Multiscale Modeling and Simulation Approaches to Lipid–Protein Interactions

Part of the Methods in Molecular Biology book series (MIMB, volume 2003)


Lipid membranes play a crucial role in living systems by compartmentalizing biological processes and forming a barrier between these processes and the environment. Naturally, a large apparatus of biomolecules is responsible for construction, maintenance, transport, and degradation of these lipid barriers. Additional classes of biomolecules are tasked with transport of specific substances or transduction of signals from the environment across lipid membranes. In this article, we intend to describe a set of techniques that enable one to build accurate models of lipid systems and their associated proteins, and to simulate their dynamics over a variety of time and length scales. We discuss the methods and challenges that allow us to derive structural, mechanistic, and thermodynamic information from these models. We also show how these models have recently been applied in research to study some of the most complex lipid–protein systems to date, including bacterial and viral envelopes, neuronal membranes, and mammalian signaling systems.

Key words

Molecular dynamics (MD) simulation Molecular modeling Protein–lipid interactions Lipid-binding protein Membrane proteins Membrane peptides Multiscale Coarse-grained (CG) models 



T.S.C. and H.I.I. acknowledge that this work has been supported in part by the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) program established by the U.S. Department of Energy (DOE) and the National Cancer Institute (NCI) of the National Institutes of Health. T.S.C. and H.I.I. note that this work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-JRNL-752805. J.R.A. acknowledges the following sources of funding: Rutherford Discovery Fellowship (15-MAU-001); Marsden grant (15-UOA-105); New Zealand Ministry of Business, Innovation and Employment (MBIE) Endeavour Smart Ideas grant (UOCX1706); Maurice Wilkins Centre for Molecular Biodiscovery Flagship Project grant (MWC 3716850). W.A.I. was supported by the following sources of funding: Massey University Doctoral Scholarship; Massey University Doctoral Dissemination grant. N.D. thanks the Nehru trust for Cambridge University and Rajiv Gandhi (UK) foundation for funding. P.J.B. and J.K.M. acknowledge funding from the Ministry of Education in Singapore (MOE AcRF Tier 3 Grant Number MOE2012-T3-1-008).


  1. 1.
    Bernlohr DA, Simpson MA, Hertzel AV, Banaszak LJ (1997) Intracellular lipid-binding proteins and their genes. Annu Rev Nutr 17:277–303PubMedGoogle Scholar
  2. 2.
    De Libero G, Mori L (2005) Recognition of lipid antigens by T cells. Nat Rev Immunol 5:485–496PubMedGoogle Scholar
  3. 3.
    Russ AP, Lampel S (2005) The druggable genome: an update. Drug Discov Today 10:1607–1610PubMedGoogle Scholar
  4. 4.
    Overington JP, Al-Lazikani B, Hopkins AL (2006) How many drug targets are there? Nat Rev Drug Discov 5:993–996PubMedGoogle Scholar
  5. 5.
    Arora A, Tamm LK (2001) Biophysical approaches to membrane protein structure determination. Curr Opin Struct Biol 11:540–547PubMedGoogle Scholar
  6. 6.
    Phillips R, Ursell T, Wiggins P, Sens P (2009) Emerging roles for lipids in shaping membrane-protein function. Nature 459:379–385PubMedPubMedCentralGoogle Scholar
  7. 7.
    Karplus M, McCammon JA (2002) Molecular dynamics simulations of biomolecules. Nat Struct Biol 9:646–652PubMedGoogle Scholar
  8. 8.
    Karplus M, Kuriyan J (2005) Molecular dynamics and protein function. Proc Natl Acad Sci U S A 102:6679–6685PubMedPubMedCentralGoogle Scholar
  9. 9.
    Durrant JD, McCammon JA (2011) Molecular dynamics simulations and drug discovery. BMC Biol 9:71PubMedPubMedCentralGoogle Scholar
  10. 10.
