Skip to main content
SpringerLink
Log in

The use of docking-based comparative intermolecular contacts analysis to identify optimal docking conditions within glucokinase and to discover of new GK activators

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

Abstract

Glucokinase (GK) is involved in normal glucose homeostasis and therefore it is a valid target for drug design and discovery efforts. GK activators (GKAs) have excellent potential as treatments of hyperglycemia and diabetes. The combined recent interest in GKAs, together with docking limitations and shortages of docking validation methods prompted us to use our new 3D-QSAR analysis, namely, docking-based comparative intermolecular contacts analysis (dbCICA), to validate docking configurations performed on a group of GKAs within GK binding site. dbCICA assesses the consistency of docking by assessing the correlation between ligands’ affinities and their contacts with binding site spots. Optimal dbCICA models were validated by receiver operating characteristic curve analysis and comparative molecular field analysis. dbCICA models were also converted into valid pharmacophores that were used as search queries to mine 3D structural databases for new GKAs. The search yielded several potent bioactivators that experimentally increased GK bioactivity up to 7.5-folds at 10 μM.

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Brocklehurst JK, Payne AV, Davies AR, Carroll D, Vertigan LH, Wightman JH, Aiston S, Waddell DI, Leighton B, Coghlan PM, Agius L (2004) Stimulation of hepatocyte glucose metabolism by novel small molecule glucokinase activators. Diabetes 53:535–541

    Article  CAS  Google Scholar 

  2. Leighton B, Atkinson A, Coghlan PM (2005) Small molecule glucokinase activators as novel anti-diabetic agents. Biochem Soc Trans 33:371–374

    Article  CAS  Google Scholar 

  3. Kietzmann T, Ganjam KG (2005) Glucokinase: old enzyme, new target. Expert Opin Ther Pat 15:705–713

    Article  CAS  Google Scholar 

  4. Sarabu R, Taub R, Grimsby J (2007) Glucokinase activation—a strategy for T2D therapy: recent developments. Drug Discov Today Ther Strateg Drug 4:111–115ti

    Google Scholar 

  5. Heuser S, Barrett GD, Berg M, Bonnier B, Kahl A, Puente LM, Oram N, Ried R, Roettig U, Gil SG, Seger E, Steggles JD, Wannera J, Weicherta JA (2006) Synthesis of novel cyclopropylic sulfones and sulfonamides acting as glucokinase activators. Tetrahedron Lett 47:2675–2678

    Article  CAS  Google Scholar 

  6. Ishikawa M, Nonoshita K, Ogino Y, Nagae Y, Tsukahara D, Hosaka H, Maruki H, Ohyama S, Yoshimoto R, Sasaki K, Nagata Y, Eiki J, Nishimura T (2009) Discovery of novel 2-(pyridine-2-yl)-1H-benzimidazole derivatives as potent glucokinase activators. Bioorg Med Chem Lett 19:4450–4454

    Article  CAS  Google Scholar 

  7. Zhang L, Li H, Zhu Q, Liu J, Chen L, Leng Y, Jiang H, Liu H (2009) Benzamide derivatives as dual-action hypoglycemic agents that inhibit glycogen phosphorylase and activate glucokinase. Bioorg Med Chem 13:4385–4388

    Google Scholar 

  8. Nishimura T, Iino T, Mitsuya M, Bamba M, Watanabe H, Tsukahara D, Kamata K, Sasaki K, Ohyama S, Hosaka H, Futamura M, Nagata Y, Eiki J (2009) Identification of novel and potent 2-amino benzamide derivatives as allosteric glucokinase activators. Bioorg Med Chem Lett 19:1357–1360

    Article  CAS  Google Scholar 

  9. Petit P, Antoine M, Ferry G, Boutin JA, Lagarde A, Gluais L, Vincentelli R, Vuillard L (2011) The active conformation of Glucokinase is not altered by allosteric activators. Acta Crystallogr D Biol Crystallogr 67:929–935

    Article  CAS  Google Scholar 

  10. Takahashi K, Hashimoto N, Nakama C, Kamata K, Sasaki K, Yoshimoto R, Ohyama S, Hosaka H, Maruki H, Nagata Y, Eiki J, Nishimura T (2009) The design and optimization of a series of 2-(pyridin-2-yl)-1H-benzimidazole compounds as allosteric glucokinase activators. Bioorg Med Chem 17:7042–7051

