The Interaction of a Novel Drug with β-secretase-1 and Acetylcholinesterase: A Computational Investigation from Both Dynamics and Thermodynamics Viewpoints

Document Type : Research Article


1 Molecular Simulation Research Laboratory, Department of Chemistry, Iran University of Science and Technology, P.O. Box16846-13114 Tehran, I. R. IRAN

2 Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, I.R. IRAN


Inhibition of glycogen synthase kinase-3 (GSK-3), β-secretase 1 (BACE-1), and acetylcholinesterase (AChE) could block one of the initial pathological events of Alzheimer's disease (AD). Recently, a new class of drugs has been developed with significant potential for GSK-3 inhibition. In this research, to the discovery of the new ligand as the potential multi-target drug with effective anti-Alzheimer's action a detailed computational investigation has been carried out on the effect of one of the most important drugs of such class on BACE-1 and AChE enzymes. The results of the binding free energies (∆GBind) showed that the binding of this drug to AChE (-67.77 kJ/mol) is thermodynamically more favorable than BACE-1 (-22.35 kJ/mol). Examination of dynamic properties such as the root mean square fluctuation (RMSF) and the propensity for the secondary structure demonstrated that due to the decrease in the β-sheet and β-bridge content as well as the increase in the random coil content of BACE-1 in the presence of the drug, this enzyme was completely more flexible than AChE. The free-energy landscape (FEL) based on the first and second motion modes (PC1 and PC2) indicated that the large concerted motions of BACE-1 found in the simulations were particularly more sensitive to this drug than AChE.


Main Subjects

[1] Rochais C., Lecoutey C., Gaven F., Giannoni P., Hamidouche K., Hedou D., Dubost E., Genest D., Yahiaoui S., Freret T., Novel multitarget-directed ligands (MTDLs) with Acetylcholinesterase (AChE) Inhibitory And Serotonergic Subtype 4 Receptor (5-HT4R) Agonist Activities as Potential Agents Against Alzheimer’s Disease: the Design of Donecopride, J. Med. Chem., 58: 3172–3187 (2015).
[2] Gazova Z., Soukup O., Sepsova V., Siposova K., Drtinova L., Jost P., Spilovska K., Korabecny J., Nepovimova E., Fedunova D., Multi-target-directed Therapeutic Potential of 7-Methoxytacrine-Adamantylamine Heterodimers in the Alzheimer’s Disease Treatment, Biochim. Biophys. Acta (BBA)-Molecular Basis Dis., 1863: 607–619 (2017).
[4] Melnikova I., Therapies for Alzheimer’s Disease, Nat. Rev. Drug Discov., 6: 341–342 (2007).
[5] Moussa‐Pacha N.M., Abdin S.M., Omar H.A., Alniss H., Al‐Tel T.H., BACE1 Inhibitors: Current Status And Future Directions In Treating Alzheimer’s Disease, Med. Res. Rev., 40: 339–384 (2020).
[6] Pohanka M., Inhibitors of Acetylcholinesterase And Butyrylcholinesterase Meet Immunity, Int. J. Mol. Sci., 15: 9809–9825 (2014).
[8] Tougu V., Acetylcholinesterase: Mechanism of Catalysis and Inhibition, Curr. Med. Chem. Nerv. Syst. Agents., 1: 155–170 (2001).
[9]   Mouchlis V.D., Melagraki G., Zacharia L.C., Afantitis A., Computer-Aided Drug Design of Β-Secretase, Γ-Secretase and Anti-Tau Inhibitors for the Discovery of Novel Alzheimer’s Therapeutics, Int. J. Mol. Sci., 21: 703 (2020).
[10] Ullah M., Johora F.T., Sarkar B., Araf Y., Ahmed N., Nahar A.N., Akter T., Computer-Assisted Evaluation of Plant-Derived β-Secretase Inhibitors in Alzheimer’s Disease, Egypt. J. Med. Hum. Genet., 22: 1–15 (2021).
[11] Citron M., Alzheimer’s Disease: Strategies for Disease Modification, Nat. Rev. Drug Discov., 9: 387–398 (2010).
