Advertisement

Journal of Molecular Modeling

, Volume 18, Issue 11, pp 4843–4863 | Cite as

Application of docking-based comparative intermolecular contacts analysis to validate Hsp90α docking studies and subsequent in silico screening for inhibitors

  • Mahmoud A. Al-Sha’er
  • Mutasem O. TahaEmail author
Original Paper

Abstract

Heat shock protein (Hsp90α) has been recently implicated in cancer, prompting several attempts to discover and optimize new Hsp90α inhibitors. Towards this end, we docked 83 diverse Hsp90α inhibitors into the ATP-binding site of this chaperone using several docking–scoring settings. Subsequently, we applied our newly developed computational tool—docking-based comparative intramolecular contacts analysis (dbCICA)—to assess the different docking conditions and select the best settings. dbCICA is based on the number and quality of contacts between docked ligands and amino acid residues within the binding pocket. It assesses a particular docking configuration based on its ability to align a set of ligands within a corresponding binding pocket in such a way that potent ligands come into contact with binding site spots distinct from those approached by low-affinity ligands, and vice versa. The optimal dbCICA models were translated into valid pharmacophore models that were used as 3D search queries to mine the National Cancer Institute’s structural database for new inhibitors of Hsp90α that could potentially be used as anticancer agents. The process culminated in 15 micromolar Hsp90α ATPase inhibitors.

Figure

Validating docking settings and Building pharmacophore models against Hsp90 based on successful dbCICA models

Keywords

Docking LigandFit dbCICA Heat shock protein 90α Anticancer 

Supplementary material

894_2012_1479_MOESM1_ESM.doc (7.4 mb)
ESM 1 (DOC 7628 kb)

