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Molecular Modeling Studies of C-Glycosylfavone Derivatives as GSK-3β Inhibitors Based on QSAR and Docking Analysis

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Abstract

Glycogen synthase kinase-3 beta (GSK-3β) is implicated in abnormal hyperphosphorylation of the tau protein and its inhibitors may be a promising therapeutic approach for treating Alzheimer’s disease. Here, a series of C-glycosylfavone derivatives as GSK-3β inhibitors was selected to perform two-dimensional quantitative structure activity relationship (2D-QSAR) method and docking analysis. The 2D-QSAR model was generated and validated using a dataset of 23 compounds and a test set of 5 compounds, respectively. The best model selected by the partial-least-squares (PLS) regression method revealed a regression coefficient (r2) value of 0.85 and the mean-square-error (MSE) value of 0.04. The predictive ability and stability of the generated model was verified by external and internal validations, and gave the regression coefficient values of 0.93 and 0.72, respectively. Molecular docking analysis using AutoDock vina was carried out to explain the binding modes of C-glycosylfavone ligands with the GSK-3β receptor. Based on the obtained results, a novel series of C-glycosylfavone derivative was designed and their activity and binding affinity were predicted. The generated work could be helpful for the design and development of novel GSK-3β inhibitors.

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Data Availability

The data of this work is available at https://github.com/elai-ssouq/GSK-3-beta

References

  1. Dokken, B.B., Henriksen, E.J.: Chronic selective glycogen synthase kinase-3 inhibition enhances glucose disposal and muscle insulin action in prediabetic obese Zucker rats. Am. J. Physiol. Endocrinol. Metab. (2006). https://doi.org/10.1152/ajpendo.00628.2005

    Article  PubMed  Google Scholar 

  2. Llorens-Martín, M., Jurado, J., Hernández, F., Ávila, J.: GSK-3β, a pivotal kinase in Alzheimer disease. Front. Mol. Neurosci. 7, 1–11 (2014). https://doi.org/10.3389/fnmol.2014.00046

    Article  CAS  Google Scholar 

  3. Dugo, L., Collin, M., Thiemermann, C.: Glycogen synthase kinase 3β as a target for the therapy of shock and inflammation. Shock 27, 113–123 (2007). https://doi.org/10.1097/01.shk.0000238059.23837.68

    Article  CAS  PubMed  Google Scholar 

  4. Woodgett, J.R.: Molecular cloning and expression of glycogen synthase kinase-3/factor A. EMBO J. 9, 2431–2438 (1990). https://doi.org/10.1002/j.1460-2075.1990.tb07419.x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Martinez, A., Castro, A., Medina, M.: Glycogen Synthase Kinase 3 (GSK-3) and its Inhibitors: Drug Discovery and Development. Wiley, Hoboken (2006)

    Book  Google Scholar 

  6. Soutar, M.P.M., Kim, W.Y., Williamson, R., Peggie, M., Hastie, C.J., McLauchlan, H., Snider, W.D., Gordon-Weeks, P.R., Sutherland, C.: Evidence that glycogen synthase kinase-3 isoforms have distinct substrate preference in the brain. J. Neurochem. 115, 974–983 (2010). https://doi.org/10.1111/j.1471-4159.2010.06988.x

    Article  CAS  PubMed  Google Scholar 

  7. Busciglio, J., Lorenzo, A., Yeh, J., Yankner, B.A.: β-Amyloid fibrils induce tau phosphorylation and loss of microtubule binding. Neuron 14, 879–888 (1995). https://doi.org/10.1016/0896-6273(95)90232-5

    Article  CAS  PubMed  Google Scholar 

  8. Acker, B.A., Jacobsen, E.J., Rogers, B.N., Wishka, D.G., Reitz, S.C., Piotrowski, D.W., Myers, J.K., Wolfe, M.L., Groppi, V.E., Thornburgh, B.A., Tinholt, P.M., Walters, R.R., Olson, B.A., Fitzgerald, L., Staton, B.A., Raub, T.J., Krause, M., Li, K.S., Hoffmann, W.E., Hajos, M., Hurst, R.S., Walker, D.P.: 2,3-cpyridine-5-carboxamide as an agonist of the a 7 nicotinic acetylcholine receptor: in vitro and in vivo activity. Bioorg. Med. Chem. Lett. 18, 3611–3615 (2008). https://doi.org/10.1016/j.bmcl.2008.04.070

    Article  CAS  PubMed  Google Scholar 

  9. Ryder, J., Su, Y., Liu, F., Li, B., Zhou, Y., Ni, B.: Divergent roles of GSK3 and CDK5 in APP processing. Biochem. Biophys. Res. Commun. 312, 922–929 (2003). https://doi.org/10.1016/j.bbrc.2003.11.014

