
  22. 22.
    V. Aleksa, G.A. Guirgis, A. Horn, P. Klaeboe, R.J. Liberatore, C.J. Nielsen, Vibrational spectra, conformations, quantum chemical calculations and spectral assignments of 1-chloro-1-silacyclohexane. Vib. Spectrosc. 61, 167–175 (2012)CrossRefGoogle Scholar
  23. 23.
    E. Lindahl, B. Hess, D. van der Spoel, GROMACS 3.0: a package for molecular simulation and trajectory analysis. J. Mol. Model. 7(8), 306–317 (2001)CrossRefGoogle Scholar
  24. 24.
    J.C. Phillips et al., Scalable molecular dynamics with NAMD. J. Comput. Chem. 26(16), 1781–1802 (2005)CrossRefGoogle Scholar
  25. 25.
    D.A. Case et al., The Amber biomolecular simulation programs. J. Comput. Chem. 26(16), 1668–1688 (2005)CrossRefGoogle Scholar
  26. 26.
    P. Eastman et al., OpenMM 7: rapid development of high performance algorithms for molecular dynamics. PLoS Comput Biol 13(7), e1005659 (2017)CrossRefGoogle Scholar
  27. 27.
    B.R. Brooks, R.E. Bruccoleri, B.D. Olafson, D.J. States, S. Swaminathan, M. Karplus, CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. J. Comput. Chem. 4(2), 187–217 (1983)CrossRefGoogle Scholar
  28. 28.
    S.J. Marrink, A.H. de Vries, D.P. Tieleman, Lipids on the move: simulations of membrane pores, domains, stalks and curves. Biochim. Biophys. Acta Biomembr. 1788(1), 149–168 (2009)CrossRefGoogle Scholar
  29. 29.
    P.J. Stansfeld, M.S.P. Sansom, Molecular simulation approaches to membrane proteins. Structure 19(11), 1562–1572 (2011)CrossRefGoogle Scholar
  30. 30.
    B. Roux, K. Schulten, Computational studies of membrane channels. Structure 12(8), 1343–1351 (2004)CrossRefGoogle Scholar
  31. 31.
    J.S. Hub, B.L. De Groot, Mechanism of selectivity in aquaporins and aquaglyceroporins. Proc Natl Acad Sci U S A 105, 1198 (2008)CrossRefGoogle Scholar
  32. 32.
    J.D. Faraldo-Gómez, L.R. Forrest, Modeling and simulation of ion-coupled and ATP-driven membrane proteins. Curr. Opin. Struct. Biol. 21(2), 173–179 (2011)CrossRefGoogle Scholar
  33. 33.
    E.B. Watkins, C.E. Miller, J. Majewski, T.L. Kuhl, Membrane texture induced by specific protein binding and receptor clustering: active roles for lipids in cellular function. Proc. Natl. Acad. Sci. 108(17), 6975–6980 (2011)CrossRefGoogle Scholar
  34. 34.
    S. Lal Badshah, A.N. Khan, Y.N. Mabkhot, Molecular dynamics simulation of cholera toxin A-1 polypeptide. Open Chem 14, 188–196 (2016)CrossRefGoogle Scholar
  35. 35.
    J.J. Blessy, D.J.S. Sharmila, Molecular modeling of methyl-α-Neu5Ac analogues docked against cholera toxin - a molecular dynamics study. Glycoconj. J. 32(1–2), 49–67 (2015)CrossRefGoogle Scholar
  36. 36.
    R.P. Saha, P. Chakrabarti, Molecular modeling and characterization of Vibrio cholerae transcription regulator HlyU. BMC Struct Biol 6, 24 (2006)CrossRefGoogle Scholar
  37. 37.
    A. Sridhar, A. Kumar, A.K. Dasmahapatra, Multi-scale molecular dynamics study of cholera pentamer binding to a GM1-phospholipid membrane. J. Mol. Graph. Model. 68, 236–251 (2016)CrossRefGoogle Scholar
  38. 38.
    K. Geleijns et al., Mannose-binding lectin contributes to the severity of Guillain-Barre syndrome. J. Immunol. 177(6), 4211–4217 (2006)CrossRefGoogle Scholar
  39. 39.
    S. Kusunoki, D. Morita, S. Ohminami, S. Hitoshi, I. Kanazawa, Binding of immunoglobulin G antibodies in Guillain-Barré syndrome sera to a mixture of GM1 and a phospholipid: possible clinical implications. Muscle Nerve 27(3), 302–306 (2003)CrossRefGoogle Scholar
  40. 40.
