Journal of Molecular Modeling

, Volume 17, Issue 3, pp 443–464 | Cite as

Discovery of new β-d-glucosidase inhibitors via pharmacophore modeling and QSAR analysis followed by in silico screening

  • Reema Abu Khalaf
  • Ahmed Mutanabbi Abdula
  • Mohammad S. Mubarak
  • Mutasem O. TahaEmail author
Original Paper


Glycosidases, including β-d-glucosidase, are involved in a variety of metabolic disorders such as diabetes, viral or bacterial infections and cancer. Accordingly, we were prompted to find new β-d-glucosidase inhibitors. Towards this end we scanned the pharmacophoric space of this enzyme using a set of 41 known inhibitors. Genetic algorithm and multiple linear regression analyses were employed to select an optimal combination of pharmacophoric models and physicochemical descriptors to yield self-consistent and predictive quantitative structure-activity relationship (QSAR). Three pharmacophores emerged in the QSAR equations, suggesting the existence of more than one binding mode accessible to ligands within the β-d-glucosidase pocket. The successful pharmacophores were complemented with strict shape constraints in an attempt to optimize their receiver-operating characteristic (ROC) curve profiles. The validity of the QSAR equations and the associated pharmacophoric models were established experimentally by the identification of several β-d-glucosidase inhibitors retrieved via in silico search of two structural databases, namely the National Cancer Institute (NCI) list of compounds, and our in-house structural database of established drugs and agrochemicals (DAC).


β-d-glucosidase inhibitor Quantitative structure-activity relationship In silico screening Pharmacophore modeling Shape constraints Receiver-operating characteristic curve 



This work has been financially supported by the Deanship of Scientific Research at the University of Jordan; this support is highly acknowledged.

Supplementary material

894_2010_737_MOESM1_ESM.doc (95 kb)
ESM 1 (DOC 95 kb)