    Article  CAS  Google Scholar 

  11. Bebernitz GR, Beaulieu V, Dale BA, Deacon R, Duttaroy A, Gao J, Grondine MS, Gupta RC, Kakmak M, Kavana M, Kirman LC, Liang J, Maniara WM, Munshi S, Nadkarni SS, Schuster HF, Stams T, St Denny I, Taslimi PM, Vash B, Caplan SL (2009) Investigation of functionally liver selective glucokinase activators for the treatment of type 2 diabetes. J Med Chem 52:6142–6152

    Article  CAS  Google Scholar 

  12. Tagami S, Sekine SI, Kumarevel T, Hino N, Murayama Y, Kamegamori S, Yamamoto M, Sakamoto K, Yokoyama S (2010) Crystal structure of bacterial RNA polymerase bound with a transcription inhibitor protein. Nature 468:978–982

    Article  CAS  Google Scholar 

  13. Mitsuya M, Kamata K, Bamba M, Watanabe H, Sasaki Y, Sasaki K, Ohyama S, Hosaka H, Nagata Y, Eiki J, Nishimura T (2009) Discovery of novel 3,6-disubstituted 2-pyridinecarboxamide derivatives as GK activators. Bioorg Med Chem Lett 19:2718–2721

    Article  CAS  Google Scholar 

  14. Diaz A, Guivovart JJ, Fita I, Ferrer JC (2011) Crystal structure of Pyrococcus abyssi glycogen synthase with open and closed conformations. Protein Databank Entry: 3FRO

  15. 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 44:793–806

    Article  CAS  Google Scholar 

  16. Kamata K, Mitsuya M, Nishimura T, Eiki J, Nagata Y (2004) Structural basis for allosteric regulation of the monomeric allosteric enzyme human glucokinase. Structure 12:429–438

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  18. Steuber H, Zentgraf M, Gerlach C, Sotriffer CA, Heine A, Klebe G (2006) Expect the unexpected or caveat for drug designers: multiple structure determinations using aldose reductase crystals treated under varying conditions. J Mol Biol 363:174–187

    Article  CAS  Google Scholar 

  19. Stubbs MT, Reyda S, Dullweber F, Moller M, Klebe G, Dorsch D, Mederski W, Wurziger H (2002) pH-dependent binding modes observed in trypsin crystals: lessons for structure-based drug design. ChemBioChem 3:246–249

    Article  CAS  Google Scholar 

  20. DePristo MA, de Bakker PIW, Blundell TL (2004) Heterogeneity and inaccuracy in protein structures solved by X-ray crystallography. Structure 12:831–838

    Article  CAS  Google Scholar 

  21. Song CM, Lim SJ, Tong JC (2009) Recent advances in computer-aided drug design. Brief Bioinform 10:579–591

    Article  CAS  Google Scholar 

  22. Menikarachchi LC, Gascon JA (2010) QM/MM approaches in medicinal chemistry research. Curr Top Med Chem 10:46–54

    Article  CAS  Google Scholar 

  23. Jorgensen WL (2009) Efficient drug lead discovery and optimization accounts. Chem Res 42:724–733

    Article  CAS  Google Scholar 

  24. Hecht D, Fogel GB (2009) Computational intelligence methods for docking scores. Curr Comput Aided Drug 5:56–68

    Article  CAS  Google Scholar 

  25. Beeley NRA, Sage C (2003) GPCRs: An update on structural approaches to drug discovery. Targets 2:19–25

    Article  CAS  Google Scholar 

  26. Morris GM, Olson AJ, Goodsell DS (2000) Protein–Ligand docking methods. Princ Med Chem 8:31–48

    Article  CAS  Google Scholar 

  27. Kontoyianni M, McClellan LM, Sokol GS (2004) Evaluation of docking performance: comparative data on docking algorithms. J Med Chem 47:558–565

    Article  CAS  Google Scholar 

  28. Beier C, Zacharias M (2010) Tackling the challenges posed by target flexibility in drug design. Expert Opin Drug Discov 5:347–359

    Article  CAS  Google Scholar 

  29. Boyd S (2007) FlexX suite. Chem World-UK 4:72

    Google Scholar 

  30. Rarey M, Kramer B, Lengauer T, Klebe G (1996) A fast flexible docking method using an incremental construction algorithm. J Mol Biol 261:470–489

