Skip to main content
SpringerLink
Log in

Chemosensorial G-proteins-Coupled Receptors: A Perspective from Computational Methods

  • Chapter
  • First Online:
Protein Conformational Dynamics

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 805))

Abstract

G-protein coupled receptors (GPCRs) constitute the targets of about 40 % of all the pharmaceutical drugs in the market and, among other functions, a large portion of the family detects odorants and a variety of tastant molecules. Computational techniques are instrumental to understand structure, dynamics and function of the cascades triggered by these receptors. As an example, here we report our own computational work aimed to dissect GPCR molecular mechanisms for chemical senses. The implications of our work for systems biology and for pharmacology are discussed.

Supported by Programma Operativo del Fondo Sociale Europeo 2007/2013 of Regione Autonoma Friuli Venezia Giulia.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Gα subunits are divided in classes. In mammals, these are: the stimulatory Gαs family (which comprises Gαs and Gαolf), the inhibitory Gαi family (which includes Gαi/o, Gαt, Gαgus, and Gαz), Gαq, Gα12/13 and rod transducin Gαt1. They feature from 35 to 95 % sequence identity (SI) among each other [6]. Gβ In mammals, isoforms (Gβ1–5), with splice variants of Gβ3 (Gβ3s, Gβ3s2 and Gβ3v) and of Gβ5 (Gβ5L), feature 50–90 % SI [7]. In mammals, 12 isoforms of Gγ (GγT1, GγT2, Gγ2-4, Gγ5, Gγ5ps, Gγ7,8,10–12) share 31–77 % SI [7].

  2. 2.

    It includes ORs and rhodopsin [49], along with the structures of other GPCRs (such as the human β2AR [4], the turkey β1AR [50] and the human A2AAR [51]).

  3. 3.

    The identified residues were 3.40, 5.45, 5.46, 5.50, 5.51, 6.44, 6.47, 6.48, and 6.51; following the numbering of Ballesteros et al. [54].

References

  1. Schoneberg T, Schulz A, Biebermann H, Hermsdorf T, Rompler H et al (2004) Mutant G-protein-coupled receptors as a cause of human diseases. Pharmacol Ther 104:173–206

    PubMed  Google Scholar 

  2. Overington JP, Al-Lazikani B, Hopkins AL (2006) How many drug targets are there? Nat Rev Drug Discov 5:993–996

    CAS  PubMed  Google Scholar 

  3. Palczewski K, Kumasaka T, Hori T, Behnke CA, Motoshima H et al (2000) Crystal structure of rhodopsin: a G protein-coupled receptor. Science 289:739–745

    CAS  PubMed  Google Scholar 

  4. Cherezov V, Rosenbaum DM, Hanson MA, Rasmussen SGF, Thian FS et al (2007) High-resolution crystal structure of an engineered human β2-adrenergic G protein–coupled receptor. Science 318:1258–1265

    CAS  PubMed Central  PubMed  Google Scholar 

  5. Rasmussen SG, Choi HJ, Rosenbaum DM, Kobilka TS, Thian FS et al (2007) Crystal structure of the human beta2 adrenergic G-protein-coupled receptor. Nature 450:383–387

    CAS  PubMed  Google Scholar 

  6. Downes GB, Gautam N (1999) The G protein subunit gene families. Genomics 62:544–552

    CAS  PubMed  Google Scholar 

  7. McIntire WE (2009) Structural determinants involved in the formation and activation of G protein betagamma dimers. Neurosignals 17:82–99

    CAS  PubMed  Google Scholar 

  8. Wall MA, Coleman DE, Lee E, Iñiguez-Lluhi JA, Posner BA et al (1995) The structure of the G protein heterotrimer Gi alpha 1 beta 1 gamma 2. Cell 83:1047–1058