  1. 1.
    Scofield AM, Witham P, Nash RJ, Kite GC, Fellows LE (1995) Castanospermine and other polyhydroxy alkaloids as inhibitors of insect glycosidases. Comp Biochem Phys 112A:187–196Google Scholar
  2. 2.
    Scofield AM, Witham P, Nash RJ, Kite GC, Fellows LE (1995) Differentiation of glycosidase activity in some Hemiptera and Lepidoptera by means of castanospermine and other polyhydroxy alkaloids. Comp Biochem Phys 112A:197–205Google Scholar
  3. 3.
    Gerber-Lemairer S, Juillerat-Jeanneret L (2006) Glycosylation pathways as drug targets for cancer: glycosidase inhibitors. Mini-Rev Med Chem 6:1043–1052CrossRefGoogle Scholar
  4. 4.
    Lillelund VH, Jensen HH, Liang X, Bols M (2002) Recent developments of transition-state analogue glycosidase inhibitors of non-natural product origin. Chem Rev 102:515–553CrossRefGoogle Scholar
  5. 5.
    Markad SD, Karanjule NS, Sharma T, Sabharwal SG, Dhavale DD (2006) Synthesis and evaluation of glycosidase inhibitory activity of N-butyl 1-deoxy-d-gluco-homonojirimycin and N-butyl 1-deoxy-l-ido-homonojirimycin. Bioorg Med Chem 14:5535–5539CrossRefGoogle Scholar
  6. 6.
    Merrer YL, Gauzy L, Gravier-Pelletier C, Depezay JC (2000) Synthesis of C2-symmetric guanidino-sugars as potent inhibitors of glycosidases. Bioorg Med Chem 8:307–320CrossRefGoogle Scholar
  7. 7.
    Robina I, Vogel P (2005) Synthesis of aza-C-disaccharides (dideoxyimino-alditols C-linked to monosaccharides) and analogues. Synthesis 5:675–702Google Scholar
  8. 8.
    Shitara E, Nishimura Y, Kojima F, Takeuchi T (1999) A facile synthesis of d-glucose-type gem-diamine 1-N-iminosugars: a new family of glucosidase inhibitors. Bioorg Med Chem 7:1241–1246CrossRefGoogle Scholar
  9. 9.
    Asano A, Nash RG, Molyneuxc RJ, Fleet GWG (2000) Sugar-mimic glycosidase inhibitors: natural occurrence, biological activity and prospects for therapeutic application. Tetrahedron-Asymmetr 11:1645–1680CrossRefGoogle Scholar
  10. 10.
    Asano N (2003) Glycosidase inhibitors: update and perspectives on practical use. Glycobiology 13:93–104CrossRefGoogle Scholar
  11. 11.
    Berecibar A, Grandjean C, Siriwardena A (1999) Synthesis and biological activity of natural aminocyclopentitol glycosidase inhibitors: mannostatins, trehazolin, allosamidins, and their analogues. Chem Rev 99:779–844CrossRefGoogle Scholar
  12. 12.
    Kim JH, Ryu YB, Kang NS, Lee BW, Heo JS, Jeong IY, Park KH (2006) Glycosidase inhibitory flavonoids from Sophora flavescens. Biol Pharm Bull 29:302–305CrossRefGoogle Scholar
  13. 13.
    Li H, Schütz C, Favre S, Zhang Y, Vogel P, Sinay P, Blériot Y (2006) Nucleophilic opening of epoxyazepanes: expanding the family of polyhydroxyazepane-based glycosidase inhibitors. Org Biomol Chem 4:1653–1662CrossRefGoogle Scholar
  14. 14.
    Pandey G, Dumbre SG, Khan MI, Shabab M (2006) Convergent approach toward the synthesis of the stereoisomers of C-6 homologues of 1-deoxynojirimycin and their analogues: evaluation as specific glycosidase inhibitors. J Org Chem 71:8481–8488CrossRefGoogle Scholar
  15. 15.
    Schramm V (2003) Enzymatic transition state poise and transition state analogues. Acc Chem Res 36:588–596CrossRefGoogle Scholar
  16. 16.
    Schramm V (2005) Enzymatic transition states and transition state analogues. Curr Opin Struct Biol 15:604–613CrossRefGoogle Scholar
  17. 17.
    Amyes T, Richard J (2007) Rational design of transition-state analogues as potent enzyme inhibitors with therapeutic applications. ACS Chem Biol 2:711–714CrossRefGoogle Scholar
  18. 18.
    Sutherland J, O’Brien L, Weaver D (2004) Pruned receptor surface models and pharmacophores for three-dimensional database searching. J Med Chem 47:3777–3787CrossRefGoogle Scholar
  19. 19.
    Taha MO, Bustanji Y, Al-Ghussein M, Mohammad M, Zalloum H, Al-Masri IM, Atallah N (2008) Pharmacophore modeling, quantitative structure-activity relationship analysis, and in-silico screening reveal potent glycogen synthase kinase-3beta inhibitory activities for cimetidine, hydroxychloroquine, and gemifloxacin. J Med Chem 51:2062–2077CrossRefGoogle Scholar
  20. 20.
    Al-masri IM, Mohammad K, Taha MO (2008) Discovery of DPP IV inhibitors by pharmacophore modeling and QSAR analysis followed by in silico screening. ChemMedChem 3:1763–1779CrossRefGoogle Scholar
  21. 21.
    Taha MO, Dahabiyeh LA, Bustanji Y, Zalloum H, Saleh S (2008) Combining ligand-based pharmacophore modeling, QSAR analysis and in-silico screening for the discovery of new potent hormone sensitive lipase inhibitors. J Med Chem 51:6478–6494CrossRefGoogle Scholar
  22. 22.
