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GPCRs: What Can We Learn from Molecular Dynamics Simulations?

Protocol
First Online:
Part of the Methods in Molecular Biology book series (MIMB, volume 1705)

Abstract

Advances in the structural biology of G-protein Coupled Receptors have resulted in a significant step forward in our understanding of how this important class of drug targets function at the molecular level. However, it has also become apparent that they are very dynamic molecules, and moreover, that the underlying dynamics is crucial in shaping the response to different ligands. Molecular dynamics simulations can provide unique insight into the dynamic properties of GPCRs in a way that is complementary to many experimental approaches. In this chapter, we describe progress in three distinct areas that are particularly difficult to study with other techniques: atomic level investigation of the conformational changes that occur when moving between the various states that GPCRs can exist in, the pathways that ligands adopt during binding/unbinding events and finally, the influence of lipids on the conformational dynamics of GPCRs.

Key words

Simulation Ligand binding Computational Lipid Metadynamics Enhanced sampling 
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Notes

Acknowledgments

NV funded by the MRC/EPSRC and Pfizer via the Systems Approaches to Biomedical Sciences Doctoral Training Centre (EP/G037280/1). Research in the laboratory of PCB is supported by the BBSRC and MRC. G.H. is funded by a fellowship from the MRC.

References

  1. 1.
    Adcock SA, Mccammon JA (2006) Molecular dynamics: survey of methods for simulating the activity of proteins. Chem Rev 106:1589–1615PubMedPubMedCentralCrossRefGoogle Scholar
  2. 2.
    Karplus M, McCammon JA (2002) Molecular dynamics simulations of biomolecules. Nat Struct Biol 9:646–652PubMedCrossRefGoogle Scholar
  3. 3.
    McRobb FM, Negri A, Beuming T, Sherman W (2016) Molecular dynamics techniques for modeling G protein-coupled receptors. Curr Opin Pharm 30:69–75. https://doi.org/10.1016/j.coph.2016.07.001 CrossRefGoogle Scholar
  4. 4.
    Filmore D (2004) It’s a GPCR world. J Modern D Discov 7:24–27Google Scholar
  5. 5.
    Salon JA, Lodowski DT, Palczewski K (2011) The significance of G protein-coupled receptor crystallography for drug discovery. Pharm Rev 63:901–937. https://doi.org/10.1124/pr.110.003350.901 PubMedPubMedCentralCrossRefGoogle Scholar
  6. 6.
    Martins SMA, Trabuco JRG, Monteiro GA, Prazeres DM (2012) GPCR screening and drug discovery: challenges and latest trends. Eur Pharm Rev 17Google Scholar
  7. 7.
    Ciancetta A, Sabbadin D, Federico S, Spalluto G, Moro S (2015) Advances in computational techniques to study GPCR–ligand recognition. Trends Pharm Sci 36(12):878–890. https://doi.org/10.1016/j.tips.2015.08.006 PubMedCrossRefGoogle Scholar
  8. 8.
    Johnston JM, Filizola M (2011) Showcasing modern molecular dynamics simulations of membrane proteins through G protein-coupled receptors. Curr Opin Struc Biol 21:552–558. https://doi.org/10.1016/j.sbi.2011.06.008 CrossRefGoogle Scholar
  9. 9.
    Fredriksson R, Lagerström MC, Lundin L-G, Schiöth HB (2003) The G-protein-coupled receptors in the human genome form five main families. Phylogenetic analysis, paralogon groups, and fingerprints. Mol Endocrinol 63:1256–1272. https://doi.org/10.1124/mol.63.6.1256 Google Scholar
  10. 10.
    Hill SJ (2006) G-protein-coupled receptors: past, present and future. Brit J Pharm 147:S27–S37. https://doi.org/10.1038/sj.bjp.0706455 CrossRefGoogle Scholar
  11. 11.
    Xiang J, Chun E, Liu C, Jing L, Al-Sahouri Z, Zhu L, Liu W (2016) Successful strategies to determine high-resolution structures of GPCRs. Trends Pharm Sci (in press). https://doi.org/10.1016/j.tips.2016.09.009.
  12. 12.
    Strahs D, Weinstein H (1997) Comparative modeling and molecular dynamics studies of the delta, kappa and mu opioid receptors. Protein Eng 10(9):1019–1038PubMedCrossRefGoogle Scholar
  13. 13.
    Scheer A, Fanelli F, Costa T, Benedetti PGD, Cotecchia S (1996) Constitutively active mutants of the a1B-adrenergic receptor: role of highly conserved polar amino acids in receptor activation. EMBO J 15:3566–3578PubMedPubMedCentralGoogle Scholar
  14. 14.
    Czaplewski C, Kazmierkiewicz R, Ciarkowski J (1998) Molecular modeling of the human vasopressin V2 receptor/agonist complex. J Comput Aided Mol Des 12:275–287PubMedCrossRefGoogle Scholar
  15. 15.
    Sansom MSP, Weinstein H (2000) Hinges, swivels and switches: the role of prolines in signalling via transmembrane alpha helices. TIPS 21:445–451PubMedGoogle Scholar
  16. 16.