    C.A. Taft, V.B. Da Silva, C.H.T.D.P. Da Silva, Current topics in computer-aided drug design. J. Pharm. Sci. 97(3), 1089–1098 (2008)CrossRefGoogle Scholar
  41. 41.
    S.J.Y. Macalino, V. Gosu, S. Hong, S. Choi, Role of computer-aided drug design in modern drug discovery. Arch. Pharm. Res. 38(9), 1686–1701 (2015)CrossRefGoogle Scholar
  42. 42.
    X. Huang, C.-G. Zhan, How dopamine transporter interacts with dopamine: insights from molecular modeling and simulation. Biophys J 93(10), 3627–3639 (2007)CrossRefGoogle Scholar
  43. 43.
    A.W. Ravna, I. Sylte, S.G. Dahl, Molecular mechanism of citalopram and cocaine interactions with neurotransmitter transporters. J. Pharmacol. Exp. Ther. 307(1), 34–41 (2003)CrossRefGoogle Scholar
  44. 44.
    H. Remaut, R. Fronzes, Bacterial Membranes_ Structural and Molecular Biology (Caister Academic Press, Norfolk, 2014)Google Scholar
  45. 45.
    R.G. Ramos, D. Libong, M. Rakotomanga, K. Gaudin, P.M. Loiseau, P. Chaminade, Comparison between charged aerosol detection and light scattering detection for the analysis of Leishmania membrane phospholipids. J. Chromatogr. A 1209(1–2), 88–94 (2008)CrossRefGoogle Scholar
  46. 46.
    F.J. van Eerden, D.H. de Jong, A.H. de Vries, T.A. Wassenaar, S.J. Marrink, Characterization of thylakoid lipid membranes from cyanobacteria and higher plants by molecular dynamics simulations. Biochim. Biophys. Acta Biomembr. 1848(6), 1319–1330 (2015)CrossRefGoogle Scholar
  47. 47.
    K. Zhang, S.M. Beverley, Phospholipid and sphingolipid metabolism in Leishmania. Mol Biochem Parasitol 170(2), 55–64 (2010)CrossRefGoogle Scholar
  48. 48.
    N. Unubol et al., Peptide Antibiotics Developed by Mimicking Natural Antimicrobial Peptides, vol 06 (Clin. Microbiol, Open Access, 2017)Google Scholar
  49. 49.
    E. Matyus, C. Kandt, D. Tieleman, Computer simulation of antimicrobial peptides. Curr. Med. Chem. 14(26), 2789–2798 (2007)CrossRefGoogle Scholar
  50. 50.
    A. Liwo, Computational Methods to Study the Structure and Dynamics of Biomolecules and Biomolecular Processes: From Bioinformatics to Molecular Quantum Mechanics (Springer, Berlin/Heidelberg, 2014)CrossRefGoogle Scholar
  51. 51.
    C.P. Fall, Computational Cell Biology (Springer, New York, 2002)Google Scholar
  52. 52.
    J. Moult, K. Fidelis, A. Kryshtafovych, T. Schwede, A. Tramontano, Critical assessment of methods of protein structure prediction (CASP)—Round XII. Proteins Struct. Funct. Bioinforma 86(August 2017), 7–15 (2018)CrossRefGoogle Scholar
  53. 53.
    M.G. Wolf, M. Hoefling, C. Aponte-Santamaría, H. Grubmüller, G. Groenhof, g_membed: efficient insertion of a membrane protein into an equilibrated lipid bilayer with minimal perturbation. J. Comput. Chem. 31(11), 2169–2174 (2010)CrossRefGoogle Scholar
  54. 54.
    A.C. Kalli, B.A. Hall, I.D. Campbell, M.S.P. Sansom, A helix heterodimer in a lipid bilayer: prediction of the structure of an integrin transmembrane domain via multiscale simulations. Structure 19(10), 1477–1484 (2011)CrossRefGoogle Scholar
  55. 55.
    P.R. Cullis, B. De Kruijff, Lipid polymorphism and the functional roles of lipids in biological membranes. Biochim. Biophys. Acta - Rev. Biomembr. 559(4), 399–420 (1979)CrossRefGoogle Scholar
  56. 56.
