Skip to main content
SpringerLink
Log in

Chemical space sampling by different scoring functions and crystal structures

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

Abstract

Virtual screening has become a popular tool to identify novel leads in the early phases of drug discovery. A variety of docking and scoring methods used in virtual screening have been the subject of active research in an effort to gauge limitations and articulate best practices. However, how to best utilize different scoring functions and various crystal structures, when available, is not yet well understood. In this work we use multiple crystal structures of PI3 K-γ in both prospective and retrospective virtual screening experiments. Both Glide SP scoring and Prime MM-GBSA rescoring are utilized in the prospective and retrospective virtual screens, and consensus scoring is investigated in the retrospective virtual screening experiments. The results show that each of the different crystal structures that was used, samples a different chemical space, i.e. different chemotypes are prioritized by each structure. In addition, the different (re)scoring functions prioritize different chemotypes as well. Somewhat surprisingly, the Prime MM-GBSA scoring function generally gives lower enrichments than Glide SP. Finally we investigate the impact of different ligand preparation protocols on virtual screening enrichment factors. In summary, different crystal structures and different scoring functions are complementary to each other and allow for a wider variety of chemotypes to be considered for experimental follow-up.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Brooijmans N, Kuntz ID (2003) Molecular recognition and docking algorithms. Annu Rev Biophys Biomol Struct 32(1):335–373

    Article  CAS  Google Scholar 

  2. Warren GL, Andrews CW, Capelli AM, Clarke B, LaLonde J, Lambert MH, Lindvall M, Nevins N, Semus SF, Senger S, Tedesco G, Wall ID, Woolven JM, Peishoff CE, Head MS (2006) A critical assessment of docking programs and scoring functions. J Med Chem 49(20):5912–5931

    Article  CAS  Google Scholar 

  3. Hartshorn MJ, Verdonk ML, Chessari G, Brewerton SC, Mooij WT, Mortenson PN, Murray CW (2007) Diverse, high-quality test set for the validation of protein–ligand docking performance. J Med Chem 50(4):726–741

    Article  CAS  Google Scholar 

  4. Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD (2003) Improved protein–ligand docking using GOLD. Proteins 52(4):609–623

    Article  CAS  Google Scholar 

  5. Jones G, Willett P, Glen RC, Leach AR, Taylor R (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267:727–748

    Article  CAS  Google Scholar 

  6. Kramer B, Rarey M, Lengauer T (1999) Evaluation of the FLEXX incremental construction algorithm for protein–ligand docking. Proteins 37(2):228–241

    Article  CAS  Google Scholar 

  7. 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) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47(7):1739–1749

    Article  CAS  Google Scholar 

  8. Perola E, Walters WP, Charifson PS (2004) A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance. Proteins 56(2):235–249

    Article  CAS  Google Scholar 

  9. Cross JB, Thompson DC, Rai BK, Baber JC, Fan KY, Hu Y, Humblet C (2009) Comparison of several molecular docking programs: pose prediction and virtual screening accuracy. J Chem Inf Model 49(6):1455–1474

    Article  CAS  Google Scholar 

  10. Sheridan RP, McGaughey GB, Cornell WD (2008) Multiple protein structures and multiple ligands: effects on the apparent goodness of virtual screening results. J Comput Aided Mol Des 22(3–4):257–265

    Article  CAS  Google Scholar 

  11. Kontoyianni M, Sokol GS, McClellan LM (2005) Evaluation of library ranking efficacy in virtual screening. J Comput Chem 26(1):11–22

    Article  CAS  Google Scholar 

  12. Muegge I, Enyedy IJ (2004) Virtual screening for kinase targets. Curr Med Chem 11(6):693–707

    Article  CAS  Google Scholar 

  13. Cummings MD, DesJarlais RL, Gibbs AC, Mohan V, Jaeger EP (2005) Comparison of automated docking programs as virtual screening tools. J Med Chem 48(4):962–976

    Article  CAS  Google Scholar 

  14. Alvarez JC (2004) High-throughput docking as a source of novel drug leads. Curr Opin Chem Biol 8(4):365–370

    Article  CAS  Google Scholar 

  15. Babaoglu K, Simeonov A, Irwin JJ, Nelson ME, Feng B, Thomas CJ, Cancian L, Costi MP, Maltby DA, Jadhav A, Inglese J, Austin CP, Shoichet BK (2008) Comprehensive mechanistic analysis of hits from high-throughput and docking screens against beta-lactamase. J Med Chem 51(8):2502–2511

    Article  CAS  Google Scholar 

  16. Klebe G (2006) Virtual ligand screening: strategies, perspectives and limitations. Drug Discov Today 11(13–14):580–594