    Palczewski K, Kumasaka T, Hori T, Behnke CA, Motoshima H, Fox BA, Le Trong I, Teller DC, Okada T, Stenkamp RE, Yamamoto M, Miyano M (2000) Crystal structure of rhodopsin: a G protein-coupled receptor. Science 289:739–745PubMedCrossRefGoogle Scholar
  17. 17.
    Rohrig UF, Guidoni L, Rothlisberger U (2002) Early steps of the intramolecular signal transduction in rhodopsin explored by molecular dynamics simulations. Biochemistry 41:10799–10809PubMedCrossRefGoogle Scholar
  18. 18.
    Crozier PS, Stevens MJ, Forrest LR, Woolf TB (2003) Molecular dynamics simulation of dark-adapted rhodopsin in an explicit membrane bilayer: coupling between local retinal and larger scale conformational change. J Mol Biol 333:493–514. https://doi.org/10.1016/j.jmb.2003.08.045 PubMedCrossRefGoogle Scholar
  19. 19.
    Huber T, Botelho AV, Beyer K, Brown MF (2004) Membrane model for the G-protein-coupled receptor rhodopsin : hydrophobic interface and dynamical structure. Biophys J 86:2078–2100. https://doi.org/10.1016/S0006-3495(04)74268-X PubMedPubMedCentralCrossRefGoogle Scholar
  20. 20.
    Pitman MC, Grossfield A, Suits F, Feller SE (2005) Role of cholesterol and polyunsaturated chains in lipid - protein interactions: molecular dynamics simulation of rhodopsin in a realistic membrane environment. J Am Chem Soc 127:4576–4577PubMedCrossRefGoogle Scholar
  21. 21.
    Seeber M, Benedetti PGD, Fanelli F (2003) Molecular dynamics simulations of the ligand-induced chemical information transfer in the 5-HT1A receptor. J Chem Inf Comp Sci 43:1520–1531CrossRefGoogle Scholar
  22. 22.
    Huang X, Shen J, Cui M, Shen L, Luo X, Ling K, Pei G, Jiang H, Chen K (2003) Molecular dynamics simulations on SDF-1α: binding with CXCR4 receptor. Biophys J 84:171–184. https://doi.org/10.1016/S0006-3495(03)74840-1 PubMedPubMedCentralCrossRefGoogle Scholar
  23. 23.
    Zhang Y, Sham YY, Rajamani R, Gao J, Portoguese PS (2005) Homology modeling and molecular dynamics simulations of the mu opioid receptor in a membrane – aqueous system. Chem Bio Chem 6:853–859. https://doi.org/10.1002/cbic.200400207 PubMedCrossRefGoogle Scholar
  24. 24.
    Iadanza M, Holtje M, Ronsisvalle G, Holtje H-D (2002) k-Opioid receptor model in a phospholipid bilayer: molecular dynamics simulation. J Med Chem 45:4838–4846PubMedCrossRefGoogle Scholar
  25. 25.
    Fenalti G, Giguere PM, Katritch V, Huang X-P, Thompson AA, Cherezov V, Roth BL, Stevens RC (2014) Molecular control of [dgr]-opioid receptor signalling. Nature 506(7487):191–196. https://doi.org/10.1038/nature12944 PubMedPubMedCentralCrossRefGoogle Scholar
  26. 26.
    Kang Y, Zhou XE, Gao X, He Y, Liu W, Ishchenko A, Barty A, White TA, Yefanov O, Han GW, Xu Q, Waal PWD, Ke J, Tan MHE, Zhang C, Moeller A, West GM, Pascal BD, Eps NV, Caro LN, Vishnivetskiy SA, Lee RJ (2015) Crystal structure of rhodopsin bound to arrestin by femtosecond X-ray laser. Nature 523:561–567. https://doi.org/10.1038/nature14656 PubMedPubMedCentralCrossRefGoogle Scholar
  27. 27.
    Huang W, Manglik A, Venkatakrishnan AJ, Laeremans T, Feinberg EN, Sanborn AL, Kato HE, Livingston KE, Thorsen TS, Kling RC, Granier S, Gmeiner P, Husbands SM, Traynor JR, Weis WI, Steyaert J, Dror RO, Kobilka BK (2015) Structural insights into μ-opioid receptor activation. Nature 524(7565):315–321. https://doi.org/10.1038/nature14886 PubMedPubMedCentralCrossRefGoogle Scholar
  28. 28.
    Rasmussen SGF, DeVree BT, Zou Y, Kruse AC, Chung KY, Kobilka TS, Thian FS, Chae PS, Pardon E, Calinski D, Mathiesen JM, Shah STA, Lyons JA, Caffrey M, Gellman SH, Steyaert J, Skiniotis G, Weis WI, Sunahara RK, Kobilka BK (2011) Crystal structure of the β2 adrenergic receptor-Gs protein complex. Nature 477(7366):549–555PubMedPubMedCentralCrossRefGoogle Scholar
  29. 29.
    Kohlhoff KJ, Shukla D, Lawrenz M, Bowman GR, Konerding DE, Belov D, Altman RB, Pande VS (2014) Cloud-based simulations on Google Exacycle reveal ligand modulation of GPCR activation pathways. Nat Chem 6(1):15–21. https://doi.org/10.1038/nchem.1821 PubMedCrossRefGoogle Scholar
  30. 30.