    H.L. Scott, Modeling the lipid component of membranes. Curr. Opin. Struct. Biol. 12(4), 495–502 (2002)CrossRefGoogle Scholar
  57. 57.
    W.L. Jorgensen, J. Tirado-Rives, The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. J. Am. Chem. Soc. 110(6), 1657–1666 (1988)CrossRefGoogle Scholar
  58. 58.
    W.L. Jorgensen, D.S. Maxwell, J. Tirado-Rives, Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J. Am. Chem. Soc. 118(45), 11225–11236 (1996)CrossRefGoogle Scholar
  59. 59.
    W.D. Cornell et al., A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J. Am. Chem. Soc. 117(19), 5179–5197 (1995)CrossRefGoogle Scholar
  60. 60.
    H.J.C. Berendsen, J.P.M. Postma, W.F. van Gunsteren, J. Hermans, Interaction models for water in relation to protein hydration (Springer, Dordrecht, 1981), pp. 331–342Google Scholar
  61. 61.
    X. Daura, A.E. Mark, W.F. Van Gunsteren, Parametrization of aliphatic CHn united atoms of GROMOS96 force field. J. Comput. Chem. 19(5), 535–547 (1998)CrossRefGoogle Scholar
  62. 62.
    A. Kukol, Lipid models for united-atom molecular dynamics simulations of proteins. J. Chem. Theory Comput. 5(3), 615–626 (2009)CrossRefGoogle Scholar
  63. 63.
    S. Jo, T. Kim, W. Im, Automated builder and database of protein/membrane complexes for molecular dynamics simulations. PLoS One 2(9), e880 (2007)CrossRefGoogle Scholar
  64. 64.
    S. Jo, T. Kim, V.G. Iyer, W. Im, CHARMM-GUI: a web-based graphical user interface for CHARMM. J. Comput. Chem. 29(11), 1859–1865 (2008)CrossRefGoogle Scholar
  65. 65.
    E.L. Wu et al., CHARMM-GUI membrane builder toward realistic biological membrane simulations. J. Comput. Chem. 35(27), 1997–2004 (2014)CrossRefGoogle Scholar
  66. 66.
    H.M. Berman et al., The protein data bank. Nucleic Acids Res. 28(1), 235–242 (2000)CrossRefGoogle Scholar
  67. 67.
    A.L. Lomize, I.D. Pogozheva, M.A. Lomize, H.I. Mosberg, Positioning of proteins in membranes: A computational approach. Protein Sci. 15, 1318–1333 (2006)CrossRefGoogle Scholar
  68. 68.
    T.H. Schmidt, C. Kandt, LAMBADA and InflateGRO2: efficient membrane alignment and insertion of membrane proteins for molecular dynamics simulations. J. Chem. Inf. Model. 52(10), 2657–2669 (2012)CrossRefGoogle Scholar
  69. 69.
    W. Humphrey, A. Dalke, K. Schulten, VMD: visual molecular dynamics. J. Mol. Graph 14(1), 33–38. , 27–8. 1996Google Scholar
  70. 70.
    B. Sommer et al., CELLmicrocosmos 2.2 MembraneEditor: a modular interactive shape-based software approach to solve heterogeneous membrane packing problems. J. Chem. Inf. Model 51(5), 110419120935062 (2011)CrossRefGoogle Scholar
  71. 71.
    L. Martínez, R. Andrade, E.G. Birgin, J.M. Martínez, PACKMOL: a package for building initial configurations for molecular dynamics simulations. J. Comput. Chem. 30(13), 2157–2164 (2009)CrossRefGoogle Scholar
  72. 72.
    E. Wallin, G. Von Heijne, Genome-Wide Analysis of Integral Membrane Proteins from Eubacterial, Archaean, and Eukaryotic Organisms (Cambridge University Press, USA, 1998)Google Scholar
  73. 73.
    E.P. Carpenter, K. Beis, A.D. Cameron, S. Iwata, Overcoming the challenges of membrane protein crystallography this review comes from a themed issue on biophysical methods edited by Samar Hasnain and Soichi Wakatsuki. Curr. Opin. Struct. Biol. 18, 581–586 (2008)CrossRefGoogle Scholar
  74. 74.
    J.P. Overington, B. Al-Lazikani, A.L. Hopkins, How many drug targets are there? Nat. Rev. Drug Discov. 5(12), 993–996 (2006)CrossRefGoogle Scholar
  75. 75.