    Ash WL, Zlomislic MR, Oloo EO, Tieleman DP (2004) Computer simulations of membrane proteins. Biochim Biophys Acta 1666:158–189PubMedGoogle Scholar
  11. 11.
    Domene C, Bond PJ, Sansom MSP (2003) Membrane protein simulations: ion channels and bacterial outer membrane proteins. In: Protein simulations. Elsevier, Amsterdam, pp 159–193Google Scholar
  12. 12.
    Bond PJ, Sansom MSP (2006) Insertion and assembly of membrane proteins via simulation. J Am Chem Soc 128:2697–2704PubMedPubMedCentralGoogle Scholar
  13. 13.
    Sansom MSP, Bond P, Beckstein O et al (2008) Water in ion channels and pores-simulation studies. In: Ion channels: from atomic resolution physiology to functional genomics. John Wiley & Sons, Ltd, New York, NY, pp 66–83Google Scholar
  14. 14.
    Huber RG, Marzinek JK, Holdbrook DA, Bond PJ (2017) Multiscale molecular dynamics simulation approaches to the structure and dynamics of viruses. Prog Biophys Mol Biol 128:121–132PubMedGoogle Scholar
  15. 15.
    Angelescu DG, Linse P (2008) Viruses as supramolecular self-assemblies: modelling of capsid formation and genome packaging. Soft Matter 4:1981Google Scholar
  16. 16.
    Reddy T, Sansom MSP (2016) Computational virology: from the inside out. Biochim Biophys Acta 1858:1610–1618PubMedPubMedCentralGoogle Scholar
  17. 17.
    Brooks BR, Brooks CL, Mackerell AD et al (2009) CHARMM: the biomolecular simulation program. J Comput Chem 30:1545–1614PubMedPubMedCentralGoogle Scholar
  18. 18.
    Huang J, Mackerell AD (2013) CHARMM36 all-atom additive protein force field: validation based on comparison to NMR data. J Comput Chem 34:2135–2145PubMedPubMedCentralGoogle Scholar
  19. 19.
    Case DA, Cheatham TE, Darden T et al (2005) The Amber biomolecular simulation programs. J Comput Chem 26:1668–1688PubMedPubMedCentralGoogle Scholar
  20. 20.
    Salomon-Ferrer R, Götz AW, Poole D et al (2013) Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. Explicit solvent particle mesh ewald. J Chem Theory Comput 9:3878–3888PubMedGoogle Scholar
  21. 21.
    Schmid N, Eichenberger AP, Choutko A et al (2011) Definition and testing of the GROMOS force-field versions 54A7 and 54B7. Eur Biophys J 40:843PubMedGoogle Scholar
  22. 22.
    Jämbeck JPM, Lyubartsev AP (2012) Derivation and systematic validation of a refined all-atom force field for phosphatidylcholine lipids. J Phys Chem B 116:3164–3179PubMedPubMedCentralGoogle Scholar
  23. 23.
    Jämbeck JPM, Lyubartsev AP (2012) An extension and further validation of an all-atomistic force field for biological membranes. J Chem Theory Comput 8:2938–2948PubMedGoogle Scholar
  24. 24.
    Jämbeck JPM, Lyubartsev AP (2012) Another piece of the membrane puzzle: extending slipids further. J Chem Theory Comput 9:774–784PubMedGoogle Scholar
  25. 25.
    Ermilova I, Lyubartsev AP (2016) Extension of the slipids force field to polyunsaturated lipids. J Phys Chem B 120:12826–12842PubMedGoogle Scholar
  26. 26.
    Dickson CJ, Madej BD, Skjevik ÅA et al (2014) Lipid14: the amber lipid force field. J Chem Theory Comput 10:865–879PubMedPubMedCentralGoogle Scholar
  27. 27.
    Schuler LD, Daura X, van Gunsteren WF (2001) An improved GROMOS96 force field for aliphatic hydrocarbons in the condensed phase. J Comput Chem 22:1205–1218Google Scholar
  28. 28.