    Taha MO, Atallah N, Al-Bakri AG, Paradis-Bleau C, Zalloum H, Younis KS, Levesque RC (2008) Discovery of new murf inhibitors via pharmacophore modeling and QSAR analysis followed by in silico screening. Bioorg Med Chem 16:1218–1235CrossRefGoogle Scholar
  23. 23.
    Taha MO, Bustanji Y, Al-Bakri AG, Yousef M, 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 Graphics Model 25:870–884CrossRefGoogle Scholar
  24. 24.
    Abu Hammad AM, Taha MO (2009) Pharmacophore modeling, quantitative structure-activity relationship analysis, and shape-complemented in silico screening allow access to novel influenza neuraminidase inhibitors. J Chem Inf Model 49:978–996CrossRefGoogle Scholar
  25. 25.
    Abu Khalaf R, Abu Sheikha G, Bustanji Y, Taha MO (2010) Discovery of new cholesteryl ester transfer protein inhibitors via ligand-based pharmacophore modeling and QSAR analysis followed by synthetic exploration. Eur J Med Chem 45:1598–1617CrossRefGoogle Scholar
  26. 26.
    Catalyst 4.11 User Guide (2005) Accelrys Software Inc, San Diego, CAGoogle Scholar
  27. 27.
    Sprague PW, Hoffmann R (1997) CATALYST pharmacophore models and their utility as queries for searching 3D databases. In: van de Waterbeemd H, Testa B, Folkers G (eds) Computer-assisted lead finding and optimization. VHCA, Basel, pp 223–240Google Scholar
  28. 28.
    Barnum D, Greene J, Smellie A, Sprague P (1996) Identification of common functional configurations among molecules. J Chem Inf Comput Sci 36:563–571Google Scholar
  29. 29.
    Smellie A, Teig S, Towbin P (1995) Poling: promoting conformational variation. J Comput Chem 16:171–187CrossRefGoogle Scholar
  30. 30.
    Li H, Sutter J, Hoffmann R (2000) In: Güner OF (ed) Pharmacophore perception, development, and use in drug design. International University Line, La Jolla, CA, pp 173–189Google Scholar
  31. 31.
    Sutter J, Güner OF, Hoffmann R, Li H, Waldman M (2000) Effect of variable weights and tolerances on predictive model generation. In: Güner OF (ed) Pharmacophore perception, development, and use in drug design. International University Line, La Jolla, CA, pp 501–511Google Scholar
  32. 32.
    Kurogi Y, Güner O (2001) Pharmacophore modeling and three-dimensional database searching for drug design using catalyst. Curr Med Chem 8:1035–1055Google Scholar
  33. 33.
    Bersuker IB, Bahçeci S, Boggs JE (2000) In: Güner OF (ed) Pharmacophore perception, development and use in drug design. International University Line, La Jolla, CA, pp 457–473Google Scholar
  34. 34.
    Poptodorov K, Luu T, Langer T, Hoffmann R (2006) In: Langer T, Hoffmann RD (eds) Methods and principles in medicinal chemistry, pharmacophores and pharmacophores searches, vol 2. WILEY-VCH, Weinheim, pp 17–47Google Scholar
  35. 35.
    Singh J, Chuaqui CE, Boriack-Sjodin PA, Lee WC, Pontz T, Corbley MJ, Cheung HK, Arduini RM, Mead JN, Newman MN, Papadatos JL, Bowes S, Josiah S, Ling LE (2003) Successful shape-Based virtual screening: the discovery of a potent inhibitor of the type I TGFβ receptor kinase (TβRI). Bioorg Med Chem Lett 13:4355–4359CrossRefGoogle Scholar
  36. 36.
    Taha MO, Qandil AM, Zaki DD, AlDamen MA (2005) Ligand-based assessment of factor Xa binding site flexibility via elaborate pharmacophore exploration and genetic algorithm-based QSAR modeling. Eur J Med Chem 40:701–727CrossRefGoogle Scholar
  37. 37.
    Keller PA, Bowman M, Dang KH, Garner J, Leach SP, Smith R, McCluskey AJ (1999) Pharmacophore development for corticotropin-releasing hormone: new insights into inhibitor activity. J Med Chem 42:2351–2357CrossRefGoogle Scholar
  38. 38.
    Karki RG, Kulkarni VM (2001) A feature based pharmacophore for Candida albicans MyristoylCoA: protein N-myristoyltransferase inhibitors. Eur J Med Chem 36:147–163CrossRefGoogle Scholar
  39. 39.
    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 16:5902–5906CrossRefGoogle Scholar
  40. 40.
    Moffat K, Gillet VJ, Whittle M, Bravi G, Leach AR (2008) A comparison of field-based similarity searching methods, CatShape, FBSS, and ROCS. J Chem Inf Model 48:719–729CrossRefGoogle Scholar
  41. 41.
    Dubost E, Tschamber T, Streith J (2003) Increasing the inhibitory potency of l-arabino-imidazolo-[1, 2]-piperidinose towards β-d-glucosidase and β-d-galactosidase. Tetrahedron Lett 44:3667–3670CrossRefGoogle Scholar
  42. 42.
    Dubost E, Nouën DL, Streith J, Tarnus C, Tschamber T (2006) Synthesis of substituted Imidazolo[1, 2-a] piperidinoses and their evaluation as glycosidase inhibitors. Eur J Org Chem 2006:610–626CrossRefGoogle Scholar
  43. 43.
