Atul Kumar SINGH, Mohammed BOUACHRINE, Shashank KUMAR, Khalil EL KHATABI, Ilham AANOUZ, Reda El-MERNISSI, Mohammed Aziz AJANA, Tahar LAKHLIFI

Integrated 3D-QSAR, molecular docking, and molecular dynamics simulation studies on 1,2,3-triazole based derivatives for designing new acetylcholinesterase inhibitors

Alzheimer’s disease (AD) is a multifactorial and polygenic disease. It is the most prevalent reason for dementia in the aging population. A dataset of twenty-six 1,2,3-triazole-based derivatives previously synthetized and evaluated for acetylcholinesterase inhibitory activity were subjected to the three-dimensional quantitative structure-activity relationship (3D-QSAR) study. Good predictability was achieved for comparative molecular field analysis (CoMFA) $(Q^2 = 0.604, R^2 = 0.863, r_{ext}^2 = 0.701)$ and comparative molecular similarity indices analysis (CoMSIA) $(Q^2 = 0.606, R^2 = 0.854, r_{ext}^2 = 0.647)$. The molecular features characteristics provided by the 3D-QSAR contour plots were quite useful for designing and improving the activity of acetylcholinesterase of this class. Based on these findings, a new series of 1,2,3-triazole based derivatives were designed, among which compound A1 with the highest predictive activity was subjected to detailed molecular docking and compared to the most active compound. The selected compounds were further subjected to 20 ns molecular dynamics (MD) simulations to study the comparative conformation dynamics of the protein after ligand binding, revealing promising results for the designed molecule. Therefore, this study could provide worthy guidance for further experimental analysis of highly effective acetylcholinesterase inhibitors.

