Journal of Computer-Aided Molecular Design

, Volume 27, Issue 6, pp 511–524 | Cite as

Protein pocket and ligand shape comparison and its application in virtual screening

  • Matthias Wirth
  • Andrea Volkamer
  • Vincent Zoete
  • Friedrich Rippmann
  • Olivier Michielin
  • Matthias Rarey
  • Wolfgang H. B. Sauer
Article

Abstract

Understanding molecular recognition is one major requirement for drug discovery and design. Physicochemical and shape complementarity between two binding partners is the driving force during complex formation. In this study, the impact of shape within this process is analyzed. Protein binding pockets and co-crystallized ligands are represented by normalized principal moments of inertia ratios (NPRs). The corresponding descriptor space is triangular, with its corners occupied by spherical, discoid, and elongated shapes. An analysis of a selected set of sc-PDB complexes suggests that pockets and bound ligands avoid spherical shapes, which are, however, prevalent in small unoccupied pockets. Furthermore, a direct shape comparison confirms previous studies that on average only one third of a pocket is filled by its bound ligand, supplemented by a 50 % subpocket coverage. In this study, we found that shape complementary is expressed by low pairwise shape distances in NPR space, short distances between the centers-of-mass, and small deviations in the angle between the first principal ellipsoid axes. Furthermore, it is assessed how different binding pocket parameters are related to bioactivity and binding efficiency of the co-crystallized ligand. In addition, the performance of different shape and size parameters of pockets and ligands is evaluated in a virtual screening scenario performed on four representative targets.

Keywords

Protein binding sites Molecular shape Shape complementarity Molecular recognition Ligand efficiency Virtual screening 

Notes

Acknowledgments

We thank Volker Hähnke and Serge Christmann-Franck for constructive discussions and Jeffrey Shaw for performing the Glide docking runs. Andrea Volkamer acknowledges funding from the BMBF (Grant 0315292A) for the pocket analysis project as part of the Biokatalyse2021 cluster. Matthias Wirth thanks Merck Serono S.A. for a PhD fellowship.

Supplementary material

10822_2013_9659_MOESM1_ESM.pdf (11.9 mb)
PDF (12160 KB)