    Berger O, Edholm O, Jähnig F (1997) Molecular dynamics simulations of a fluid bilayer of dipalmitoylphosphatidylcholine at full hydration, constant pressure, and constant temperature. Biophys J 72:2002–2013PubMedPubMedCentralGoogle Scholar
  29. 29.
    Marrink SJ, Risselada HJ, Yefimov S et al (2007) The MARTINI force field: coarse grained model for biomolecular simulations. J Phys Chem B 111:7812–7824PubMedGoogle Scholar
  30. 30.
    Monticelli L, Kandasamy SK, Periole X et al (2008) The MARTINI coarse-grained force field: extension to proteins. J Chem Theory Comput 4:819–834PubMedGoogle Scholar
  31. 31.
    De Jong DH, Singh G, Bennett WFD et al (2013) Improved parameters for the martini coarse-grained protein force field. J Chem Theory Comput 9:687–697PubMedGoogle Scholar
  32. 32.
    Uusitalo JJ, Ingólfsson HI, Akhshi P et al (2015) Martini coarse-grained force field: extension to DNA. J Chem Theory Comput 11:3932–3945PubMedGoogle Scholar
  33. 33.
    Nielsen SO, Lopez CF, Srinivas G, Klein ML (2004) Coarse grain models and the computer simulation of soft materials. J Phys Condens Matter 16:R481–R512Google Scholar
  34. 34.
    Saunders MG, Voth GA (2013) Coarse-graining methods for computational biology. Annu Rev Biophys 42:73–93PubMedGoogle Scholar
  35. 35.
    Zhang Z, Pfaendtner J, Grafmüller A, Voth GA (2009) Defining coarse-grained representations of large biomolecules and biomolecular complexes from elastic network models. Biophys J 97:2327–2337PubMedPubMedCentralGoogle Scholar
  36. 36.
    Noid WG, Chu JW, Ayton GS et al (2008) The multiscale coarse-graining method. I. A rigorous bridge between atomistic and coarse-grained models. J Chem Phys 128:244114PubMedPubMedCentralGoogle Scholar
  37. 37.
    Noid WG, Liu P, Wang Y et al (2008) The multiscale coarse-graining method. II. Numerical implementation for coarse-grained molecular models. J Chem Phys 128:244115PubMedPubMedCentralGoogle Scholar
  38. 38.
    Izvekov S, Voth GA (2005) A multiscale coarse-graining method for biomolecular systems. J Phys Chem B 109:2469–2473PubMedGoogle Scholar
  39. 39.
    Götz AW, Williamson MJ, Xu D et al (2012) Routine microsecond molecular dynamics simulations with AMBER on GPUs. 1. generalized born. J Chem Theory Comput 8:1542–1555PubMedPubMedCentralGoogle Scholar
  40. 40.
    Shaw DE, Bowers KJ, Chow E et al (2009) Millisecond-scale molecular dynamics simulations on Anton. In: Proc Conf High Perform Comput Netw Storage Anal SC 09 1. ACM, New York, NYGoogle Scholar
  41. 41.
    Shaw DE, Grossman JP, Bank JA et al (2014) Anton 2: raising the bar for performance and programmability in a special-purpose molecular dynamics supercomputer. In: Proceedings of the International Conference for High Performance Computing, Networking. Storage and Analysis. IEEE Press, Piscataway, NJ, USA, pp 41–53Google Scholar
  42. 42.
    Marzinek JK, Holdbrook DA, Huber RG et al (2016) Pushing the envelope: dengue viral membrane coaxed into shape by molecular simulations. Structure 24:1410–1420PubMedGoogle Scholar
  43. 43.
    Petrlova J, Hansen FC, van der Plas MJA et al (2017) Aggregation of thrombin-derived C-terminal fragments as a previously undisclosed host defense mechanism. Proc Natl Acad Sci U S A 114:E4213–E4222PubMedPubMedCentralGoogle Scholar
  44. 44.
    Berman HM, Westbrook J, Feng Z et al (2000) The protein data bank. Nucleic Acids Res 28:235–242PubMedPubMedCentralGoogle Scholar
  45. 45.
