Structural Chemistry

, Volume 28, Issue 3, pp 849–857 | Cite as

Molecular Modeling of Human CCR2 Receptor within POPC Lipid Bilayer

  • Ahmad Ebadi
  • Dara Dastan
  • Mojtaba Azami
  • Adibe Karimi
  • Nima Razzaghi-Asl
Original Research

Abstract

Chemokine receptor 2 (CCR2), a G-protein coupled receptor (GPCR), is a critical target for several inflammatory and autoimmune diseases. The main restriction on designing desirable antagonists against CCR2 is the lack of appropriate crystal structure for this target. In the absence of such experimental data, computational methods triggering structure prediction provide a cost-effective option. Homology modeling has been widely used to explore GPCR structure. Within the present contribution, homology modeling, molecular docking and molecular dynamics (MD) simulation were applied to construct a reliable model for CCR2. In the present contribution, we docked INCB3344, one of the most potent CCR2 inhibitors, into the active site of the CCR2 protein. Subsequently, we studied the dynamic behavior of INCB3344-CCR2 complex in the presence of lipid membrane. Moreover; a detailed molecular mechanism of INCB3344 action has been proposed. It was revealed that Tyr120, His121, Tyr259 and Glu291 formed H-bond interactions with INCB3344 while residues such as Trp98, His202, Thr203 and Thr173 participated in hydrophobic interactions. As a consequence, a reliable homology model of CCR2 could be successfully developed on the basis of CCR5 crystallographic structure. Finally it was found that binding of INCB3344 led to the structural changes in CCR2 that provided more interaction sites. Results of this study may be useful to design further CCR2 inhibiting structures with the aim of developing desirable therapeutic agents.

Keywords

Homology modeling Docking MD simulation CCR2 Inflammation INCB3344 

Notes

Acknowledgements

Financial supports of this project by research council of Hamadan University of Medical Sciences are acknowledged.