    CAS  PubMed  Google Scholar 

  9. Zhao Q, B-l W (2012) Ice breaking in GPCR structural biology. Acta Pharmacol Sin 33:324–334

    CAS  PubMed  Google Scholar 

  10. Wang C, Wu H, Katritch V, Han GW, Huang XP et al (2013) Structure of the human smoothened receptor bound to an antitumour agent. Nature. doi:10.1038/nature12167

    Google Scholar 

  11. Venkatakrishnan AJ, Deupi X, Lebon G, Tate CG, Schertler GF et al (2013) Molecular signatures of G-protein-coupled receptors. Nature 494:185–194

    CAS  PubMed  Google Scholar 

  12. Lagerstrom MC, Schioth HB (2008) Structural diversity of G protein-coupled receptors and significance for drug discovery. Nat Rev Drug Discov 7:339–357

    PubMed  Google Scholar 

  13. Unal H, Karnik SS (2012) Domain coupling in GPCRs: the engine for induced conformational changes. Trends Pharmacol Sci 33:79–88

    CAS  PubMed Central  PubMed  Google Scholar 

  14. Depoortere I (2012) GI functions of GPR39: novel biology. Curr Opin Pharmacol 12:647–652

    CAS  PubMed  Google Scholar 

  15. White JF, Noinaj N, Shibata Y, Love J, Kloss B et al (2012) Structure of the agonist-bound neurotensin receptor. Nature 490:508–513

    CAS  PubMed Central  PubMed  Google Scholar 

  16. Sprang S (1997) G protein mechanisms: insights from structural analysis. Annu Rev Biochem 66:639–678

    CAS  PubMed  Google Scholar 

  17. Tesmer JJ (2010) The quest to understand heterotrimeric G protein signaling. Nat Struct Mol Biol 17:650–652

    CAS  PubMed Central  PubMed  Google Scholar 

  18. Bos JL, Rehmann H, Wittinghofer A (2007) GEFs and GAPs: critical elements in the control of small G proteins. Cell 129:865–877

    CAS  PubMed  Google Scholar 

  19. Chung KY, Rasmussen SG, Liu T, Li S, DeVree BT et al (2011) Conformational changes in the G protein Gs induced by the β2 adrenergic receptor. Nature 477:611–615

    CAS  PubMed Central  PubMed  Google Scholar 

  20. Westfield GH, Rasmussen SG, Su M, Dutta S, DeVree BT et al (2011) Structural flexibility of the G alpha s alpha-helical domain in the beta2-adrenoceptor Gs complex. Proc Natl Acad Sci USA 108:16086–16091

    CAS  PubMed  Google Scholar 

  21. Gaillard I, Rouquier S, Giorgi D (2004) Olfactory receptors. Cell Mol Life Sci 61:456–469

    CAS  PubMed  Google Scholar 

  22. Chandrashekar J, Hoon MA, Ryba NJ, Zuker CS (2006) The receptors and cells for mammalian taste. Nature 444:288–294

    CAS  PubMed  Google Scholar 

  23. Lupieri P, Nguyen CH, Bafghi ZG, Giorgetti A, Carloni P (2009) Computational molecular biology approaches to ligand-target interactions. HFSP J 3:228–239

    CAS  PubMed Central  PubMed  Google Scholar 

  24. Oldham W (2008) Heterotrimeric G protein activation by G-protein-coupled receptors. Nat Rev Mol Cell Biol 9:60–71

    CAS  PubMed  Google Scholar 

  25. Kaupp UB, Seifert R (2002) Cyclic nucleotide-gated ion channels. Physiol Rev 82:769–824

    CAS  PubMed  Google Scholar 

  26. Pifferi S, Boccaccio A, Menini A (2006) Cyclic nucleotide-gated ion channels in sensory transduction. FEBS Lett 580:2853–2859

    CAS  PubMed  Google Scholar 

  27. Kleene SJ (1993) The cyclic nucleotide-activated conductance in olfactory cilia: effects of cytoplasmic Mg2+ and Ca2+. J Membr Biol 131:237–243

    CAS  PubMed  Google Scholar 

  28. Lowe G, Gold GH (1993) Nonlinear amplification by calcium-dependent chloride channels in olfactory receptor cells. Nature 366:283–286

