Skip to main content

Advertisement

Log in

Binding mode similarity measures for ranking of docking poses: a case study on the adenosine A2A receptor

  • Published:
Journal of Computer-Aided Molecular Design Aims and scope Submit manuscript

Abstract

We report an investigation designed to explore alternative approaches for ranking of docking poses in the search for antagonists of the adenosine A2A receptor, an attractive target for structure-based virtual screening. Calculation of 3D similarity of docking poses to crystallographic ligand(s) as well as similarity of receptor–ligand interaction patterns was consistently superior to conventional scoring functions for prioritizing antagonists over decoys. Moreover, the use of crystallographic antagonists and agonists, a core fragment of an antagonist, and a model of an agonist placed into the binding site of an antagonist-bound form of the receptor resulted in a significant early enrichment of antagonists in compound rankings. Taken together, these findings showed that the use of binding modes of agonists and/or antagonists, even if they were only approximate, for similarity assessment of docking poses or comparison of interaction patterns increased the odds of identifying new active compounds over conventional scoring.

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

Similar content being viewed by others

References

  1. Lavecchia A, Di Giovanni C (2013) Virtual screening strategies in drug discovery: a critical review. Curr Med Chem 20:2839–2860

    Article  CAS  Google Scholar 

  2. Heikamp K, Bajorath J (2013) The future of virtual compound screening. Chem Biol Drug Des 81:33–40

    Article  CAS  Google Scholar 

  3. Cheng T, Li Q, Zhou Z, Wang Y, Bryant SH (2012) Structure-based virtual screening for drug discovery: a problem-centric review. AAPS J 14:133–141

    Article  CAS  Google Scholar 

  4. Irwin JJ, Shoichet BK (2016) Docking screens for novel ligands conferring new biology. J Med Chem. doi:10.1021/acs.jmedchem.5b02008

    Google Scholar 

  5. Ripphausen P, Nisius B, Bajorath J (2011) State-of-the-art in ligand-based virtual screening. Drug Discov Today 16:372–376

    Article  CAS  Google Scholar 

  6. Drwal MN, Griffith R (2013) Combination of ligand—and structure-based methods in virtual screening. Drug Discov Today Technol 10:e395–e401

    Article  Google Scholar 

  7. Anighoro A, Bajorath J (2016) Three-dimensional similarity in molecular docking: prioritizing ligand poses on the basis of experimental binding modes. J Chem Inf Model 56:580–587

    Article  CAS  Google Scholar 

  8. Hu Y, Furtmann N, Gütschow M, Bajorath J (2012) Systematic identification and classification of three-dimensional activity cliffs. J Chem Inf Model 52:1490–1498

    Article  CAS  Google Scholar 

  9. Peltason L, Bajorath J (2007) Molecular similarity analysis uncovers heterogeneous structure-activity relationships and variable activity landscapes. Chem Biol 14:489–497

    Article  CAS  Google Scholar 

  10. Kobilka BK (2007) G protein coupled receptor structure and activation. Biochim Biophys Acta Biomembr 1768:794–807

    Article  CAS  Google Scholar 

  11. de Lera Ruiz M, Lim Y-H, Zheng J (2014) Adenosine A2A receptor as a drug discovery target. J Med Chem 57:3623–3650

    Article  Google Scholar 

  12. Shoichet BK, Kobilka BK (2012) Structure-based drug screening for G-protein-coupled receptors. Trends Pharmacol Sci 33:268–272

    Article  CAS  Google Scholar 

  13. Tautermann CS (2014) GPCR structures in drug design, emerging opportunities with new structures. Bioorg Med Chem Lett 24:4073–4079

    Article  CAS  Google Scholar 

  14. Carlsson J, Yoo L, Gao ZG, Irwin JJ, Shoichet BK, Jacobson KA (2010) Structure-based discovery of A2A adenosine receptor ligands. J Med Chem 53:3748–3755

    Article  CAS  Google Scholar 

  15. Chemical Computing Group, Inc. Molecular operating environment, version 2014.09

  16. Mpamhanga CP, Chen B, McLay IM, Willett P (2006) Knowledge-based interaction fingerprint scoring: a simple method for improving the effectiveness of fast scoring functions. J Chem Inf Model 46:686–698

    Article  CAS  Google Scholar 

  17. Marcou G, Rognan D (2007) Optimizing fragment and scaffold docking by use of molecular interaction fingerprints. J Chem Inf Model 47:195–207

    Article  CAS  Google Scholar 

  18. Desaphy J, Raimbaud E, Ducrot P, Rognan D (2013) Encoding protein–ligand interaction patterns in fingerprints and graphs. J Chem Inf Model 53:623–637

    Article  CAS  Google Scholar 

  19. Da C, Kireev D (2014) Structural protein–ligand interaction fingerprints (SPLIF) for structure-based virtual screening: method and benchmark study. J Chem Inf Model 54:2555–2561

    Article  CAS  Google Scholar 

  20. Da C, Stashko M, Jayakody C, Wang X, Janzen W, Frye S, Kireev D (2015) Discovery of mer kinase inhibitors by virtual screening using structural protein–ligand interaction fingerprints. Bioorg Med Chem 23:1096–1101

    Article  CAS  Google Scholar 

  21. 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 337:232–236

    Article  CAS  Google Scholar 

  22. Jaakola V-P, Griffith MT, Hanson MA, Cherezov V, Chien EY, Lane JR, Ijzerman AP, Stevens RC (2008) The 2.6 Ångstrom crystal structure of a human A2A adenosine receptor bound to an antagonist. Science 322:1211–1217

