Approaches for Differentiation and Interconverting GPCR Agonists and Antagonists

  • Przemysław Miszta
  • Jakub Jakowiecki
  • Ewelina Rutkowska
  • Maria Turant
  • Dorota Latek
  • Sławomir FilipekEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1705)


Predicting the functional preferences of the ligands was always a highly demanding task, much harder that predicting whether a ligand can bind to the receptor. This is because of significant similarities of agonists, antagonists and inverse agonists which are binding usually in the same binding site of the receptor and only small structural changes can push receptor toward a particular activation state. For G protein-coupled receptors, due to a large progress in crystallization techniques and also in receptor thermal stabilization, it was possible to obtain a large number of high-quality structures of complexes of these receptors with agonists and non-agonists. Additionally, the long-time-scale molecular dynamics simulations revealed how the activation processes of GPCRs can take place. Using both theoretical and experimental knowledge it was possible to employ many clever and sophisticated methods which can help to differentiate agonists and non-agonists, so one can interconvert them in search of the optimal drug.

Key words

GPCRs Agonists Activation Ligand docking Fingerprints Molecular dynamics 



Figure 3 is reproduced from J. Chem. Inf. Model. 2015 ( with permission from American Chemical Society. Figure 9 is reproduced from ACS Chem. Biol. 2013 ( with permission from American Chemical Society. Figure 18 is reproduced from FEBS Lett. 2015 with permission from John Wiley and Sons.