References

  1. 1.
    Mahalingam D, Swords R, Carew JS, Nawrocki ST, Bhalla K, Giles FJ (2009) Targeting HSP90α for cancer therapy. Br J Cancer 100:1523–1529CrossRefGoogle Scholar
  2. 2.
    Chiosis G, Rodina A, Moulick K (2006) Emerging Hsp90α inhibitors: from discovery to clinic. Anti Cancer Agents Med Chem 6:1–8CrossRefGoogle Scholar
  3. 3.
    Dymock BW, Drysdale MJ, McDonald E, Workman P (2004) Inhibitors of Hsp90α and other chaperones for the treatment of cancer. Expert Opin Ther Pat 14:837–847CrossRefGoogle Scholar
  4. 4.
    Isaacs JS, Xu W, Neckers L (2003) Heat shock protein 90 as a molecular target for cancer therapeutics. Cancer Cell 3:213–217CrossRefGoogle Scholar
  5. 5.
    Pearl LH, Prodromou C, Workman P (2008) The Hsp90α molecular chaperone: an open and shut case for treatment. Biochem J 41:439–453CrossRefGoogle Scholar
  6. 6.
    Neckers L, Schulte TW, Mimnaugh E (1999) Geldanamycin as a potential anti-cancer agent: its molecular target and biochemical activity. Invest New Drugs 17:361–373CrossRefGoogle Scholar
  7. 7.
    Soga S, Shiotsu Y, Akinaga S, Sharma SV (2003) Development of radicicol analogues. Curr Cancer Drug Targets 3:359–369CrossRefGoogle Scholar
  8. 8.
    Chiosis G, Kang Y, Sun W (2008) Discovery and development of purine scaffold Hsp90α inhibitors. Expert Opin Drug Discov 3:99–114Google Scholar
  9. 9.
    Hwangseo P, Yun-Jung K, Ji-Sook H (2007) A novel class of Hsp90α inhibitors isolated by structure-based virtual screening. Bioorg Med Chem Lett 17:6345–6349Google Scholar
  10. 10.
    Barril X, Beswick M, Collier A, Drysdale M, Dymock B, Fink A, Grant K, Howes R, Jordan A, Massey A (2006) 4-Amino derivatives of the Hsp90α inhibitor CCT018159. Bioorg Med Chem Lett 16:2543–2548Google Scholar
  11. 11.
    Barril X, Brough P, Drysdale M, Hubbard RE, Massey A, Surgenor A, Wright L (2005) Structure-based discovery of a new class of Hsp90α inhibitors. Bioorg Med Chem Lett 15:5187–5191CrossRefGoogle Scholar
  12. 12.
    Chiosis G, Lucas B, Shtil A, Huezoa H, Rosen N (2002) Development of a purine-scaffold novel vlass of Hsp90α binders that inhibit the proliferation of cancer cells and induce the degradation of Her2tyrosine kinase. Bioorg Med Chem 10:3555–3564Google Scholar
  13. 13.
    Neckers L (2006) Using natural product inhibitors to validate Hsp90α as a molecular target in cancer. Curr Top Med Chem 6:1163–1171CrossRefGoogle Scholar
  14. 14.
    Xiao L, Lu X, Ruden DM (2006) Effectiveness of Hsp90α inhibitors as anti-cancer drugs. Mini-Rev Med Chem 6:1137–1143CrossRefGoogle Scholar
  15. 15.
    Neckers L, Mollapour M, Tsutsumi S (2009) The complex dance of the molecular chaperone Hsp90. Trends Biochem Sci 34:223–226CrossRefGoogle Scholar
  16. 16.
    Jeffrey RH, Chang P, Andrew MP, Aaron RK, Michael DW, Xilu W, Christopher LL, Jamey CM, Kerry MS, Russell AJ, Jun C, Paul LR, Sha J, Stephen KT, Edward DM, Sarah AD, Uri SL, Jean MS, Karl AW, Diane MB, Stephen WF, Steven WE, Philip JH (2007) Discovery and design of novel HSP90Α inhibitors using multiple fragment-based design strategies. Chem Biol Drug 70:1–12CrossRefGoogle Scholar
  17. 17.
    Taha MO, Habash M, Al-Hadidi Z, Al-Bakri A, Younis K, Sisan S (2011) Docking-based comparative intermolecular contacts analysis as new 3-D QSAR concept for validating docking studies and in silico screening: NMT and GP inhibitors as case studies. J Chem Inf Model 51:647–669CrossRefGoogle Scholar
  18. 18.
    Morris GM, Olson AJ, Goodsell DS (2000) Protein–ligand docking methods. Princ Med Chem 8:31–48Google Scholar
  19. 19.
    Kontoyianni M, McClellan LM, Sokol GS (2004) Evaluation of docking performance: comparative data on docking algorithms. J Med Chem 47:558–565CrossRefGoogle Scholar
  20. 20.
    Beier C, Zacharias M (2010) Tackling the challenges posed by target flexibility in drug design. Expert Opin Drug Dis 5:347–359CrossRefGoogle Scholar
  21. 21.
    Boyd S (2007) FlexX suite. Chem World UK 4:72Google Scholar
  22. 22.
    Rarey M, Kramer B, Lengauer T, Klebe G (1996) A fast flexible docking method using an incremental construction algorithm. J Mol Biol 261:470–489CrossRefGoogle Scholar
  23. 23.
    Ewing TJA, Makino S, Skillman AG, Kuntz ID (2001) DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J Comput Aid Mol Des 15:411–428Google Scholar
  24. 24.
    Jones G, Willett P, Glen RC, Leach AR, Taylor R (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267:727–748CrossRefGoogle Scholar
  25. 25.