References

  1. 1.
    Náray-Szabó GG (1993) J Mol Recognit 6(4):205CrossRefGoogle Scholar
  2. 2.
    Jennings A (2011) In: Tari LW (ed) Structure-based drug discovery methods in molecular biology. Springer Protocols, Human Press, pp 235–250Google Scholar
  3. 3.
    Chen K, Kurgan L (2009) PLoS ONE 4(2):e4473CrossRefGoogle Scholar
  4. 4.
    Kahraman A, Morris RJ, Laskowski RA, Thornton JM (2007) J Mol Biol 368(1):283CrossRefGoogle Scholar
  5. 5.
    Kahraman A, Morris RJ, Laskowski RA, Favia AD, Thornton JM (2010) Proteins 78(5):1120CrossRefGoogle Scholar
  6. 6.
    Nicholls A, McGaughey G, Sheridan R, Good A, Warren G, Mathieu M, Muchmore S, Brown S, Grant J, Haigh J, Nevins N, Jain A, Kelley B (2010) J Med Chem 53(10):3862CrossRefGoogle Scholar
  7. 7.
    Morris R, Najmanovich R, Kahraman A, Thornton J (2005) Bioinformatics. Oxford, England. 21(10):2347Google Scholar
  8. 8.
    Putta S, Beroza P (2007) Curr Top Med Chem 7(15):1514CrossRefGoogle Scholar
  9. 9.
    McGaughey G, Sheridan R, Bayly C, Culberson J, Kreatsoulas C, Lindsley S, Maiorov V, Truchon JF, Cornell W (2007) J Chem Inf Model 47(4):1504CrossRefGoogle Scholar
  10. 10.
    Kortagere S, Krasowski M, Ekins S (2009) Trends Pharmacol Sci 30(3):138CrossRefGoogle Scholar
  11. 11.
    Rush T, Grant J, Mosyak L, Nicholls A (2005) J Med Chem 48(5):1489CrossRefGoogle Scholar
  12. 12.
    Miller MD, Sheridan RP, Kearsley SK (1999) J Med Chem 42(9):1505CrossRefGoogle Scholar
  13. 13.
    Ballester P, Richards W (2007) J Comput Chem 28(10):1711CrossRefGoogle Scholar
  14. 14.
    Sauer W, Schwarz M (2003) J Chem Inf Comput Sci 43(3):987CrossRefGoogle Scholar
  15. 15.
    Akritopoulou-Zanze I, Metz J, Djuric S (2007) Drug Discov Today 12(21–22):948CrossRefGoogle Scholar
  16. 16.
    Wirth M, Sauer W (2011) Mol Inf 30:677Google Scholar
  17. 17.
    Liang J, Edelsbrunner H, Woodward C (1998) Protein Sci Publ Protein Soc 7(9):1884Google Scholar
  18. 18.
    Sonavane S, Chakrabarti P (2008) PLoS Comput Biol 4(9):e1000188CrossRefGoogle Scholar
  19. 19.
    Weisel M, Kriegl JM, Schneider G (2010) ChemBioChem 11(4):1CrossRefGoogle Scholar
  20. 20.
    Pérot S, Sperandio O, Miteva M, Camproux AC, Villoutreix B (2010) Drug Discov Today 15(15–16):656CrossRefGoogle Scholar
  21. 21.
    Meslamani J, Rognan D, Kellenberger E (2011) Bioinformatics 27(9):1324CrossRefGoogle Scholar
  22. 22.
    Volkamer A, Griewel A, Grombacher T, Rarey M (2010) J Chem Inf Model 50(11):2041CrossRefGoogle Scholar
  23. 23.
    Volkamer A, Kuhn D, Grombacher T, Rippmann F, Rarey M (2012) J Chem Inf Model 52(2):360CrossRefGoogle Scholar
  24. 24.
    Berman H, Westbrook J, Feng Z, Gilliland G, Bhat T, Weissig H, Shindyalov I, Bourne P (2000) Nucleic Acids Res 28(1):235CrossRefGoogle Scholar
  25. 25.
    Halgren T (1996) J Comput Chem 17(5–6):490CrossRefGoogle Scholar
  26. 26.
    Blow DM (2002) Acta Crystallographica Section D 58(5):792Google Scholar
  27. 27.
    Vainio MJ, Puranen JS, Johnson MS (2009) J Chem Inf Model 49(2):492CrossRefGoogle Scholar
  28. 28.
    Wang R, Fang X, Lu Y, Wang S (2004) J Med Chem 47(12):2977CrossRefGoogle Scholar
  29. 29.
    Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, Overington JP (2011) Nucleic Acids Res 40(D1):D1100CrossRefGoogle Scholar
  30. 30.
    Hawkins PCD, Skillman AG, Warren GL, Ellingson BA, Stahl MT (2010) J Chem Inf Model 50(4):572CrossRefGoogle Scholar
  31. 31.
    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) J Med Chem 47(7):1739CrossRefGoogle Scholar
  32. 32.
    Madhavi Sastry G, Adzhigirey M, Day T, Annabhimoju R, Sherman W (2013) J Comput-Aided Mol Des 27(3):221CrossRefGoogle Scholar
  33. 33.
    Kuntz ID, Chen K, Sharp KA, Kollman PA (1999) Proc Natl Acad Sci USA 96(18):9997CrossRefGoogle Scholar
  34. 34.
    Reynolds CH, Tounge BA, Bembenek SD (2008) J Med Chem 51(8):2432CrossRefGoogle Scholar
  35. 35.
    Abad-Zapatero C, Perisic O, Wass J, Bento AP, Overington J, Al-Lazikani B, Johnson ME (2010) Drug Discov Today 15(19–20):804CrossRefGoogle Scholar
  36. 36.
    Huang N, Shoichet BK, Irwin JJ (2006) J Med Chem 49(23):6789CrossRefGoogle Scholar
  37. 37.
    Cross J, Thompson D, Rai B, Baber J, Fan K, Hu Y, Humblet C (2009) J Chem Inf Model 49(6):1455CrossRefGoogle Scholar
  38. 38.
    Verdonk ML, Berdini V, Hartshorn MJ, Mooij WTM, Murray CW, Taylor RD, Watson P (2004) J Chem Inf Comput Sci 44(3):793CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Matthias Wirth
    • 1
    • 2
  • Andrea Volkamer
    • 3
  • Vincent Zoete
    • 2
  • Friedrich Rippmann
    • 4
  • Olivier Michielin
    • 2
  • Matthias Rarey
    • 3
  • Wolfgang H. B. Sauer
    • 1
  1. 1.Computational ChemistryMerck Serono S.A. GenevaGenevaSwitzerland
  2. 2.Swiss Institute of Bioinformatics, Molecular Modelling GroupUNIL Sorge-Bâtiment GénopodeLausanneSwitzerland
  3. 3.Center for BioinformaticsUniversity of HamburgHamburgGermany
  4. 4.Global Computational ChemistryMerck KGaA, Merck SeronoDarmstadtGermany

Personalised recommendations