[13] Morphy R., Rankovic Z., Designed Multiple Ligands. an Emerging Drug Discovery Paradigm, J. Med. Chem., 48: 6523–6543 (2005).
[14] Kola I., Landis J., Can the Pharmaceutical Industry Reduce Attrition Rates?, Nat. Rev. Drug Discov., 3: 711–716 (2004).
[15] Prati F., Uliassi E., Bolognesi M.L., Two Diseases, one Approach: Multitarget Drug Discovery in Alzheimer’s and Neglected Tropical Diseases, Medchemcomm, 5: 853–861 (2014).
[16] Cavalli A., Bolognesi M.L., Minarini A., Rosini M., Tumiatti V., Recanatini M., Melchiorre C., Multi-Target-Directed Ligands to Combat Neurodegenerative Diseases, J. Med. Chem., 51: 347–372 (2008).
[17] Nepovimova E., Uliassi E., Korabecny J., Pena-Altamira L.E., Samez S., Pesaresi A., Garcia G.E., Bartolini M., Andrisano V., Bergamini C., Multitarget drug Design Strategy: Quinone–Tacrine Hybrids Designed to Block Amyloid-β Aggregation and to Exert Anticholinesterase and Antioxidant Effects, J. Med. Chem., 57: 8576–8589 (2014).
[18] Nepovimova E., Korabecny J., Dolezal R., Babkova K., Ondrejicek A., Jun D., Sepsova V., Horova A., Hrabinova M., Soukup O., Tacrine–Trolox Hybrids: A Novel Class of Centrally Active, Nonhepatotoxic Multi-Target-Directed Ligands Exerting Anticholinesterase and Antioxidant Activities with Low in Vivo Toxicity, J. Med. Chem., 58: 8985–9003 (2015).
[19] Perez L.R., Franz K.J., Minding Metals: Tailoring Multifunctional Chelating Agents for Neurodegenerative Disease, Dalt. Trans., 39: 2177–2187 (2010).
[21] Ain Q., Batool M., Choi S., TLR4-Targeting Therapeutics: Structural Basis and Computer-Aided Drug Discovery Approaches, Molecules, 25: 627 (2020).
[22] Batool M., Ahmad B., Choi S., A Structure-Based Drug Discovery Paradigm, Int. J. Mol. Sci., 20: 2783 (2019).
[23] Usha T., Shanmugarajan D., Goyal A.K., Kumar C.S., Middha S.K., Recent Updates on Computer-Aided Drug Discovery: Time for a Paradigm Shift, Curr. Top. Med. Chem., 17: 3296–3307 (2017).
[24] Viana J. de O., Félix M.B., Maia M. dos S., Serafim V. de L., Scotti L., Scotti M.T., Drug Discovery and Computational Strategies in the Multitarget Drugs Era, Brazilian J. Pharm. Sci., 54: (2018).
[25] Abdolmaleki A., Ghasemi J.B., Ghasemi F., Computer Aided Drug Design for Multi-Target Drug Design: SAR/QSAR, Molecular Docking And Pharmacophore Methods, Curr. Drug Targets., 18: 556–575 (2017).
[26] Carabet L.A., Rennie P.S., Cherkasov A., Therapeutic Inhibition of Myc in Cancer. Structural Bases and Computer-aided Drug Discovery Approaches, Int. J. Mol. Sci., 20: 120 (2019).
[29] Azizi M.G.K. K., Molecular Dynamics Simulations of Oxprenolol and Propranolol in a DPPC Lipid Bilayer, J. Mol. Graph. Model., 64: 153–164 (2016).
[30] Aghazadeh H., Ganjali Koli M., Ranjbar R., Pooshang Bagheri K., Interactions of GF-17 Derived from LL-37 Antimicrobial Peptide with Bacterial Membranes: A Molecular Dynamics Simulation Study,
J. Comput. Aided. Mol. Des.
, 34: 1261–1273 (2020).