    Article  CAS  PubMed  Google Scholar 

  10. Lovestone, S., Reynolds, C.H., Latimer, D., Davis, D.R., Anderton, B.H., Gallo, J., Hanger, D., Mulot, S., Marquardt, B., Stabel, S., Woodgett, J.R., Miller, C.C.J.: Alzheimer’s disease-like phosphorylation of the microtubule-associated protein tau by glycogen synthase kinase-3 in transfected mammalian cells. Curr Biol. 4, 1077–1086 (1994). https://doi.org/10.1016/S0960-9822(00)00246-3

    Article  CAS  PubMed  Google Scholar 

  11. Imahori, K.: PDH is inactivated by betaA-induced TPKI-GSK-3beta. Proc. Natl. Acad. 93, 2719–2723 (1996). https://doi.org/10.1073/pnas.93.7.2719

    Article  Google Scholar 

  12. Pei, J.J., Tanaka, T., Tung, Y.C., Braak, E., Iqbal, K.: Distribution, levels, and activity of glycogen synthase kinase-3 in the Alzheimer disease brain. J. Neuropathol. Exp. Neurol. 56, 70–78 (1997). https://doi.org/10.1097/00005072-199701000-00007

    Article  CAS  PubMed  Google Scholar 

  13. Hardy, J.: A hundred years of Alzheimer’s disease research. Neuron 52, 3–13 (2006). https://doi.org/10.1016/j.neuron.2006.09.016

    Article  CAS  PubMed  Google Scholar 

  14. Leroy, K., Yilmaz, Z., Brion, J.: Increased level of active GSK-3ß in Alzheimer’s disease and accumulation in argyrophilic grains and in neurons at different stages of neurofibrillary degeneration. Neuropathol. Appl. Neurobiol. 33, 43–55 (2007). https://doi.org/10.1111/j.1365-2990.2006.00795.x

    Article  CAS  PubMed  Google Scholar 

  15. Liang, Z., Li, Q.X.: Discovery of selective, substrate-competitive, and passive membrane permeable glycogen synthase kinase-3 β inhibitors: synthesis, biological evaluation, and molecular modeling of new c-glycosyl flavones. ACS Chem. Neurosci. 9, 1166–1183 (2018). https://doi.org/10.1021/acschemneuro.8b00010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. El Aissouq, A., Toufik, H.: QSAR study of isonicotinamides derivatives as Alzheimr’s disease inhibitors using PLS-R and ANN methods. 2019 Int. Conf. Intell. Syst. Adv. Comput. Sci. 1–7 (2019). https://doi.org/https://doi.org/10.1109/ISACS48493.2019.9068919

  17. Stitou, M., Toufik, H., Bouachrine, M., Lamchouri, F.: Quantitative structure–activity relationships analysis, homology modeling, docking and molecular dynamics studies of triterpenoid saponins as Kirsten rat sarcoma inhibitors. J. Biomol. Struct. Dyn. (2020). https://doi.org/10.1080/07391102.2019.1707122

    Article  PubMed  Google Scholar 

  18. Tropsha, A., Gramatica, P., Gombar, V.K.: The importance of being Earnest : validation is the absolute essential for successful application and interpretation of QSPR models. Mol. Inform. 22, 69–77 (2003). https://doi.org/10.1002/qsar.200390007

    Article  CAS  Google Scholar 

  19. Raghu, T.S., Ramesh, R., Whinston, A.B.: Addressing the homeland security problem: a collaborative decision-making framework. J. Am. Soc. Inf. Sci. Technol. 56, 310–324 (2005). https://doi.org/10.1002/asi.20123

    Article  Google Scholar 

  20. El Aissouq, A., Toufik, H., Stitou, M., Ouammou, A., Lamchouri, F.: In silico design of novel tetra-substituted pyridinylimidazoles derivatives as c-jun N-terminal kinase-3 inhibitors, using 2D/3D-QSAR studies, molecular docking and ADMET prediction. Int. J. Pept. Res. Ther. 26, 1335–1351 (2020). https://doi.org/10.1007/s10989-019-09939-8

    Article  CAS  Google Scholar 

  21. Elmchichi, L., Belhassan, A., Lakhlifi, T., Bouachrine, M.: 3D-QSAR study of the chalcone derivatives as anticancer agents. J. Chem. (2020). https://doi.org/10.1155/2020/5268985

    Article  Google Scholar 

  22. Kubinyi, H., Hamprecht, F.A., Mietzner, T.: Three-dimensional quantitative similarity-activity relationships (3D QSiAR) from SEAL similarity matrices. J. Med. Chem. 41, 2553–2564 (1998). https://doi.org/10.1021/jm970732a