    Article  CAS  Google Scholar 

  17. Kraemer O, Hazemann I, Podjarny AD, Klebe G (2004) Virtual screening for inhibitors of human aldose reductase. Proteins 55(4):814–823

    Article  CAS  Google Scholar 

  18. Nagarajan S, Doddareddy M, Choo H, Cho YS, Oh KS, Lee BH, Pae AN (2009) IKKbeta inhibitors identification part I: homology model assisted structure based virtual screening. Bioorg Med Chem 17(7):2759–2766

    Article  CAS  Google Scholar 

  19. Park H, Bahn YJ, Jeong DG, Woo EJ, Kwon JS, Ryu SE (2008) Identification of novel inhibitors of extracellular signal-regulated kinase 2 based on the structure-based virtual screening. Bioorg Med Chem Lett 18(20):5372–5376

    Article  CAS  Google Scholar 

  20. Luo C, Xie P, Marmorstein R (2008) Identification of BRAF inhibitors through in silico screening. J Med Chem 51(19):6121–6127

    Article  CAS  Google Scholar 

  21. Fu DH, Jiang W, Zheng JT, Zhao GY, Li Y, Yi H, Li ZR, Jiang JD, Yang KQ, Wang Y, Si SY, Jadomycin B (2008) An Aurora-B kinase inhibitor discovered through virtual screening. Mol Cancer Ther 7(8):2386–2393

    Article  CAS  Google Scholar 

  22. Kolb P, Huang D, Dey F, Caflisch A (2008) Discovery of kinase inhibitors by high-throughput docking and scoring based on a transferable linear interaction energy model. J Med Chem 51(5):1179–1188

    Article  CAS  Google Scholar 

  23. Cavasotto CN, Ortiz MA, Abagyan RA, Piedrafita FJ (2006) In silico identification of novel EGFR inhibitors with antiproliferative activity against cancer cells. Bioorg Med Chem Lett 16(7):1969–1974

    Article  CAS  Google Scholar 

  24. Cozza G, Bonvini P, Zorzi E, Poletto G, Pagano MA, Sarno S, Donella-Deana A, Zagotto G, Rosolen A, Pinna LA, Meggio F, Moro S (2006) Identification of ellagic acid as potent inhibitor of protein kinase CK2: a successful example of a virtual screening application. J Med Chem 49(8):2363–2366

    Article  CAS  Google Scholar 

  25. Cozza G, Gianoncelli A, Montopoli M, Caparrotta L, Venerando A, Meggio F, Pinna LA, Zagotto G, Moro S (2008) Identification of novel protein kinase CK1 delta (CK1delta) inhibitors through structure-based virtual screening. Bioorg Med Chem Lett 18(20):5672–5675

    Article  CAS  Google Scholar 

  26. Foloppe N, Fisher LM, Howes R, Potter A, Robertson AGS, Surgenor AE (2006) Identification of chemically diverse Chk1 inhibitors by receptor-based virtual screening. Bioorg Med Chem 14(14):4792–4802

    Article  CAS  Google Scholar 

  27. Hancock CN, Macias A, Lee EK, Yu SY, MacKerell AD Jr, Shapiro P (2005) Identification of novel extracellular signal-regulated kinase docking domain inhibitors. J Med Chem 48(14):4586–4595

    Article  CAS  Google Scholar 

  28. Hu X, Prehna G, Stebbins CE (2007) Targeting plague virulence factors: a combined machine learning method and multiple conformational virtual screening for the discovery of yersinia protein kinase A inhibitors. J Med Chem 50(17):3980–3983

    Article  CAS  Google Scholar 

  29. Li J, Tan J-z, Chen L-l, Zhang J, Shen X, Mei C-l, Fu L-l, Lin L-p, Ding J, Xiong B, Xiong X-s, Liu H, Luo X-m, Jiang H-l (2006) Design, synthesis and antitumor evaluation of a new series of N-substituted-thiourea derivatives. Acta Pharmacol Sin 27(9):1259–1271

    Article  CAS  Google Scholar 

  30. Park H, Bahn YJ, Jeong DG, Woo EJ, Kwon JS, Ryu SE (2008) Identification of novel inhibitors of extracellular signal-regulated kinase 2 based on the structure-based virtual screening. Bioorganic & Medicinal Chemistry Letters 18(20):5372–5376

    Article  CAS  Google Scholar 

  31. Peach ML, Tan N, Choyke SJ, Giubellino A, Athauda G, Burke TR Jr, Nicklaus MC, Bottaro DP (2009) Directed discovery of agents targeting the met tyrosine kinase domain by virtual screening. J Med Chem 52(4):943–951

