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Structure-Based Virtual Screening of FGFR Inhibitors

Cross-Decoys and Induced-Fit Effect

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Abstract

Background

Receptor rearrangement upon ligand binding (induced-fit) constitutes a complicating factor in structure-based virtual screening, as protein flexibility is only partially included in many high-throughput docking programs. The effect of protein structure in these cases is rarely discussed.

Aim

Our objective was to analyze this influence on three aspects of automated ligand docking: (i) the successful reproduction of binding modes; (ii) the performance in binding site detection for a series of initial decoys positioned on the protein surface; and (iii) the extent to which the protein conformation biases the enrichment factors and the diversity in scaffold retrieval of a Virtual Screening experiment.

Methods

A fibroblast growth factor receptor (FGFR), for which several structures complexed with different inhibitors are publicly available, was selected as a study case. Besides its biological relevance, FGFR is an interesting target because of the structural changes occurring on ligand binding in receptor tyrosine kinases. Three common scoring functions (AUTODOCK, ChemScore, and GoldScore), under different parameter settings, were employed to dock a set of inhibitors of FGFR into these structures.

Results

We show how the choice of one particular protein x-ray structure restricts the docking process to the detection of those compounds that belong to the same chemical series or are similar to the chemotype of the corresponding co-crystallized ligand.

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References

  1. Wang R, Lu Y, Wang S. Comparative evaluation of 11 scoring functions for molecular docking. J Med Chem 2003 Jun; 46(12): 2287–303

    Article  PubMed  CAS  Google Scholar 

  2. Ferrara P, Gohlke H, Price DJ, et al. Assessing scoring functions for protein-ligand interactions. J Med Chem 2004 Jun; 47(12): 3032–47

    Article  PubMed  CAS  Google Scholar 

  3. Kellenberger E, Rodrigo J, Muller P, et al. Comparative evaluation of eight docking tools for docking and virtual screening accuracy. Proteins 2004 Nov; 57(2): 225–42

    Article  PubMed  CAS  Google Scholar 

  4. Perez C, Ortiz AR. Evaluation of docking functions for protein-ligand docking. J Med Chem 2001; 44: 3768–85

    Article  PubMed  CAS  Google Scholar 

  5. Kontoyianni M, McClellan LM, Sokol GS. Evaluation of docking performance: comparative data on docking algorithms. J Med Chem 2004 Jan; 47(3): 558–65

    Article  PubMed  CAS  Google Scholar 

  6. Warren GL, Andrews CV, Capelli A, et al. A critical assessment of docking programs and scoring functions. J Med Chem 2006 Oct 5; 49(20): 5912–31

    Article  PubMed  CAS  Google Scholar 

  7. Feher M. Consensus scoring for protein-ligand interactions. Drug Discov Today 2006 May; 11(9-10): 421–8

    Article  PubMed  CAS  Google Scholar 

  8. Charifson PS, Corkery JJ, Murcko MA, et al. Consensus scoring: a method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. J Med Chem 1999 Dec; 42(25): 5100–9

    Article  PubMed  CAS  Google Scholar 

  9. Wang R, Wang S. How does consensus scoring work for virtual library screening? An idealized computer experiment. J Chem Inf Comput Sci 2001 Sep–Oct; 41(5): 1422–6

    PubMed  CAS  Google Scholar 

  10. Verdonk ML, Cole JC, Hartshorn MJ, et al. Virtual screening using protein-ligand docking: avoiding artificial enrichment. J Chem Inf Comput Sci 2004 May–Jun; 44(3): 793–806

    PubMed  CAS  Google Scholar 

  11. Jones G, Willett P, Glen RC, et al. Development and validation of a genetic algorithm for flexible docking. J Mol Biol 1997 Apr; 267(3): 727–48

    Article  PubMed  CAS  Google Scholar 

  12. Ferrari AM, Wei BQ, Costantino L, et al. Soft docking and multiple receptor conformations in virtual screening. J Med Chem 2004 Oct; 47(21): 5076–84