    Article  CAS  Google Scholar 

  31. Ewing TJA, Makino S, Skillman AG, Kuntz ID (2001) DOCK 40: Search strategies for automated molecular docking of flexible molecule databases. J Comput Aided Mol Des 15:411–428

    Article  CAS  Google Scholar 

  32. 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 

  33. 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 4:1–19

    Article  CAS  Google Scholar 

  34. Cosconati S, Forli S, Perryman AL, Harris R, Goodsell DS, Olson AJ (2010) Virtual screening with AutoDock: theory and practice. Expert Opin Drug Dis 5:597–607

    Article  CAS  Google Scholar 

  35. 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 19:1639–1662

    Article  CAS  Google Scholar 

  36. 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 47:1750–1759

    Article  CAS  Google Scholar 

  37. Accelrys Inc (2000) CERIUS2 410 LigandFit user manual. San Diego, CA

  38. OpenEye Scientific Software Inc (2006) FRED: Fast rigid exhaustive docking user manual. Santa Fe

  39. Diller DJ, Merz KM (2001) High throughput docking for library design and library prioritization. Proteins 43:113–124

    Article  CAS  Google Scholar 

  40. Rao SN, Head MS, Kulkarni A, LaLonde JM (2007) Validation studies of the site-directed docking program LibDock. J Chem Inf Model 47:2159–2171

    Article  CAS  Google Scholar 

  41. Bissantz C, Folkers G, Rognan D (2000) Protein-based virtual screening of chemical databases 1 evaluation of different docking/scoring combinations. J Med Chem 43:4759–4767

    Article  CAS  Google Scholar 

  42. Gao WR, Lai YL (1998) SCORE: A new empirical method for estimating the binding affinity of a protein-ligand complex. J Mol Model 4:379–394

    Article  Google Scholar 

  43. Krammer A, Kirchhoff PD, Jiang X, Venkatachalam CM, Waldman M (2005) LigScore: A novel scoring function for predicting binding affinities. J Mol Graph Model 23:395–407

    Article  CAS  Google Scholar 

  44. 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 48:6296–6303

    Article  CAS  Google Scholar 

  45. Jain AN (2006) Scoring functions for protein-ligand docking. Curr Protein Pept Sci 7:407–420

    Article  CAS  Google Scholar 

  46. Rajamani R, Good AC (2007) Ranking poses in structure-based lead discovery and optimization: current trends in scoring function development. Curr Opin Drug Disc 10:308–315

    CAS  Google Scholar 

  47. 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 44:1123–1129

    Article  CAS  Google Scholar 

  48. Foloppe N, Hubbard R (2006) Towards predictive ligand design with free-energy based computational methods? Curr Med Chem 13:3583–3608

    Article  CAS  Google Scholar 

  49. Englebienne P, Moitessier N (2009) Docking ligands into flexible and solvated macromolecules 4 are popular scoring functions accurate for this class of proteins? J Chem Inf Model 49:1568–1580

    Article  CAS  Google Scholar 

  50. Jain AN (1996) Scoring non-covalent protein–ligand interactions: a continuous differentiable function tuned to compute binding affinities. J Comput Aided Mol Des 10:427–440

    Article  CAS  Google Scholar 

  51. 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 12:309–323

    Article  Google Scholar 

  52. 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 11:425–445

    Article  CAS  Google Scholar 

  53. 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 4:379–394

    Article  CAS  Google Scholar 

  54. 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 292–311

    Chapter  Google Scholar 

  55. 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 16:11–26

    Article  CAS  Google Scholar 

  56. Muegge I, Martin YC (1999) A general and fast scoring function for protein-ligand interactions: a simplified potential approach. J Med Chem 42:791–804

    Article  CAS  Google Scholar 

  57. Muegge I (2000) A knowledge-based scoring function for protein-ligand interactions: probing the reference state. Perspect Drug Discov 20:99–114

    Article  CAS  Google Scholar 

  58. Muegge I (2001) Effect of ligand volume correction on PMF scoring. J Comput Chem 22:418–425

    Article  CAS  Google Scholar 

  59. Gohlke H, Hendlich M, Klebe G (2000) Knowledge-based scoring function to predict protein-ligand interactions. J Mol Biol 295:337–356

    Article  CAS  Google Scholar 

  60. Muegge I (2006) PMF scoring revisited. J Med Chem 49:5895–5902

    Article  CAS  Google Scholar 

  61. Leach AR, Shoichet BK, Peishoff CE (2006) Prediction of protein-ligand interactions Docking and scoring: successes and gaps. J Med Chem 49:5851–5855