    Article  CAS  Google Scholar 

  32. Peng H, Huang N, Qi J, Xie P, Xu C, Wang J, Yang C (2003) Identification of novel inhibitors of BCR-ABL tyrosine kinase via virtual screening. Bioorg Med Chem Lett 13(21):3693–3699

    Article  CAS  Google Scholar 

  33. Pierce AC, Jacobs M, Stuver-Moody C (2008) Docking study yields four novel inhibitors of the protooncogene pim-1 kinase. J Med Chem 51(6):1972–1975

    Article  CAS  Google Scholar 

  34. Qin Z, Zhang J, Xu B, Chen L, Wu Y, Yang X, Shen X, Molin S, Danchin A, Jiang H, Qu D (2006) Structure-based discovery of inhibitors of the YycG histidine kinase: new chemical leads to combat Staphylococcus epidermidis infections. BMC Microbiol 6:96

    Article  Google Scholar 

  35. Richardson CM, Nunns CL, Williamson DS, Parratt MJ, Dokurno P, Howes R, Borgognoni J, Drysdale MJ, Finch H, Hubbard RE, Jackson PS, Kierstan P, Lentzen G, Moore JD, Murray JB, Simmonite H, Surgenor AE, Torrance CJ (2007) Discovery of a potent CDK2 inhibitor with a novel binding mode, using virtual screening and initial, structure-guided lead scoping. Bioorg Med Chem Lett 17(14):3880–3885

    Article  CAS  Google Scholar 

  36. Segura-Cabrera A, Rodriguez-Perez MA (2008) Structure-based prediction of Mycobacterium tuberculosis shikimate kinase inhibitors by high-throughput virtual screening. Bioorg Med Chem Lett 18(11):3152–3157

    Article  CAS  Google Scholar 

  37. Toledo-Sherman L, Deretey E, Slon-Usakiewicz JJ, Ng W, Dai J-R, Foster JE, Redden PR, Uger MD, Liao LC, Pasternak A, Reid N (2005) Frontal affinity chromatography with MS detection of EphB2 tyrosine kinase receptor. 2. Identification of small-molecule inhibitors via coupling with virtual screening. J Med Chem 48(9):3221–3230

    Article  CAS  Google Scholar 

  38. Warner SL, Bashyam S, Vankayalapati H, Bearss DJ, Han H, Von Hoff DD, Hurley LH (2006) Identification of a lead small-molecule inhibitor of the Aurora kinases using a structure-assisted, fragment-based approach. Mol Cancer Ther 5(7):1764–1773

    Article  CAS  Google Scholar 

  39. Duca JS, Madison VS, Voigt JH (2008) Cross-docking of inhibitors into CDK. 2 structures 1. J Chem Inf Model 48(3):659–668

    Article  CAS  Google Scholar 

  40. O’Boyle NM, Brewerton SC, Taylor R (2008) Using buriedness to improve discrimination between actives and inactives in docking. J Chem Inf Model 48(6):1269–1278

    Article  Google Scholar 

  41. Friesner RA, Murphy RB, Repasky MP, Frye LL, Greenwood JR, Halgren TA, Sanschagrin PC, Mainz DT (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein–ligand complexes. J Med Chem 49(21):6177–6196

    Article  CAS  Google Scholar 

  42. Kuhn B, Gerber P, Schulz-Gasch T, Stahl M (2005) Validation and use of the MM-PBSA approach for drug discovery. J Med Chem 48(12):4040–4048

    Article  CAS  Google Scholar 

  43. Thompson DC, Humblet C, Joseph-McCarthy D (2008) Investigation of MM-PBSA rescoring of docking poses. J Chem Inf Model 48(5):1081–1091

    Article  CAS  Google Scholar 

  44. Ruvinsky AM (2007) Role of binding entropy in the refinement of protein–ligand docking predictions: analysis based on the use of 11 scoring functions. J Comput Chem 28(8):1364–1372

    Article  CAS  Google Scholar 

  45. Ruvinsky AM, Kozintsev AV (2006) Novel statistical-thermodynamic methods to predict protein–ligand binding positions using probability distribution functions. Proteins 62(1):202–208

    Article  CAS  Google Scholar 

  46. Srinivasan J, Cheatham TE, Cieplak P, Kollman PA, Case DA (1998) Continuum solvent studies of the stability of DNA, RNA, and phosphoramidate-DNA helices. J Am Chem Soc 120(37):9401–9409