    Article  PubMed  CAS  Google Scholar 

  13. Claussen H, Bunning C, Rarey M, et al. FlexE: Efficient molecular docking considering protein structure variations. J Mol Biol 2001 Apr; 308(2): 377–95

    Article  PubMed  CAS  Google Scholar 

  14. Sherman W, Beard HS, Farid R. Use of an induced fit receptor structure in virtual screening. Chem Biol Drug Design 2006; 67(1): 83–4

    Article  CAS  Google Scholar 

  15. Murray CW, Baxter CA, Frenkel AD. The sensitivity of the results of molecular docking to induced-fit effects: application to thrombin, thermolysin and neuraminidase. J Comp-Aided Mol Design 1999 Nov; 13(6): 547–62

    Article  CAS  Google Scholar 

  16. Birch L, Murray CW, Hartshorn MJ, et al. Sensitivity of molecular docking to induced fit effects in influenza virus neuraminidase. J Comp-Aided Mol Design 2002 Dec; 16(12): 855–69

    Article  CAS  Google Scholar 

  17. Erickson JA, Jalaie M, Robertson DH, et al. Lessons in molecular recognition: the effects of ligand and protein flexibility on molecular docking accuracy. J Med Chem 2004 Jan; 47(1): 45–55

    Article  PubMed  CAS  Google Scholar 

  18. Thomas MP, McInnes C, Fischerr P. Protein structures in virtual screening: a case study with CDK2. J Med Chem 2006 Jan; 49(1): 92–104

    Article  PubMed  CAS  Google Scholar 

  19. Hubbard SR, Till JH. Protein tyrosine kinase structure and function. Annu Rev Biochem 2000; 69: 373–98

    Article  PubMed  CAS  Google Scholar 

  20. McGovern SL, Shoichet BK. Information decay in molecular docking screens against holo, apo, and modeled conformations of enzymes. J Med Chem 2003 Jul; 46(14): 2895–907

    Article  PubMed  CAS  Google Scholar 

  21. Cavasotto CN, Abagyan RA. Protein flexibility in ligand docking and virtual screening to protein kinases. J Mol Biol 2004 Mar; 337(1): 209–25

    Article  PubMed  CAS  Google Scholar 

  22. Wei BQ, Weaver LH, Ferrari AM, et al. Testing a flexible-receptor docking algorithm in a model binding site. J Mol Biol 2004 Apr; 337(5): 1161–82

    Article  PubMed  CAS  Google Scholar 

  23. Cavasotto CN, Kovacs JA, Abagyan RA. Representing receptor flexibility in ligand docking through relevant normal modes. J Am Chem Soc 2005 Jul; 127(6): 9632–40

    Article  PubMed  CAS  Google Scholar 

  24. Kovacs JA, Cavasotto CN, Abagyan RA. Conformational sampling of protein flexibility in generalized coordinates: application to ligand docking. J Comput Theor Nanosci 2005 Sep; 2(3): 354–61

    Article  CAS  Google Scholar 

  25. Cavasotto CN, Orry AJ, Abagyan RA. The challenge of considering receptor flexibility in ligand docking and virtual screening. Curr Comput Aided Drug Des 2005 Oct; 1(4): 423–40

    Article  CAS  Google Scholar 

  26. May A, Zacharias M. Accounting for global protein deformability during protein-protein and protein-ligand docking. Biochim Biophys Acta 2005 Dec; 1754(1-2): 225–31

    Article  PubMed  CAS  Google Scholar 

  27. Teague SJ. Implications of protein flexibility for drug discovery. Nat Rev Drug Discov 2003 Jul; 2(7): 527–41

    Article  PubMed  CAS  Google Scholar 

  28. Presta M, Dell'Era P, Mitola S, et al. Fibroblast growth factor/fibroblast growth factor receptor system in angiogenesis. Cytokine Growth Factor Rev 2005 Apr; 16(2): 159–78

    Article  PubMed  CAS  Google Scholar 

  29. Grose R, Dickson C. Fibroblast growth factor signalling in tumorigenesis. Cytokine Growth Factor Rev 2005 Apr; 16(2): 179–86

