Login Register Cart 0
Generic placeholder image

Central Nervous System Agents in Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 1871-5249
ISSN (Online): 1875-6166

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Lead Identification Through In Silico Studies: Targeting Acetylcholinesterase Enzyme Against Alzheimer’s Disease

Author(s): Dhairiya Agarwal, Sumit Kumar, Ramesh Ambatwar, Neeru Bhanwala, Lokesh Chandrakar and Gopal L. Khatik*

Volume 24, Issue 2, 2024

Published on: 26 January, 2024

Page: [219 - 242] Pages: 24

DOI: 10.2174/0118715249268585240107184956

Price: $65

Abstract

Aims: In this work, we aimed to acquire the best potential small molecule for Alzheimer's disease (AD) treatment using different models in Biovia Discovery Studio to identify new potential inhibitors of acetylcholinesterase (AChE) via in silico studies.

Background: The prevalence of cognitive impairment-related neurodegenerative disorders, such as AD, has been observed to escalate rapidly. However, we still know little about the underlying functions, outcome predictors, or intervention targets causing AD.

Objectives: The objective of the study was to optimize and identify the lead compound to target AChE against Alzheimer’s disease.

Methods: Different in silico studies were employed, including the pharmacophore model, virtual screening, molecular docking, de novo evolution model, and molecular dynamics.

Results: The pharmacophoric features of AChE inhibitors were determined by ligand-based pharmacophore models and 3D QSAR pharmacophore generation. Further validation of the best pharmacophore model was done using the cost analysis method, Fischer’s randomization method, and test set. The molecules that harmonized the best pharmacophore model with the estimated activity < 1 nM and ADMET parameters were filtered, and 12 molecules were subjected to molecular docking studies to obtain binding energy. 3vsp_EK8_1 secured the highest binding energy of 65.60 kcal/mol. Further optimization led to a 3v_Evo_4 molecule with a better binding energy of 70.17 kcal/mol. The molecule 3v_evo_4 was subjected to 100 ns molecular simulation compared to donepezil, which showed better stability at the binding site.

Conclusion: A lead compound, 3v_Evo_4 molecule, was identified to inhibit AChE, and it could be further studied to develop as a drug with better efficacy than the existing available drugs for treating AD.

Keywords: Alzheimer's disease, molecular docking, pharmacophore models, de novo evolution, molecular simulation, in silico.