[31] Gobbo D., Cavalli A., Ballone P., Benedetto A., Computational Analysis of the Effect of [Tea][Ms] and [Tea][H 2 PO 4] Ionic Liquids on the Structure and Stability of Aβ (17–42) Amyloid Fibrils, Phys. Chem. Chem. Phys., 23: 6695–6709 (2021).
[32] Yu W., MacKerell A.D., Computer-Aided Drug Design Methods, In: Antibiotics, Springer, 85–106 (2017).
[36] Neumann U., Ufer M., Jacobson L.H., Rouzade‐Dominguez M., Huledal G., Kolly C., Lüönd R.M., Machauer R., Veenstra S.J., Hurth K., The BACE‐1 inhibitor CNP 520 for prevention trials in Alzheimer’s Disease, EMBO Mol. Med., 10: e9316 (2018).
[37] Cheung J., Gary E.N., Shiomi K., Rosenberry T.L., Structures of Human Acetylcholinesterase Bound
to Dihydrotanshinone I and Territrem B Show Peripheral Site Flexibility
, ACS Med. Chem. Lett., 4: 1091–1096 (2013).
[38] Davies M., Nowotka M., Papadatos G., Dedman N., Gaulton A., Atkinson F., Bellis L., Overington J.P., ChEMBL web Services: Streamlining Access to Drug Discovery Data and Utilities, Nucleic Acids Res., 43: W612–W620 (2015).
[39] Mendez D., Gaulton A., Bento A.P., Chambers J., De Veij M., Félix E., Magariños M.P., Mosquera J.F., Mutowo P., Nowotka M., ChEMBL: Towards Direct Deposition of Bioassay Data, Nucleic Acids Res., 47: D930–D940 (2019).
[40] Gilson M.K., Liu T., Baitaluk M., Nicola G., Hwang L., Chong J., BindingDB in 2015: A Public Database for Medicinal Chemistry, Computational Chemistry and Systems Pharmacology, Nucleic Acids Res., 44: D1045–D1053 (2016).
[41] Lo Monte F., Kramer T., Gu J., Anumala U.R., Marinelli L., La Pietra V., Novellino E., Franco B., Demedts D., Van Leuven F., Identification of Glycogen Synthase Kinase-3 Inhibitors with a Selective Sting for Glycogen Synthase Kinase-3α, J. Med. Chem., 55: 4407–4424 (2012).
[42] Word J.M., Lovell S.C., Richardson J.S., Richardson D.C., Asparagine and Glutamine: Using Hydrogen Atom Contacts in the Choice of Side-Chain Amide Orientation, J. Mol. Biol., 285: 1735–1747 (1999).
[43] Morris G.M., Huey R., Lindstrom W., Sanner M.F., Belew R.K., Goodsell D.S., Olson A.J., AutoDock4 and AutoDockTools4: Automated Docking with Selective Receptor Flexibility, J. Comput. Chem., 30: 2785–2791 (2009).
[44] Berendsen H.J.C., van der Spoel D., van Drunen R., GROMACS: A Message-Passing Parallel Molecular Dynamics Implementation, Comput. Phys. Commun., 91: 43–56 (1995).
[45] Pall S., Abraham M.J., Kutzner C., Hess B., Lindahl E., Tackling Exascale Software Challenges in Molecular Dynamics Simulations with GROMACS, in: Int. Conf. Exascale Appl. Softw., Springer, pp. 3–27 (2014).
[46] Abraham M.J., Murtola T., Schulz R., Páll S., Smith J.C., Hess B., Lindahl E., GROMACS: High Performance Molecular Simulations Through Multi-Level Parallelism from Laptops to Supercomputers, SoftwareX., 1: 19–25 (2015).
[47] Vanommeslaeghe K., Hatcher E., Acharya C., Kundu S., Zhong S., Shim J., Darian E., Guvench O., Lopes P., Vorobyov I., CHARMM General Force Field: A Force Field for Drug‐Like Molecules Compatible with the CHARMM All‐Atom Additive Biological Force Fields, J. Comput. Chem., 31: 671–690 (2010).
