Skip to main content
Log in

Computational analysis of ligand recognition mechanisms by prostaglandin E2 (subtype 2) and D2 receptors

  • Regular Article
  • Published:
Theoretical Chemistry Accounts Aims and scope Submit manuscript

Abstract

The eight members of the prostanoid receptor family belong to the class A G protein-coupled receptors. We investigated the evolutionary relationship of the eight members by a molecular phylogenetic analysis and found that prostaglandin E2 receptor subtype 2 (EP2) and prostaglandin D2 receptor (DP) were closely related. The structures of the ligands for the two receptors are similar to each other but are distinguished by the exchanged locations of the carbonyl oxygen and the hydroxy group in the cyclopentane ring. The ligand recognition mechanisms of the receptors were examined by an integrated approach using several computational methods, such as amino acid sequence comparison, homology modeling, docking simulation, and molecular dynamics simulation. The results revealed the similar location of the ligand between the two receptors. The common carboxy group of the ligands interacts with the Arg residue on the seventh transmembrane (TM) helix, which is invariant among the prostanoid receptors. EP2 uses a Ser on TM1 to recognize the carbonyl oxygen in the cyclopentane ring of the ligand. The Ser is specifically conserved within EP2. On the other hand, DP uses a Lys on TM2 to recognize the hydroxy group of the ω chain of the ligand. The Lys is also specifically conserved within DP. The interaction network between the D(E)RY motif and TM6 was found in EP2. However, DP lacked this network, due to the mutation in the D(E)RY motif. Based on these observations and the previously published mutational studies on the motif, the possibility of another activation mechanism that does not involve the interaction between the D(E)RY motif and TM6 will be discussed.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Funk CD (2001) Science 294:1871–1875

    Article  CAS  Google Scholar 

  2. Samuelsson B, Morgenstern R, Jakobsson PJ (2007) Pharmacol Rev 59:207–224

    Article  CAS  Google Scholar 

  3. Wang MT, Honn KV, Nie D (2007) Cancer Metastasis Rev 26:525–534

    Article  CAS  Google Scholar 

  4. Schuligoi R et al (2010) Pharmacology 85:372–382

    Article  CAS  Google Scholar 

  5. Jones RL, Giembycz MA, Woodward DF (2009) Br J Pharmacol 158:104–145

    Article  CAS  Google Scholar 

  6. Matsuoka T, Narumiya S (2007) Sci World J 7:1329–1347

    CAS  Google Scholar 

  7. Hirai H et al (2001) J Exp Med 193:255–261

    Article  CAS  Google Scholar 

  8. Hata AN, Breyer RM (2004) Pharmacol Ther 103:147–166

    Article  CAS  Google Scholar 

  9. Kedzie KM, Donello JE, Krauss HA, Regan JW, Gil DW (1998) Mol Pharmacol 54:584–590

    CAS  Google Scholar 

  10. Chang C, Negishi M, Nishigaki N, Ichikawa A (1997) Biochem J 322:597–601

    CAS  Google Scholar 

  11. Stitham J, Stojanovic A, Merenick BL, O’Hara KA, Hwa J (2002) J Biol Chem 278:4250–4257

    Article  Google Scholar 

  12. Funk CD, Furci L, Moran N, Fitzgerald GA (1993) Mol Pharmacol 44:934–939

    CAS  Google Scholar 

  13. Neuschäfer-Rube F, Engemaier E, Koch S, Böer U, Püschel GP (2003) Biochem J 371:443–449

    Article  Google Scholar 

  14. Li Y et al (2007) J Am Chem Soc 129:10720–10731

    Article  CAS  Google Scholar 

  15. Rosenbaum DM, Rasmussen SG, Kobilka BK (2009) Nature 459:356–363

    Article  CAS  Google Scholar 

  16. Scheer A, Fanelli F, Costa T, De Benedetti PG, Cotecchia S (1996) EMBO J 15:3566–3578

    CAS  Google Scholar 

  17. Wess J (1998) Pharmacol Ther 80:231–264

    Article  CAS  Google Scholar 

  18. Ballesteros J, Palczewski K (2001) Curr Opin Drug Discov Devel 4:561–574

    CAS  Google Scholar 

  19. Rosenkilde MM, Kledal TN, Schwartz TW (2005) Mol Pharmacol 68:11–19

    CAS  Google Scholar 

  20. Lu ZL, Curtis CA, Jones PG, Pavia J, Hulme EC (1997) Mol Pharmacol 51:234–241

    CAS  Google Scholar 

  21. Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ (1997) Nucleic Acids Res 25:3389–3402

    Article  CAS  Google Scholar 

  22. Ballesteros JA, Weinstein H (1995) Methods Neurosci 25:366–428

    Article  CAS  Google Scholar 

  23. Katoh K, Misawa K, Kuma K, Miyata T (2002) Nucleic Acids Res 30:3059–3066

    Article  CAS  Google Scholar 

  24. Katoh K, Toh H (2008) Brief Bioinform 9:286–298

    Article  CAS  Google Scholar 

  25. Saitou N, Nei M (1987) Mol Biol Evol 4:406–425

    CAS  Google Scholar 

  26. Felsenstein J (1996) Methods Enzymol 266:418–427

    Article  CAS  Google Scholar 

  27. Jones DT, Taylor WR, Thornton JM (1992) Comput Appl Biosci 8:275–282

    CAS  Google Scholar 

  28. Felsenstein J (1985) Evolution 39:783–791

    Article  Google Scholar 

  29. Felsenstein J (1993) PHYLIP (phylogeny inference package), version 3.5c. University of Washington, Seattle

    Google Scholar 

  30. Adachi J, Hasegawa M (1996) MOLPHY (programs for molecular phylogenetics), version 2.3b3. Institute of Statistical Mathematics, Tokyo