    CAS  PubMed  Google Scholar 

  29. Frings S, Hackos DH, Dzeja C, Ohyama T, Hagen V et al (2000) Determination of fractional calcium ion current in cyclic nucleotide-gated channels. Methods Enzymol 315:797–817

    CAS  PubMed  Google Scholar 

  30. Menini A (1999) Calcium signalling and regulation in olfactory neurons. Curr Opin Neurobiol 9:419–426

    CAS  PubMed  Google Scholar 

  31. Firestein S (2001) How the olfactory system makes sense of scents. Nature 413:211–218

    CAS  PubMed  Google Scholar 

  32. Nadler W, Brunger AT, Schulten K, Karplus M (1987) Molecular and stochastic dynamics of proteins. Proc Natl Acad Sci USA 84:7933–7937

    CAS  PubMed  Google Scholar 

  33. Matsunami H, Montmayeur J-P, Buck LB (2000) A family of candidate taste receptors in human and mouse. Nature 404:601–604

    CAS  PubMed  Google Scholar 

  34. Biarnes X, Marchiori A, Giorgetti A, Lanzara C, Gasparini P et al (2010) Insights into the binding of phenyltiocarbamide (PTC) agonist to its target human TAS2R38 bitter receptor. PLoS One 5:e12394

    PubMed Central  PubMed  Google Scholar 

  35. Shi P, Zhang J (2006) Contrasting modes of evolution between vertebrate sweet/umami receptor genes and bitter receptor genes. Mol Biol Evol 23:292–300

    CAS  PubMed  Google Scholar 

  36. Bufe B, Breslin PA, Kuhn C, Reed DR, Tharp CD et al (2005) The molecular basis of individual differences in phenylthiocarbamide and propylthiouracil bitterness perception. Curr Biol 15:322–327

    CAS  PubMed Central  PubMed  Google Scholar 

  37. Lee JH, Lee IH, Choe YJ, Kang S, Kim HY et al (2009) Real-time analysis of amyloid fibril formation of alpha-synuclein using a fibrillation-state-specific fluorescent probe of JC-1. Biochem J 418:311–323

    CAS  PubMed  Google Scholar 

  38. Margolskee RF (2002) Molecular mechanisms of bitter and sweet taste transduction. J Biol Chem 277:1–4

    CAS  PubMed  Google Scholar 

  39. Berridge MJ, Lipp P, Bootman MD (2000) The versatility and universality of calcium signalling. Nat Rev Mol Cell Biol 1:11–21

    CAS  PubMed  Google Scholar 

  40. Nei M, Niimura Y, Nozawa M (2008) The evolution of animal chemosensory receptor gene repertoires: roles of chance and necessity. Nat Rev Genet 9:951–963

    CAS  PubMed  Google Scholar 

  41. Dong D, Jin K, Wu X, Zhong Y (2012) CRDB: database of chemosensory receptor gene families in vertebrate. PLoS One 7:e31540

    CAS  PubMed Central  PubMed  Google Scholar 

  42. Grossfield A (2011) Recent progress in the study of G protein-coupled receptors with molecular dynamics computer simulations. Biochim Biophys Acta 1808:1868–1878

    CAS  PubMed  Google Scholar 

  43. Johnston JM, Filizola M (2011) Showcasing modern molecular dynamics simulations of membrane proteins through G protein-coupled receptors. Curr Opin Struct Biol 21:552–558

    CAS  PubMed Central  PubMed  Google Scholar 

  44. Bruno A, Costantino G (2012) Molecular dynamics simulations of G protein-coupled receptors. Mol Inform 31:222–230

    CAS  Google Scholar 

  45. Khafizov K, Lattanzi G, Carloni P (2009) G protein inactive and active forms investigated by simulation methods. Proteins 75:919–930

    CAS  PubMed  Google Scholar 

  46. Singer MS (2000) Analysis of the molecular basis for octanal interactions in the expressed rat 17 olfactory receptor. Chem Senses 25:155–165