    Article  PubMed  CAS  Google Scholar 

  30. Mohammadi M, Froum S, Hamby JM, et al. Crystal structure of an angiogenesis inhibitor bound to the FGF receptor tyrosine kinase domain. Embo J 1998 Oct; 17(20): 5896–904

    Article  PubMed  CAS  Google Scholar 

  31. Mohammadi M, McMahon G, Sun L, et al. Structures of the tyrosine kinase domain of fibroblast growth factor receptor in complex with inhibitors. Science 1997 May; 276: 955–60

    Article  PubMed  CAS  Google Scholar 

  32. Alasdair TRL, Jackson RM. Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites. Bioinformatics 2005 May; 21(9): 1908–16

    Article  Google Scholar 

  33. Brady GP, Stouten PFW. Fast prediction and visualization of protein binding pockets with PASS. J Comp-Aided Mol Design 2000 May; 14(4): 383–401

    Article  CAS  Google Scholar 

  34. Hetenyi C, Spoel D. Efficient docking of peptides to proteins without prior knowledge of the binding site. Protein Sci 2002 Jul; 11(7): 1729–37

    Article  PubMed  CAS  Google Scholar 

  35. Morris GM, Goodsell DS, Huey R, et al. Automated docking using a lamarckian genetic algorithm and a empirical binding free energy function. J Comput Chem 1998; 19: 1639–62

    Article  CAS  Google Scholar 

  36. MOE, molecular operating environment [computer program]. Montreal (PQ): Chemical Computing Group, 2004

  37. Hamby JM, Connolly CJC, Schroeder MC, et al. Structure-activity relationships for a novel series of Pyrido[2,3-d] pyrimidine tyrosine kinase inhibitors. J Med Chem 1997 Jul; 40(15): 2296–303

    Article  PubMed  CAS  Google Scholar 

  38. Klutchko SR, Hamby JM, Boschelli DH, et al. 2-Substituted aminopyrido[2,3-d] pyrimidin-7(8H)-ones: structure-activity relationships against selected tyrosine kinases and in vitro and in vitro anticancer activity. J Med Chem 1998 Aug; 41(17): 3276–92

    Article  PubMed  CAS  Google Scholar 

  39. Boschelli DH, Wu Z, Klutchko SR, et al. Synthesis and tyrosine kinase inhibitory activity of a series of 2-amino-8H-pyrido[2,3-d]pyrimidines: identification of potent, selective platelet-derived growth factor receptor tyrosine kinase inhibitors. J Med Chem 1998 Oct; 41(22): 4365–77

    Article  PubMed  CAS  Google Scholar 

  40. Connolly CJC, Hamby JM, Schroeder MC, et al. Discovery and structure-activity studies of a novel series of pyrido[2,3-d]Pyrimidine tyrosine kinase inhibitors. Bioorg Med Chem Lett Letters 1997; 7(18): 2415–20

    Article  CAS  Google Scholar 

  41. Schroeder M, Hamby JM, Connolly CJC, et al. Soluble 2-substituted aminopyrido [2,3-d]pyrimidin-7-yl ureas: structure-activity relationships against selected tyrosine kinases and exploration of in vitro and in vitro anticancer activity. J Med Chem 2001 Jun; 44(12): 1915–26

    Article  PubMed  CAS  Google Scholar 

  42. Thompson AM, Rewcastle GW, Boushelle SL, et al. Synthesis and structure-activity relationships of 7-subsituted 3-(2,6-Dichlorophenyl)-1,-naphthyridin-2 (1H)-ones as selective inhibitors of pp60c-src. J Med Chem 2000 Aug; 43(16): 3134–47

    Article  PubMed  CAS  Google Scholar 

  43. Sun L, Tran N, Liang C, et al. Design, synthesis, and evaluations of substituted 3-[(3-or 4-Carboxyethylpyrrol-2-yl)methylidenyl]indolin-2-ones as inhibitors of VEGF, FGF and PDGF receptor tyrosine kinases. J Med Chem 1999 Dec; 42(25): 5120–30