    Kleywegt GJ, Jones TA (1998) Databases in protein crystallography. Acta Crystallogr Sect D Biol Crystallogr 54:1119–1131Google Scholar
  46. 46.
    Wuthrich K (1986) NMR of proteins and nucleic acids. Wiley, New York, NYGoogle Scholar
  47. 47.
    Cheng Y (2015) Single-particle Cryo-EM at crystallographic resolution. Cell 161:450–457PubMedPubMedCentralGoogle Scholar
  48. 48.
    Wlodawer A, Minor W, Dauter Z, Jaskolski M (2008) Protein crystallography for non‐crystallographers, or how to get the best (but not more) from published macromolecular structures. FEBS J 275:1–21PubMedGoogle Scholar
  49. 49.
    Brown EN, Ramaswamy S (2007) Quality of protein crystal structures. Acta Crystallogr Sect D Biol Crystallogr 63:941–950Google Scholar
  50. 50.
    Hryc CF, Chen DH, Chiu W (2011) Near-atomic resolution cryo-EM for molecular virology. Curr Opin Virol 1:110–117PubMedPubMedCentralGoogle Scholar
  51. 51.
    Carpenter EP, Beis K, Cameron AD, Iwata S (2008) Overcoming the challenges of membrane protein crystallography. Curr Opin Struct Biol 18:581–586PubMedPubMedCentralGoogle Scholar
  52. 52.
    Fiser A, Šali A (2003) Modeller: generation and refinement of homology-based protein structure models. In: Methods in enzymology. Elsevier, Amsterdam, pp 461–491Google Scholar
  53. 53.
    Sali A (2008) MODELLER a program for protein structure modeling release 9v4, r6262. Structure:779–815Google Scholar
  54. 54.
    Shen M, Sali A (2006) Statistical potential for assessment and prediction of protein structures. Protein Sci 15:2507–2524PubMedPubMedCentralGoogle Scholar
  55. 55.
    Wolf MG, Hoefling M, Aponte-Santamaría C et al (2010) g_membed: efficient insertion of a membrane protein into an equilibrated lipid bilayer with minimal perturbation. J Comput Chem 31:2169–2174PubMedGoogle Scholar
  56. 56.
    Jo S, Lim JB, Klauda JB, Im W (2009) CHARMM-GUI membrane builder for mixed bilayers and its application to yeast membranes. Biophys J 97:50–58PubMedPubMedCentralGoogle Scholar
  57. 57.
    Qi Y, Ingólfsson HI, Cheng X et al (2015) CHARMM-GUI martini maker for coarse-grained simulations with the martini force field. J Chem Theory Comput 11:4486–4494PubMedGoogle Scholar
  58. 58.
    Wassenaar TA, Ingólfsson HI, Böckmann RA et al (2015) Computational lipidomics with insane: a versatile tool for generating custom membranes for molecular simulations. J Chem Theory Comput 11:2144–2155PubMedGoogle Scholar
  59. 59.
    Chang R, Ayton GS, Voth GA (2005) Multiscale coupling of mesoscopic- and atomistic-level lipid bilayer simulations. J Chem Phys 122:244716PubMedGoogle Scholar
  60. 60.
    Abraham MJ, Murtola T, Schulz R et al (2015) GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2:19–25Google Scholar
  61. 61.
    Berendsen HJC, van der Spoel D, van Drunen R (1995) GROMACS: a message-passing parallel molecular dynamics implementation. Comput Phys Commun 91:43–56Google Scholar
  62. 62.
    Van Der Spoel D, Lindahl E, Hess B et al (2005) GROMACS: fast, flexible, and free. J Comput Chem 26:1701–1718Google Scholar
  63. 63.
    Phillips JC, Braun R, Wang W et al (2005) Scalable molecular dynamics with NAMD. J Comput Chem 26:1781–1802PubMedPubMedCentralGoogle Scholar
  64. 64.