    CAS  PubMed  Google Scholar 

  47. Floriano WB, Vaidehi N, Goddard WA 3rd (2004) Making sense of olfaction through predictions of the 3-D structure and function of olfactory receptors. Chem Senses 29:269–290

    CAS  PubMed  Google Scholar 

  48. Goddard WA 3rd, Abrol R (2007) 3-Dimensional structures of G protein-coupled receptors and binding sites of agonists and antagonists. J Nutr 137:1528S–1538S; discussion 1548S

    CAS  PubMed  Google Scholar 

  49. Kolakowski LF Jr (1994) GCRDb: a G-protein-coupled receptor database. Receptors Channels 2:1–7

    CAS  PubMed  Google Scholar 

  50. Warne T, Serrano-Vega MJ, Baker JG, Moukhametzianov R, Edwards PC et al (2008) Structure of a beta1-adrenergic G-protein-coupled receptor. Nature 454:486–491

    CAS  PubMed Central  PubMed  Google Scholar 

  51. Jaakola VP, Griffith MT, Hanson MA, Cherezov V, Chien EY et al (2008) The 2.6 angstrom crystal structure of a human A2A adenosine receptor bound to an antagonist. Science 322:1211–1217

    CAS  PubMed Central  PubMed  Google Scholar 

  52. Costanzi S (2008) On the applicability of GPCR homology models to computer-aided drug discovery: a comparison between in silico and crystal structures of the beta2-adrenergic receptor. J Med Chem 51:2907–2914

    CAS  PubMed Central  PubMed  Google Scholar 

  53. Jorgensen AM, Tagmose L, Jorgensen AM, Topiol S, Sabio M et al (2007) Homology modeling of the serotonin transporter: insights into the primary escitalopram-binding site. ChemMedChem 2:815–826

    PubMed  Google Scholar 

  54. Ballesteros JA, Weinstein H, Stuart CS (1995) Integrated methods for the construction of three-dimensional models and computational probing of structure-function relations in G protein-coupled receptors. In: Methods in neurosciences. Academic Press, San Diego, pp 366–428

    Google Scholar 

  55. Katada S, Hirokawa T, Oka Y, Suwa M, Touhara K (2005) Structural basis for a broad but selective ligand spectrum of a mouse olfactory receptor: mapping the odorant-binding site. J Neurosci 25:1806–1815

    CAS  PubMed  Google Scholar 

  56. Oldham WM, Van Eps N, Preininger AM, Hubbell WL, Hamm HE (2006) Mechanism of the receptor-catalyzed activation of heterotrimeric G proteins. Nat Struct Mol Biol 13:772–777

    CAS  PubMed  Google Scholar 

  57. Gales C, Van Durm JJ, Schaak S, Pontier S, Percherancier Y et al (2006) Probing the activation-promoted structural rearrangements in preassembled receptor-G protein complexes. Nat Struct Mol Biol 13:778–786

    CAS  PubMed  Google Scholar 

  58. Abdulaev NG, Ngo T, Ramon E, Brabazon DM, Marino JP et al (2006) The receptor-bound “empty pocket” state of the heterotrimeric G-protein alpha-subunit is conformationally dynamic. Biochemistry 45:12986–12997

    CAS  PubMed  Google Scholar 

  59. Kristiansen K (2004) Molecular mechanisms of ligand binding, signaling, and regulation within the superfamily of G-protein-coupled receptors: molecular modeling and mutagenesis approaches to receptor structure and function. Pharmacol Ther 103:21–80

    CAS  PubMed  Google Scholar 

  60. Oldham WM, Hamm HE (2006) Structural basis of function in heterotrimeric G proteins. Q Rev Biophys 39:117–166

    CAS  PubMed  Google Scholar 

  61. Johnston CA, Siderovski DP (2007) Receptor-mediated activation of heterotrimeric G-proteins: current structural insights. Mol Pharmacol 72:219–230