    Prioleau C, Visiers I, Ebersole BJ, Weinstein H, Sealfon SC (2002) Conserved helix 7 tyrosine acts as a multistate conformational switch in the 5HT2C receptor. Identification of a novel "locked-on" phenotype and double revertant mutations. J Biol Chem 277:36577–36584. https://doi.org/10.1074/jbc.M206223200 PubMedCrossRefGoogle Scholar
  31. 31.
    Sealfon SC, Chi L, Ebersole BJ, Rodic V, Zhang D, Ballesteros J, Weinstein H (1995) Related contribution of specific heix 2 and 7 residues to conformational activation of the serotonin 5-HT2A receptor. J Bio Chem 270:16683–16688. https://doi.org/10.1074/jbc.270.28.16683 CrossRefGoogle Scholar
  32. 32.
    Tiburu EK, Bowman AL, Struppe JO, Janero DR, Avraham HK, Makriyannis A (2009) Solid-state NMR and molecular dynamics characterization of cannabinoid receptor-1 (CB1) helix 7 conformational plasticity in model membranes. Biochim Biophys Acta 1788:1159–1167. https://doi.org/10.1016/j.bbamem.2009.02.002 PubMedPubMedCentralCrossRefGoogle Scholar
  33. 33.
    Venkatakrishnan AJ, Deupi X, Lebon G, Tate CG, Schertler GF, Babu MM (2013) Molecular signatures of G-protein-coupled receptors. Nature 494(7436):185–194PubMedCrossRefGoogle Scholar
  34. 34.
    Negri A et al (2013) Discovery of a novel selective kappa-opioid receptor agonist using crystal structure-based virtual screening. J Chem Inf Model 53(3):521–526. https://doi.org/10.1021/ci400019t PubMedPubMedCentralCrossRefGoogle Scholar
  35. 35.
    Weiss DR et al (2013) Conformation guides molecular efficacy in docking screens of activated β-2 adrenergic G protein coupled receptor. ACS Chem Biol 8(5):1018–1026. https://doi.org/10.1021/cb400103f PubMedPubMedCentralCrossRefGoogle Scholar
  36. 36.
    Manglik A et al (2016) Structure-based discovery of opioid analgesics with reduced side effects. Nature 537(7619):185–190. https://doi.org/10.1038/nature19112 PubMedPubMedCentralCrossRefGoogle Scholar
  37. 37.
    Lebon G et al (2012) Agonist-bound structures of G protein-coupled receptors. Curr Opin Struct Biol 22(4):482–490. https://doi.org/10.1016/j.sbi.2012.03.007 PubMedCrossRefGoogle Scholar
  38. 38.
    Verdonk ML et al (2003) Improved protein-ligand docking using GOLD. Proteins 52(4):609–623. https://doi.org/10.1002/prot.10465 PubMedCrossRefGoogle Scholar
  39. 39.
    Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455–461. https://doi.org/10.1002/jcc.21334. PubMedPubMedCentralGoogle Scholar
  40. 40.
    Friesner RA et al (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein−ligand complexes. J Med Chem 49(21):6177–6196. https://doi.org/10.1021/jm051256o PubMedCrossRefGoogle Scholar
  41. 41.
    Chen YC (2015) Beware of docking! Trends Pharmacol Sci 36(2):78–95. https://doi.org/10.1016/j.tips.2014.12.001 PubMedCrossRefGoogle Scholar
  42. 42.
    Amaro RE et al (2008) An improved relaxed complex scheme for receptor flexibility in computer-aided drug design. J Comput Aided Mol Des 22(9):693–705. https://doi.org/10.1007/s10822-007-9159-2 PubMedPubMedCentralCrossRefGoogle Scholar
  43. 43.
    Gater DL, Saurel O, Iordanov I, Liu W, Cherezov V, Milon A (2014) Two classes of cholesterol binding sites for the β2AR revealed by thermostability and NMR. Biophys J 107:2305–2312. https://doi.org/10.1016/j.bpj.2014.10.011 PubMedPubMedCentralCrossRefGoogle Scholar
  44. 44.
    Harvey MJ, De Fabritiis G (2012) High-throughput molecular dynamics: the powerful new tool for drug discovery. Drug Discov Today 17:1059–1062. https://doi.org/10.1016/j.drudis.2012.03.017 PubMedCrossRefGoogle Scholar
  45. 45.
    Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, Lindahl E (2015) GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2:19–25. https://doi.org/10.1016/j.softx.2015.06.001 CrossRefGoogle Scholar
  46. 46.
    Case DA, Betz RM, Cerutti DS, Cheatham TE III, Darden TA, Duke RE, Giese TJ, Gohlke H, Goetz AW, Homeyer N, Izadi S, Janowski P, Kaus J, Kovalenko A, Lee TS, LeGrand S, Li P, Lin C, Luchko T, Luo R, Madej BD, Mermelstein D, Merz KM, Monard G, Nguyen H, Nguyen HT, Omelyan I, Onufriev A, Roe DR, Roitberg A, Sagui C, Simmerling CL, Botello-Smith WM, Swails J, Walker RC, Wang J, Wolf RM, Wu X, Xiao L, Kollman PA (2016) AMBER 2016. University of California, San FranciscoGoogle Scholar
  47. 47.