    Article  CAS  Google Scholar 

  62. Krissinel E (2009) Crystal contacts as nature’s docking solutions. J Comput Chem 31:133–143

    Article  Google Scholar 

  63. Steinbrecher T, Labahn A (2010) Towards accurate free energy calculations in ligand protein-binding studies. Curr Med Chem 17:767–785

    Article  CAS  Google Scholar 

  64. 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 48:8016–8034

    Article  CAS  Google Scholar 

  65. Tame JRH (1999) Scoring functions: a view from the bench. J Comput Aided Mol Des 13:99–108

    Article  CAS  Google Scholar 

  66. Garcia-Sosa AT, Hetenyi C, Maran U (2010) Drug efficiency indices for improvement of molecular docking scoring functions. J Comput Chem 31:174–184

    Article  CAS  Google Scholar 

  67. Homans SW (2007) Water, water everywhere—except where it matters. Drug Discov Today 12:534–539

    Article  CAS  Google Scholar 

  68. 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 9:500–512

    Article  CAS  Google Scholar 

  69. Poornima CS, Dean PM (1995) Hydration in drug design 2 influence of local site surface shape on water binding. J Comput Aided Mol Des 9:513–520

    Article  CAS  Google Scholar 

  70. 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 9:521–531

    Article  CAS  Google Scholar 

  71. Koehler KF, Rao SN, Snyder JP (1996) Modeling drug-receptor interactions. In: Cohen NC (ed) Guidebook on molecular modeling in drug design. Academic Press, San Diego, pp 235–336

    Chapter  Google Scholar 

  72. Pastor M, Cruciani G, Watson KA (1997) 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 40:4089–4102

    Article  CAS  Google Scholar 

  73. Garcia-Sosa AT, Mancera RL, Dean PM (2003) WaterScore: a novel method for distinguishing between bound and displaceable water molecules in the crystal structure of the binding site of protein-ligand complexes. J Mol Model 9:172–182

    Article  CAS  Google Scholar 

  74. Garcia-Sosa AT (2013) Hydration properties of ligands and drugs in protein binding sites: tightly-bound, bridging water molecules and their effects and consequences on molecular design strategies. J Chem Inf Model 53:1388–1405

    Article  CAS  Google Scholar 

  75. Martin YC (2009) Let’s not forget tautomers. J Comput Aided Mol Des 23:693–704

    Article  CAS  Google Scholar 

  76. Waszkowycz B (1998) New methods for structure-based de novo drug design. In: Harvey AL (ed) Advances in drug discovery techniques. Wiley, UK, pp 150–153

    Google Scholar 

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

    Article  CAS  Google Scholar 

  78. 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 48:2534–2547

    Article  CAS  Google Scholar 

  79. Triballeau N, Bertrand HO, Acher F (2006) Are you sure you have a good model? In: Langer T, Hoffmann RD (eds) Pharmacophores and pharmacophore searches. Wiley, Weinheim, pp 325–364

    Chapter  Google Scholar 

  80. 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 22:213–228

    Article  CAS  Google Scholar 

  81. Wang R, Lu Y, Wang S (2003) Comparative evaluation of 11 scoring functions for molecular docking. J Med Chem 46:2287–2303

    Article  CAS  Google Scholar 

  82. 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 26:443–456

    Article  CAS  Google Scholar 

  83. 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 44:2583–2596

    Article  CAS  Google Scholar 

  84. Taha MO, Habash M, Al-Hadidi Z, Al-Bakri A, Younis K, Sisan S (2011) Docking-based comparative intermolecular contacts analysis as new-3D QSAR concept for validating docking studies and in silico screening: NMT and GP inhibitors as case studies. J Chem Inf Model 51:647–669

    Article  CAS  Google Scholar 

  85. Al-Sha’er MA, Taha MO (2012) Application of docking-based comparative intermolecular contacts analysis to validate Hsp90α docking studies and subsequent in silico screening for inhibitors. J Mol Model 18:4843–4863

    Article  Google Scholar 

  86. Castelhano LA, Dong H, Fyfe MCT, Gardner LS, Kamikozawa Y, Kurabayashi S, Nawano M, Ohashi R, Procter MJ, Qiu L, Rasamison CM, Schofield KL, Shah VK, Ueta K, Williams GM, Wittera D, Yasuda K (2005) Glucokinase-activating ureas. Bioorg Med Chem Lett 15:1501–1504