    CAS  PubMed  Google Scholar 

  62. Ceruso MA, Periole X, Weinstein H (2004) Molecular dynamics simulations of transducin: interdomain and front to back communication in activation and nucleotide exchange. J Mol Biol 338:469–481

    CAS  PubMed  Google Scholar 

  63. Mello LV, van Aalten DM, Findlay JB (1998) Dynamic properties of the guanine nucleotide binding protein alpha subunit and comparison of its guanosine triphosphate hydrolase domain with that of ras p21. Biochemistry 37:3137–3142

    CAS  PubMed  Google Scholar 

  64. Wall MA, Posner BA, Sprang SR (1998) Structural basis of activity and subunit recognition in G protein heterotrimers. Structure 6:1169–1183

    CAS  PubMed  Google Scholar 

  65. Hanoune J, Defer N (2001) Regulation and role of adenylyl cyclase isoforms. Annu Rev Pharmacol Toxicol 41:145–174

    CAS  PubMed  Google Scholar 

  66. Tesmer JJ, Sunahara RK, Gilman AG, Sprang SR (1997) Crystal structure of the catalytic domains of adenylyl cyclase in a complex with Gsalpha. GTPgammaS. Science 278:1907–1916

    CAS  PubMed  Google Scholar 

  67. Biel M, Michalakis S (2007) Function and dysfunction of CNG channels: insights from channelopathies and mouse models. Mol Neurobiol 35:266–277

    CAS  PubMed  Google Scholar 

  68. Anselmi C, Carloni P, Torre V (2007) Origin of functional diversity among tetrameric voltage-gated channels. Proteins 66:136–146

    CAS  PubMed  Google Scholar 

  69. Giorgetti A, Nair AV, Codega P, Torre V, Carloni P (2005) Structural basis of gating of CNG channels. FEBS Lett 579:1968–1972

    CAS  PubMed  Google Scholar 

  70. Nair AV, Mazzolini M, Codega P, Giorgetti A, Torre V (2006) Locking CNGA1 channels in the open and closed state. Biophys J 90:3599–3607

    CAS  PubMed Central  PubMed  Google Scholar 

  71. Pifferi S, Cenedese V, Menini A (2012) Anoctamin 2/TMEM16B: a calcium-activated chloride channel in olfactory transduction. Exp Physiol 97:193–199

    CAS  PubMed  Google Scholar 

  72. Hartzell HC, Qu Z, Yu K, Xiao Q, Chien LT (2008) Molecular physiology of bestrophins: multifunctional membrane proteins linked to best disease and other retinopathies. Physiol Rev 88:639–672

    CAS  PubMed  Google Scholar 

  73. Pifferi S, Pascarella G, Boccaccio A, Mazzatenta A, Gustincich S et al (2006) Bestrophin-2 is a candidate calcium-activated chloride channel involved in olfactory transduction. Proc Natl Acad Sci USA 103:12929–12934

    CAS  PubMed  Google Scholar 

  74. Boccaccio A, Menini A (2007) Temporal development of cyclic nucleotide-gated and Ca2+ -activated Cl− currents in isolated mouse olfactory sensory neurons. J Neurophysiol 98:153–160

    CAS  PubMed  Google Scholar 

  75. Pifferi S, Dibattista M, Sagheddu C, Boccaccio A, Al Qteishat A et al (2009) Calcium-activated chloride currents in olfactory sensory neurons from mice lacking bestrophin-2. J Physiol 587:4265–4279

    CAS  PubMed  Google Scholar 

  76. Jentsch TJ, Stein V, Weinreich F, Zdebik AA (2002) Molecular structure and physiological function of chloride channels. Physiol Rev 82:503–568

    CAS  PubMed  Google Scholar 

  77. Loewen ME, Forsyth GW (2005) Structure and function of CLCA proteins. Physiol Rev 85:1061–1092

    CAS  PubMed  Google Scholar 

  78. Xiao Q, Prussia A, Yu K, Cui YY, Hartzell HC (2008) Regulation of bestrophin Cl channels by calcium: role of the C terminus. J Gen Physiol 132:681–692