    Liu W, Schmidt B, Voss G, Müller-Wittig W (2008) Accelerating molecular dynamics simulations using graphics processing units with CUDA. Compu Phys Comm 179:634–641. https://doi.org/10.1016/j.cpc.2008.05.008 CrossRefGoogle Scholar
  48. 48.
    Bowers KJ, Chow E, Xu H, Dror RO, Eastwood MP, Gregersen BA, Klepeis JL, Kolossvary I, Moraes MA, Sacerdoti FD, Salmon JK, Shan Y, Shaw DE (2006) Scalable algorithms for molecular dynamics simulations on commodity clusters. In: Proceedings of the 2006 ACMI/IEEE conference on supercomputing. ACM Press, New yorkGoogle Scholar
  49. 49.
    Klepeis JL, Lindorff-Larsen K, Dror RO, Shaw DE (2009) Long-timescale molecular dynamics simulations of protein structure and function. Curr Opin Struct Biol 19(2):120–127PubMedCrossRefGoogle Scholar
  50. 50.
    Shaw DE et al (2008) Anton, a special-purpose machine for molecular dynamics simulation. Comm ACM 51(7):91–97. https://doi.org/10.1145/1364782.1364802 CrossRefGoogle Scholar
  51. 51.
    Rosenbaum DM, Zhang C, Lyons JA, Holl R, Aragao D, Arlow DH, Rasmussen SGF, Choi H-j, Devree BT, Sunahara RK, Chae PS, Gellman SH, Dror RO, Shaw DE, Weis WI, Caffrey M, Gmeiner P, Kobilka BK (2011) Structure and function of an irreversible agonist-β2 adrenoceptor complex. Nature 469:236–240. https://doi.org/10.1038/nature09665 PubMedPubMedCentralCrossRefGoogle Scholar
  52. 52.
    Dror RO, Arlow DH, Maragakis P, Mildorf TJ, Pan AC, Xu H, Borhani DW, Shaw DE (2011) Activation mechanism of the β2-adrenergic receptor. Proc Natl Acad Sci U S A 108(46):18684–18689PubMedPubMedCentralCrossRefGoogle Scholar
  53. 53.
    Rasmussen SGF, Choi H-J, Fung JJ, Pardon E, Casarosa P, Chae PS, Devree BT, Rosenbaum DM, Thian FS, Kobilka TS, Schnapp A, Konetzki I, Sunahara RK, Gellman SH, Pautsch A, Steyaert J, Weis WI, Kobilka BK (2011) Structure of a nanobody-stabilized active state of the β(2) adrenoceptor. Nature 469:175–180. https://doi.org/10.1038/nature09648 PubMedPubMedCentralCrossRefGoogle Scholar
  54. 54.
    Cherezov V, Rosenbaum DM, Hanson MA, Rasmussen SGF, Thian FS, Kobilka TS, Choi H-J, Kuhn P, Weis WI, Kobilka BK, Stevens RC (2007) High-resolution crystal structure of an engineered human β2-adrenergic G protein–coupled receptor. Science 318(5854):1258–1265PubMedPubMedCentralCrossRefGoogle Scholar
  55. 55.
    Marti-Solano M, Sanz F, Pastor M, Selent J (2014) A dynamic view of molecular switch behavior at serotonin receptors: implications for functional selectivity. PLoS One 9:e109312. https://doi.org/10.1371/journal.pone.0109312 PubMedPubMedCentralCrossRefGoogle Scholar
  56. 56.
    Hellerstein JL, Kohlhoff KJ, Konerding DE (2012) Science in the cloud: accelerating discovery in the 21st century. IEEE Internet Comput 16(4):64–68. https://doi.org/10.1109/MIC.2012.87 CrossRefGoogle Scholar
  57. 57.
    Nygaard R, Frimurer TM, Holst B, Rosenkilde MM, Schwartz TW (2009) Ligand binding and micro-switches in 7TM receptor structures. Trends Pharm Sci 30:249–259. https://doi.org/10.1016/j.tips.2009.02.006 PubMedCrossRefGoogle Scholar
  58. 58.
    Schneider S, Provasi D, Filizola M (2016) How Oliceridine (TRV-130) binds and stabilizes a μ-opioid receptor conformational state that selectively triggers G protein-signaling pathways. Biochemistry 55:6456–6466. https://doi.org/10.1021/acs.biochem.6b00948 PubMedCrossRefGoogle Scholar
  59. 59.
    Rodríguez-Espigares I, Kaczor AA, Selent J (2016) In silico exploration of the conformational universe of GPCRs. Mol Inform 35:227–237. https://doi.org/10.1002/minf.201600012 PubMedCrossRefGoogle Scholar
  60. 60.
    Pierce LCT, Salomon-Ferrer R, De Oliveira C AF, JA MC, Walker RC (2012) Routine access to millisecond time scale events with accelerated molecular dynamics. J Chem Theory Comput 8:2997–3002PubMedPubMedCentralCrossRefGoogle Scholar
  61. 61.
    Bussi G, Laio A, Parrinello M (2006) Equilibrium free energies from nonequilibrium metadynamics. Phys Rev Lett 96(9):090601PubMedCrossRefGoogle Scholar
  62. 62.
    Leone V, Marinelli F, Carloni P, Parrinello M (2010) Targeting biomolecular flexibility with metadynamics. Curr Opin Struc Biol 20:148–154. https://doi.org/10.1016/j.sbi.2010.01.011 CrossRefGoogle Scholar
  63. 63.
