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

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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.

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References

  1. Náray-Szabó GG (1993) J Mol Recognit 6(4):205

    Article  Google Scholar 

  2. Jennings A (2011) In: Tari LW (ed) Structure-based drug discovery methods in molecular biology. Springer Protocols, Human Press, pp 235–250

  3. Chen K, Kurgan L (2009) PLoS ONE 4(2):e4473

    Article  Google Scholar 

  4. Kahraman A, Morris RJ, Laskowski RA, Thornton JM (2007) J Mol Biol 368(1):283

    Article  CAS  Google Scholar 

  5. Kahraman A, Morris RJ, Laskowski RA, Favia AD, Thornton JM (2010) Proteins 78(5):1120

    Article  CAS  Google Scholar 

  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):3862

    Article  CAS  Google Scholar 

  7. Morris R, Najmanovich R, Kahraman A, Thornton J (2005) Bioinformatics. Oxford, England. 21(10):2347

  8. Putta S, Beroza P (2007) Curr Top Med Chem 7(15):1514

    Article  CAS  Google Scholar 

  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):1504

    Article  CAS  Google Scholar 

  10. Kortagere S, Krasowski M, Ekins S (2009) Trends Pharmacol Sci 30(3):138

    Article  CAS  Google Scholar 

  11. Rush T, Grant J, Mosyak L, Nicholls A (2005) J Med Chem 48(5):1489

    Article  CAS  Google Scholar 

  12. Miller MD, Sheridan RP, Kearsley SK (1999) J Med Chem 42(9):1505

    Article  CAS  Google Scholar 

  13. Ballester P, Richards W (2007) J Comput Chem 28(10):1711

    Article  CAS  Google Scholar 

  14. Sauer W, Schwarz M (2003) J Chem Inf Comput Sci 43(3):987

    Article  CAS  Google Scholar 

  15. Akritopoulou-Zanze I, Metz J, Djuric S (2007) Drug Discov Today 12(21–22):948

    Article  CAS  Google Scholar 

  16. Wirth M, Sauer W (2011) Mol Inf 30:677

    CAS  Google Scholar 

  17. Liang J, Edelsbrunner H, Woodward C (1998) Protein Sci Publ Protein Soc 7(9):1884

    Google Scholar 

  18. Sonavane S, Chakrabarti P (2008) PLoS Comput Biol 4(9):e1000188

    Article  Google Scholar 

  19. Weisel M, Kriegl JM, Schneider G (2010) ChemBioChem 11(4):1

    Article  Google Scholar 

  20. Pérot S, Sperandio O, Miteva M, Camproux AC, Villoutreix B (2010) Drug Discov Today 15(15–16):656

    Article  Google Scholar 

  21. Meslamani J, Rognan D, Kellenberger E (2011) Bioinformatics 27(9):1324

    Article  CAS  Google Scholar 

  22. Volkamer A, Griewel A, Grombacher T, Rarey M (2010) J Chem Inf Model 50(11):2041

    Article  CAS  Google Scholar 

  23. Volkamer A, Kuhn D, Grombacher T, Rippmann F, Rarey M (2012) J Chem Inf Model 52(2):360

    Article  CAS  Google Scholar 

  24. Berman H, Westbrook J, Feng Z, Gilliland G, Bhat T, Weissig H, Shindyalov I, Bourne P (2000) Nucleic Acids Res 28(1):235

    Article  CAS  Google Scholar 

  25. Halgren T (1996) J Comput Chem 17(5–6):490

    Article  CAS  Google Scholar 

  26. Blow DM (2002) Acta Crystallographica Section D 58(5):792

    CAS  Google Scholar 

  27. Vainio MJ, Puranen JS, Johnson MS (2009) J Chem Inf Model 49(2):492

    Article  CAS  Google Scholar 

  28. Wang R, Fang X, Lu Y, Wang S (2004) J Med Chem 47(12):2977

    Article  CAS  Google Scholar 

  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):D1100

    Article  Google Scholar 

  30. Hawkins PCD, Skillman AG, Warren GL, Ellingson BA, Stahl MT (2010) J Chem Inf Model 50(4):572

    Article  CAS  Google Scholar 

  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):1739

    Article  CAS  Google Scholar 

  32. Madhavi Sastry G, Adzhigirey M, Day T, Annabhimoju R, Sherman W (2013) J Comput-Aided Mol Des 27(3):221

    Article  CAS  Google Scholar 

  33. Kuntz ID, Chen K, Sharp KA, Kollman PA (1999) Proc Natl Acad Sci USA 96(18):9997

    Article  CAS  Google Scholar 

  34. Reynolds CH, Tounge BA, Bembenek SD (2008) J Med Chem 51(8):2432

    Article  CAS  Google Scholar 

  35. Abad-Zapatero C, Perisic O, Wass J, Bento AP, Overington J, Al-Lazikani B, Johnson ME (2010) Drug Discov Today 15(19–20):804

    Article  CAS  Google Scholar 

  36. Huang N, Shoichet BK, Irwin JJ (2006) J Med Chem 49(23):6789

    Article  CAS  Google Scholar 

  37. Cross J, Thompson D, Rai B, Baber J, Fan K, Hu Y, Humblet C (2009) J Chem Inf Model 49(6):1455

    Article  CAS  Google Scholar 

  38. Verdonk ML, Berdini V, Hartshorn MJ, Mooij WTM, Murray CW, Taylor RD, Watson P (2004) J Chem Inf Comput Sci 44(3):793

    Article  CAS  Google Scholar 

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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.

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Correspondence to Matthias Wirth.

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Matthias Wirth and Andrea Volkamer contributed equally to this manuscript.

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Wirth, M., Volkamer, A., Zoete, V. et al. Protein pocket and ligand shape comparison and its application in virtual screening. J Comput Aided Mol Des 27, 511–524 (2013). https://doi.org/10.1007/s10822-013-9659-1

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  • DOI: https://doi.org/10.1007/s10822-013-9659-1

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