    Article  CAS  PubMed  Google Scholar 

  23. Kasmi, R., Hadaji, E., Chedadi, O., El Aissouq, A., Bouachrine, M., Ouammou, A.: Of CDK (anticancer activity) with an application of the molecular docking method. Heliyon. 6, e04514 (2020). https://doi.org/10.1016/j.heliyon.2020.e04514

    Article  PubMed  PubMed Central  Google Scholar 

  24. Goudzal, A., El Aissouq, A., El Hamdani, H., Ouammou, A.: Materials today: proceedings QSAR modeling, molecular docking sudies and ADMET prediction on a series of henylaminopyrimidine-(thio)urea derivatives as CK2 inhibitors. Mater. Today Proc. (2020). https://doi.org/10.1016/j.matpr.2020.08.044

    Article  Google Scholar 

  25. Gramatica, P.: Principles of QSAR models validation: internal and external. QSAR Comb. Sci. 26, 694–701 (2007). https://doi.org/10.1002/qsar.200610151

    Article  CAS  Google Scholar 

  26. Bertrand, J.A., Thieffine, S., Vulpetti, A., Cristiani, C., Valsasina, B., Knapp, S., Kalisz, H.M., Flocco, M.: Structural characterization of the GSK-3b active site using selective and non-selective ATP-mimetic inhibitors. J. Mol. Biol. 333, 393–407 (2003). https://doi.org/10.1016/j.jmb.2003.08.031

    Article  CAS  PubMed  Google Scholar 

  27. Morris, G.M., Huey, R., Lindstrom, W., Sanner, M.F., Belew, R.K., Goodsell, D.S., Olson, A.J.: Software news and updates AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J. Comput. Chem. 30, 2785–2791 (2009). https://doi.org/10.1002/jcc

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Trott, O., Olson, A.J.: Software news and update AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31, 455–461 (2009). https://doi.org/10.1002/jcc

    Article  Google Scholar 

  29. Petitjean, M.: Applications of the radius-diameter diagram to the classification of topological and geometrical shapes of chemical compounds. J. Chem. Inf. Comput. Sci. 32, 331–337 (1992). https://doi.org/10.1021/ci00008a012

    Article  CAS  Google Scholar 

  30. Wildman, S.A., Crippen, G.M.: Prediction of physicochemical parameters by atomic contributions. J. Chem. Inf. Comput. Sci. 39, 868–873 (1999). https://doi.org/10.1021/ci990307l

    Article  CAS  Google Scholar 

  31. Hari, N.S., Moorthy, N., Ramos, M.J., Fernandes, P.A.: Prediction of the relationship between the structural features of andrographolide derivatives and α-glucosidase inhibitory activity: a quantitative structure-activity­relationship (QSAR) study. J. Enzyme Inhib. Med. Chem. 26, 78–87 (2011). https://doi.org/10.3109/14756361003724760

    Article  CAS  Google Scholar 

  32. Hall, L.H., Mohney, B.: The electrotopological state: an atom index for QSAR. Mol. Inform. 51, 43–51 (1991). https://doi.org/10.1002/qsar.19910100108

    Article  Google Scholar 

  33. Kitchen, D.B., Decornez, H., Furr, J.R., Bajorath, J.: Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug Discov. 3, 4 (2004). https://doi.org/10.1038/nrd1549

    Article  CAS  Google Scholar 

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Acknowledgment

The authors want to thank the Moroccan Association of Theoretical Chemistry (MATC) for its relevant help regarding to the Software.

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Authors and Affiliations

  1. Laboratory of Processes, Materials and Environment (LPME), Faculty of Science and Technology, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Abdellah El Aissouq & Fouad Khalil

  2. Engineering Laboratory of Organometallic, Molecular Materials and Environment (LIMOME), Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Oussama Chedadi, Rania Kasmi, Amina Goudzal & Abdelkrim Ouammou

  3. MCNS Laboratory, Faculty of Sciences, Moulay Ismail University, Meknes, Morocco

    Larbi Elmchichi, Fatima En-nahli & Mohammed Bouachrine

  4. EST Khenifra, Sultan Moulay Sliman University, Beni-Mellal, Morocco

    Mohammed Bouachrine

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  1. Abdellah El Aissouq
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Correspondence to Abdelkrim Ouammou.

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El Aissouq, A., Chedadi, O., Kasmi, R. et al. Molecular Modeling Studies of C-Glycosylfavone Derivatives as GSK-3β Inhibitors Based on QSAR and Docking Analysis. J Solution Chem 50, 808–822 (2021). https://doi.org/10.1007/s10953-021-01083-6

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