    Schlitter J, Engels M, Krüger P, Jacoby E, Wollmer A (1993) Targeted molecular dynamics simulation of conformational change-application to the T ↔ R transition in insulin. Mol Sim 10:291–308. https://doi.org/10.1080/08927029308022170 CrossRefGoogle Scholar
  64. 64.
    Schlitter J, Engels M, Krueger P (1994) Targeted molecular dynamics: a new approach for searching pathways of conformational transitions. J Mol Graph 1994:84–89CrossRefGoogle Scholar
  65. 65.
    Huang H, Ozkirimli E, Post CB (2009) Comparison of three perturbation molecular dynamics methods for modeling conformational transitions. J Chem Theor Comput 5:1304–1314. https://doi.org/10.1021/ct9000153 CrossRefGoogle Scholar
  66. 66.
    Grubmuller H, Heymann B, Tavan P (1996) Ligand binding: molecular mechanics calculation of the streptavidin-biotin rupture force. Science 271:997–999PubMedCrossRefGoogle Scholar
  67. 67.
    Marchi M, Ballone P (1999) Adiabatic bias molecular dynamics : a method to navigate the conformational space of complex molecular systems. J Chem Phys 110:3697–3702. https://doi.org/10.1063/1.478259 CrossRefGoogle Scholar
  68. 68.
    Paci E, Karplus M (1999) Forced unfolding of fibronectin type 3 modules: an analysis by biased molecular dynamics simulations. J Mol Bio 288:441–459CrossRefGoogle Scholar
  69. 69.
    Hamelberg D, De Oliveira CAF, McCammon JA (2007) Sampling of slow diffusive conformational transitions with accelerated molecular dynamics. J Chem Phys 127:155102. https://doi.org/10.1063/1.2789432 PubMedCrossRefGoogle Scholar
  70. 70.
    Markwick PRL, McCammon JA (2011) Studying functional dynamics in bio-molecules using accelerated molecular dynamics. Phys Chem Chem Phys 13:20053–20065. https://doi.org/10.1039/c1cp22100k PubMedCrossRefGoogle Scholar
  71. 71.
    Miao Y, Nichols SE, Gasper PM, Metzger VT, McCammon JA (2013) Activation and dynamic network of the M2 muscarinic receptor. Proc Natl Acad Sci U S A 110(27):10982–10987. https://doi.org/10.1073/pnas.1309755110 PubMedPubMedCentralCrossRefGoogle Scholar
  72. 72.
    Bonomi M, Branduardi D, Bussi G, Camilloni C, Provasi D, Raiteri P, Donadio D, Marinelli F, Pietrucci F, Broglia RA, Parrinello M (2009) PLUMED: a portable plugin for free-energy calculations with molecular dynamics. Comput Phys Comm 180(10):1961–1972. https://doi.org/10.1016/j.cpc.2009.05.011 CrossRefGoogle Scholar
  73. 73.
    Provasi D, Artacho MC, Negri A, Mobarec JC, Filizola M (2011) Ligand-induced modulation of the free-energy landscape of G protein-coupled receptors explored by adaptive biasing techniques. PLoS Comput Biol 7:e1002193. https://doi.org/10.1371/journal.pcbi.1002193 PubMedPubMedCentralCrossRefGoogle Scholar
  74. 74.
    Bonomi M, Barducci A, Parrinello M (2009) Reconstructing the equilibrium boltzmann distribution from well-tempered metadynamics. J Comput Chem 30:1615–1621. https://doi.org/10.1002/jcc PubMedCrossRefGoogle Scholar
  75. 75.
    Provasi D, Filizola M (2010) Putative active states of a prototypic g-protein-coupled receptor from biased molecular dynamics. Biophys J 98:2347–2355. https://doi.org/10.1016/j.bpj.2010.01.047 PubMedPubMedCentralCrossRefGoogle Scholar
  76. 76.
    Barducci A, Bussi G, Parrinello M (2008) Well-tempered metadynamics: a smoothly converging and tunable free-energy method. Phys Rev Lett 100(2):020603PubMedCrossRefGoogle Scholar
  77. 77.
    Ballesteros JA, Jensen AD, Liapakis G, Rasmussen SGF, Shi L, Gether U, Javitch JA (2001) Activation of the β2 -adrenergic receptor involves disruption of an ionic lock between the cytoplasmic ends of transmembrane segments 3 and 6. J Biol Chem 276:29171–29177. https://doi.org/10.1074/jbc.M103747200 PubMedCrossRefGoogle Scholar
  78. 78.
    Shi L, Liapakis G, Xu R, Guarnieri F, Ballesteros JA, Javitch JA (2002) β2 adrenergic receptor activation: modulation of the proline kink in transmembrane 6 by a rotamer toggle switch. J Biol Chem 277:40989–40996. https://doi.org/10.1074/jbc.M206801200 PubMedCrossRefGoogle Scholar
  79. 79.
    Li J, Jonsson AL, Beuming T, Shelley JC, Voth GA (2013) Ligand-dependent activation and deactivation of the human adenosine A2A receptor. J Am Chem Soc 135:8749–8759PubMedPubMedCentralCrossRefGoogle Scholar
  80. 80.
