Advertisement

Medicinal Chemistry Research

, Volume 26, Issue 11, pp 2768–2784 | Cite as

Docking-based comparative intermolecular contacts analysis and in silico screening reveal new potent acetylcholinesterase inhibitors

  • Maha Habash
  • Sawsan Abuhamdah
  • Khaled Younis
  • Mutasem O. TahaEmail author
Original Research

Abstract

The positive impact of acetylcholinesterase enzyme inhibitors on neurodegenerative diseases impelled continuous attempts to discover and optimize new acetylcholinesterase enzyme inhibitors. The combined recent interest inacetylcholinesterase enzyme inhibitors, together with known shortages of docking and docking validation methods prompted us to use our new 3D-QSAR method, namely, docking-based comparative intermolecular contacts analysis, to identify optimal docking conditions required to dock certain group of inhibitors into acetylcholinesterase enzyme binding site. Additionally, optimal docking-based comparative intermolecular contacts analysis models were converted into pharmacophore models, which were validated by receiver operating characteristic curve analysis. The pharmacophores were subsequently used as search queries to mine the national cancer institute list of compounds for new acetylcholinesterase enzyme inhibitors. Five low micromolar acetylcholinesterase enzyme inhibitors were identified. The most potent gave IC50 value of 2.55 μM.

Keywords

Acetylcholinesterase Docking-based Comparative Intermolecular Contacts Analysis Libdock virtual screening 

Notes

Acknowledgements

This project was sponsored by the Deanship of Scientific Research at the University of Jordan. The authors wish to thank the National Cancer Institute for freely providing hit compounds for experimental validation.

Compliance with ethical standards

Conflict of Interest

The authors declare that they have no competing interests.