    Google Scholar 

  31. Halgren TA (1996) J Comput Chem 17:490–519

    Article  CAS  Google Scholar 

  32. Labute P (2008) J Comput Chem 29:1693–1698

    Article  CAS  Google Scholar 

  33. Wiederstein M, Sippl MJ (2007) Nucleic Acids Res 35:W407–W410

    Article  Google Scholar 

  34. Sippl MJ (1993) Proteins 17:355–362

    Article  CAS  Google Scholar 

  35. Goto J, Kataoka R (2008) J Chem Inf Model 48:583–590

    Article  CAS  Google Scholar 

  36. 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) In: Proceedings of the ACM/IEEE conference on Supercomputing, Tampa, November 11-17, ACM New York, USA. doi:10.1145/1188455.1188544

  37. Banks JL, Beard HS, Cao Y, Cho AE, Damm W, Farid B, Felts AK, Halgren TA, Mainz DT, Maple JR, Murphy R, Philipp DM, Repasky MP, Zhang LY, Berne BJ, Friesner RA, Gallicchio E, Levy RM (2005) J Comput Chem 26:1752–1780

    Article  CAS  Google Scholar 

  38. Krautler V (2001) J Comput Chem 22:501–508

    Article  CAS  Google Scholar 

  39. Darden T, York D, Pedersen L (1993) J Chem Phys 98:10089–10092

    Article  CAS  Google Scholar 

  40. Lyne PD, Lamb M, Saeh JC (2006) J Med Chem 49:4805–4808

    Article  CAS  Google Scholar 

  41. Toh H, Ichikawa A, Narumiya S (1995) FEBS Lett 361:17–21

    Article  CAS  Google Scholar 

  42. Fritze O et al (2003) Proc Natl Acad Sci USA 100:2290–2295

    Article  CAS  Google Scholar 

  43. Paila YD, Tiwari S, Chattopadhyay A (2008) Biochim Biophys Acta 1788:295–302

    Google Scholar 

  44. Dundas J, Ouyang Z, Tseng J, Binkowski A, Turpaz Y, Liang J (2006) Nucleic Acids Res 34:W116–W118

    Article  CAS  Google Scholar 

  45. Jaakola V-P, Prilusky J, Sussman JL, Goldman A (2005) Protein Eng Des Sel 18:103–110

    Article  CAS  Google Scholar 

  46. Kobayashi T, Ushikubi F, Narumiya S (2000) J Biol Chem 275:24294–24303

    Article  CAS  Google Scholar 

  47. Tsuboi K, Sugimoto Y, Ichikawa A (2002) Prostaglandins Other Lipid Mediat 68–69:535–556

    Article  Google Scholar 

  48. Vogel R, Mahalingam M, Lüdeke S, Huber T, Siebert F, Sakmar TP (2008) J Mol Biol 380:648–655

    Article  CAS  Google Scholar 

  49. Rovati GE, Capra V, Neubig RR (2007) Mol Pharmacol 71:959–964

    Article  CAS  Google Scholar 

  50. Pathe-Neuschäfer-Rube A, Neuschäfer-Rube F, Püschel GP (2005) Biochem J 388:317–324

    Article  Google Scholar 

  51. Ambrosio M, Fanelli F, Brocchetti S, Raimondi F, Mauri M, Rovati GE, Capra V (2010) Cell Mol Life Sci 67:2979–2989

    Article  CAS  Google Scholar 

  52. Tusnády GE, Dosztányi Z, Simon I (2005) Bioinformatics 21:1276–1277

    Article  Google Scholar 

Download references

Acknowledgments

We thank Drs. Wataru Nemoto, Kentaro Tomii, and Makiko Suwa of CBRC for useful discussions on this work. HD was supported in part by the Global COE program, “an In Silico Medicine”, at Osaka University and Grants-in-Aid (Nos. 20650012 and 19650072) from the Ministry of Education, Culture, Sports, Science and Technology of Japan. HT was partially supported by Targeted Proteins Research Program (TPRP).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hiromi Daiyasu.

Additional information

Dedicated to Professor Akira Imamura on the occasion of his 77th birthday and published as part of the Imamura Festschrift Issue.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplement 1 Database search results (EPS 209 kb)

214_2011_1034_MOESM2_ESM.eps

Supplement 2 Multiple alignment of prostanoid receptor homologues. The alignment was used to construct the phylogenetic tree shown in Fig. 2. The gi number for each sequence is shown on the left side of the alignment. The information about the source organism and the receptor name are provided in Fig. 2. The D(E)RY motif and the NPxxY motif are indicated by blue rectangles. The second residue in the D(E)RY motif of DP is colored pink (EPS 1,637 kb)

Supplement 3 Validation of model structures (EPS 994 kb)

Supplement 4 Docking results (EPS 218 kb)

Supplement 5 Cα RMSD for the EP2 and DP models (EPS 947 kb)

Supplement 6 Potential energies for the EP2 and DP models (EPS 692 kb)

214_2011_1034_MOESM7_ESM.eps

Supplement 7 Time series of the distances between the atom pairs involved in the formation of the intramolecular networks for the EP2 and DP models (EPS 9,880 kb)

Supplement 8 Cα RMSF for the EP2 and DP models (EPS 2,315 kb)

Supplement 9 Interaction energies of the ligand-receptor complexes for the EP2 and DP models (EPS 210 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Daiyasu, H., Hirokawa, T., Kamiya, N. et al. Computational analysis of ligand recognition mechanisms by prostaglandin E2 (subtype 2) and D2 receptors. Theor Chem Acc 130, 1131–1143 (2011). https://doi.org/10.1007/s00214-011-1034-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00214-011-1034-5

Keywords

Navigation