Journal of Computer-Aided Molecular Design

, Volume 26, Issue 5, pp 595–601 | Cite as

Evaluation of docking performance in a blinded virtual screening of fragment-like trypsin inhibitors

  • Georgiana Surpateanu
  • Bogdan I. IorgaEmail author


In this study, we have “blindly” assessed the ability of several combinations of docking software and scoring functions to predict the binding of a fragment-like library of bovine trypsine inhibitors. The most suitable protocols (involving Gold software and GoldScore scoring function, with or without rescoring) were selected for this purpose using a training set of compounds with known biological activities. The selected virtual screening protocols provided good results with the SAMPL3-VS dataset, showing enrichment factors of about 10 for Top 20 compounds. This methodology should be useful in difficult cases of docking, with a special emphasis on the fragment-based virtual screening campaigns.


Virtual screening Docking Scoring function Fragment-like compounds Trypsin inhibitors 



Root mean square deviation


Receiver operating characteristic


Area under curve


Confidence interval


QM-Polarized ligand docking

Supplementary material

10822_2011_9526_MOESM1_ESM.pdf (229 kb)
Supplementary material 1 (PDF 229 kb)


  1. 1.
    Huang D, Caflisch A (2011) Fragment-based approaches in virtual screening. In: Sotriffer C (ed) Virtual screening. Wiley-VCH Verlag, Weinheim, pp 467–489CrossRefGoogle Scholar
  2. 2.
    Kawatkar S, Wang H, Czerminski R, Joseph-McCarthy D (2009) Virtual fragment screening: an exploration of various docking and scoring protocols for fragments using glide. J Comput Aided Mol Des 23:527–539CrossRefGoogle Scholar
  3. 3.
    Sandor M, Kiss R, Keseru GM (2010) Virtual fragment docking by Glide: a validation study on 190 protein-fragment complexes. J Chem Inf Model 50:1165–1172CrossRefGoogle Scholar
  4. 4.
    Joseph-McCarthy D (2009) Challenges of fragment screening. J Comput Aided Mol Des 23:449–451CrossRefGoogle Scholar
  5. 5.
    Law R, Barker O, Barker J, Hesterkamp T, Godemann R, Andersen O, Fryatt T, Courtney S, Hallett D, Whittaker M (2009) The multiple roles of computational chemistry in fragment-based drug design. J Comput Aided Mol Des 23:459–473CrossRefGoogle Scholar
  6. 6.
    Warr W (2011) Fragment-based drug discovery: what really works. J Comput Aided Mol Des 25:599–605CrossRefGoogle Scholar
  7. 7.
    Rabal O, Urbano-Cuadrado M, Oyarzabal J (2010) Computational medicinal chemistry in fragment-based drug discovery: what, how and when. Future Med Chem 3:95–134CrossRefGoogle Scholar
  8. 8.
    Huang N, Jacobson MP (2010) Binding-site assessment by virtual fragment screening. PLoS ONE 5:e10109CrossRefGoogle Scholar
  9. 9.
    Schulz M, Landström J, Bright K, Hubbard R (2011) Design of a fragment library that maximally represents available chemical space. J Comput Aided Mol Des 25:611–620CrossRefGoogle Scholar
  10. 10.
    Chen IJ, Hubbard R (2009) Lessons for fragment library design: analysis of output from multiple screening campaigns. J Comput Aided Mol Des 23:603–620CrossRefGoogle Scholar
  11. 11.
    Verdonk ML, Berdini V, Hartshorn MJ, Mooij WT, Murray CW, Taylor RD, Watson P (2004) Virtual screening using protein-ligand docking: avoiding artificial enrichment. J Chem Inf Comput Sci 44:793–806CrossRefGoogle Scholar
  12. 12.
    Chen Y, Shoichet BK (2009) Molecular docking and ligand specificity in fragment-based inhibitor discovery. Nat Chem Biol 5:358–364CrossRefGoogle Scholar
  13. 13.
    Chen Y, Pohlhaus DT (2010) In silico docking and scoring of fragments. Drug Discov Today Technol 7:e149–e156CrossRefGoogle Scholar
  14. 14.
    Rummey C, Nordhoff S, Thiemann M, Metz G (2006) In silico fragment-based discovery of DPP-IV S1 pocket binders. Bioorg Med Chem Lett 16:1405–1409CrossRefGoogle Scholar
  15. 15.