    Article  CAS  Google Scholar 

  47. Massova I, Kollman PA (1999) Computational alanine scanning to probe protein–protein interactions: a novel approach to evaluate binding free energies. J Am Chem Soc 121(36):8133–8143

    Article  CAS  Google Scholar 

  48. Bashford D, Case DA (2000) Generalized born models of macromolecular solvation effects. Annu Rev Phys Chem 51(1):129–152

    Article  CAS  Google Scholar 

  49. Gilson MK, Rashin A, Fine R, Honig B (1985) On the calculation of electrostatic interactions in proteins. J Mol Biol 184(3):503–516

    Article  CAS  Google Scholar 

  50. Warwicker J, Watson HC (1982) Calculation of the electric potential in the active site cleft due to [alpha]-helix dipoles. J Mol Biol 157(4):671–679

    Article  CAS  Google Scholar 

  51. Sharp KA, Nicholls A, Fine RF, Honig B (1991) Reconciling the magnitude of the microscopic and macroscopic hydrophobic effects. Science 252(5002):106–109

    Article  CAS  Google Scholar 

  52. Sitkoff D, Ben-Tal N, Honig B (1996) Calculation of alkane to water solvation free energies using continuum solvent models. J Phys Chem 100(7):2744–2752

    Article  CAS  Google Scholar 

  53. Guimarães CR, Cardozo M (2008) MM-GB/SA rescoring of docking poses in structure-based lead optimization. J Chem Inf Model 48(5):958–970

    Article  Google Scholar 

  54. Huo S, Wang J, Cieplak P, Kollman PA, Kuntz ID (2002) Molecular dynamics and free energy analyses of cathepsin D-inhibitor interactions: insight into structure-based ligand design. J Med Chem 45(7):1412–1419

    Article  CAS  Google Scholar 

  55. Charifson PS, Corkery JJ, Murcko MA, Walters WP (1999) Consensus scoring: a method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. J Med Chem 42(25):5100–5109

    Article  CAS  Google Scholar 

  56. O’Boyle NM, Liebeschuetz JW, Cole JC (2009) Testing assumptions and hypotheses for rescoring success in protein–ligand docking. J Chem Inf Model 49:1871–1878

    Article  Google Scholar 

  57. Krovat EM, Langer T (2004) Impact of scoring functions on enrichment in docking-based virtual screening: an application study on renin inhibitors. J Chem Inf Comput Sci 44(3):1123–1129

    CAS  Google Scholar 

  58. 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(3):793–806

    CAS  Google Scholar 

  59. Stahl M, Rarey M (2001) Detailed analysis of scoring functions for virtual screening. J Med Chem 44:1035–1042

    Article  CAS  Google Scholar 

  60. Sutherland JJ, Nandigam RK, Erickson JA, Vieth M (2007) Lessons in molecular recognition. 2. Assessing and improving cross-docking accuracy. J Chem Inf Model 47(6):2293–2302

    Article  CAS  Google Scholar 

  61. Erickson JA, Jalaie M, Robertson DH, Lewis RA, Vieth M (2004) Lessons in molecular recognition: the effects of ligand and protein flexibility on molecular docking accuracy. J Med Chem 47(1):45–55

    Article  CAS  Google Scholar 

  62. Birch L, Murray CW, Hartshorn MJ, Tickle IJ, Verdonk ML (2002) Sensitivity of molecular docking to induced fit effects in influenza virus neuraminidase. J Comput Aided Mol Des 16(12):855–869

    Article  CAS  Google Scholar 

  63. Murray CW, Baxter CA, Frenkel AD (1999) The sensitivity of the results of molecular docking to induced fit effects: application to thrombin, thermolysin and neuraminidase. J Comput Aided Mol Des 13(6):547–562

    Article  CAS  Google Scholar 

  64. Ragno R, Frasca S, Manetti F, Brizzi A, Massa S (2005) HIV-reverse transcriptase inhibition: inclusion of ligand-induced fit by cross-docking studies. J Med Chem 48(1):200–212

    Article  CAS  Google Scholar 

  65. May A, Zacharias M (2008) Protein–ligand docking accounting for receptor side chain and global flexibility in normal modes: evaluation on kinase inhibitor cross docking. J Med Chem 51(12):3499–3506

    Article  CAS  Google Scholar 

  66. Jain AN (2009) Effects of protein conformation in docking: improved pose prediction through protein pocket adaptation. J Comput Aided Mol Des 23(6):355–374

    Article  CAS  Google Scholar 

  67. Ferrari AM, Wei BQ, Costantino L, Shoichet BK (2004) Soft docking and multiple receptor conformations in virtual screening. J Med Chem 47(21):5076–5084