    Vaque M, Ardrevol A, Blade C, Salvado MJ, Blay M, Fernandez-Larrea J, Arola L, Pujadas G (2008) Protein–ligand docking: a review of recent advances and future perspectives. Curr Pharm Anal 4:1–19Google Scholar
  26. 26.
    Cosconati S, Forli S, Perryman AL, Harris R, Goodsell DS, Olson AJ (2010) Virtual screening with AutoDock: theory and practice. Expert Opin Drug Dis 5:597–607CrossRefGoogle Scholar
  27. 27.
    Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ (1998) Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem 19:1639–1662CrossRefGoogle Scholar
  28. 28.
    Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT, Banks JL (2004) Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 47:1750–1759CrossRefGoogle Scholar
  29. 29.
    Accelrys Inc. (2000) Cerius2 LigandFit 4.10. Accelrys Inc., San DiegoGoogle Scholar
  30. 30.
    Diller DJ, Merz KM (2001) High throughput docking for library design and library prioritization. Proteins 43:113–124CrossRefGoogle Scholar
  31. 31.
    Hecht D, Fogel GB (2009) Computational intelligence methods for docking scores. Curr Comput Aid Drug 5:56–68CrossRefGoogle Scholar
  32. 32.
    Bissantz C, Folkers G, Rognan D (2000) Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations. J Med Chem 43:4759–4767CrossRefGoogle Scholar
  33. 33.
    Gao WR, Lai YL (1998) SCORE: a new empirical method for estimating the binding affinity of a protein–ligand complex. J Mol Model 4:379–394Google Scholar
  34. 34.
    Krammer A, Kirchhoff PD, Jiang X, Venkatachalam CM, Waldman M (2005) LigScore: a novel scoring function for predicting binding affinities. J Mol Graph Model 23:395–407CrossRefGoogle Scholar
  35. 35.
    Velec HFG, Gohlke H, Klebe G (2005) Drug score-knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better affinity prediction. J Med Chem 48:6296–6303CrossRefGoogle Scholar
  36. 36.
    Jain AN (2006) Scoring functions for protein–ligand docking. Curr Protein Pept Sci 7:407–420Google Scholar
  37. 37.
    Rajamani R, Good AC (2007) Ranking poses in structure-based lead discovery and optimization: current trends in scoring function development. Curr Opin Drug Disc 10:308–315Google Scholar
  38. 38.
    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 44:1123–1129CrossRefGoogle Scholar
  39. 39.
    Foloppe N, Hubbard R (2006) Towards predictive ligand design with free-energy based computational methods? Curr Med Chem 13:3583–3608CrossRefGoogle Scholar
  40. 40.
    Englebienne P, Moitessier N (2009) Docking ligands into flexible and solvated macromolecules. Are popular scoring functions accurate for this class of proteins? J Chem Inf Model 49:1568–1580CrossRefGoogle Scholar
  41. 41.
    Jain AN (1996) Scoring non-covalent protein–ligand interactions: a continuous differentiable function tuned to compute binding affinities. J Comput Aided Mol Des 10:427–440CrossRefGoogle Scholar
  42. 42.
    Böhm HJ (1998) Prediction of binding constants of protein ligands: a fast method for the prioritization of hits obtained from de novo design or 3D database search programs. J Comput Aided Mol Des 12:309–323CrossRefGoogle Scholar
  43. 43.
    Eldridge MD, Murray CW, Auton TR, Paolini GV, Mee RP (1997) Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J Comput Aided Mol Des 11:425–445CrossRefGoogle Scholar
  44. 44.
    Wang R, Gao Y, Lai L (1998) SCORE: a new empirical method for estimating the binding affinity of a protein–ligand complex. J Mol Model 4:379–394Google Scholar
  45. 45.
    Gehlhaar DK, Bouzida D, Rejto P (1999) Reduced dimensionality in ligand–protein structure prediction: covalent inhibitors of serine proteases and design of site-directed combinatorial libraries. In: Parrill L, Rami Reddy M (eds) Rational drug design: novel methodology and practical applications. American Chemical Society, Washington, DC, pp 292–311Google Scholar
  46. 46.
    Wang R, Lai L, Wang S (2002) Further development and of empirical scoring functions for structure-based binding validation affinity prediction. J Comput Aided Mol Des 16:11–26CrossRefGoogle Scholar
  47. 47.
    Muegge I, Martin YC (1999) A general and fast scoring function for protein–ligand interactions: a simplified potential approach. J Med Chem 42:791–804Google Scholar
  48. 48.
    Muegge I (2000) A knowledge-based scoring function for protein–ligand interactions: probing the reference state. Perspect Drug Discov 20:99–114Google Scholar
  49. 49.
    Muegge I (2001) Effect of ligand volume correction on PMF scoring. J Comput Chem 22:418–425CrossRefGoogle Scholar
  50. 50.