  1. 1.
    Pierce KL, Premont RT, Lefkowitz RJ (2002) Seven-transmembrane receptors. Nat Rev Mol Cell Biol 3:639–650CrossRefPubMedGoogle Scholar
  2. 2.
    Moreno JL, Holloway T, Gonzalez-Maeso J (2013) G protein-coupled receptor heterocomplexes in neuropsychiatric disorders. Prog Mol Biol Transl Sci 117:187–205CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    O'Hayre M, Degese MS, Gutkind JS (2014) Novel insights into G protein and G protein-coupled receptor signaling in cancer. Curr Opin Cell Biol 27:126–135CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Sodhi A, Montaner S, Gutkind JS (2004) Viral hijacking of G-protein-coupled-receptor signalling networks. Nat Rev Mol Cell Biol 5:998–1012CrossRefPubMedGoogle Scholar
  5. 5.
    Lundstrom K (2006) Latest development in drug discovery on G protein-coupled receptors. Curr Protein Pept Sci 7:465–470CrossRefPubMedGoogle Scholar
  6. 6.
    Schioth HB, Fredriksson R (2005) The GRAFS classification system of G-protein coupled receptors in comparative perspective. Gen Comp Endocrinol 142:94–101CrossRefPubMedGoogle Scholar
  7. 7.
    Katritch V, Cherezov V, Stevens RC (2011) Diversity and modularity of G protein-coupled receptor structures. Trends Pharmacol Sci 33(1):17–27CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Kenakin T, Miller LJ (2010) Seven transmembrane receptors as shapeshifting proteins: the impact of allosteric modulation and functional selectivity on new drug discovery. Pharmacol Rev 62:265–304CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Trzaskowski B, Latek D, Yuan S, Ghoshdastider U, Debinski A, Filipek S (2012) Action of molecular switches in GPCRs–theoretical and experimental studies. Curr Med Chem 19:1090–1109CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Vass M, Kooistra AJ, Ritschel T, Leurs R, de Esch IJ, de Graaf C (2016) Molecular interaction fingerprint approaches for GPCR drug discovery. Curr Opin Pharmacol 30:59–68CrossRefPubMedGoogle Scholar
  11. 11.
    Cereto-Massague A, Ojeda MJ, Valls C, Mulero M, Garcia-Vallve S, Pujadas G (2015) Molecular fingerprint similarity search in virtual screening. Methods 71:58–63CrossRefPubMedGoogle Scholar
  12. 12.
    Deng Z, Chuaqui C, Singh J (2004) Structural interaction fingerprint (SIFt): a novel method for analyzing three-dimensional protein-ligand binding interactions. J Med Chem 47:337–344CrossRefPubMedGoogle Scholar
  13. 13.
    Marcou G, Rognan D (2007) Optimizing fragment and scaffold docking by use of molecular interaction fingerprints. J Chem Inf Model 47:195–207CrossRefPubMedGoogle Scholar
  14. 14.
    Desaphy J, Raimbaud E, Ducrot P, Rognan D (2013) Encoding protein-ligand interaction patterns in fingerprints and graphs. J Chem Inf Model 53:623–637CrossRefPubMedGoogle Scholar
  15. 15.
    Da C, Kireev D (2014) Structural protein-ligand interaction fingerprints (SPLIF) for structure-based virtual screening: method and benchmark study. J Chem Inf Model 54:2555–2561CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Baroni M, Cruciani G, Sciabola S, Perruccio F, Mason JS (2007) A common reference framework for analyzing/comparing proteins and ligands. Fingerprints for ligands and proteins (FLAP): theory and application. J Chem Inf Model 47:279–294CrossRefPubMedGoogle Scholar
  17. 17.
    Wood DJ, de Vlieg J, Wagener M, Ritschel T (2012) Pharmacophore fingerprint-based approach to binding site subpocket similarity and its application to bioisostere replacement. J Chem Inf Model 52:2031–2043CrossRefPubMedGoogle Scholar
  18. 18.
    Lavecchia A (2015) Machine-learning approaches in drug discovery: methods and applications. Drug Discov Today 20:318–331CrossRefPubMedGoogle Scholar
  19. 19.
    Mordalski S, Kosciolek T, Kristiansen K, Sylte I, Bojarski AJ (2011) Protein binding site analysis by means of structural interaction fingerprint patterns. Bioorg Med Chem Lett 21:6816–6819CrossRefPubMedGoogle Scholar
  20. 20.
    Cao R, Wang Y (2016) Predicting molecular targets for small-molecule drugs with a ligand-based interaction fingerprint approach. ChemMedChem 11:1352–1361CrossRefPubMedGoogle Scholar
  21. 21.
    Kooistra AJ, Leurs R, de Esch IJ, de Graaf C (2015) Structure-based prediction of G-protein-coupled receptor ligand function: a beta-adrenoceptor case study. J Chem Inf Model 55:1045–1061CrossRefPubMedGoogle Scholar
  22. 22.
    Kooistra AJ, Vischer HF, McNaught-Flores D, Leurs R, de Esch IJ, de Graaf C (2016) Function-specific virtual screening for GPCR ligands using a combined scoring method. Sci Rep 6:28288CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Yuan S, Peng Q, Palczewski K, Vogel H, Filipek S (2016) Mechanistic studies on the stereoselectivity of the serotonin 5-HT1A receptor. Angew Chem Int Ed 55:8661–8665CrossRefGoogle Scholar
  24. 24.
    Latek D, Pasznik P, Carlomagno T, Filipek S (2013) Towards improved quality of GPCR models by usage of multiple templates and profile-profile comparison. PLoS One 8:e56742CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Vroling B, Sanders M, Baakman C, Borrmann A, Verhoeven S, Klomp J, Oliveira L, de Vlieg J, Vriend G (2011) GPCRDB: information system for G protein-coupled receptors. Nucleic Acids Res 39:D309–D319CrossRefPubMedGoogle Scholar