    Article  CAS  Google Scholar 

  68. Claussen H, Buning C, Rarey M, Lengauer T (2001) FlexE: efficient molecular docking considering protein structure variations. J Mol Biol 308(2):377–395

    Article  CAS  Google Scholar 

  69. Osterberg F, Morris GM, Sanner MF, Olson AJ, Goodsell DS (2002) Automated docking to multiple target structures: incorporation of protein mobility and structural water heterogeneity in AutoDock. Proteins 46(1):34–40

    Article  CAS  Google Scholar 

  70. Knegtel RM, Kuntz ID, Oshiro CM (1997) Molecular docking to ensembles of protein structures. J Mol Biol 266(2):424–440

    Article  CAS  Google Scholar 

  71. Leach AR (1994) Ligand docking to proteins with discrete side-chain flexibility. J Mol Biol 235(1):345–356

    Article  CAS  Google Scholar 

  72. Zavodszky MI, Kuhn LA (2005) Side-chain flexibility in protein–ligand binding: the minimal rotation hypothesis. Protein Sci 14(4):1104–1114

    Article  CAS  Google Scholar 

  73. Leach AR, Lemon AP (1998) Exploring the conformational space of protein side chains using dead-end elimination and the A* algorithm. Proteins 33(2):227–239

    Article  CAS  Google Scholar 

  74. Wei BQ, Weaver LH, Ferrari AM, Matthews BW, Shoichet BK (2004) Testing a flexible-receptor docking algorithm in a model binding site. J Mol Biol 337(5):1161–1182

    Article  CAS  Google Scholar 

  75. Cavasotto CN, Abagyan RA (2004) Protein flexibility in ligand docking and virtual screening to protein kinases. J Mol Biol 337(1):209–225

    Article  CAS  Google Scholar 

  76. Barril X, Morley SD (2005) Unveiling the full potential of flexible receptor docking using multiple crystallographic structures. J Med Chem 48(13):4432–4443

    Article  CAS  Google Scholar 

  77. Ruckle T, Schwarz MK, Rommel C (2006) PI3 K[gamma] inhibition: towards an ‘aspirin of the 21st century’? Nat Rev Drug Discov 5(11):903–918

    Article  Google Scholar 

  78. Macromodel. Schrodinger LLC, New York (2005)

  79. Schrodinger (2007) Maestro 8.0. Manual, p 294

  80. In: iResearchLibrary, ChemNavigator, San Diego, CA

  81. OEChemTK (2008) OpenEye Scientific Software, Inc, Santa Fe, NM

  82. OMEGA (2007) OpenEye Scientific Software: Santa Fe, NM

  83. Glide. In: Manual. Schrodinger, LLC, Portland, OR, USA, p 112 (2007

  84. Prime (2007) Schrodinger LLC, New York

  85. Feher M (2006) Consensus scoring for protein–ligand interactions. Drug Discov Today 11(9–10):421–428

    Article  CAS  Google Scholar 

  86. Jain AN, Nicholls A (2008) Recommendations for evaluation of computational methods. J Comput Aided Mol Des 22(3–4):133–139

    Article  CAS  Google Scholar 

  87. Benchware DataMiner (2007) Tripos, L.P. Saint Louis, MO

  88. 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 23:527–539

    Article  CAS  Google Scholar 

  89. Mpamhanga CP, Chen B, McLay IM, Ormsby DL, Lindvall MK (2005) Retrospective docking study of PDE4B ligands and an analysis of the behavior of selected scoring functions. J Chem Inf Model 45(4):1061–1074

    Article  CAS  Google Scholar 

Download references

Acknowledgments

NB thanks Derek Cole and Mike Bowman for support of the VS efforts, Jason Jussif for experimental testing of VS hits, Joel Bard and Kris Svenson for PI3 K-γ crystal structures, and Yongbo Hu for preparation of CORP and CNAV VS libraries.

Author information

Authors and Affiliations

  1. Structural Biology and Computational Chemistry, Wyeth Research, 401 N. Middletown Road, Pearl River, NY, USA

    Natasja Brooijmans

  2. Lead Finding Platform, Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, MA, USA

    Natasja Brooijmans

  3. Structural Biology and Computational Chemistry, Wyeth Research, CN 8000, Princeton, NJ, USA

    Christine Humblet

Authors
  1. Natasja Brooijmans
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christine Humblet
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Natasja Brooijmans.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Cite this article

Brooijmans, N., Humblet, C. Chemical space sampling by different scoring functions and crystal structures. J Comput Aided Mol Des 24, 433–447 (2010). https://doi.org/10.1007/s10822-010-9356-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-010-9356-2

Keywords

Search

Navigation