    Gohlke H, Hendlich M, Klebe G (2000) Knowledge-based scoring function to predict protein–ligand interactions. J Mol Biol 295:337–356Google Scholar
  51. 51.
    Muegge I (2006) PMF scoring revisited. J Med Chem 49:5895–5902CrossRefGoogle Scholar
  52. 52.
    Song CM, Lim SJ, Tong JC (2009) Recent advances in computer-aided drug design. Brief Bioinform 10:579–591CrossRefGoogle Scholar
  53. 53.
    Jorgensen WL (2009) Efficient drug lead discovery and optimization. Acc Chem Res 42:724–733CrossRefGoogle Scholar
  54. 54.
    Leach AR, Shoichet BK, Peishoff CE (2006) Prediction of protein–ligand interactions. Docking and scoring: successes and gaps. J Med Chem 49:5851–5855Google Scholar
  55. 55.
    Klebe G (2006) Virtual ligand screening: strategies, perspectives and limitations. Drug Discov Today 11:580–594CrossRefGoogle Scholar
  56. 56.
    Krissinel E (2009) Crystal contacts as nature’s docking solutions. J Comput Chem 31:133–143CrossRefGoogle Scholar
  57. 57.
    Steinbrecher T, Labahn A (2010) Towards accurate free energy calculations in ligand protein-binding studies. Curr Med Chem 17:767–785CrossRefGoogle Scholar
  58. 58.
    Taha MO, AlDhamin M (2005) Effects of variable docking conditions and scoring functions on the qualities of protein aligned CoMFA models constructed from diverse h-PTP 1B inhibitors. J Med Chem 48:8016–8034CrossRefGoogle Scholar
  59. 59.
    Tame JRH (1999) Scoring functions: a view from the bench. J Comput Aided Mol Des 13:99–108CrossRefGoogle Scholar
  60. 60.
    Kollman P (1993) Free energy calculations: applications to chemical and biochemical phenomena. Chem Rev 93:2395–2417CrossRefGoogle Scholar
  61. 61.
    Homans SW (2007) Water, water everywhere—except where it matters. Drug Discov Today 12:534–539Google Scholar
  62. 62.
    Poornima CS, Dean PM (1995) Hydration in drug design. 1. Multiple hydrogen-bonding features of water molecules in mediating protein–ligand interactions. J Comput Aided Mol Des 9:500–512Google Scholar
  63. 63.
    Poornima CS, Dean PM (1995) Hydration in drug design. 2. Influence of local site surface shape on water binding. J Comput Aided Mol Des 9:513–520CrossRefGoogle Scholar
  64. 64.
    Poornima CS, Dean PM (1995) Hydration in drug design. 3. Conserved water molecules at the ligand-binding sites of homologous proteins. J Comput Aided Mol Des 9:521–531Google Scholar
  65. 65.
    Koehler KF, Rao SN, Snyder JP (1996) Modeling drug–receptor interactions. In: Cohen NC (ed) Guidebook on molecular modeling in drug design. Academic, San Diego, pp 235–336Google Scholar
  66. 66.
    Pastor M, Cruciani G, Watson K (1997) A strategy for the incorporation of water molecules present in a ligand binding site into a three-dimensional quantitative structure–activity relationship analysis. J Med Chem 40:4089–4102CrossRefGoogle Scholar
  67. 67.
    Silverman RA (1991) The organic chemistry of drug design and drug action. Academic, San Diego, pp 62–65Google Scholar
  68. 68.
    Waszkowycz B (1998) New methods for structure-based de novo drug design. In: Harvey AL (ed) Advances in drug discovery techniques. Wiley, Chichester, pp 150–153Google Scholar
  69. 69.
    Sutherland JJ, Nandigam RK, Erickson JA, Vieth M (2007) Lessons in molecular recognition. 2. Assessing and improving cross-docking accuracy. J Chem Inf Model 47:2293–2302CrossRefGoogle Scholar
  70. 70.
    Verdonk ML, Berdini V, Hartshorn MJ, Mooij WTM, Murray CW, Watson P (2004) Virtual screening using protein–ligand docking: avoiding artificial enrichment. J Chem Inf Comput Sci 44:793–806Google Scholar
  71. 71.
    Wang R, Lu Y, Wang S (2003) Comparative evaluation of 11 scoring functions for molecular docking. J Med Chem 46:2287–2303CrossRefGoogle Scholar
  72. 72.
    Abu-Hammad AM, Afifi F, Taha MO (2007) Combining docking, scoring and molecular field analyses to probe influenza neuraminidase–ligand interactions. J Mol Graph Model 26:443–456Google Scholar
  73. 73.
    Abu-Hammad A, Zalloum WA, Zalloum H, Abu-Sheikha G, Taha MO (2009) Homology modeling of MCH1 receptor and validation by docking/scoring and protein-aligned CoMFA. Eur J Med Chem 44:2583–2596CrossRefGoogle Scholar
  74. 74.
    Venkatachalam CM, Jiang X, Oldfield T, Waldman M (2003) LigandFit: a novel method for the shape-directed rapid docking of ligands to protein active sites. J Mol Graph Model 21:289–307CrossRefGoogle Scholar
  75. 75.
    Brough PA, Aherne W, Barril X, Borgognoni J, Boxall K, Cansfield JE, Cheung K-MJ et al (2008) 4,5-Diarylisoxazole Hsp90α chaperone inhibitors: potential therapeutic agents for the treatment of cancer. J Med Chem 51:196–218Google Scholar