References

  1. 1.
    Congreve M, Murray CW, Blundell TL (2005) Keynote review: Structural biology and drug discovery. Drug Discov Today 10(13):895–907CrossRefGoogle Scholar
  2. 2.
    Kubinyi H (2001) HTS Technologies-IBC Informa Conference. IDrugs 4(2):168–173Google Scholar
  3. 3.
    Blundell TL, Sibanda BL, Montalvão RW, Brewerton S, Chelliah V, Worth CL, Harmer NJ, Davies O, Burke D (2006) Structural biology and bioinformatics in drug design: opportunities and challenges for target identification and lead discovery. Philos Trans R Soc Lond Ser B Biol Sci 361(1467):413–423CrossRefGoogle Scholar
  4. 4.
    Anderson AC (2003) The process of structure-based drug design. Chem Biol 10(9):787–797CrossRefGoogle Scholar
  5. 5.
    Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28(1):235–242CrossRefGoogle Scholar
  6. 6.
    Cavasotto CN, Phatak SS (2009) Homology modeling in drug discovery: current trends and applications. Drug Discov Today 14(13):676–683CrossRefGoogle Scholar
  7. 7.
    Dalton JA, Jackson RM (2007) An evaluation of automated homology modelling methods at low target–template sequence similarity. Bioinformatics 23(15):1901–1908CrossRefGoogle Scholar
  8. 8.
    Marti-Renom MA, Stuart AC, Fiser A, Sánchez R, Melo F, Šali A (2000) Comparative protein structure modeling of genes and genomes. Annu Rev Biophys Biomol Struct 29(1):291–325CrossRefGoogle Scholar
  9. 9.
    Charo IF, Ransohoff RM (2006) The many roles of chemokines and chemokine receptors in inflammation. N Engl J Med Chem 354(6):610–621CrossRefGoogle Scholar
  10. 10.
    Baggiolini M, Dewald B, Moser B (1993) Interleukin-8 and related chemotactic cytokines-CXC and CC chemokines. Adv Immunol 55:97–179CrossRefGoogle Scholar
  11. 11.
    Gu L, Tseng SC, Rollins BJ (1999) Monocyte chemoattractant protein-1. Chemokines Chem Immunol Basel Karger 72:7–29Google Scholar
  12. 12.
    Thelen M (2001) Dancing to the tune of chemokines. Nat Immunol 2(2):129–134CrossRefGoogle Scholar
  13. 13.
    Craig MJ, Loberg RD (2006) CCL2 (Monocyte Chemoattractant Protein-1) in cancer bone metastases. Cancer Metast Rev 25(4):611–619CrossRefGoogle Scholar
  14. 14.
    Buckle DR, Hedgecock CJ (1997) Drug targets in inflammation and immunomodulation. Drug Discov Today 2(8):325–332CrossRefGoogle Scholar
  15. 15.
    Reape TJ, Groot PH (1999) Chemokines and atherosclerosis. Atherosclerosis 147(2):213–225CrossRefGoogle Scholar
  16. 16.
    Boring L, Gosling J, Cleary M, Charo IF (1998) Decreased lesion formation in CCR2−/− mice reveals a role for chemokines in the initiation of atherosclerosis. Nature 394(6696):894–897CrossRefGoogle Scholar
  17. 17.
    Gosling J, Slaymaker S, Gu L, Tseng S, Zlot CH, Young SG, Rollins BJ, Charo IF (1999) MCP-1 deficiency reduces susceptibility to atherosclerosis in mice that overexpress human apolipoprotein B. J Clin Invest 103(6):773–778CrossRefGoogle Scholar
  18. 18.
    Gong J-H, Ratkay LG, Waterfield JD, Clark-Lewis I (1997) An antagonist of monocyte chemoattractant protein 1 (MCP-1) inhibits arthritis in the MRL-lpr mouse model. J Exp Med 186(1):131–137CrossRefGoogle Scholar
  19. 19.
    Berkhout TA, Blaney FE, Bridges AM, Cooper DG, Forbes IT, Gribble AD, Groot PH, Hardy A, Ife RJ, Kaur R, Moores KE, Shillito H, Willetts J, Witherington J (2003) CCR2: characterization of the antagonist binding site from a combined receptor modeling/mutagenesis approach. J Med Chem 46(19):4070–4086CrossRefGoogle Scholar
  20. 20.
    Cherezov V, Rosenbaum DM, Hanson MA, Rasmussen SG, Thian FS, Kobilka TS, Choi HJ, 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):1258–1265CrossRefGoogle Scholar
  21. 21.
    Kim J-H, Lim JW, Lee S-W, Kim K, No KT (2011) Ligand supported homology modeling and docking evaluation of CCR2: docked pose selection by consensus scoring. J Mol Model 17(10):2707–2016CrossRefGoogle Scholar