PDF

___

1. Barai P, Raval N, Acharya S, Borisa A, Bhatt H, et al. Neuroprotective effects of bergenin in Alzheimer’s disease: Investigation through molecular docking, in vitro and in vivo studies. Behavioural Brain Research 2019; 356: 18-40. doi:10.1016/j.bbr.2018.08.010
2. Iqbal K, Grundke-Iqbal I. Alzheimer’s disease, a multifactorial disorder seeking multitherapies. Alzheimer’s & Dementia 2010; 6: 420-424. doi:10.1016/j.jalz.2010.04.006
3. Stanciu GD, Luca A, Rusu RN, Bild V, Beschea Chiriac SI, et al. Alzheimer’s disease pharmacotherapy in relation to cholinergic system ınvolvement. Biomolecules 2019; 10: 40. doi:10.3390/biom10010040
4. Bush AI. The metal theory of Alzheimer’s disease. Journal of Alzheimer’s Disease 2012; 33: S277-281. doi:10.3233/JAD-2012-129011
5. Robert A, Liu Y, Nguyen M, Meunier B. Regulation of copper and ıron homeostasis by metal chelators: a possible chemotherapy for Alzheimer’s disease. Accounts of Chemical Research 2015; 48: 13321339. doi:10.1021/acs.accounts.5b00119
6. Hardy J, Allsop D. Amyloici deposition as the central event in the aetiology of Alzheimer’s disease. Trends in Pharmacological Sciences 1991; 12 (10): 383-388. doi: 10.1016/0165-6147(91)90609-v.
7. Tramutola A, Lanzillotta C, Perluigi M, Butterfield DA. Oxidative stress, protein modification and Alzheimer disease. Brain Research Bulletin 2017; 133: 88-96. doi:10.1016/j.brainresbull.2016.06.005
8. Mattson MP, Tomaselli KJ, Rydel RE. Calcium-destabilizing and neurodegenerative effects of aggregated /3-amyloid peptide are attenuated by basic FGF. Brain Research 1993; 621 (1): 35-49. doi: 10.1016/0006-8993(93)90295-x.
9. LaFerla FM, Green KN, Oddo S. Intracellular amyloid-β in Alzheimer’s disease. Nature Reviews Neuroscience 2007; 8: 499-509. doi:10.1038/nrn2168
10. Agatonovic-Kustrin S, Kettle C, Morton DW. A molecular approach in drug development for Alzheimer’s disease. Biomedicine & Pharmacotherapy 2018; 106: 553-565. doi:10.1016/j.biopha.2018.06.147
11. Hampel H, Mesulam M-M, Cuello AC, Farlow MR, Giacobini E, et al. The cholinergic system in the pathophysiology and treatment of Alzheimer’s disease. Brain 2018; 141: 1917-1933. doi:10.1093/brain/awy132
12. Stanton FM, Hua-Yu LW, Shelby VM, Matthew CV. Recent advances in acetylcholinesterase Inhibitors and Reactivators: an update on the patent literature (2012-2015). Expert Opinion on Therapeutic Patents 2017; 27 (4): 455-476. doi:10.1080/13543776.2017.1272571
13. Sharma K. Cholinesterase inhibitors as Alzheimer’s therapeutics (Review). Molecular Medicine REPORTS 2019; 20: 1479-1487. doi:10.3892/mmr.2019.10374
14. Colovic MB, Krstic DZ, Lazarevic-Pasti TD, Bondzic AM, Vasic VM. Acetylcholinesterase ınhibitors: pharmacology and toxicology. Current Neuropharmacology 2013; 11: 315-335. doi:10.2174/1570159X11311030006
15. Huang C, Wang Y, Li X, Ren L, Zhao J, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet 2020; 395: 497-506. doi:10.1186/s12929-019-0609-7
16. Rastegari A, Nadri H, Mahdavi M, Moradi A, Mirfazli SS, et al. Design, synthesis and anti-Alzheimer’s activity of novel 1,2,3-triazolechromenone carboxamide derivatives. Bioorganic Chemistry 2019; 83: 391-401.
17. Xu M, Peng Y, Zhu L, Wang S, Ji J, Rakesh KP. Triazole derivatives as inhibitors of Alzheimer’s disease: current developments and structureactivity relationships. European Journal of Medicinal Chemistry 2019; 180: 656-672.
18. Sybyl 8.1; Tripos Inc.: St. Louis, MO, USA, 2008.
19. Clark M, Cramer RD, Van Opdenbosch N. Validation of the general purpose tripos 5.2 force field. Journal of Computer Chemistry. 1989; 10: 982-1012. doi:10.1002/jcc.540100804
20. Purcell WP, Singer JA. A brief review and table of semiempirical parameters used in the Hueckel molecular orbital method. Journal of Chemical and Engeenring Data. 1967; 12: 235–46. doi:10.1021/je60033a020
21. Lee C, Yang W, Parr RG. Development of the colle-salvetti correlation-energy formula into a functional of the electron density. Physical Review. 1988; 37: 785–789.
22. AbdulHameed MDM, Hamza A, Liu J, Zhan C-G. Combined 3D-QSAR Modeling and Molecular Docking Study on Indolinone Derivatives as Inhibitors of 3-Phosphoinositide-Dependent Protein Kinase-1.Journal of Chemical Information and Modeling. 2008; 48: 1760–72. doi:10.1021/ci800147v
23. Cramer RD, Patterson DE, Bunce JD. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. Journal of the American Chemical Society. 1988; 110: 5959–67. doi:10.1021/ja00226a005
24. Klebe G, Abraham U, Mietzner T. Molecular Similarity Indices in a Comparative Analysis (CoMSIA) of Drug Molecules to Correlate and Predict Their Biological Activity. Journal of Medicinal Chemistry. 1994; 37: 4130–46. doi:10.1021/jm00050a010
25. Ståhle L, Wold S. 6 Multivariate Data Analysis and Experimental Design in Biomedical Research. Progress in Medicinal Chemistry 1988; 25: 291–338. doi:10.1016/S0079-6468(08)70281-9
26. Bush BL, Nachbar RB. Sample-distance partial least squares: PLS optimized for many variables, with application to CoMFA. Journal of Computer-Aided Molecular Design. 1993; 7: 587–619. doi:10.1007/BF00124364
27. Golbraikh A, Tropsha A. Beware of q2! Journal of Molecular Graphics and Modelling. 2002; 20: 269–76. doi:10.1016/S1093-3263(01)00123- 1
28. Tropsha A, Gramatica P, Gombar V. The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models. QSAR & Combinatorial Science. 2003; 22: 69–77. doi:10.1002/qsar.200390007
29. Baroni M, Clementi S, Cruciani G, Costantino G, Riganelli D, Oberrauch E. Predictive ability of regression models. Part II: Selection of the best predictive PLS model. QSAR & Combinatorial Science. 1992; 6: 347–56. doi:10.1002/cem.1180060605
30. Rücker C, Rücker G, Meringer M. y-Randomization and Its Variants in QSPR/QSAR. Journal of Chemical Information and Modeling. 2007; 47: 2345–57. doi:10.1021/ci700157b
31. DeLano WL. DeLano. PyMOL Mol Graph Syst DeLano Sci San Carlos CA USA. Published online 2002
32. Discovery Studio Predictive Science Application | Dassault Systèmes BIOVIA. Accessed February 6, 2020.
33. Schlagenhauf P, Adamcova M, Regep L, Schaerer MT, Rhein H-G. The position of mefloquine as a 21st century malaria chemoprophylaxis. Malaria Journal. 2010; 9: 357. doi:10.1107/S0907444904011679
34. Parrinello M, Rahman A. Polymorphic transitions in single crystals: A new molecular dynamics method. Journal of Applied Physics. 1981; 52: 7182–90. doi:10.1063/1.328693
35. Hess B, Bekker H, Berendsen HJC. LINCS: A linear constraint solver for molecular simulations. JOURNAL OF COMPUTATIONAL CHEMISTRY. Journal of Computational Chemistry 1997; 18 (12): 1463-1472 1997. https://doi.org/10.1002/(SICI)1096- 987X(199709)18:12%3C1463::AID-JCC4%3E3.0.CO;2-H
36. Darden T, York D, Pedersen L. Particle mesh Ewald: An N ⋅log( N ) method for Ewald sums in large systems. The Journal of Chemical Physics. 1993; 98: 10089–92. doi:10.1063/1.464397
37. Gohlke H, Kiel C, Case DA. Insights into Protein–Protein Binding by Binding Free Energy Calculation and Free Energy Decomposition for the Ras–Raf and Ras–RalGDS Complexes. Journal of Molecular Biology. 2003; 330: 891–913. doi:10.1016/S0022-2836(03)00610-7