    Zia SR, Gaspari R, Decherchi S, Rocchia W (2016) Probing hydration patterns in class-a GPCRs via biased MD: the A2A receptor. J Chem Theor Comput 12:6049–6061. https://doi.org/10.1021/acs.jctc.6b00475 CrossRefGoogle Scholar
  81. 81.
    Liu W, Chun E, Thompson AA, Chubukov P, Xu F, Katritch V, Han GW, Roth CB, Heitman LH, Ijzerman AP, Cherezov V, Stevens RC (2012) Structural basis for allosteric regulation of GPCRs by sodium ions. Science 337(6091):232–236PubMedPubMedCentralCrossRefGoogle Scholar
  82. 82.
    Angel TE, Chance MR, Palczewski K (2009) Conserved waters mediate structural and functional activation of family a (rhodopsin-like) G protein-coupled receptors. Proc Natl Acad Sci U S A 106:8555–8560PubMedPubMedCentralCrossRefGoogle Scholar
  83. 83.
    Kaszuba K, Róg T, Bryl K, Vattulainen I, Karttunen M (2010) Molecular dynamics simulations reveal fundamental role of water as factor determining affinity of binding of β-blocker nebivolol to β-adrenergic receptor. J Phys Chem B 114:8374–8386. https://doi.org/10.1021/jp909971f PubMedCrossRefGoogle Scholar
  84. 84.
    Ross GA, Morris GM, Biggin PC (2012) Rapid and accurate prediction and scoring of water molecules in protein binding sites. PLoS One 7(3):e32036PubMedPubMedCentralCrossRefGoogle Scholar
  85. 85.
    Ross GA, Bodnarchuk MS, Essex JW (2015) Water sites, networks, and free energies with grand canonical Monte Carlo. J Am Chem Soc 137(47):14930–14943. https://doi.org/10.1021/jacs.5b07940 PubMedCrossRefGoogle Scholar
  86. 86.
    Gerogiokas G, Southey MWY, Mazanetz MP, Hefeitz A, Bodkin M, Law RJ, Michel J (2015) Evaluation of water displacement energetics in protein binding sites with grid cell theory. Phys Chem Chem Phys 17:8416–8426. https://doi.org/10.1039/C4CP05572A PubMedCrossRefGoogle Scholar
  87. 87.
    Gerogiokas G, Southey MWY, Mazanetz MP, Heifetz A, Bodkin M, Law RJ, Henchman RH, Michel J (2016) Assessment of hydration thermodynamics at protein interfaces with grid cell theory. J Phys Chem B 120:10442–10452. https://doi.org/10.1021/acs.jpcb.6b07993 PubMedCrossRefGoogle Scholar
  88. 88.
    Michel J, Tirado-Rives J, Jorgensen WL (2009) Prediction of the water content in protein binding sites. J Phys Chem 113:13337–13346. https://doi.org/10.1021/jp9047456 CrossRefGoogle Scholar
  89. 89.
    Sciabola S, Stanton RV, Mills JE, Flocco MM, Baroni M, Cruciani G, Perruccio F, Mason JS (2010) High-throughput virtual screening of proteins using GRID molecular interaction fields. J Chem Inf Model 50:155–169PubMedCrossRefGoogle Scholar
  90. 90.
    Uehara S, Tanaka S (2016) AutoDock-GIST: incorporating thermodynamics of active-site water into scoring function for accurate protein-ligand docking. Molecules 21:E1604. https://doi.org/10.3390/molecules21111604 PubMedCrossRefGoogle Scholar
  91. 91.
    Dror RO, Pan AC, Arlow DH, Borhani DW, Maragakis P, Shan Y, Xu H, Shaw DE (2011) Pathway and mechanism of drug binding to G-protein-coupled receptors. Proc Natl Acad Sci U S A 108(32):13118–13123PubMedPubMedCentralCrossRefGoogle Scholar
  92. 92.
    Guo D, Hillger JM, Ijzerman AP, Heitman LH (2014) Drug-target residence time—a case for G protein-coupled receptors. Med Res Rev 34(4):856–892. https://doi.org/10.1002/med.21307 PubMedCrossRefGoogle Scholar
  93. 93.
    Lu H, Tonge PJ (2010) Drug-target residence time: critical information for lead optimization. Curr Opin Chem Biol 14:467–474. https://doi.org/10.1016/j.cbpa.2010.06.176 PubMedPubMedCentralCrossRefGoogle Scholar
  94. 94.
    Swinney DC (2008) Applications of binding kinetics to drug discovery: translation of binding mechanisms to clinically differentiated therapeutic responses. Pharmaceut Med 22:23–34. https://doi.org/10.1007/BF03256679 Google Scholar
  95. 95.
    Swinney DC (2009) The role of binding kinetics in therapeutically useful drug action. Curr Opin Drug Discov Devel 12:31–39PubMedGoogle Scholar
  96. 96.
    Swinney DC, Haubrich BA, Van Liefde I, Vauquelin G (2015) The role of binding kinetics in GPCR drug discovery. Curr Top Med Chem 15:2504–2522PubMedCrossRefGoogle Scholar
  97. 97.
    Warne T, Serrano-Vega MJ, Baker JG, Moukhametzianov R, Edwards PC, Henderson R, Leslie AGW, Tate CG, Schertler GFX (2008) Structure of a β1-adrenergic G-protein-coupled receptor. Nature 454:486–491. https://doi.org/10.1038/nature07101 PubMedPubMedCentralCrossRefGoogle Scholar
  98. 98.