  26. 26.
    Chan HC, Filipek S, Yuan S (2016) The principles of ligand specificity on beta-2-adrenergic receptor. Sci Rep 6:34736CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Ballesteros JA, Weinstein H (1995) Integrated methods for the construction of three-dimensional models and computational probing of structure-function relations in G protein-coupled receptors. Methods Neurosci 25:366–428CrossRefGoogle Scholar
  28. 28.
    Rodriguez D, Ranganathan A, Carlsson J (2015) Discovery of GPCR ligands by molecular docking screening: novel opportunities provided by crystal structures. Curr Top Med Chem 15:2484–2503CrossRefPubMedGoogle Scholar
  29. 29.
    Gutierrez-de-Teran H, Sallander J, Sotelo E (2017) Structure-based rational design of adenosine receptor ligands. Curr Top Med Chem 17:40–58CrossRefPubMedGoogle Scholar
  30. 30.
    Kolb P, Rosenbaum DM, Irwin JJ, Fung JJ, Kobilka BK, Shoichet BK (2009) Structure-based discovery of beta2-adrenergic receptor ligands. Proc Natl Acad Sci U S A 106:6843–6848CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Bissantz C, Bernard P, Hibert M, Rognan D (2003) Protein-based virtual screening of chemical databases. II. Are homology models of G-protein coupled receptors suitable targets? Proteins 50:5–25CrossRefPubMedGoogle Scholar
  32. 32.
    Korb O, Stutzle T, Exner TE (2009) Empirical scoring functions for advanced protein-ligand docking with PLANTS. J Chem Inf Model 49:84–96CrossRefPubMedGoogle Scholar
  33. 33.
    de Graaf C, Kooistra AJ, Vischer HF, Katritch V, Kuijer M, Shiroishi M, Iwata S, Shimamura T, Stevens RC, de Esch IJ, Leurs R (2011) Crystal structure-based virtual screening for fragment-like ligands of the human histamine H(1) receptor. J Med Chem 54:8195–8206CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    O'Boyle NM, Liebeschuetz JW, Cole JC (2009) Testing assumptions and hypotheses for rescoring success in protein-ligand docking. J Chem Inf Model 49:1871–1878CrossRefPubMedGoogle Scholar
  35. 35.
    Shimamura T, Shiroishi M, Weyand S, Tsujimoto H, Winter G, Katritch V, Abagyan R, Cherezov V, Liu W, Han GW, Kobayashi T, Stevens RC, Iwata S (2011) Structure of the human histamine H1 receptor complex with doxepin. Nature 475:65–70CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50:742–754CrossRefPubMedGoogle Scholar
  37. 37.
    Weiss DR, Ahn S, Sassano MF, Kleist A, Zhu X, Strachan R, Roth BL, Lefkowitz RJ, Shoichet BK (2013) Conformation guides molecular efficacy in docking screens of activated beta-2 adrenergic G protein coupled receptor. ACS Chem Biol 8:1018–1026CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Liapakis G, Ballesteros JA, Papachristou S, Chan WC, Chen X, Javitch JA (2000) The forgotten serine. A critical role for Ser-2035.42 in ligand binding to and activation of the beta 2-adrenergic receptor. J Biol Chem 275:37779–37788CrossRefPubMedGoogle Scholar
  39. 39.
    Huang N, Shoichet BK, Irwin JJ (2006) Benchmarking sets for molecular docking. J Med Chem 49:6789–6801CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Mysinger MM, Shoichet BK (2010) Rapid context-dependent ligand desolvation in molecular docking. J Chem Inf Model 50:1561–1573CrossRefPubMedGoogle Scholar
  41. 41.
    Costanzi S, Vilar S (2012) In silico screening for agonists and blockers of the beta(2) adrenergic receptor: implications of inactive and activated state structures. J Comput Chem 33:561–572CrossRefPubMedGoogle Scholar
  42. 42.
    Jazayeri A, Andrews SP, Marshall FH (2017) Structurally enabled discovery of adenosine A2A receptor antagonists. Chem Rev 117:21–37CrossRefPubMedGoogle Scholar
  43. 43.
    Liu W, Chun E, Thompson AA, Chubukov P, Xu F, Katritch V, Han GW, Roth CB, Heitman LH, AP IJ, Cherezov V, Stevens RC (2012) Structural basis for allosteric regulation of GPCRs by sodium ions. Science 337:232–236CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Bortolato A, Tehan BG, Bodnarchuk MS, Essex JW, Mason JS (2013) Water network perturbation in ligand binding: adenosine a(2A) antagonists as a case study. J Chem Inf Model 53:1700–1713CrossRefPubMedGoogle Scholar
  45. 45.
    Lenselink EB, Beuming T, Sherman W, van Vlijmen HW, AP IJ (2014) Selecting an optimal number of binding site waters to improve virtual screening enrichments against the adenosine A2A receptor. J Chem Inf Model 54:1737–1746CrossRefPubMedGoogle Scholar
  46. 46.
    Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin PS (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47:1739–1749CrossRefPubMedGoogle Scholar
  47. 47.
    Yuan S, Hu Z, Filipek S, Vogel H (2015) W246(6.48) opens a gate for a continuous intrinsic water pathway during activation of the adenosine A2A receptor. Angew Chem Int Ed 54:556–559Google Scholar
  48. 48.
    Skjaerven L, Yao XQ, Scarabelli G, Grant BJ (2014) Integrating protein structural dynamics and evolutionary analysis with Bio3D. BMC Bioinformatics 15:399CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Grant BJ, Rodrigues AP, ElSawy KM, McCammon JA, Caves LS (2006) Bio3d: an R package for the comparative analysis of protein structures. Bioinformatics 22:2695–2696CrossRefPubMedGoogle Scholar
  50. 50.