    Frankowski A, Deredas D, Dubost E, Gessier F, Jankowski S, Neuburger M, Seliga C, Tschamber T, Weinberg K (2003) Stereocontrolled synthesis of imidazolo[1, 5]hexopiperidinoses and imidazol-4(5)-yl-C-glycosides. Tetrahedron 59:6503–6520CrossRefGoogle Scholar
  44. 44.
    Gessier F, Tschamber T, Tarnus C, Neuburger M, Huber W, Streith J (2001) Synthesis of imidazolo-piperidinopentoses as nagstatine analogues. Eur J Org Chem 2001:4111–4125CrossRefGoogle Scholar
  45. 45.
    Tschamber T, Gessier F, Dubost E, Newsome J, Tarnus C, Kohler J, Neuburger M, Streith J (2003) Carbohydrate transition state mimics: synthesis of imidazolo-pyrrolidinoses as potential nectrisine surrogates. Bioorg Med Chem 11:3559–3568CrossRefGoogle Scholar
  46. 46.
    Fisher R (1966) The principle of experimentation illustrated by a psycho-physical experiment, 8th edn. Hafner, New YorkGoogle Scholar
  47. 47.
    Krovat EM, Langer T (2003) Non-peptide angiotensin II receptor antagonists: chemical feature based pharmacophore identification. J Med Chem 46:716–726CrossRefGoogle Scholar
  48. 48.
    CERIUS2, Version 4.10. QSAR Users’ Manual (2005) Accelrys Inc, San Diego, CA, pp 221–235Google Scholar
  49. 49.
    Hahn M (1997) Three-dimensional shape-based searching of conformationally flexible compounds. J Chem Inf Comput Sci 37:80–86Google Scholar
  50. 50.
    Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 46:3–26CrossRefGoogle Scholar
  51. 51.
    Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD (2002) Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 45:2615–2623CrossRefGoogle Scholar
  52. 52.
    Sheridan RP, Kearsley SK (2002) Why do we need so many chemical similarity search methods. Drug Discovery Today 7:903–911CrossRefGoogle Scholar
  53. 53.
    Ramsey LF, Schafer WD (1997) The statistical sleuth, 1st edn. Wadesworth, Belmont, CAGoogle Scholar
  54. 54.
    Tropsha A, Gramatica P, Gombar VK (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR & Comb Sci 22:69–77CrossRefGoogle Scholar
  55. 55.
    Sivakumar PM, Babu SKG, Doble M (2008) Impact of topological and electronic descriptors in the QSAR of pyrazine containing thiazolines and thiazolidinones as antitubercular and antibacterial agents. Chem Biol Drug Des 71:447–463CrossRefGoogle Scholar
  56. 56.
    Verdonk ML, Marcel L, Berdini V, Hartshorn MJ, Mooij WTM, Murray CW, Taylor RD, Watson P (2004) Virtual screening using protein-ligand docking: avoiding artificial enrichment. J Chem Inf Comput Sci 44:793–806Google Scholar
  57. 57.
    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 22:213–228CrossRefGoogle Scholar
  58. 58.
    Irwin JJ, Shoichet BK (2005) ZINC—a free database of commercially available compounds for virtual screening. J Chem Inf Comput Sci 45:177–182Google Scholar
  59. 59.
    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 4. J Med Chem 48:2534–2547CrossRefGoogle Scholar
  60. 60.
    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
  61. 61.
    Gao H, Williams C, Labute P, Bajorath J (1999) Binary quantitative structure-activity relationship (QSAR) analysis of estrogen receptor ligands. J Chem Inf Comput Sci 39:164–168Google Scholar
  62. 62.
    Gloster TM, Roberts S, Perugino G, Rossi M, Moracci M, Panday N, Terinek M, Vasella A, Davies GJ (2006) Structural, kinetic, and thermodynamic analysis of glucoimidazole-derived glycosidase inhibitors. Biochemistry 45:11879–11884CrossRefGoogle Scholar
  63. 63.
    Cronin MTD, Schultz TW (2003) Pitfalls in QSAR. J Mol Struct 622:39–51Google Scholar
  64. 64.
    Buser S, Vasella A (2006) Norbornane mimics of distorted β-d-glucopyranosides inhibitors of β-d-glucopyranosidases. Helv Chim Acta 89:614–621CrossRefGoogle Scholar
  65. 65.
    Pabba J, Vasella A (2006) Probing the interaction of the C(4) hydroxy group of lactone-type inhibitors with beta-glucosidases and beta-galactosidases. Helv Chim Acta 89:2006–2019CrossRefGoogle Scholar
  66. 66.
    Falshaw A, Hart JB, Tyler PC (2000) New syntheses of 1D- and 1L-1, 2-anhydro-myo-inositol and assessment of their glycosidase inhibitory activities. Carbohydr Res 329:301–308CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Reema Abu Khalaf
    • 1
  • Ahmed Mutanabbi Abdula
    • 2
  • Mohammad S. Mubarak
    • 2
  • Mutasem O. Taha
    • 3
    Email author
  1. 1.Department of Pharmaceutical Sciences, Faculty of PharmacyAl Zaytoonah Private University of JordanAmmanJordan
  2. 2.Department of Chemistry, Faculty of ScienceUniversity of JordanAmmanJordan
  3. 3.Drug Discovery Unit, Department of Pharmaceutical Sciences, Faculty of PharmacyUniversity of JordanAmmanJordan

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