  22. 22.
    Shahlaei M, Fassihi A, Papaleo E, Pourfarzam M (2013) Molecular dynamics simulation of chemokine receptors in lipid bilayer: a case study on C-C chemokine receptor type 2. Chem Biol Drug Des 82(5):534–545CrossRefGoogle Scholar
  23. 23.
    Hall SE, Mao A, Nicolaidou V, Finelli M, Wise EL, Nedjai B, Kanjanapangka J, Harirchian P, Chen D, Selchau V, Ribeiro S, Schyler S, Pease JE, Horuk R, Vaidehi N (2009) Elucidation of binding sites of dual antagonists in the human chemokine receptors CCR2 and CCR5. Mol Pharmacol 75(6):1325–1336CrossRefGoogle Scholar
  24. 24.
    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
  25. 25.
    Kothandan G, Gadhe CG, Cho SJ (2012) Structural insights from binding poses of CCR2 and CCR5 with clinically important antagonists: a combined in silico study. PLoS One 7(3):e32864CrossRefGoogle Scholar
  26. 26.
    Skelton AA, Maharaj YR, Soliman ME (2014) Target-bound generated pharmacophore model to improve the pharmacophore-based virtual screening: Identification of G-protein coupled human CCR2 receptors inhibitors as anti-Inflammatory drugs. Cell Mol Bioeng 7(1):45–57CrossRefGoogle Scholar
  27. 27.
    Chavan S, Pawar S, Singh R, Sobhia ME (2012) Binding site characterization of G protein-coupled receptor by alanine-scanning mutagenesis using molecular dynamics and binding free energy approach: application to CC chemokine receptor-2 (CCR2). Mol Divers 16(2):401–413CrossRefGoogle Scholar
  28. 28.
    Singh R, Sobhia ME (2011) Homology modeling of human CCR2 receptor. Med Chem Res 20(9):1704–1712CrossRefGoogle Scholar
  29. 29.
    Burley SK, Joachimiak A, Montelione GT, Wilson IA (2008) Contributions to the NIH-NIGMS protein structure initiative from the PSI production centers. Structure 16(1):5–11CrossRefGoogle Scholar
  30. 30.
    Ginalski K (2006) Comparative modeling for protein structure prediction. Curr Opin Struct Biol 16(2):172–177CrossRefGoogle Scholar
  31. 31.
    Hillisch A, Pineda LF, Hilgenfeld R (2004) Utility of homology models in the drug discovery process. Drug Discov Today 9(15):659–669CrossRefGoogle Scholar
  32. 32.
    Ebadi SA, Razzaghi-Asl N, Khoshneviszadeh M, Miri R (2015) Detailed atomistic molecular modeling of a potent type ΙΙ p38α inhibitor. Struct Chem 26(4):1125–1137CrossRefGoogle Scholar
  33. 33.
    Tan Q, Zhu Y, Li J, Chen Z, Han GW, Kufareva I, Li T, Ma L, Fenalti G, Li J, Zhang W, Xie X, Yang H, Jiang H, Cherezov V, Liu H, Stevens RC, Zhao Q, Wu B (2013) Structure of the CCR5 chemokine receptor–HIV entry inhibitor maraviroc complex. Science 341(6152):1387–1390CrossRefGoogle Scholar
  34. 34.
    Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215(3):403–410CrossRefGoogle Scholar
  35. 35.
    Tusnady GE, Simon I (1998) Principles governing amino acid composition of integral membrane proteins: application to topology prediction. Journal Mol Biol 283(2):489–506CrossRefGoogle Scholar
  36. 36.
    Viklund H, Elofsson A (2008) OCTOPUS: improving topology prediction by two-track ANN-based preference scores and an extended topological grammar. Bioinformatics 24(15):1662–1668CrossRefGoogle Scholar
  37. 37.
    Krogh A, Larsson B, Von Heijne G, Sonnhammer EL (2001) Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 305(3):567–580CrossRefGoogle Scholar
  38. 38.
    Reynolds SM, Käll L, Riffle ME, Bilmes JA, Noble WS (2008) Transmembrane topology and signal peptide prediction using dynamic bayesian networks. PLoS Comput Biol 4(11):e1000213CrossRefGoogle Scholar
  39. 39.
    Käll L, Krogh A, Sonnhammer EL (2004) A combined transmembrane topology and signal peptide prediction method. J Mol Biol 338(5):1027–1036CrossRefGoogle Scholar
  40. 40.
    Bernsel A, Viklund H, Falk J, Lindahl E, von Heijne G, Elofsson A (2008) Prediction of membrane-protein topology from first principles. P Natl Acad Sci 105(20):7177–7181CrossRefGoogle Scholar