    Sanbonmatsu KY, Tung CS (2007) High performance computing in biology: multimillion atom simulations of nanoscale systems. J Struct Biol 157:470–480PubMedGoogle Scholar
  65. 65.
    Götz AW, Williamson MJ, Xu D et al (2012) Routine microsecond molecular dynamics simulations with amber - Part I: Generalized born. J Chem Theory Comput 8:1542–1555PubMedPubMedCentralGoogle Scholar
  66. 66.
    Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14(27-28):33–38PubMedGoogle Scholar
  67. 67.
    Ulmschneider MB, Sansom MSP (2001) Amino acid distributions in integral membrane protein structures. Biochim Biophys Acta 1512:1–14PubMedGoogle Scholar
  68. 68.
    Lomize MA, Lomize AL, Pogozheva ID, Mosberg HI (2006) OPM: orientations of proteins in membranes database. Bioinformatics 22:623–625PubMedGoogle Scholar
  69. 69.
    Yesylevskyy SO (2007) ProtSqueeze: simple and effective automated tool for setting up membrane protein simulations. J Chem Inf Model 47:1986–1994PubMedGoogle Scholar
  70. 70.
    Faraldo-Gómez J, Smith G, Sansom M (2002) Setting up and optimization of membrane protein simulations. Eur Biophys J 31:217–227PubMedGoogle Scholar
  71. 71.
    Schmidt TH, Kandt C (2012) LAMBADA and InflateGRO2: efficient membrane alignment and insertion of membrane proteins for molecular dynamics simulations. J Chem Inf Model 52:2657–2669PubMedGoogle Scholar
  72. 72.
    Shirts MR, Mobley DL, Chodera JD (2007) Chapter 4 Alchemical free energy calculations: ready for prime time? Annu Rep Comput Chem 3:41–59Google Scholar
  73. 73.
    Xu C, Gagnon E, Call ME et al (2008) Regulation of T cell receptor activation by dynamic membrane binding of the CD3ε cytoplasmic tyrosine-based motif. Cell 135:702–713PubMedPubMedCentralGoogle Scholar
  74. 74.
    Shirts MR, Pande VS (2005) Solvation free energies of amino acid side chain analogs for common molecular mechanics water models. J Chem Phys 122:134508PubMedGoogle Scholar
  75. 75.
    Jefferys E, Sands ZA, Shi J et al (2015) Alchembed: a computational method for incorporating multiple proteins into complex lipid geometries. J Chem Theory Comput 11:2743–2754PubMedPubMedCentralGoogle Scholar
  76. 76.
    Bond PJ, Cuthbertson JM, Deol SS, Sansom MSP (2004) MD simulations of spontaneous membrane protein/detergent micelle formation. J Am Chem Soc 126:15948–15949PubMedGoogle Scholar
  77. 77.
    Scott KA, Bond PJ, Ivetac A et al (2008) Coarse-grained MD simulations of membrane protein-bilayer self-assembly. Structure 16:621–630PubMedGoogle Scholar
  78. 78.
    Saravanan R, Holdbrook DA, Petrlova J et al (2018) Structural basis for endotoxin neutralization and anti-inflammatory activity of thrombin-derived C-terminal peptides. Nat Commun 9:2762PubMedPubMedCentralGoogle Scholar
  79. 79.
    Stansfeld PJ, Hopkinson R, Ashcroft FM, Sansom MSP (2009) PIP2-binding site in kir channels: definition by multiscale biomolecular simulations. Biochemistry 48:10926–10933PubMedPubMedCentralGoogle Scholar
  80. 80.
    Wassenaar TA, Pluhackova K, Bockmann RA et al (2014) Going backward: a flexible geometric approach to reverse transformation from coarse grained to atomistic models. J Chem Theory Comput 10:676–690PubMedGoogle Scholar
  81. 81.
    Kargas V, Marzinek JK, Holdbrook DA et al (2017) A polar SxxS motif drives assembly of the transmembrane domains of Toll-like receptor 4. Biochim Biophys Acta 1859:2086–2095Google Scholar
  82. 82.