Supplementary material

44_2017_1976_MOESM1_ESM.doc (2.9 mb)
Supplementary Information

References

  1. Abuhamdah S, Habash M, Taha MO (2013) Elaborate ligand-based modeling coupled with QSAR analysis and in silico screening reveal new potent acetylcholinesterase inhibitors. J Comput Aided Mol Des 27:1075–1092CrossRefPubMedGoogle Scholar
  2. Abuhammad A, Taha MO (2016) Innovative computer-aided methods for the discovery of new kinase ligands. Future Med Chem 8:509–526CrossRefPubMedGoogle Scholar
  3. Alabed SJ, Khanfar M, Taha MO (2016) Computer-aided discovery of new FGFR-1 inhibitors followed by in vitro validation. Future Med Chem 8:1841–1869CrossRefPubMedGoogle Scholar
  4. Al-Sha’er MA, Taha MO (2012) Application of docking-based comparative intermolecular contacts analysis to validate Hsp90alpha docking studies and subsequent in silico screening for inhibitors. J Mol Model 18:4843–4863CrossRefPubMedGoogle Scholar
  5. Bachurin SO (2003) Medicinal chemistry approaches for the treatment and prevention of Alzheimer’s disease. Med Res Rev 23:48–88CrossRefPubMedGoogle Scholar
  6. Beeley NRA, Sage C (2003) GPCRs: an update on structural approaches to drug discovery. Targets 2:19–25CrossRefGoogle Scholar
  7. Beier C, Zacharias M (2010) Tackling the challenges posed by target flexibility in drug design. Expert Opin Drug Dis 5:347–359CrossRefGoogle Scholar
  8. Bissantz C, Folkers G, Rognan D (2000) Protein-based virtual screening of chemical databases 1 Evaluation of different docking/scoring combinations. J Med Chem 43:4759–4767CrossRefPubMedGoogle Scholar
  9. Bodnarchuk MS (2016) Water, water, everywhere… It’s time to stop and think. Drug Discov Today 21:1139–1146CrossRefPubMedGoogle Scholar
  10. Boyd S (2007) FlexX suite. Chem World 4:72Google Scholar
  11. Chen S, Zhang X-J, Li L, Le W-D (2007) Current Experimental Therapy for Alzheimer’s Disease. Curr Neuropharmacol 5:127–134CrossRefPubMedPubMedCentralGoogle Scholar
  12. Clark CM, Karlawish JH (2003) Alzheimer disease: current concept and emerging diagnostic and therapeutic strategies. Ann Intern Med 138:400–410CrossRefPubMedGoogle Scholar
  13. 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
  14. De Ferrari GV, Canales MA, Shin I, Weiner LM, Silman I, Inestrosa NC (2001) A structural motif of acetylcholinesterase that promotes amyloid beta-peptide fibril formation. Biochem 40:10447–10457CrossRefGoogle Scholar
  15. Decker M (2006) Homo bivalent quinazolinimines as novel nanomolar inhibitors of cholinesterases with dirigible selectivity toward butyrylcholinesterase. J Med Chem 49:5411–5413CrossRefPubMedGoogle Scholar
  16. Decker M, Kraus B, Heilmann J (2008) Design, synthesis and pharmacological evaluation of hybrid molecules out of quinazolinimines and lipoic acid lead to highly potent and selective butyrylcholinesterase inhibitors with antioxidant properties. Bioorg Med Chem 16:4252–4261CrossRefPubMedGoogle Scholar
  17. Decker M, Krauth F, Lehmann J (2006) Novel tricyclic quinazolinimines and related tetracyclic nitrogen bridgehead compounds as cholinesterase inhibitors with selectivity towards butyrylcholinesterase. Bioorg Med Chem 14:1966–1977CrossRefPubMedGoogle Scholar
  18. Diller DJ, Merz KM (2001) High throughput docking for library design and library prioritization. Proteins 43:113–124CrossRefPubMedGoogle Scholar
  19. Ellman G, Courtney K, Andresjr V, Featherstone R (1961) A new and rapid colorimetric determination of acetylcholinesterase activity. Biochem Pharmacol 7:88–95CrossRefPubMedGoogle Scholar
  20. 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 49:1568–1580CrossRefPubMedGoogle Scholar
  21. 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 15:411–428CrossRefGoogle Scholar
  22. Fang L, Decker M, Roegler C, Lehmann J, Appenroth D, Fleck C, Kiehntopf M, Deufel T, Peng S, Zhang Y (2008) Synthesis and biological evaluation of NO-donor-tacrine hybrids as hepatoprotective anti-Alzheimer drug candidates. J Med Chem 51:713–716CrossRefPubMedGoogle Scholar
  23. Fang L, Kraus B, Lehmann J, Heilmann J, Zhang Y, Decker M (2008) Design and synthesis of tacrine-ferulic acid hybrids as multi-potent anti-Alzheimer drug candidates. Bioorg Med Chem Lett 18:2905–2909CrossRefPubMedGoogle Scholar
  24. Fang L, Zhang Y, Decker M, Appenroth D, Fleck C, Jumpertz S, Mohr K, Trankle C (2010) Hybrid molecules from xanomeline and tacrine: enhanced tacrine actions on cholinesterases and muscarinic M1 receptors. J Med Chem 53:2094–2103CrossRefPubMedGoogle Scholar
  25. Foloppe N, Hubbard R (2006) Towards predictive ligand design with free-energy based computational methods? Curr Med Chem 13:3583–3608CrossRefPubMedGoogle Scholar
  26. Gao WR, Lai YL (1998) SCORE: A new empirical method for estimating the binding affinity of a protein-ligand complex. J Mol Model 4:379–394CrossRefGoogle Scholar
  27. Garcia-Sosa AT, Hetenyi C, Maran U (2010) Drug efficiency indices for improvement of molecular docking scoring functions. J Comput Chem 31:174–184CrossRefPubMedGoogle Scholar