    A.S. Robinson, Production of Membrane Proteins : Strategies for Expression and Isolation (Wiley-VCH, Weinheim, 2011)CrossRefGoogle Scholar
  76. 76.
    A. 1969- Kukol, Molecular Modeling of Proteins (Humana Press, New York, 2015)Google Scholar
  77. 77.
    C. Kandt, W.L. Ash, D. Peter Tieleman, Setting up and running molecular dynamics simulations of membrane proteins. Methods 41(4), 475–488 (2007)CrossRefGoogle Scholar
  78. 78.
    M.A. Lomize, I.D. Pogozheva, H. Joo, H.I. Mosberg, A.L. Lomize, OPM database and PPM web server: resources for positioning of proteins in membranes. Nucleic Acids Res. 40(D1), 370–376 (2012)CrossRefGoogle Scholar
  79. 79.
    A.L. Lomize, I.D. Pogozheva, M.A. Lomize, H.I. Mosberg, The role of hydrophobic interactions in positioning of peripheral proteins in membranes. BMC Struct. Biol. 7, 1–30 (2007)CrossRefGoogle Scholar
  80. 80.
    A.L. Lomize, I.D. Pogozheva, H.I. Mosberg, Anisotropic solvent model of the lipid bilayer. 2. Energetics of insertion of small molecules, peptides, and proteins in membranes. J. Chem. Inf. Model. 51(4), 930–946 (2011)CrossRefGoogle Scholar
  81. 81.
    V.A. Harmandaris, N.P. Adhikari, N.F.A. van der Vegt, K. Kremer, Hierarchical modeling of polystyrene: from atomistic to coarse-grained simulations. Macromolecules 39(19), 6708–6719 (2006)CrossRefGoogle Scholar
  82. 82.
    M. Praprotnik, L.D. Site, K. Kremer, Multiscale simulation of soft matter: from scale bridging to adaptive resolution. Annu Rev Phys Chem 59, 545–571 (2008)CrossRefGoogle Scholar
  83. 83.
    C. Peter, K. Kremer, Multiscale simulation of soft matter systems – from the atomistic to the coarse-grained level and back. Soft Matter 5(22), 4357 (2009)CrossRefGoogle Scholar
  84. 84.
    J.B. Klauda et al., Update of the CHARMM all-atom additive force field for lipids: validation on six lipid types NIH Public Access. J. Phys. Chem. B 114(23), 7830–7843 (2010)CrossRefGoogle Scholar
  85. 85.
    R.W. Pastor, A.D. Mackerell, Development of the CHARMM force field for lipids NIH public access. J. Phys. Chem. Lett. 2(13), 1526–1532 (2011)CrossRefGoogle Scholar
  86. 86.
    J.P. Ulmschneider, M.B. Ulmschneider, United atom lipid parameters for combination with the optimized potentials for liquid simulations all-atom force field. J. Chem. Theory Comput. 5(7), 1803–1813 (2009)CrossRefGoogle Scholar
  87. 87.
    S.W.I. Siu, R. Vácha, P. Jungwirth, R.A. Böckmann, Biomolecular simulations of membranes: Physical properties from different force fields. J. Chem. Phys 128(12), 125103 (2008)CrossRefGoogle Scholar
  88. 88.
    B.J. Reynwar, G. Illya, V.A. Harmandaris, M.M. Müller, K. Kremer, M. Deserno, Aggregation and vesiculation of membrane proteins by curvature-mediated interactions. Nature 447(7143), 461–464 (2007)CrossRefGoogle Scholar
  89. 89.
    W.L. Jorgensen, J. Chandrasekhar, J.D. Madura, R.W. Impey, M.L. Klein, Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 79(2), 926–935 (1983)CrossRefGoogle Scholar
  90. 90.
    D.J. Price, C.L. Brooks, A modified TIP3P water potential for simulation with Ewald summation. J. Chem. Phys. 121(20), 10096–10103 (2004)CrossRefGoogle Scholar
  91. 91.
    P. Mark, L. Nilsson, Structure and dynamics of the TIP3P, SPC, and SPC/E Water Models at 298 K. J. Phys. Chem. A 105(43), 9954–9960 (2001)CrossRefGoogle Scholar
  92. 92.
    E. Neria, S. Fischer, M. Karplus, Simulation of activation free energies in molecular systems. J. Chem. Phys 105(5), 1902 (1998)CrossRefGoogle Scholar
  93. 93.