    Friedman R, Caflisch A (2009) Discovery of plasmepsin inhibitors by fragment-based docking and consensus scoring. ChemMedChem 4:1317–1326CrossRefGoogle Scholar
  16. 16.
    Verdonk ML, Giangreco I, Hall RJ, Korb O, Mortenson PN, Murray CW (2011) Docking performance of fragments and druglike compounds. J Med Chem 54:5422–5431CrossRefGoogle Scholar
  17. 17.
    Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28:235–242CrossRefGoogle Scholar
  18. 18.
    Berman H, Henrick K, Nakamura H (2003) Announcing the worldwide protein data bank. Nat Struct Mol Biol 10:980CrossRefGoogle Scholar
  19. 19.
    Dullweber F, Stubbs MT, Musil Đ, Stürzebecher J, Klebe G (2001) Factorising ligand affinity: a combined thermodynamic and crystallographic study of trypsin and thrombin inhibition. J Mol Biol 313:593–614CrossRefGoogle Scholar
  20. 20.
    Nar H, Bauer M, Schmid A, Stassen J-M, Wienen W, Priepke HWM, Kauffmann IK, Ries UJ, Hauel NH (2001) Structural basis for inhibition promiscuity of dual specific thrombin and factor Xa blood coagulation inhibitors. Structure 9:29–37Google Scholar
  21. 21.
    Brandt T, Holzmann N, Muley L, Khayat M, Wegscheid-Gerlach C, Baum B, Heine A, Hangauer D, Klebe G (2011) Congeneric but still distinct: how closely related trypsin ligands exhibit different thermodynamic and structural properties. J Mol Biol 405:1170–1187CrossRefGoogle Scholar
  22. 22.
    Presnell SR, Patil GS, Mura C, Jude KM, Conley JM, Bertrand JA, Kam C-M, Powers JC, Williams LD (1998) Oxyanion-mediated inhibition of serine proteases. Biochemistry 37:17068–17081CrossRefGoogle Scholar
  23. 23.
    Perilo CS, Pereira MT, Santoro MM, Nagem RAP (2010) Structural binding evidence of the trypanocidal drugs Berenil® and Pentacarinate® active principles to a serine protease model. Int J Biol Macromol 46:502–511CrossRefGoogle Scholar
  24. 24.
    Rauh D, Klebe G, Stubbs MT (2004) Understanding protein-ligand interactions: the price of protein flexibility. J Mol Biol 335:1325–1341CrossRefGoogle Scholar
  25. 25.
    Katz BA, Elrod K, Luong C, Rice MJ, Mackman RL, Sprengeler PA, Spencer J, Hataye J, Janc J, Link J, Litvak J, Rai R, Rice K, Sideris S, Verner E, Young W (2001) A novel serine protease inhibition motif involving a multi-centered short hydrogen bonding network at the active site. J Mol Biol 307:1451–1486CrossRefGoogle Scholar
  26. 26.
    Katz BA, Elrod K, Verner E, Mackman RL, Luong C, Shrader WD, Sendzik M, Spencer JR, Sprengeler PA, Kolesnikov A, Tai VWF, Hui HC, Breitenbucher JG, Allen D, Janc JW (2003) Elaborate manifold of short hydrogen bond arrays mediating binding of active site-directed serine protease inhibitors. J Mol Biol 329:93–120CrossRefGoogle Scholar
  27. 27.
    Whitlow M, Arnaiz DO, Buckman BO, Davey DD, Griedel B, Guilford WJ, Koovakkat SK, Liang A, Mohan R, Phillips GB, Seto M, Shaw KJ, Xu W, Zhao Z, Light DR, Morrissey MM (1999) Crystallographic analysis of potent and selective factor Xa inhibitors complexed to bovine trypsin. Acta Crystallogr Sect D 55:1395–1404CrossRefGoogle Scholar
  28. 28.
    Toyota E, Ng KKS, Sekizaki H, Itoh K, Tanizawa K, James MNG (2001) X-ray crystallographic analyses of complexes between bovine β-trypsin and schiff base copper(II) or iron(III) chelates. J Mol Biol 305:471–479CrossRefGoogle Scholar
  29. 29.
    Sherawat M, Kaur P, Perbandt M, Betzel C, Slusarchyk WA, Bisacchi GS, Chang C, Jacobson BL, Einspahr HM, Singh TP (2007) Structure of the complex of trypsin with a highly potent synthetic inhibitor at 0.97 A resolution. Acta Crystallogr Sect D 63:500–507CrossRefGoogle Scholar
  30. 30.
    Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD (2003) Improved protein-ligand docking using Gold. Protein Struct Funct Bioinf 52:609–623CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Institut de Chimie des Substances Naturelles, CNRS UPR 2301, Centre de Recherche de Gif-sur-YvetteGif-sur-YvetteFrance

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