    Article  PubMed  CAS  Google Scholar 

  44. Sun L, Tran N, Liang C, et al. Identification of substituted 3-[(4,5,6,7-Tetrahydro-1H, indol-2-yl)methylene]-1,3-dihydroindol-2-ones as growth factor receptor inhibitors for VEGF-R2 (Flk-1/KDR), FGFR-R1, and PDGFR-Rβ tyrosine kinases. J Med Chem 2000 Jul; 43(14): 2655–63

    Article  PubMed  CAS  Google Scholar 

  45. Palmer BD, Kraker AJ, Hartl BG, et al. Structure-activity relationships for 5-substituted 1-Phenylbenzimidazoles as selective inhibitors of the platelet-derived growth factor receptor. J Med Chem 1999 Jul; 42(13): 2373–82

    Article  PubMed  CAS  Google Scholar 

  46. Hennequin LF, Thomas AP, Johnstone C, et al. Design and structure-activity relationship of a new class of potent VEGF receptor tyrosine kinase inhibitors. J Med Chem 1999 Dec; 42(26): 5369–89

    Article  PubMed  CAS  Google Scholar 

  47. Hennequin LF, Stokes ES, Thomas AP, et al. Novel 4-anilinoquinazolines with C-7 basic side chains: design and structure activity relationship of a series of potent, orally active, VEGF receptor tyrosine kinase inhibitors. J Med Chem 2002 Mar; 45(6): 1300–12

    Article  PubMed  CAS  Google Scholar 

  48. Buzko OV, Bishop AC, Shokat KM. Modified AutoDock for accurate docking of protein kinase inhibitors. J Comp-Aided Mol Design 2002; 16: 113–27

    Article  CAS  Google Scholar 

  49. Pierce AC, Sandretto KL, Bermis GW. Kinase inhibitors and the case for C-H… O hydrogen bonds in protein-ligand binding. Proteins 2002 Dec; 49(4): 567–76

    Article  PubMed  CAS  Google Scholar 

  50. Morris GM. Autodock: parameters. The Scripps Research Institute (La Jolla) [online]. Available from URL: http://www.URL.scripps.edu/mb/olson/doc/autodock/parameters.html/ [Accessed 2006 Apr 19]

  51. Gasteiger J, Marsili M. Iterative partial equalization of orbital electronegativity: a rapid access to atomic charges. Tetrahedron 1980; 36: 3219–88

    Article  CAS  Google Scholar 

  52. Stewart JJP. MOPAC 7/MOPAC 93 [computer program]. Tokyo: Fujitsu Limited, 1993

    Google Scholar 

  53. Verdonk ML, Cole JC, Hartshorn MJ, et al. Improved protein-ligand docking using GOLD. Proteins Struct Funct Genet 2003; 52: 609–23

    Article  PubMed  CAS  Google Scholar 

  54. Schneider P, Schneider G. Collection of bioactive reference compounds for focused library design. QSAR Comb Sci 2003 Dec; 22(7): 713–8

    Article  CAS  Google Scholar 

  55. Diller DJ, Li RX. Kinases, homology models, and high throughput docking. J Med Chem 2003 Oct; 46(22): 4638–47

    Article  PubMed  CAS  Google Scholar 

  56. Sali A, Blundell TL. Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 1993 Dec; 234(3): 779–815

    Article  PubMed  CAS  Google Scholar 

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Acknowledgments

The authors are grateful to Mr Norbert Dichter for technical assistance. This research was supported by the Beilstein-Institut zur Förderung der Chemischen Wissenschaften, Frankfurt am Main. Obdulia Rabal would like to thank the Generalitat de Catalunya — DURSI for a grant of the Formaciñ de Personal Investigador (2003FI) program. Financial support by the Spanish Ministerio de Ciencia y Tecnología is gratefully acknowledged (Grant No. BQU2003-07852).

The authors have no conflicts of interest that are directly relevant to the content of this study.

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Correspondence to Jordi Teixidó.

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Rabal, O., Schneider, G., Borrell, J.I. et al. Structure-Based Virtual Screening of FGFR Inhibitors. BioDrugs 21, 31–45 (2007). https://doi.org/10.2165/00063030-200721010-00005

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