  28. Greenblatt HM, Kryger G, Lewis T, Silman I, Sussman JL (1999) Structure of acetylcholinesterase complexed with (-)-galanthamine at 2.3 A resolution. FEBS Lett 463:321–326CrossRefPubMedGoogle Scholar
  29. Guzior N, Wieckowska A, Panek D, Malawska B (2015) Recent development of multifunctional agents as potential drug candidates for the treatment of Alzheimer’s disease. Curr Med Chem 22:373–404CrossRefPubMedPubMedCentralGoogle Scholar
  30. Habash M, Taha MO (2011) Ligand-based modelling followed by synthetic exploration unveil novel glycogen phosphorylase inhibitory leads. Bioorg Med Chem 19:4746–4771CrossRefPubMedGoogle Scholar
  31. 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 47:1750–1759CrossRefPubMedGoogle Scholar
  32. Harel M, Schalk I, Ehret-Sabatier L, Bouet F, Goeldner M, Hirth C, Axelsen PH, Silman I, Sussman JL (1993) Quaternary ligand binding to aromatic residues in the active-site gorge of acetylcholinesterase. Proc Natl Acad Sci USA 90:9031–9035CrossRefPubMedGoogle Scholar
  33. Harel M, Silman I, Quinn DM, Nair HK, Sussman JL (1996) The X-ray structure of a transition state analogue complex reveals the molecular origins of the catalytic power and substrate specificity of acetylcholinesterase. J Am Chem Soc 118:2340–2346CrossRefGoogle Scholar
  34. Hecht D, Fogel GB (2009) Computational intelligence methods for docking scores. Curr Comput Aided Drug Des 5:56–68CrossRefGoogle Scholar
  35. Homans SW (2007) Water, water everywhere - except where it matters. Drug Discov Today 12:534–539CrossRefPubMedGoogle Scholar
  36. Inestrosa NC, Dinamarca MC, Alvarez A (2008) Amyloid-cholinesterase interactions. Implications for Alzheimer’s disease. FEBS J 275:625–632CrossRefPubMedGoogle Scholar
  37. Irwin JJ, Shoichet BK (2005) ZINC−A free database of commercially available compounds for virtual screening. J Chem Inf Model 45:177–182CrossRefPubMedPubMedCentralGoogle Scholar
  38. 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 46:5781–5789CrossRefPubMedGoogle Scholar
  39. Jain AN (2006) Scoring functions for protein-ligand docking. Curr Protein Pept Sc 7:407–420CrossRefGoogle Scholar
  40. Jaradat NJ, Khanfar MA, Habash M, Taha MO (2015) Combining docking-based comparative intermolecular contacts analysis and k-nearest neighbor correlation for the discovery of new check point kinase 1 inhibitors. J Comput Aided Mol Des 29:561–581CrossRefPubMedGoogle Scholar
  41. Jones G, Willett P, Glen RC, Leach AR, Taylor R (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267:727–748CrossRefPubMedGoogle Scholar
  42. Jorgensen WL (2009) Efficient drug lead discovery and optimization. Accounts Chem Res 42:724–733CrossRefGoogle Scholar
  43. 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 22:213–228CrossRefPubMedGoogle Scholar
  44. Kontoyianni M, McClellan LM, Sokol GS (2004) Evaluation of docking performance: comparative data on docking algorithms. J Med Chem 47:558–565CrossRefPubMedGoogle Scholar
  45. Kryger G, Silman I, Sussman JL (1998) Three-dimensional structure of a complex of E2020 with acetylcholinesterase from Torpedo californica. J Physiol 92:191–194Google Scholar
  46. Krammer A, Kirchhoff PD, Jiang X, Venkatachalam CM, Waldman M (2005) LigScore: a novel scoring function for predicting binding affinities. J Mol Graphics Modell 23:395–407CrossRefGoogle 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 44:1123–1129CrossRefPubMedGoogle Scholar
  48. 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 Delivery Rev 46:3–26CrossRefGoogle Scholar
  49. Lu SH, Wu JW, Liu HL, Zhao JH, Liu KT, Chuang CK, Lin HY, Tsai WB, Ho Y (2011) The discovery of potential acetylcholinesterase inhibitors: a combination of pharmacophore modeling, virtual screening, and molecular docking studies. J Biomed Sci 18:8–18CrossRefPubMedPubMedCentralGoogle Scholar
  50. Menikarachchi LC, Gascon JA (2010) QM/MM approaches in medicinal chemistry research. Curr Top Med Chem 10:46–54CrossRefPubMedGoogle Scholar
  51. 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
  52. Morris GM, Olson AJ, Goodsell DS (2000) Protein-ligand docking. Methods Princ Med Chem 8:31–48Google Scholar
  53. Muegge I (2000) A knowledge-based scoring function for protein-ligand interactions: Probing the reference state. Perspect Drug Discov 20:99–114CrossRefGoogle Scholar
  54. Mukherjee PK, Satheeshkumar N, Venkatesh P, Venkatesh M (2011) Lead finding for Acetyl cholinesterase inhibitors from natural origin: structure activity relationship and scope. Mini-Rev Med Chem 11:247–262CrossRefPubMedGoogle Scholar
  55. 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 9:500–512CrossRefPubMedGoogle Scholar
  56. Quinn DM (1987) Acetylcholinesterase: enzyme structure, reaction dynamics, and virtual transition states. Chem Rev 87:955–979CrossRefGoogle Scholar
  57. 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
  58. Rao SN, Head MS, Kulkarni A, LaLonde JM (2007) Validation studies of the site-directed docking program LibDock. J Chem Inf Model 47:2159–2171CrossRefPubMedGoogle Scholar
  59. Rarey M, Kramer B, Lengauer T, Klebe G (1996) A fast flexible docking method using an incremental construction algorithm. J Mol Biol 261:470–489CrossRefPubMedGoogle Scholar