    Article  CAS  Google Scholar 

  87. Bertram LS, Black D, Briner PH, Chatfield R, Cooke A, Fyfe MC, Murray PJ, Naud F, Nawano M, Procter MJ, Rakipovski G, Rasamison CM, Reynet C, Schofield KL, Shah VK, Spindler F, Taylor A, Turton R, Williams GM, Wong-Kai-In P, Yasuda K (2008) SAR, pharmacokinetics, safety, and efficacy of glucokinase activating 2-(4-sulfonylphenyl)-N-thiazol-2-ylacetamides: discovery of PSN-GK). J Med Chem 51(14):4340–4345

    Article  CAS  Google Scholar 

  88. McKerrecher D, Allen JV, Bowker SS, Boyd S, Caulkett PWR, Currie GS, Davies CD, Fenwick ML, Gaskin H, Grange E, Hargreaves RB, Hayter BR, James R, Keith M, Johnson KM, Johnstone C, Jones CD, Lackie S, Rayner JW, Walker RP (2005) Discovery, synthesis and biological evaluation of novel glucokinase activators. Bioorg Med Chem Lett 15(8):2103–2106

    Article  CAS  Google Scholar 

  89. McKerrecher D, Allen JV, Caulkett PW, Donald CS, Fenwick ML, Grange E, Johnson KM, Johnstone C, Jones CD, Pike KG, Rayner JW, Walker RP (2006) Design of a potent, soluble glucokinase activator with excellent in vivo efficacy. Bioorg Med Chem Lett 16:2705–2709

    Article  CAS  Google Scholar 

  90. Gasteiger J, Marsili M (1980) Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges. Tetrahedron 36:3219–3228

    Article  CAS  Google Scholar 

  91. 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 21:289–307

    Article  CAS  Google Scholar 

  92. 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 2:317–324

    Article  CAS  Google Scholar 

  93. Accelrys Inc (2009) Discovery Studio version 2.5 (DS 2.5) user manual. San Diego, CA

  94. Accelrys Inc (2005) CERIUS2 QSAR users’ manual. San Diego, CA

  95. Accelrys Inc (1997) CERIUS2 OFF. San Diego, pp 5–109

  96. Irwin JJ, Shoichet BK (2005) ZINC—A free database of commercially available compounds for virtual screening. J Chem Inf Model 45:177–182

    Article  CAS  Google Scholar 

  97. 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 46:5781–5789

    Article  CAS  Google Scholar 

  98. 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 3:1763–1779

    Article  CAS  Google Scholar 

  99. 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 40:701–727

    Article  CAS  Google Scholar 

  100. Taha MO, Bustanji Y, Al-Bakri AG, Yousef A-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 Graph Model 25:870–884

    Article  CAS  Google Scholar 

  101. Taha MO, Atallah N, Al-Bakri AG, Paradis-Bleau C, Zalloum H, Younis K, Levesque RC (2008) Discovery of new murf inhibitors via pharmacophore modeling and QSAR analysis followed by in silico screening. Bioorg Med Chem 16:1218–1235

    Article  CAS  Google Scholar 

  102. 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 45:4316–4330

    Article  Google Scholar 

  103. Goward CR, Hartwell R, Atkinson T, Scawen MD (1986) The purification and characterization of glucokinase from the thermophile Bacillus stearothermophilus. Biochem J 15:415–420

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Drug Discovery Unit, Department of Pharmaceutical Sciences, Faculty of Pharmacy, The University of Jordan, Amman, Jordan

    Mutasem O. Taha & Mohammad A. Khanfar

  2. Department of Pharmaceutical Chemistry and Pharmacognosy, Faculty of Pharmacy, Applied Science University, Amman, Jordan

    Maha Habash

Authors
  1. Mutasem O. Taha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maha Habash
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad A. Khanfar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mutasem O. Taha.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOC 42 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Taha, M.O., Habash, M. & Khanfar, M.A. The use of docking-based comparative intermolecular contacts analysis to identify optimal docking conditions within glucokinase and to discover of new GK activators. J Comput Aided Mol Des 28, 509–547 (2014). https://doi.org/10.1007/s10822-014-9740-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-014-9740-4

Keywords

Search

Navigation