[48] Bjelkmar P., Larsson P., Cuendet M.A., Hess B., Lindahl E., Implementation of the CHARMM Force Field in GROMACS: Analysis of Protein Stability Effects from Correction Maps, Virtual Interaction Sites, and Water Models, J. Chem. Theory Comput., 6: 459–466 (2010).
[49] Hoover W.G., Constant-Pressure Equations of Motion, Phys. Rev. A., 34: 2499 (1986).
[50] Parrinello M., Rahman A., Crystal Structure and Pair Potentials: A Molecular-Dynamics Study, Phys. Rev. Lett., 45: 1196 (1980).
[51] Hess B., Bekker H., Berendsen H.J.C., Fraaije J.G.E.M., LINCS: A Linear Constraint Solver for Molecular Simulations, J. Comput. Chem., 18: 1463–1472 (1997).
[52] Hockney R.W., Goel S.P., Eastwood J.W., Quiet High-Resolution Computer Models of a Plasma, J. Comput. Phys., 14: 148–158 (1974).
[53] Darden T., York D., Pedersen L., Particle Mesh Ewald: An N⋅ log (N) Method for Ewald Sums in Large Systems, J. Chem. Phys., 98: 10089–10092 (1993).
[54] Jorgensen W.L., Chandrasekhar J., Madura J.D., Impey R.W., Klein M.L., Comparison of Simple Potential Functions for Simulating Liquid Water, J. Chem. Phys. 79: 926–935 (1983).
[56] Humphrey W., Dalke A., Schulten K., VMD: Visual Molecular Dynamics, J. Mol. Graph., 14: 33–38 (1996).
[57] Kumari R., Kumar R., Consortium O.S.D.D., Lynn A., g_mmpbsa-A GROMACS Tool for High-Throughput MM-PBSA Calculations, J. Chem. Inf. Model., 54: 1951–1962 (2014).
[61] Zou B., Lee V.H.F., Chen L., Ma L., Wang D.D., Yan H., Deciphering Mechanisms of Acquired T790M Mutation After EGFR Inhibitors for NSCLC by Computational Simulations, Sci. Rep., 7: 1–13 (2017).
[62] Ducati R.G., Basso L.A., Santos D.S., de Azevedo Jr W.F., Crystallographic and Docking Studies of Purine Nucleoside Phosphorylase from Mycobacterium Tuberculosis, Bioorg. Med. Chem., 18: 4769–4774 (2010).
[63] Morrone Xavier M., Sehnem Heck G., Boff de Avila M., Maria Bernhardt Levin N., Oliveira Pintro V., Lemes Carvalho N., Filgueira de Azevedo W., SAnDReS a Computational Tool for Statistical Analysis of Docking Results and Development of Scoring Functions, Comb. Chem. High Throughput Screen., 19: 801–812 (2016).
[65] Zhang L., Wang P., Yang Z., Du F., Li Z., Wu C., Fang A., Xu X., Zhou G., Molecular Dynamics Simulation Exploration of the Interaction Between Curcumin and Myosin Combined with the Results of Spectroscopy Techniques, Food Hydrocoll., 101: 105455 (2020).
[66] Amir M., Ahmad S., Ahamad S., Kumar V., Mohammad T., Dohare R., Alajmi M.F., Rehman T., Hussain A., Islam A., Impact of Gln94Glu Mutation on the Structure And Function of Protection of Telomere 1, A Cause of Cutaneous Familial Melanoma, J. Biomol. Struct. Dyn. (2019).
[68] Soni S., Tyagi C., Grover A., Goswami S.K., Molecular Modeling and Molecular Dynamics Simulations Based Structural Analysis of the SG2NA Protein Variants, BMC Res. Notes., 7: 1–16 (2014).
[69] Laskowski R.A., MacArthur M.W., Moss D.S., Thornton J.M., PROCHECK: A Program to Check the Stereochemical Quality of Protein Structures, J. Appl. Crystallogr., 26: 283–291 (1993).
[70] Laskowski R.A., Rullmann J.A.C., MacArthur M.W., Kaptein R., Thornton J.M., AQUA and PROCHECK-NMR: Programs for Checking the Quality of Protein Structures Solved by NMR, J. Biomol. NMR., 8: 477–486 (1996).