  41. 41.
    Tsirigos KD, Peters C, Shu N, Käll L, Elofsson A (2015) The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides. Nucleic Acids Res 43(web server issue):W401–W407CrossRefGoogle Scholar
  42. 42.
    Thompson JD, Higgins DG, Gibson TJ (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 22(22):4673–4680CrossRefGoogle Scholar
  43. 43.
    Sali A, Blundell T (1994) Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 234(3):779–815CrossRefGoogle Scholar
  44. 44.
    Shen M, Sali A (2006) Statistical potential for assessment and prediction of protein structures. Protein Sci 15(11):2507–2524CrossRefGoogle Scholar
  45. 45.
    Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993) PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr 26(2):283–291CrossRefGoogle Scholar
  46. 46.
    Hess B, Kutzner C, Van Der Spoel D, Lindahl E (2008) GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput 4(3):435–447CrossRefGoogle Scholar
  47. 47.
    van Gunsteren WF, Billeter S, Eising A, Hünenberger PH, Krüger P, Mark AE, Scott WRP, Tironi IG (1996) Biomolecular simulation: the GROMOS96 manual and user guide. Zurich, Switzerland: Hochschulverlag AG an der ETH Z¨urichGoogle Scholar
  48. 48.
    Tieleman DP, Berendsen H (1996) Molecular dynamics simulations of a fully hydrated dipalmitoylphosphatidylcholine bilayer with different macroscopic boundary conditions and parameters. J Chem Phys 105(11):4871–4880CrossRefGoogle Scholar
  49. 49.
    Tieleman DP, MacCallum JL, Ash WL, Kandt C, Xu Z, Monticelli L (2006) Membrane protein simulations with a united-atom lipid and all-atom protein model: lipid–protein interactions, side chain transfer free energies and model proteins. J Phys Condens Matter 18(28):S1221CrossRefGoogle Scholar
  50. 50.
    Bussi G, Donadio D, Parrinello M (2007) Canonical sampling through velocity rescaling. J Chem Phys 126(1):014101CrossRefGoogle Scholar
  51. 51.
    Parrinello M, Rahman A (1981) Polymorphic transitions in single crystals: A new molecular dynamics method. J Appl Phys 52(12):7182–7190CrossRefGoogle Scholar
  52. 52.
    Tom D, Darrin Y, Lee P (1993) Particle mesh Ewald: an N [center-dot] log (N) method for Ewald sums in large systems. J Chem Phys 98(12):10089–10092CrossRefGoogle Scholar
  53. 53.
    Hess B, Bekker H, Berendsen HJ, Fraaije JG (1997) LINCS: a linear constraint solver for molecular simulations. J Comput Chem 18(12):1463–1472CrossRefGoogle Scholar
  54. 54.
    Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791CrossRefGoogle Scholar
  55. 55.
    Baldwin JM, Schertler GF, Unger VM (1997) An alpha-carbon template for the transmembrane helices in the rhodopsin family of G-protein-coupled receptors. J Mol Biol 272(1):144–164CrossRefGoogle Scholar
  56. 56.
    Colovos C, Yeates TO (1993) Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 2(9):1511–1519CrossRefGoogle Scholar
  57. 57.
    Gonzalez A, Duran LS, Araya-Secchi R, Garate JA, Pessoa-Mahana CD, Lagos CF, Perez-Acle T (2008) Computational modeling study of functional microdomains in cannabinoid receptor type 1. Bioorg Med Chem 16(8):4378–4389CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Ahmad Ebadi
    • 1
    • 2
  • Dara Dastan
    • 2
    • 3
  • Mojtaba Azami
    • 1
  • Adibe Karimi
    • 1
  • Nima Razzaghi-Asl
    • 4
  1. 1.Department of Medicinal Chemistry, School of PharmacyHamadan University of Medical SciencesHamadanIran
  2. 2.Medicinal Plants and Natural Products Research CenterHamadan University of Medical SciencesHamadanIran
  3. 3.Department of Pharmacognosy and Pharmaceutical Biotechnology, School of PharmacyHamadan University of Medical SciencesHamadanIran
  4. 4.Department of Medicinal Chemistry, School of PharmacyArdabil University of Medical SciencesArdabilIran

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