    Van Wart AT, Durrant J, Votapka L, Amaro RE (2014) Weighted implementation of suboptimal paths (WISP): an optimized algorithm and tool for dynamical network analysis. J Chem Theory Comput 10:511–517CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Lee Y, Choi S, Hyeon C (2015) Communication over the network of binary switches regulates the activation of A2A adenosine receptor. PLoS Comput Biol 11:e1004044CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Manglik A, Kruse AC, Kobilka TS, Thian FS, Mathiesen JM, Sunahara RK, Pardo L, Weis WI, Kobilka BK, Granier S (2012) Crystal structure of the micro-opioid receptor bound to a morphinan antagonist. Nature 485:321–326CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Yuan S, Vogel H, Filipek S (2013) The role of water and sodium ions in the activation of the mu-opioid receptor. Angew Chem Int Ed 52:10112–10115CrossRefGoogle Scholar
  54. 54.
    Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E, Villa E, Chipot C, Skeel RD, Kale L, Schulten K (2005) Scalable molecular dynamics with NAMD. J Comput Chem 26:1781–1802CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Pronk S, Pall S, Schulz R, Larsson P, Bjelkmar P, Apostolov R, Shirts MR, Smith JC, Kasson PM, van der Spoel D, Hess B, Lindahl E (2013) GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 29:845–854CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Salomon-Ferrer R, Gotz AW, Poole D, Le Grand S, Walker RC (2013) Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. Explicit solvent particle mesh ewald. J Chem Theory Comput 9:3878–3888CrossRefPubMedGoogle Scholar
  57. 57.
    Harvey MJ, Giupponi G, Fabritiis GD (2009) ACEMD: accelerating biomolecular dynamics in the microsecond time scale. J Chem Theory Comput 5:1632–1639CrossRefPubMedGoogle Scholar
  58. 58.
    Bergdorf M, Baxter S, Rendleman CA, Shaw DE (2016) Desmond/GPU performance as of November 2016, D. E. Shaw Research Technical Report DESRES/TRGoogle Scholar
  59. 59.
    Klauda JB, Venable RM, Freites JA, O'Connor JW, Tobias DJ, Mondragon-Ramirez C, Vorobyov I, AD MK Jr, Pastor RW (2010) Update of the CHARMM all-atom additive force field for lipids: validation on six lipid types. J Phys Chem B 114:7830–7843CrossRefPubMedPubMedCentralGoogle Scholar
  60. 60.
    Yuan S, Filipek S, Palczewski K, Vogel H (2014) Activation of G-protein-coupled receptors correlates with the formation of a continuous internal water pathway. Nat Commun 5:4733CrossRefPubMedGoogle Scholar
  61. 61.
    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 Commun 180:1961–1972CrossRefGoogle Scholar
  62. 62.
    Barducci A, Bussi G, Parrinello M (2008) Well-tempered metadynamics: a smoothly converging and tunable free-energy method. Phys Rev Lett 100:020603CrossRefPubMedGoogle Scholar
  63. 63.
    Li JN, Jonsson AL, Beuming T, Shelley JC, Voth GA (2013) Ligand-dependent activation and deactivation of the human adenosine A(2A) receptor. J Am Chem Soc 135:8749–8759CrossRefPubMedPubMedCentralGoogle Scholar
  64. 64.
    Yuan S, Filipek S, Vogel H (2016) A gating mechanism of the serotonin 5-HT3 receptor. Structure 24:816–825CrossRefPubMedGoogle Scholar
  65. 65.
    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:e1002193CrossRefPubMedPubMedCentralGoogle Scholar
  66. 66.
    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-beta(2) adrenoceptor complex. Nature 469:236–240CrossRefPubMedPubMedCentralGoogle Scholar
  67. 67.
    Yuan S, Palczewski K, Peng Q, Kolinski M, Vogel H, Filipek S (2015) The mechanism of ligand-induced activation or inhibition of mu- and kappa-opioid receptors. Angew Chem Int Ed 54:7560–7563CrossRefGoogle Scholar
  68. 68.
    Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph Model 14:33–38CrossRefGoogle Scholar
  69. 69.
    Kuo CL, Wang RB, Shen LJ, Lien LL, Lien EJ (2004) G-protein coupled receptors: SAR analyses of neurotransmitters and antagonists. J Clin Pharm Ther 29:279–298CrossRefPubMedGoogle Scholar
  70. 70.
    Zhu XL, Cai HY, Xu ZJ, Wang Y, Wang HY, Zhang A, Zhu WL (2011) Classification of 5-HT(1A) receptor agonists and antagonists using GA-SVM method. Acta Pharmacol Sin 32:1424–1430CrossRefPubMedPubMedCentralGoogle Scholar
  71. 71.
    Oh SJ (2012) Characteristics in molecular vibrational frequency patterns between agonists and antagonists of histamine receptors. Genomics Inform 10:128–132CrossRefPubMedPubMedCentralGoogle Scholar
  72. 72.
    Chee HK, Oh SJ (2013) Molecular vibration-activity relationship in the agonism of adenosine receptors. Genomics & informatics 11:282–288CrossRefGoogle Scholar
  73. 73.
    Chee HK, Yang JS, Joung JG, Zhang BT, Oh SJ (2015) Characteristic molecular vibrations of adenosine receptor ligands. FEBS Lett 589:548–552CrossRefPubMedGoogle Scholar
  74. 74.
    Frank E, Hall M, Trigg L, Holmes G, Witten IH (2004) Data mining in bioinformatics using Weka. Bioinformatics 20:2479–2481CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2018

Authors and Affiliations

  • Przemysław Miszta
    • 1
  • Jakub Jakowiecki
    • 1
  • Ewelina Rutkowska
    • 1
  • Maria Turant
    • 1
  • Dorota Latek
    • 1
  • Sławomir Filipek
    • 1
    Email author
  1. 1.Biological and Chemical Research Centre, Faculty of ChemistryUniversity of WarsawWarsawPoland

Personalised recommendations