    Irvine WA, Flanagan JU, Allison JR (2018) Computational prediction of amino acids governing protein-membrane interaction for the PIP3 cell signalling system. Structure 27:371PubMedGoogle Scholar
  83. 83.
    Wu EL, Cheng X, Jo S et al (2014) CHARMM-GUI membrane builder toward realistic biological membrane simulations. J Comput Chem 35:1997–2004PubMedPubMedCentralGoogle Scholar
  84. 84.
    Cheng X, Jo S, Lee HS et al (2013) CHARMM-GUI micelle builder for pure/mixed micelle and protein/micelle complex systems. J Chem Inf Model 53:2171–2180PubMedGoogle Scholar
  85. 85.
    Michaud‐Agrawal N, Denning EJ, Woolf TB, Beckstein O (2011) MDAnalysis: a toolkit for the analysis of molecular dynamics simulations. J Comput Chem 32:2319–2327PubMedPubMedCentralGoogle Scholar
  86. 86.
    Huang CC, Couch GS, Pettersen EF, Ferrin TE (1996) Chimera: an extensible molecular modeling application constructed using standard components. In: Pac. Symp. Biocomput. World Scientific, Hackensack, NJ, p 724Google Scholar
  87. 87.
    Ingólfsson HI, Melo MN, van Eerden FJ et al (2014) Lipid organization of the plasma membrane. J Am Chem Soc 136:14554–14559PubMedGoogle Scholar
  88. 88.
    Reddy T, Sansom MSP (2016) The role of the membrane in the structure and biophysical robustness of the dengue virion envelope. Structure 24(3):375–382PubMedPubMedCentralGoogle Scholar
  89. 89.
    Ingólfsson HI, Carpenter TS, Bhatia H et al (2017) Computational lipidomics of the neuronal plasma membrane. Biophys J 113:2271–2280PubMedPubMedCentralGoogle Scholar
  90. 90.
    Koldsø H, Shorthouse D, Hélie J, Sansom MSP (2014) Lipid clustering correlates with membrane curvature as revealed by molecular simulations of complex lipid bilayers. PLoS Comput Biol 10:e1003911PubMedPubMedCentralGoogle Scholar
  91. 91.
    Vollmer W, Blanot D, De Pedro MA (2008) Peptidoglycan structure and architecture. FEMS Microbiol Rev 32:149–167PubMedGoogle Scholar
  92. 92.
    Vollmer W, Seligman SJ (2010) Architecture of peptidoglycan: more data and more models. Trends Microbiol 18:59–66PubMedGoogle Scholar
  93. 93.
    Braun V (1975) Covalent lipoprotein from the outer membrane of Escherichia coli. Biochim Biophys Acta 415:335–377PubMedGoogle Scholar
  94. 94.
    Koebnik R (1995) Proposal for a peptidoglycan‐associating alpha‐helical motif in the C‐terminal regions of some bacterial cell‐surface proteins. Mol Microbiol 16:1269–1270PubMedGoogle Scholar
  95. 95.
    Parsons LM, Lin F, Orban J (2006) Peptidoglycan recognition by Pal, an outer membrane lipoprotein. Biochemistry 45:2122–2128PubMedGoogle Scholar
  96. 96.
    Roujeinikova A (2008) Crystal structure of the cell wall anchor domain of MotB, a stator component of the bacterial flagellar motor: implications for peptidoglycan recognition. Proc Natl Acad Sci 105:10348–10353PubMedGoogle Scholar
  97. 97.
    Gumbart JC, Beeby M, Jensen GJ, Roux B (2014) Escherichia coli peptidoglycan structure and mechanics as predicted by atomic-scale simulations. PLoS Comput Biol 10:e1003475PubMedPubMedCentralGoogle Scholar
  98. 98.
    Samsudin F, Ortiz-Suarez ML, Piggot TJ et al (2016) OmpA: a flexible clamp for bacterial cell wall attachment. Structure 24:2227–2235PubMedGoogle Scholar
  99. 99.