    CAS  PubMed Central  PubMed  Google Scholar 

  79. Kranjc A, Grillo FW, Rievaj J, Boccaccio A, Pietrucci F et al (2009) Regulation of bestrophins by Ca2+: a theoretical and experimental study. PLoS One 4:e4672

    PubMed Central  PubMed  Google Scholar 

  80. Fiorin G, Pastore A, Carloni P, Parrinello M (2006) Using metadynamics to understand the mechanism of calmodulin/target recognition at atomic detail. Biophys J 91:2768–2777

    CAS  PubMed Central  PubMed  Google Scholar 

  81. Laio A, Parrinello M (2002) Escaping free-energy minima. Proc Natl Acad Sci USA 99:12562–12566

    CAS  PubMed  Google Scholar 

  82. Ehrhardt MR, Urbauer JL, Wand AJ (1995) The energetics and dynamics of molecular recognition by calmodulin. Biochemistry 34:2731–2738

    CAS  PubMed  Google Scholar 

  83. Sakurai T, Misaka T, Ishiguro M, Masuda K, Sugawara T et al (2010) Characterization of the beta-D-glucopyranoside binding site of the human bitter taste receptor hTAS2R16. J Biol Chem 285:28373–28378

    CAS  PubMed  Google Scholar 

  84. Singh UC, Kollman PA (1986) A combined ab initio quantum mechanical and molecular mechanical method for carrying out simulations on complex molecular systems: applications to the CH3Cl + Cl− exchange reaction and gas phase protonation of polyethers. J Comput Chem 7:718–730

    CAS  Google Scholar 

  85. Kim UK, Jorgenson E, Coon H, Leppert M, Risch N et al (2003) Positional cloning of the human quantitative trait locus underlying taste sensitivity to phenylthiocarbamide. Science 299:1221–1225

    CAS  PubMed  Google Scholar 

  86. de Vries SJ, van Dijk AD, Krzeminski M, van Dijk M, Thureau A et al (2007) HADDOCK versus HADDOCK: new features and performance of HADDOCK2.0 on the CAPRI targets. Proteins 69:726–733

    PubMed  Google Scholar 

  87. Dominguez C, Boelens R, Bonvin AM (2003) HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. J Am Chem Soc 125:1731–1737

    CAS  PubMed  Google Scholar 

  88. Marchiori A, Capece L, Giorgetti A, Gasparini P, Behrens M et al (2013) Coarse-grained/molecular mechanics of the TAS2R38 bitter taste receptor: experimentally-validated detailed structural prediction of agonist binding. PLoS One 8(5):e64675

    CAS  PubMed Central  PubMed  Google Scholar 

  89. Levit A, Barak D, Behrens M, Meyerhof W, Niv M (2012) Homology model-assisted elucidation of binding sites in GPCRs. In: Vaidehi N, Klein-Seetharaman J (eds) Membrane protein structure and dynamics. Humana Press, pp 179–205

    Google Scholar 

  90. Chang X, Xu T, Li Y, Wang K (2013) Dynamic modular architecture of protein-protein interaction networks beyond the dichotomy of ‘date’ and ‘party’ hubs. Sci Rep 3:1691

    CAS  PubMed Central  PubMed  Google Scholar 

  91. Pujol A, Mosca R, Farrés J, Aloy P (2010) Unveiling the role of network and systems biology in drug discovery. Trends Pharmacol Sci 31:115–123

    CAS  PubMed  Google Scholar 

  92. Wass MN, Fuentes G, Pons C, Pazos F, Valencia A (2011) Towards the prediction of protein interaction partners using physical docking. Mol Syst Biol 7:1–8