    W. Shinoda, M. Shimizu, S. Okazaki, Molecular dynamics study on electrostatic properties of a lipid bilayer: polarization, electrostatic potential, and the effects on structure and dynamics of water near the interface. J. Phys. Chem. B 102(34), 6647–6654 (1998)CrossRefGoogle Scholar
  94. 94.
    J.E. Davis, O. Rahaman, S. Patel, Molecular dynamics simulations of a DMPC bilayer using nonadditive interaction models. Biophys J 96(2), 385–402 (2009)CrossRefGoogle Scholar
  95. 95.
    J.S. Hub, T. Salditt, M.C. Rheinstä, B.L. De Groot, Short-range order and collective dynamics of DMPC bilayers: a comparison between molecular dynamics simulations, X-ray, and neutron scattering experiments. Biophys. J. 93, 3156–3168 (2007)CrossRefGoogle Scholar
  96. 96.
    B. Hess, S. León, N. van der Vegt, K. Kremer, Long time atomistic polymer trajectories from coarse grained simulations: bisphenol-A polycarbonate. Soft Matter 2(5), 409–414 (2006)CrossRefGoogle Scholar
  97. 97.
    S.K. Kandasamy, R.G. Larson, Molecular dynamics simulations of model trans-membrane peptides in lipid bilayers: A systematic investigation of hydrophobic mismatch. Biophys. J. 90(7), 2326–2343 (2006)CrossRefGoogle Scholar
  98. 98.
    T. Mori, N. Miyashita, W. Im, M. Feig, Y. Sugita, Molecular dynamics simulations of biological membranes and membrane proteins using enhanced conformational sampling algorithms. Biochim. Biophys. Acta Biomembr. 1858(7), 1635–1651 (2016)CrossRefGoogle Scholar
  99. 99.
    M. Chavent, A.L. Duncan, M.S. Sansom, Molecular dynamics simulations of membrane proteins and their interactions: from nanoscale to mesoscale. Curr. Opin. Struct. Biol. 40, 8–16 (2016)CrossRefGoogle Scholar
  100. 100.
    T. Apajalahti et al., Concerted diffusion of lipids in raft-like membranes. Faraday Discuss 144, 411–430. ; discussion 445–81 (2010)CrossRefGoogle Scholar
  101. 101.
    H. Koldsø, D. Shorthouse, J. Hélie, M.S.P. Sansom, Lipid clustering correlates with membrane curvature as revealed by molecular simulations of complex lipid bilayers. PLoS Comput. Biol 10(10), e1003911 (2014)CrossRefGoogle Scholar
  102. 102.
    H.I. Ingólfsson et al., Lipid organization of the plasma membrane. J. Am. Chem. Soc. 136(41), 14554–14559 (2014)CrossRefGoogle Scholar
  103. 103.
    J.E. Goose, M.S.P. Sansom, Reduced lateral mobility of lipids and proteins in crowded membranes. PLoS Comput. Biol 9(4), e1003033 (2013)CrossRefGoogle Scholar
  104. 104.
    G. Guigas, M. Weiss, Effects of protein crowding on membrane systems. Biochim. Biophys. Acta Biomembr. 1858(10), 2441–2450 (2016)CrossRefGoogle Scholar
  105. 105.
    M. Javanainen, H. Martinez-Seara, Efficient preparation and analysis of membrane and membrane protein systems. Biochim. Biophys. Acta Biomembr. 1858(10), 2468–2482 (2016)CrossRefGoogle Scholar
  106. 106.
    W.J. Allen, J.A. Lemkul, D.R. Bevan, GridMAT-MD: A grid-based membrane analysis tool for use with molecular dynamics. J. Comput. Chem. 30(12), 1952–1958 (2009)CrossRefGoogle Scholar
  107. 107.
    T.A. Özal, C. Peter, B. Hess, N.F.A. van der Vegt, Modeling solubilities of additives in polymer microstructures: single-step perturbation method based on a soft-cavity reference state. Macromolecules 41(13), 5055–5061 (2008)CrossRefGoogle Scholar
  108. 108.
    S. Noskov, J.C. Gumbart, Membrane proteins: where theory meets experiment. BBA-Biomembranes 1858, 1553–1555 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Acıbadem Mehmet Ali Aydınlar University, Faculty of Engineering, Medical Engineering DepartmentIstanbulTurkey

Personalised recommendations