    Samsudin F, Boags A, Piggot TJ, Khalid S (2017) Braun’s lipoprotein facilitates OmpA interaction with the escherichia coli cell wall. Biophys J 113:1496–1504PubMedPubMedCentralGoogle Scholar
  100. 100.
    Cohen EJ, Ferreira JL, Ladinsky MS et al (2017) Nanoscale-length control of the flagellar driveshaft requires hitting the tethered outer membrane. Science 356:197–200PubMedPubMedCentralGoogle Scholar
  101. 101.
    Zheng C, Yang L, Hoopmann MR et al (2011) Cross-linking measurements of in vivo protein complex topologies. Mol Cell Proteomics 10:M110-006841PubMedPubMedCentralGoogle Scholar
  102. 102.
    Marcoux J, Politis A, Rinehart D et al (2014) Mass spectrometry defines the C-terminal dimerization domain and enables modeling of the structure of full-length OmpA. Structure 22:781–790PubMedPubMedCentralGoogle Scholar
  103. 103.
    Doudou S, Burton NA, Henchman RH (2009) Standard free energy of binding from a one-dimensional potential of mean force. J Chem Theory Comput 5:909–918PubMedGoogle Scholar
  104. 104.
    Kumar S, Rosenberg JM, Bouzida D et al (1992) THE weighted histogram analysis method for free-energy calculations on biomolecules. I. The method. J Comput Chem 13:1011–1021Google Scholar
  105. 105.
    Kästner J (2011) Umbrella sampling. Wiley Interdiscip Rev Comput Mol Sci 1:932–942Google Scholar
  106. 106.
    Huber RG, Berglund NA, Kargas V et al (2018) A thermodynamic funnel drives bacterial lipopolysaccharide transfer in the TLR4 pathway. Structure 26:1151–1161PubMedGoogle Scholar
  107. 107.
    Lu Y-C, Yeh W-C, Ohashi PS (2008) LPS/TLR4 signal transduction pathway. Cytokine 42:145–151PubMedGoogle Scholar
  108. 108.
    Kim HM, Park BS, Kim J-I et al (2007) Crystal structure of the TLR4-MD-2 complex with bound endotoxin antagonist eritoran. Cell 130:906–917PubMedGoogle Scholar
  109. 109.
    Brandenburg K, Seydel U (1984) Physical aspects of structure and function of membranes made from lipopolysaccharides and free lipid A. Biochim Biophys Acta 775:225–238Google Scholar
  110. 110.
    Ryu J-K, Kim SJ, Rah S-H et al (2017) Reconstruction of LPS transfer cascade reveals structural determinants within LBP, CD14, and TLR4-MD2 for efficient LPS recognition and transfer. Immunity 46:38. Scholar
  111. 111.
    Juan TS-C, Kelley MJ, Johnson DA et al (1995) Soluble CD14 truncated at amino acid 152 binds lipopolysaccharide (LPS) and enables cellular response to LPS. J Biol Chem 270:1382–1387PubMedGoogle Scholar
  112. 112.
    Kelley SL, Lukk T, Nair SK, Tapping RI (2013) The crystal structure of human soluble CD14 reveals a bent solenoid with a hydrophobic amino-terminal pocket. J Immunol 190:1304–1311PubMedGoogle Scholar
  113. 113.
    Kim J-I, Lee CJ, Jin MS et al (2005) Crystal structure of CD14 and its implications for lipopolysaccharide signaling. J Biol Chem 280:11347–11351PubMedGoogle Scholar
  114. 114.
    Zimmer SM, Liu J, Clayton JL et al (2008) Paclitaxel binding to human and murine MD-2. J Biol Chem 283:27916–27926PubMedPubMedCentralGoogle Scholar
  115. 115.
    Ohto U, Fukase K, Miyake K, Shimizu T (2012) Structural basis of species-specific endotoxin sensing by innate immune receptor TLR4/MD-2. Proc Natl Acad Sci U S A 109:7421–7426PubMedPubMedCentralGoogle Scholar
  116. 116.