    Google Scholar 

  93. Vidal M, Cusick ME, Barabási A-L (2011) Interactome networks and human disease. Cell 144:986–998

    CAS  PubMed Central  PubMed  Google Scholar 

  94. Ayton GS, Noid WG, Voth GA (2007) Multiscale modeling of biomolecular systems: in serial and in parallel. Curr Opin Struct Biol 17:192–198

    CAS  PubMed  Google Scholar 

  95. Hyeon C, Thirumalai D (2011) Capturing the essence of folding and functions of biomolecules using coarse-grained models. Nat Commun 2:487

    PubMed  Google Scholar 

  96. Monticelli L, Kandasamy SK, Periole X, Larson RG, Tieleman DP et al (2008) The MARTINI coarse-grained force field: extension to proteins. J Chem Theory Comput 4:819–834

    CAS  Google Scholar 

  97. Kalli AC, Campbell ID, Sansom MSP (2011) Multiscale simulations suggest a mechanism for integrin inside-out activation. Proc Natl Acad Sci USA 108:11890–11895

    CAS  PubMed  Google Scholar 

  98. Messer BM, Roca M, Chu ZT, Vicatos S, Kilshtain AV et al (2010) Multiscale simulations of protein landscapes: using coarse-grained models as reference potentials to full explicit models. Proteins 78:1212–1227

    CAS  PubMed Central  PubMed  Google Scholar 

  99. Shi Q, Izvekov S, Voth GA (2006) Mixed atomistic and coarse-grained molecular dynamics: simulation of a membrane-bound ion channel. J Phys Chem B 110:15045–15048

    CAS  PubMed  Google Scholar 

  100. Villa E, Balaeff A, Schulten K (2005) Structural dynamics of the lac repressor-DNA complex revealed by a multiscale simulation. Proc Natl Acad Sci USA 102:6783–6788

    CAS  PubMed  Google Scholar 

  101. Neri M, Baaden M, Carnevale V, Anselmi C, Maritan A et al (2008) Microseconds dynamics simulations of the outer-membrane protease T. Biophys J 94:71–78

    CAS  PubMed Central  PubMed  Google Scholar 

  102. Neri M, Anselmi C, Cascella M, Maritan A, Carloni P (2005) Coarse-grained model of proteins incorporating atomistic detail of the active site. Phys Rev Lett 95:218102

    PubMed  Google Scholar 

  103. Leguebe M, Nguyen C, Capece L, Hoang Z, Giorgetti A et al (2012) Hybrid molecular mechanics/coarse-grained simulations for structural prediction of G-protein coupled receptor/ligand complexes. PLoS One 7:e47332

    CAS  PubMed Central  PubMed  Google Scholar 

  104. Vanni S, Neri M, Tavernelli I, Rothlisberger U (2011) Predicting novel binding modes of agonists to B adrenergic receptors using all-atom molecular dynamics simulations. PLoS Comput Biol 7:e1001053

    CAS  PubMed Central  PubMed  Google Scholar 

  105. Bjarnadottir TK, Gloriam DE, Hellstrand SH, Kristiansson H, Fredriksson R et al (2006) Comprehensive repertoire and phylogenetic analysis of the G protein-coupled receptors in human and mouse. Genomics 88:263–273