  60. Rees TM, Berson A, Sklan EH, Younkin L, Younkin S, Brimijoin S, Soreq H (2005) Memory deficits correlating with acetylcholinesterase splice shift and amyloid burden in doubly transgenic mice. Curr Alzheimer Res 2:291–300CrossRefPubMedGoogle Scholar
  61. Rees TM, Hammond PI, Soreq H, Younkin S, Brimijoin S (2003) Acetylcholinesterase promotes beta-amyloid plaques in cerebral cortex. Neurobiol Aging 24:777–787CrossRefPubMedGoogle Scholar
  62. Rook Y, Schmidtke K-U, Gaube F, Schepmann D, Wünsch B, Heilmann J, Lehmann J, Winckler T (2010) Bivalent beta-carbolines as potential multitarget anti-Alzheimer agents. J Med Chem 53:3611–3617CrossRefPubMedGoogle Scholar
  63. Schott Y, Decker M, Rommelspacher H, Lehmann J (2006) 6-Hydroxy- and 6-methoxy-beta-carbolines as acetyl- and butyrylcholinesterase inhibitors. Bioorg Med Chem Lett 16:5840–5843CrossRefPubMedGoogle Scholar
  64. Shahin R, AlQtaishat S, Taha MO (2012) Elaborate ligand-based modeling reveal new submicromolar Rho kinase inhibitors. J Comput -Aided Mol Des 26:249–266CrossRefPubMedGoogle Scholar
  65. Shoichet BK (2006) Interpreting steep dose-response curves in early inhibitor discovery. J Med Chem 49:7274–7277CrossRefPubMedGoogle Scholar
  66. Silman I, Harel M, Axelsen P, Raves M, Sussman JL (1994) Three-dimensional structures of acetylcholine-esterase and of its complexes with anticholinesterase agents. Biochem Soc Trans 22:745–749CrossRefPubMedGoogle Scholar
  67. Song CM, Lim SJ, Tong JC (2009) Recent advances in computer-aided drug design. Brief Bioinform 10:579–591CrossRefPubMedGoogle Scholar
  68. Sussman JL, Harel M, Frolow F, Oefner C, Goldman A, Toker L, Silman I (1991) Atomic structure of acetylcholinesterase from Torpedo californica: a prototypic acetylcholine-binding protein. Science 253:872–879CrossRefPubMedGoogle Scholar
  69. Sussman JL, Harel M, Silman I (1993) Three-dimensional structure of acetylcholinesterase and of its complexes with anticholinesterase drugs. Chem Biol Interact 87:187–197CrossRefPubMedGoogle Scholar
  70. Taha MO, Dahbiyeh LA, Bustanji Y, Zalloum H, Saleh S (2008) Combining ligand-based pharmacophore modeling, quantitative structure−activity relationship analysis and in silico screening for the discovery of new potent hormone sensitive lipase inhibitors. J Med Chem 51:6478–6494CrossRefPubMedGoogle Scholar
  71. 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 51:647–669CrossRefPubMedGoogle Scholar
  72. Taha MO, Habash M, Khanfar MA (2014) 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 28:509–547CrossRefPubMedGoogle Scholar
  73. Taylor P, Radic Z (1994) The cholinesterases: from genes to proteins. Annu Rev Pharmacol Toxicol 34:281–320CrossRefPubMedGoogle Scholar
  74. Terry AV, Buccafusco JJ (2003) The cholinergic hypothesis of age and Alzheimer’s disease-related cognitive deficits: recent challenges and their implications for novel drug development. J Pharm Exp Ther 306:821–827CrossRefGoogle Scholar
  75. 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-VCH Verlag GmbH & Co, Weinheim, pp 325–364CrossRefGoogle Scholar
  76. Van Heel W, Hachimi-Idrissi S (2011) Accidental organophosphate insecticide intoxication in children: a reminder. Int J Emerg Med 4:32CrossRefPubMedPubMedCentralGoogle Scholar
  77. 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 4:1–19CrossRefGoogle Scholar
  78. 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 45:2615–2623CrossRefPubMedGoogle Scholar
  79. 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 48:6296–6303CrossRefPubMedGoogle Scholar
  80. 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 21:289–307CrossRefPubMedGoogle Scholar
  81. 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 44:793–806CrossRefPubMedGoogle Scholar
  82. Walters W, Namchuk M (2003) Designing screens: how to make your hits a hit. Nat Rev Drug Discov 2:259–266CrossRefPubMedGoogle Scholar
  83. Wang Y, Wang H, Chen H-Z (2016) AChE inhibition-based multi-target-directed ligands, a novel pharmacological approach for the symptomatic and disease-modifying therapy of Alzheimer’s disease. Curr Neuropharmacol 14:364–375CrossRefPubMedPubMedCentralGoogle Scholar
  84. Whittaker VP (1990) The contribution of drugs and toxins to understanding of cholinergic function. Trends Pharmacol Sci 11:8–13CrossRefPubMedGoogle Scholar
  85. Woodruff-Pak DS, Vogel RW, Wenk GL (2001) Galantamine: Effect on nicotinic receptor binding, acetylcholinesterase inhibition, and learning. Proc Natl Acad Sci USA 98:2089–2094CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Pharmaceutics and Pharmaceutical Sciences, Faculty of PharmacyAqaba University of TechnologyAqabaJordan
  2. 2.Department of Biopharmaceutics and Clinical Pharmacy, Faculty of PharmacyUniversity of JordanAmmanJordan
  3. 3.Department of Computer Engineering, Faculty of EngineeringUniversity of JordanAmmanJordan
  4. 4.Drug Discovery Unit, Department of Pharmaceutical Sciences, Faculty of PharmacyUniversity of JordanAmmanJordan

Personalised recommendations