    Sabbadin D, Moro S (2014) Supervised molecular dynamics (SuMD) as a helpful tool to depict GPCR-ligand recognition pathway in a nanosecond time scale. J Chem Inf Model 54:372–376. https://doi.org/10.1021/ci400766b PubMedCrossRefGoogle Scholar
  99. 99.
    Jaakola VP, Griffith MT, Hanson MA, Cherezov V, Chien EY, Lane JR, Ijzerman AP, Stevens RC (2008) The 2.6 angstrom crystal structure of a human A2A adenosine receptor bound to an antagonist. Science 322(5905):1211–1217. https://doi.org/10.1126/science.1164772 PubMedPubMedCentralCrossRefGoogle Scholar
  100. 100.
    Congreve M, Andrews SP, Dore AS, Hollenstein K, Hurrell E, Langmead CJ, Mason JS, Ng IW, Tehan B, Zhukov A, Weir M, Marshall FH (2012) Discovery of 1,2,4-triazine derivatives as adenosine A2A antagonists using structure based drug design. J Med Chem 55:1898–1903PubMedPubMedCentralCrossRefGoogle Scholar
  101. 101.
    Doré AS, Robertson N, Errey JC, Ng I, Hollenstein K, Tehan B, Hurrell E, Bennett K, Congreve M, Magnani F, Tate CG, Weir M, Marshall FH (2011) Structure of the adenosine a 2A receptor in complex with ZM241385 and the xanthines XAC and caffeine. Structure 19:1283–1293. https://doi.org/10.1016/j.str.2011.06.014 PubMedPubMedCentralCrossRefGoogle Scholar
  102. 102.
    Deganutti G, Cuzzolin A, Ciancetta A, Moro S (2015) Understanding allosteric interactions in G protein-coupled receptors using supervised molecular dynamics: a prototype study analysing the human A3 adenosine receptor positive allosteric modulator. Bioorg Med Chem 23:4065–4071. https://doi.org/10.1016/j.bmc.2015.03.039 PubMedCrossRefGoogle Scholar
  103. 103.
    Provasi D, Bortolato A, Filizola M (2009) Exploring molecular mechanisms of ligand recognition by opioid receptors with metadynamics. Biochemistry 48:10020–10029. https://doi.org/10.1021/bi901494n PubMedPubMedCentralCrossRefGoogle Scholar
  104. 104.
    Dickson CJ, Hornak V, Velez-Vega C, McKay DJJ, Reilly J, Sandham DA, Shaw D, Fairhurst RA, Charlton SJ, Sykes DA, Pearlstein RA, Duca JS (2016) Uncoupling the structure-activity relationships of β2 adrenergic receptor ligands from membrane binding. J Med Chem 59:5780–5789. https://doi.org/10.1021/acs.jmedchem.6b00358 PubMedCrossRefGoogle Scholar
  105. 105.
    Hedger G, Sansom MSP (2016) Lipid interaction sites on channels, transporters and receptors: recent insights from molecular dynamics simulations. Biochim Biophys Acta 1858(10):2390–2400. https://doi.org/10.1016/j.bbamem.2016.02.037 PubMedPubMedCentralCrossRefGoogle Scholar
  106. 106.
    Lebon G, Warne T, Edwards PC, Bennett K, Langmead CJ, Leslie AGW, Tate CG (2011) Agonist-bound adenosine A2A receptor structures reveal common features of GPCR activation. Nature 474:521–525. https://doi.org/10.1038/nature10136 PubMedPubMedCentralCrossRefGoogle Scholar
  107. 107.
    Lee JY, Lyman E (2012) Predictions for cholesterol interaction sites on the A2A adenosine receptor. J Am Chem Soc 134:16512–16515PubMedCrossRefGoogle Scholar
  108. 108.
    Neale C, Herce HD, Pomès R, García AE (2015) Can specific protein-lipid interactions stabilize an active state of the Beta 2 adrenergic receptor? Biophys J 109:1652–1662. https://doi.org/10.1016/j.bpj.2015.08.028 PubMedPubMedCentralCrossRefGoogle Scholar
  109. 109.
    Dawaliby R, Trubbia C, Delporte C, Masureel M, Van Antwerpen P, Kobilka BK, Govaerts C (2016) Allosteric regulation of G protein–coupled receptor activity by phospholipids. Nature Chem Biol 12:35–41. https://doi.org/10.1038/nchembio.1960 CrossRefGoogle Scholar
  110. 110.
    Manna M, Niemelä M, Tynkkynen J, Javanainen M, Kulig W, Müller DJ, Rog T, Vattulainen I (2016) Mechanism of allosteric regulation of β2-adrenergic receptor by cholesterol. eLife 5:e18432. https://doi.org/10.7554/eLife.18432 PubMedPubMedCentralCrossRefGoogle Scholar
  111. 111.
    Periole X, Huber T, Marrink S-j, Sakmar TP (2007) G Protein-coupled receptors self-assemble in dynamics simulations of model bilayers. J Am Chem Soc 129:10126–10132. https://doi.org/10.1021/ja0706246 PubMedCrossRefGoogle Scholar
  112. 112.