    CAS  PubMed  Google Scholar 

Download references

Author information

Author notes

    Authors and Affiliations

    1. Scuola Internazionale Superiore di Studi Avanzati (SISSA/ISAS), Trieste, Italy

      Francesco Musiani

    2. Institute for Research in Biomedicine (IRB Barcelona), Barcelona, Spain

      Giulia Rossetti

    3. Joint IRB-BSC Program in Computational Biology, Barcelona, Spain

      Giulia Rossetti

    4. Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich, Jülich, Germany

      Giulia Rossetti

    5. Computational Biophysics, German Research School for Simulation Sciences, Jülich, Germany

      Giulia Rossetti, Alejandro Giorgetti & Paolo Carloni

    6. Institute for Advanced Simulation, Forschungszentrum Jülich, Jülich, Germany

      Giulia Rossetti, Alejandro Giorgetti & Paolo Carloni

    7. Department of Biotechnology, University of Verona, Ca’ Vignal 1, Strada le Grazie 15, Verona, 37134, Italy

      Alejandro Giorgetti

    Authors
    1. Francesco Musiani
      View author publications

      You can also search for this author in PubMed Google Scholar

    2. Giulia Rossetti
      View author publications

      You can also search for this author in PubMed Google Scholar

    3. Alejandro Giorgetti
      View author publications

      You can also search for this author in PubMed Google Scholar

    4. Paolo Carloni
      View author publications

      You can also search for this author in PubMed Google Scholar

    Corresponding author

    Correspondence to Alejandro Giorgetti .

    Editor information

    Editors and Affiliations

    1. Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, People's Republic of China

      Ke-li Han

    2. The Scripps Research Institute, La Jolla, California, USA

      Xin Zhang

    3. Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, People's Republic of China

      Ming-jun Yang

    Appendix: Molecular Mechanics/Coarse-Grained Hybrid Approach

    Appendix: Molecular Mechanics/Coarse-Grained Hybrid Approach

    Coarse-grained (CG)-based MD approaches allow the study of longer timescales than all-atom force field simulations [94, 95]. The reduction of the number of degrees of freedom makes the model computationally very efficient, allowing a reduction of the simulation time by ca. 2–3 orders of magnitude compared to full atom force fields [96]. Unfortunately, without a detailed description of the side-chains, as in cases such as GPCRs, these approaches cannot describe in detail the intermolecular ligand/protein interactions. Thus a possible solution to this problem may be to combine atomistic with CG modeling [97–101]. Indeed, a hybrid/multi-scale approach in which different representations of the system are modeled concurrently was proposed, i.e. Molecular Mechanics/Coarse-Grained (MM/CG) simulations. In this kind of procedure, a coupling scheme is needed to connect the boundary of the different models. This approach has been developed for proteins by several groups, including ours [99–102]. In our scheme, a region of interest (i.e. the active site of an enzyme, MM region) is treated at molecular level using an atomistic force field and the protein frame is described at CG level using a Go-like model. Recently we modified and extended the use of the method, previously developed for soluble enzymes, to the case of GPCRs [103] in which the presence of the lipid bilayer must be imposed. In addition, one has to avoid that water from the binding site diffuses into the hydrophobic regions of the lipid bilayer. The accuracy of the new version of our MM/CG method was established by comparing MM/CG simulations with all-atom MD calculations on the human β2AR (hβ2AR) [103], in complex with two different ligands: the co-crystallized ligand and inverse agonist S-Carazolol (S-Car) [5] and its agonist R-Isoprenaline (R-ISO). The MM region consisted of 476 and 486 atoms, while the overall system was made of only 4,597 and 4,587 atoms, for the hβ2AR/S-Car and hβ2AR/R-ISO complexes, respectively. This allowed us to simulate more than 70 ns/day on 16 CPUs, which is a speed up of 15 times compared to the MD simulations of the same system. The trajectory obtained with our MM/CG scheme are able to reproduce the key structural features of the active site found in the MD simulations [104]. With these results, due to both its low cost and high reliability, using the MM/CG methodology emerges as a useful approach to study the ligand cavity of these proteins [103], indeed we have extensively used it for characterizing the binding cavity of the human TAS2R38 bitter taste receptor (see above).

    Rights and permissions

    Reprints and permissions

    Copyright information

    © 2014 Springer International Publishing Switzerland

    About this chapter

    Cite this chapter

    Musiani, F., Rossetti, G., Giorgetti, A., Carloni, P. (2014). Chemosensorial G-proteins-Coupled Receptors: A Perspective from Computational Methods. In: Han, Kl., Zhang, X., Yang, Mj. (eds) Protein Conformational Dynamics. Advances in Experimental Medicine and Biology, vol 805. Springer, Cham. https://doi.org/10.1007/978-3-319-02970-2_18

    Download citation

    Publish with us

    Policies and ethics

    Search

    Navigation