    Article  CAS  Google Scholar 

  23. Hino T, Arakawa T, Iwanari H, Yurugi-Kobayashi T, Ikeda-Suno C, Nakada-Nakura Y, Kusano-Arai O, Weyand S, Shimamura T, Nomura N, Cameron AD, Kobayashi T, Hamakubo T, Iwata S, Murata T (2012) G-protein-coupled receptor inactivation by an allosteric inverse-agonist antibody. Nature 482:237–240

    CAS  Google Scholar 

  24. Congreve M, Andrews SP, Doré 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 55:1898–1903

    Article  CAS  Google Scholar 

  25. 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

    Article  Google Scholar 

  26. 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

    Article  CAS  Google Scholar 

  27. Lebon G, Edwards PC, Leslie AGW, Tate CG (2015) Molecular determinants of CGS21680 binding to the human adenosine A2A receptor. Mol Pharmacol 87:907–915

    Article  CAS  Google Scholar 

  28. Xu F, Wu H, Katritch V, Han GW, Jacobson KA, Gao Z-G, Cherezov V, Stevens RC (2011) Structure of an agonist-bound human A2A adenosine receptor. Science 332:322–327

    Article  CAS  Google Scholar 

  29. Lenselink EB, Beuming T, Sherman W, van Vlijmen HWT, Ijzerman AP (2014) Selecting an optimal number of binding site waters to improve virtual screening enrichments against the adenosine A2A receptor. J Chem Inf Model 54:1737–1746

    Article  CAS  Google Scholar 

  30. Bauer MR, Ibrahim TM, Vogel SM, Boeckler FM (2013) Evaluation and optimization of virtual screening workflows with DEKOIS 2.0—a public library of challenging docking benchmark sets. J Chem Inf Model 53:1447–1462

    Article  CAS  Google Scholar 

  31. Liu T, Lin Y, Wen X, Jorissen RN, Gilson MK (2007) BindingDB: a web-accessible database of experimentally determined protein–ligand binding affinities. Nucleic Acids Res 35:D198–D201

    Article  CAS  Google Scholar 

  32. Irwin JJ, Shoichet BK (2005) ZINC—a free database of commercially available compounds for virtual screening. J Chem Inf Model 45:177–182

    Article  CAS  Google Scholar 

  33. Anighoro A, Rastelli G (2013) Enrichment factor analyses on G-protein coupled receptors with known crystal structure. J Chem Inf Model 53:739–743

    Article  CAS  Google Scholar 

  34. Corbeil CR, Williams CI, Labute P (2012) Variability in docking success rates due to dataset preparation. J Comput Aided Mol Des 26:775–786

    Article  CAS  Google Scholar 

  35. Planesas JM, Claramunt RM, Teixidó J, Borrell JI, Pérez-Nueno VI (2011) Improving VEGFR-2 docking-based screening by pharmacophore postfiltering and similarity search postprocessing. J Chem Inf Model 51:777–787

    Article  CAS  Google Scholar 

  36. Bender A, Glen RC (2005) A discussion of measures of enrichment in virtual screening: comparing the information content of descriptors with increasing levels of sophistication. J Chem Inf Model 45:1369–1375

    Article  CAS  Google Scholar 

  37. 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. Neurosci Methods 25:366–428

    Article  CAS  Google Scholar 

  38. Rodríguez D, Gao Z-G, Moss SM, Jacobson KA, Carlsson J (2015) Molecular docking screening using agonist-bound GPCR structures: probing the A2A adenosine receptor. J Chem Inf Model 55:550–563

    Article  Google Scholar 

  39. Federico S, Paoletta S, Cheong SL, Pastorin G, Cacciari B, Stragliotto S, Klotz KN, Siegel J, Gao Z-G, Jacobson KA, Moro S, Spalluto G (2011) Synthesis and biological evaluation of a new series of 1,2,4-triazolo[1,5-a]-1,3,5-triazines as human A2A adenosine receptor antagonists with improved water solubility. J Med Chem 54:877–889

    Article  CAS  Google Scholar 

  40. Bottegoni G, Kufareva I, Totrov M, Abagyan R (2009) Four-dimensional docking: a fast and accurate account of discrete receptor flexibility in ligand docking. J Med Chem 52:397–406

    Article  CAS  Google Scholar 

  41. Rueda M, Bottegoni G, Abagyan R (2010) Recipes for the selection of experimental protein conformations for virtual screening. J Chem Inf Model 50:186–193

    Article  CAS  Google Scholar 

  42. Bottegoni G, Rocchia W, Rueda M, Abagyan R, Cavalli A (2011) Systematic exploitation of multiple receptor conformations for virtual ligand screening. PLoS One 6:e18845

    Article  CAS  Google Scholar 

  43. Sgobba M, Caporuscio F, Anighoro A, Portioli C, Rastelli G (2012) Application of a post-docking procedure based on MM–PBSA and MM–GBSA on single and multiple protein conformations. Eur J Med Chem 58:431–440

    Article  CAS  Google Scholar 

  44. Hou X, Li K, Yu X, Sun J-P, Fang H (2015) Protein flexibility in docking-based virtual screening: discovery of novel lymphoid-specific tyrosine phosphatase inhibitors using multiple crystal structures. J Chem Inf Model 55:1973–1983

    Article  CAS  Google Scholar 

  45. Stevens RC, Cherezov V, Katritch V, Abagyan R, Kuhn P, Rosen H, Wüthrich K (2013) The GPCR network: a large-scale collaboration to determine human GPCR structure and function. Nat Rev Drug Discov 12:25–34

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jürgen Bajorath.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Anighoro, A., Bajorath, J. Binding mode similarity measures for ranking of docking poses: a case study on the adenosine A2A receptor. J Comput Aided Mol Des 30, 447–456 (2016). https://doi.org/10.1007/s10822-016-9918-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-016-9918-z

Keywords

Navigation