    Wang J, Wang W, Kollman PA, Case DA (2001) Antechamber, an accessory software package for molecular mechanical calculations. J Am Chem Soc 222:U403Google Scholar
  117. 117.
    Johnson GT, Autin L, Al-Alusi M et al (2014) cellPACK: a virtual mesoscope to model and visualize structural systems biology. Nat Methods 12:85–91PubMedPubMedCentralGoogle Scholar
  118. 118.
    Sommer B, Dingersen T, Gamroth C et al (2011) CELLmicrocosmos 2.2 MembraneEditor: a modular interactive shape-based software approach to solve heterogeneous membrane packing problems. J Chem Inf Model 51:1165PubMedGoogle Scholar
  119. 119.
    Jo S, Kim T, Iyer VG, Im W (2008) CHARMM-GUI: a web-based graphical user interface for CHARMM. J Comput Chem 29:1859–1865PubMedGoogle Scholar
  120. 120.
    Bovigny C, Tamò G, Lemmin T et al (2015) LipidBuilder: a framework to build realistic models for biological membranes. J Chem Inf Model 55:2491–2499PubMedGoogle Scholar
  121. 121.
    Durrant JD, Amaro RE (2014) LipidWrapper: an algorithm for generating large-scale membrane models of arbitrary geometry. PLoS Comput Biol 10:e1003720PubMedPubMedCentralGoogle Scholar
  122. 122.
    Ghahremanpour MM, Arab SS, Aghazadeh SB et al (2013) MemBuilder: a web-based graphical interface to build heterogeneously mixed membrane bilayers for the GROMACS biomolecular simulation program. Bioinformatics 30:439–441PubMedGoogle Scholar
  123. 123.
    Knight CJ, Hub JS (2015) MemGen: a general web server for the setup of lipid membrane simulation systems: Fig. 1. Bioinformatics 31:2897–2899PubMedGoogle Scholar
  124. 124.
    Martinez L, Andrade R, Birgin EG, Martínez JM (2009) PACKMOL: a package for building initial configurations for molecular dynamics simulations. J Comput Chem 30:2157–2164PubMedGoogle Scholar
  125. 125.
    Vergara-Jaque A, Fenollar-Ferrer C, Kaufmann D, Forrest LR (2015) Repeat-swap homology modeling of secondary active transporters: updated protocol and prediction of elevator-type mechanisms. Front Pharmacol 6:183PubMedPubMedCentralGoogle Scholar
  126. 126.
    Darden T, York D, Pedersen L (1993) Particle mesh Ewald: an N⋅log(N) method for Ewald sums in large systems. J Chem Phys 98:10089–10092Google Scholar
  127. 127.
    Patra M, Karttunen M, Hyvönen MT et al (2003) Molecular dynamics simulations of lipid bilayers: major artifacts due to truncating electrostatic interactions. Biophys J 84:3636–3645PubMedPubMedCentralGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Bioinformatics Institute (BII)Agency for Science, Technology and Research (A*STAR)SingaporeSingapore
  2. 2.Biosciences and Biotechnology Division, Physical and Life Sciences DirectorateLawrence Livermore National LaboratoryLivermoreUSA
  3. 3.Department of ChemistryUniversity of CambridgeCambridgeUK
  4. 4.Centre for Theoretical Chemistry and Physics, Institute of Natural and Mathematical SciencesMassey UniversityAucklandNew Zealand
  5. 5.School of ChemistryUniversity of SouthamptonSouthamptonUK
  6. 6.School of Biological Sciences and Maurice Wilkins Centre for Molecular BiodiscoveryThe University of AucklandAucklandNew Zealand
  7. 7.Biomolecular Interaction CentreUniversity of CanterburyChristchurchNew Zealand
  8. 8.Department of Biological SciencesNational University of SingaporeSingaporeSingapore

Personalised recommendations