  76. 76.
    McDonald E, Jones K, Brough PA, Drysdale MJ, Workman P (2006) Discovery and development of pyrazole-scaffold Hsp90α inhibitors. Curr Top Med Chem 6:1193–1203Google Scholar
  77. 77.
    Gopalsamy A, Shi M, Golas J, Vogan E, Jacob J, Johnson M, Lee F, Nilakantan R, Petersen R, Svenson K, Chopra R, Tam MS, Wen Y, Ellingboe J, Arndt K, Boschelli F (2008) Discovery of benzisoxazoles as potent inhibitors of chaperone heat shock protein 90. J Med Chem 51:373–375CrossRefGoogle Scholar
  78. 78.
    Gasteiger J, Marsili M (1980) Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges. Tetrahedron 36:3219–3228Google Scholar
  79. 79.
    Gehlhaar DK, Verkhivker GM, Rejto PA, Sherman CJ, Fogel DB, Fogel LJ, Freer ST (1995) Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally flexible docking by evolutionary programming. Chem Biol 2:317–324CrossRefGoogle Scholar
  80. 80.
    Accelrys Inc. (2009) Discovery Studio 2.5. Accelrys Inc., San DiegoGoogle Scholar
  81. 81.
    Taha MO, Al-Bakri AG, Zalloum WA (2006) Discovery of potent inhibitors of pseudomonal quorum sensing via pharmacophore modeling and in silico screening. Bioorg Med Chem Lett 16:5902–5906Google Scholar
  82. 82.
    Taha MO, Bustanji Y, Al-Bakri AG, Al-Motassem Y, Zalloum WA, Al-Masri IM, Atallah N (2007) Discovery of new potent human protein tyrosine phosphatase inhibitors via pharmacophore and QSAR analysis followed by in silico screening. J Mol Graph Model 25:870–884Google Scholar
  83. 83.
    Avila C, Hadden MK, Ma Z, Kornilayev BA, Ye QZ, Blagg BS (2006) Highthroughput screening for Hsp90α ATPase inhibitors. Bioorg Med Chem Lett 16:3005–3008CrossRefGoogle Scholar
  84. 84.
    Lanzetta PA, Alvarez LJ, Reinach PS, Candia OA (1979) An improved assay for nanomole amounts of inorganic phosphate. Anal Biochem 100:95–97CrossRefGoogle Scholar
  85. 85.
    Christopher A, Boris AK, Brian SJ (2006) Development and optimization of a useful assay for determining Hsp90s inherent ATPase activity. Bioorg Med Chem 14:1134–1142CrossRefGoogle Scholar
  86. 86.
    Abu Sheikha G, Al-Sha’er MA, Taha MO (2011) Some sulfonamide drugs inhibit ATPase activity of heat shock protein 90: investigation by docking simulation and experimental validation. J Enzym Inhibit Med Chem 26:603–609Google Scholar
  87. 87.
    Al-Sha’er MA, Taha MO (2010) Elaborate ligand-based modeling reveal new nanomolar heat shock protein 90a inhibitors. J Chem Inf Model 50:1706–1723CrossRefGoogle Scholar
  88. 88.
    Al-masri IM, Mohammad MK, Taha MO (2008) Discovery of DPP IV inhibitors by pharmacophore modeling and QSAR analysis followed by in silico screening. Chem Med Chem 3:1763–1779Google Scholar
  89. 89.
    Al-Sha’er MA, Taha MO (2010) Discovery of novel CDK1 inhibitors by combining pharmacophore modeling, QSAR analysis and in silico screening followed by in vitro bioassay. Eur J Med Chem 45:4316–4330Google Scholar
  90. 90.
    Irwin JJ, Shoichet BK (2005) ZINC—a free database of commercially available compounds for virtual screening. J Chem Inf Model 45:177–182Google Scholar
  91. 91.
    Kirchmair J, Markt P, Distinto S, Wolber G, Langer T (2008) Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection. What can we learn from earlier mistakes? J Comput Aided Mol Des 22:213–228Google Scholar
  92. 92.
    Jacobsson M, Liden P, Stjernschantz E, Bostroem H, Norinder U (2003) Improving structure-based virtual screening by multivariate analysis of scoring data. J Med Chem 46:5781–5789CrossRefGoogle Scholar
  93. 93.
    Triballeau N, Acher F, Brabet I, Pin JP, Bertrand HO (2005) Virtual screening workflow development guided by the “receiver operating characteristic” curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype. J Med Chem 48:2534–2547Google Scholar
  94. 94.
    Lipinski CA (2004) Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol 1:337–341CrossRefGoogle Scholar
  95. 95.
    Howes R, Barril X, Dymock BW, Grant K, NorthWeld CJ, Robertson AGS, Surgenor A, Wayne J, Wright L, James K, Matthews T, Cheung KM, McDonald E, Workman P, Drysdale MJ (2006) A fluorescence polarization assay for inhibitors of Hsp90. Anal Biochem 350:202–213Google Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Faculty of PharmacyZarqa UniversityZarqaJordan
  2. 2.Drug Discovery Unit, Department of Pharmaceutical Sciences, Faculty of PharmacyUniversity of JordanAmmanJordan

Personalised recommendations