    Marrink SJ, Tieleman DP (2013) Perspective on the Martini model. Chem Soc Rev 42(16):6801–6822. https://doi.org/10.1039/C3CS60093A PubMedCrossRefGoogle Scholar
  113. 113.
    Killian JA (1998) Hydrophobic mismatch between proteins and lipids in membranes. Biochim Biophys Acta 1376:401–415. https://doi.org/10.1016/S0304-4157(98)00017-3 PubMedCrossRefGoogle Scholar
  114. 114.
    Casuso I, Khao J, Chami M, Paul-Gilloteaux P, Husain M, Duneau J-P, Stahlberg H, Sturgis JN, Scheuring S (2012) Characterization of the motion of membrane proteins using high-speed atomic force microscopy. Nature Nanotech 7:525–529. https://doi.org/10.1038/nnano.2012.109 CrossRefGoogle Scholar
  115. 115.
    Botelho AV, Huber T, Sakmar TP, Brown MF (2006) Curvature and hydrophobic forces drive oligomerization and modulate activity of rhodopsin in membranes. Biophys J 91:4464–4477. https://doi.org/10.1529/biophysj.106.082776 PubMedPubMedCentralCrossRefGoogle Scholar
  116. 116.
    Periole X (2016) Interplay of G protein-coupled receptors with the membrane: insights from supra-atomic coarse grain molecular dynamics simulations. Chem Rev 117:156–185. https://doi.org/10.1021/acs.chemrev.6b00344 PubMedCrossRefGoogle Scholar
  117. 117.
    Prasanna X et al (2014) Cholesterol modulates the dimer interface of the β2-adrenergic receptor via cholesterol occupancy sites. Biophys J 106(6):1290–1300. https://doi.org/10.1016/j.bpj.2014.02.002 PubMedPubMedCentralCrossRefGoogle Scholar
  118. 118.
    Provasi D et al (2015) Preferred supramolecular organization and dimer interfaces of opioid receptors from simulated self-association. PLoS Comp Biol 11(3):1–21. https://doi.org/10.1371/journal.pcbi.1004148 CrossRefGoogle Scholar
  119. 119.
    Koldsø H, Sansom MSP (2015) Organization and dynamics of receptor proteins in a plasma membrane. J Am Chem Soc 137:14694–14704. https://doi.org/10.1021/jacs.5b08048 PubMedPubMedCentralCrossRefGoogle Scholar
  120. 120.
    Reinhart G, Desale H, Clemons B, Cahalan SM, Schuerer SC (2012) Crystal structure of a lipid G protein – coupled receptor. Science 335:851–856PubMedPubMedCentralCrossRefGoogle Scholar
  121. 121.
    Jiang Q, Mosberg HI, Porreca F (1990) Modulation of the potency and efficacy of μ-mediated antinociception by delta agonists in the mouse. J Pharmacol Exp Ther 254:683–689Google Scholar
  122. 122.
    Isberg V, Mordalski S, Munk C, Rataj K, Harpsøe K, Hauser AS, Vroling B, Bojarski AJ, Vriend G, Gloriam DE (2016) GPCRdb: an information system for G protein-coupled receptors. Nucleic Acids Res 44(D1):D356–D364. https://doi.org/10.1093/nar/gkv1178 PubMedCrossRefGoogle Scholar
  123. 123.
    Bartuzi D, Kaczor AA, Matosiuk D (2015) Activation and allosteric modulation of human μ opioid receptor in molecular dynamics. J Chem Inf Model 55(11):2421–2434. https://doi.org/10.1021/acs.jcim.5b00280 PubMedCrossRefGoogle Scholar
  124. 124.
    Stansfeld PJ, Sansom MSP (2011) From coarse grained to atomistic: a serial multiscale approach to membrane protein simulations. J Chem Theory Comput 7(4):1157–1166. https://doi.org/10.1021/ct100569y PubMedCrossRefGoogle Scholar
  125. 125.
    Wassenaar TA, Pluhackova K, Böckmann RA, Marrink SJ, Tieleman DP (2014) Going backward: a flexible geometric approach to reverse transformation from coarse grained to atomistic models. J Chem Theor Comput 10(2):676–690. https://doi.org/10.1021/ct400617g CrossRefGoogle Scholar
  126. 126.
    Lee J, Cheng X, Swails JM, Yeom MS, Eastman PK, Lemkul JA, Wei S, Buckner J, Jeong JC, Qi Y, Jo S, Pande VS, Case DA, Brooks CL, MacKerell AD, Klauda JB, Im W (2016) CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. J Chem Theor Comput 12(1):405–413. https://doi.org/10.1021/acs.jctc.5b00935 CrossRefGoogle Scholar
  127. 127.
    Wassenaar TA, Ingólfsson HI, Böckmann RA, Tieleman DP, Marrink SJ (2015) Computational lipidomics with insane: a versatile tool for generating custom membranes for molecular simulations. J Chem Theor Comput 11(5):2144–2155. https://doi.org/10.1021/acs.jctc.5b00209 CrossRefGoogle Scholar

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© Springer Science+Business Media LLC 2018

Authors and Affiliations

  1. 1.Department of Biochemistry, Structural Bioinformatics and Computational BiochemistryUniversity of OxfordOxfordUK

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