Journal of Computer-Aided Molecular Design

, Volume 22, Issue 9, pp 621–627 | Cite as

Improving database enrichment through ensemble docking

  • Shashidhar Rao
  • Paul C. Sanschagrin
  • Jeremy R. Greenwood
  • Matthew P. Repasky
  • Woody Sherman
  • Ramy FaridEmail author


While it may seem intuitive that using an ensemble of multiple conformations of a receptor in structure-based virtual screening experiments would necessarily yield improved enrichment of actives relative to using just a single receptor, it turns out that at least in the p38 MAP kinase model system studied here, a very large majority of all possible ensembles do not yield improved enrichment of actives. However, there are combinations of receptor structures that do lead to improved enrichment results. We present here a method to select the ensembles that produce the best enrichments that does not rely on knowledge of active compounds or sophisticated analyses of the 3D receptor structures. In the system studied here, the small fraction of ensembles of up to 3 receptors that do yield good enrichments of actives were identified by selecting ensembles that have the best mean GlideScore for the top 1% of the docked ligands in a database screen of actives and drug-like “decoy” ligands. Ensembles of two receptors identified using this mean GlideScore metric generally outperform single receptors, while ensembles of three receptors identified using this metric consistently give optimal enrichment factors in which, for example, 40% of the known actives outrank all the other ligands in the database.


Enrichment Ensemble docking Virtual screening p38 MAP kinase Glide 


  1. 1.
    Leach AR, Shoichet BK (2006) J Med Chem 49:5851CrossRefGoogle Scholar
  2. 2.
    Perola E, Walters WP, Charifson PS (2004) Proteins 56:235CrossRefGoogle Scholar
  3. 3.
    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 47:1739CrossRefGoogle Scholar
  4. 4.
    Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT, Banks JL (2004) J Med Chem 47:1750CrossRefGoogle Scholar
  5. 5.
    Friesner RA, Murphy RB, Repasky MP, Frye LL, Greenwood JR, Halgren TA, Sanschagrin PC, Mainz DT (2006) J Med Chem 49:6177CrossRefGoogle Scholar
  6. 6.
    Zhou Z, Felts AK, Friesner RA, Levy RM (2007) J Chem Inf Model 47:1599CrossRefGoogle Scholar
  7. 7.
    Bernstein FC, Koetzle TF, Williams GJ, Meyer EF Jr, Brice MD, Rodgers JR, Kennard O, Shimanouchi T, Tasumi M (1977) J Mol Biol 112:535CrossRefGoogle Scholar
  8. 8.
    Sutherland JJ, Nandigam RK, Erickson JA, Vieth M (2007) Lessons in molecular recognition. 2. Assessing and improving cross-docking accuracy. J Chem Inf Model Google Scholar
  9. 9.
    Sherman W, Day T, Jacobson MP, Friesner RA, Farid R (2006) J Med Chem 49:534CrossRefGoogle Scholar
  10. 10.
    Bastard K, Prevost C, Zacharias M (2006) Proteins 62:956CrossRefGoogle Scholar
  11. 11.
    Cavasotto CN, Kovacs JA, Abagyan RA (2005) J Am Chem Soc 127:9632CrossRefGoogle Scholar
  12. 12.
    Schnecke V, Swanson CA, Getzoff ED, Tainer JA, Kuhn LA (1998) Proteins 33:74CrossRefGoogle Scholar
  13. 13.
    Zavodszky MI, Lei M, Thorpe MF, Day AR, Kuhn LA (2004) Proteins 57:243CrossRefGoogle Scholar
  14. 14.
    Alberts IL, Todorov NP, Dean PM (2005) J Med Chem 48:6585CrossRefGoogle Scholar
  15. 15.
    Limongelli V, Marinelli L, Cosconati S, Braun HA, Schmidt B, Novellino E (2007) ChemMedChem 2:667CrossRefGoogle Scholar
  16. 16.
    Huang SY, Zou X (2007) Proteins 66:399CrossRefGoogle Scholar
  17. 17.
    Claussen H, Buning C, Rarey M, Lengauer T (2001) J Mol Biol 308:377CrossRefGoogle Scholar
  18. 18.
    Lorber DM, Shoichet BK (1998) Protein Sci 7:938CrossRefGoogle Scholar
  19. 19.
    Polgar T, Keseru GM (2006) J Chem Inf Model 46:1795CrossRefGoogle Scholar
  20. 20.
    Only CA atoms are available in the PDB, however, one of the authors of the 1IAN structure (Liang Tong) generously provided us with a refined, all-atom model Google Scholar
  21. 21.
    Maestro v8.0, Schrödinger, Inc.: Portland, ORGoogle Scholar
  22. 22.
    Pargellis C, Tong L, Churchill L, Cirillo PF, Gilmore T, Graham AG, Grob PM, Hickey ER, Moss N, Pav S, Regan J (2002) Nat Struct Biol 9:268CrossRefGoogle Scholar
  23. 23.
    Epik v1.5, Schrödinger, Inc.: Portland, ORGoogle Scholar
  24. 24.
    The 1000 drug-like set of decoy ligands is available for download from the Schrödinger website Google Scholar
  25. 25.
    LigPrep v2.1, Schrödinger, Inc.: Portland, ORGoogle Scholar
  26. 26.
    Glide v4.5, Schrödinger, Inc.: Portland, ORGoogle Scholar
  27. 27.
    Frembgen-Kesner T, Elcock AH (2006) J Mol Biol 359:202CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Shashidhar Rao
    • 1
  • Paul C. Sanschagrin
    • 1
  • Jeremy R. Greenwood
    • 1
  • Matthew P. Repasky
    • 1
  • Woody Sherman
    • 1
  • Ramy Farid
    • 1
    Email author
  1. 1.Schrödinger, IncNew YorkUSA

Personalised recommendations