« Previous
Graphical Abstract

[1]
Gong, C.X.; Dai, C.L.; Liu, F.; Iqbal, K. Multi-targets: An unconventional drug development strategy for alzheimer’s disease. Front. Aging Neurosci., 2022, 14, 837649.
[http://dx.doi.org/10.3389/fnagi.2022.837649] [PMID: 35222001]
[2]
2020 Alzheimer’s disease facts and figures. Alzheimers Dement., 2020, 16(3), 391-460.
[http://dx.doi.org/10.1002/alz.12068]
[3]
Chen, Y.G. Research progress in the pathogenesis of alzheimer’s disease. Chin. Med. J., 2018, 131(13), 1618-1624.
[http://dx.doi.org/10.4103/0366-6999.235112] [PMID: 29941717]
[4]
Uddin, M.S.; Kabir, M.T.; Jalouli, M.; Rahman, M.A.; Jeandet, P.; Behl, T.; Alexiou, A.; Albadrani, G.M.; Abdel-Daim, M.M.; Perveen, A.; Ashraf, G.M. Neuroinflammatory signaling in the pathogenesis of alzheimer’s disease. Curr. Neuropharmacol., 2022, 20(1), 126-146.
[http://dx.doi.org/10.2174/1570159X19666210826130210] [PMID: 34525932]
[5]
Chaudhary, A.; Singh, V.; Varadwaj, P.K.; Mani, A. Screening natural inhibitors against upregulated G-protein coupled receptors as potential therapeutics of Alzheimer’s disease. J. Biomol. Struct. Dyn., 2022, 40(2), 673-684.
[http://dx.doi.org/10.1080/07391102.2020.1817784] [PMID: 32900274]
[6]
Lamptey, R.N.L.; Chaulagain, B.; Trivedi, R.; Gothwal, A.; Layek, B.; Singh, J. A review of the common neurodegenerative disorders: Current therapeutic approaches and the potential role of nanotherapeutics. Int. J. Mol. Sci., 2022, 23(3), 1851.
[http://dx.doi.org/10.3390/ijms23031851] [PMID: 35163773]
[7]
Akbaba, E.; Bagci, E. Memory-enhancing, antioxidant, and anticholinesterase effects of inhaled achillea pseudoaleppica essential oil on scopolamine-induced amnesic rats. J. Essent. Oil-Bear. Plants, 2022, 25(4), 859-875.
[http://dx.doi.org/10.1080/0972060X.2022.2121617]
[8]
Chaudhary, A.; Maurya, P.K.; Yadav, B.S.; Singh, S.; Mani, A. Current therapeutic targets for alzheimer’s disease. J. Biomed., 2018, 3, 74-84.
[http://dx.doi.org/10.7150/jbm.26783]
[9]
Global action plan on the public health response to dementia 2017 - 2025; World Health Organization: Geneva, 2017.
[10]
Yin, Z.; Zhang, Z.; Gao, D.; Luo, G.; Ma, T.; Wang, Y.; Lu, L.; Gao, X. Stepwise coordination-driven metal–phenolic nanoparticle as a neuroprotection enhancer for alzheimer’s disease therapy. ACS Appl. Mater. Interfaces, 2023, 15(1), 524-540.
[http://dx.doi.org/10.1021/acsami.2c18060] [PMID: 36542560]
[11]
Turgutalp, B.; Bhattarai, P.; Ercetin, T.; Luise, C.; Reis, R.; Gurdal, E.E.; Isaak, A.; Biriken, D.; Dinter, E.; Sipahi, H.; Schepmann, D.; Junker, A.; Wünsch, B.; Sippl, W.; Gulcan, H.O.; Kizil, C.; Yarim, M. Discovery of potent cholinesterase inhibition-based multi-target-directed lead compounds for synaptoprotection in alzheimer’s disease. J. Med. Chem., 2022, 65(18), 12292-12318.
[http://dx.doi.org/10.1021/acs.jmedchem.2c01003] [PMID: 36084304]
[12]
Rao, YL; Ganaraja, B; Murlimanju, B V; Joy, T; Krishnamurthy, A Agrawal, A Hippocampus and its involvement in Alzheimer’s disease: A review. 3 Biotech, 2022, 12(2), 55.
[13]
Serrano-Pozo, A.; Frosch, M.P.; Masliah, E.; Hyman, B.T. Neuropathological alterations in Alzheimer disease. Cold Spring Harb. Perspect. Med., 2011, 1(1), a006189.
[http://dx.doi.org/10.1101/cshperspect.a006189] [PMID: 22229116]
[14]
Rossi, M.; Freschi, M.; de Camarga, N.L.; Salerno, A.; de Melo Viana, T.S.; Nachon, F.; Chantegreil, F.; Soukup, O.; Prchal, L.; Malaguti, M.; Bergamini, C.; Bartolini, M.; Angeloni, C.; Hrelia, S.; Soares Romeiro, L.A.; Bolognesi, M.L. Sustainable drug discovery of multi-target-directed ligands for alzheimer’s disease. J. Med. Chem., 2021, 64(8), 4972-4990.
[http://dx.doi.org/10.1021/acs.jmedchem.1c00048] [PMID: 33829779]
[15]
Tran, K.N.; Nguyen, N.P.K.; Nguyen, L.T.H.; Shin, H.M.; Yang, I.J. Screening for neuroprotective and rapid antidepressant-like effects of 20 essential oils. Biomedicines, 2023, 11(5), 1248.
[http://dx.doi.org/10.3390/biomedicines11051248] [PMID: 37238920]
[16]
Miles, J.A.; Ross, B.P. Recent advances in virtual screening for cholinesterase inhibitors. ACS Chem. Neurosci., 2021, 12(1), 30-41.
[http://dx.doi.org/10.1021/acschemneuro.0c00627] [PMID: 33350300]
[17]
Costanzi, S.; Machado, J.H.; Mitchell, M. Nerve agents: What they are, how they work, how to counter them. ACS Chem. Neurosci., 2018, 9(5), 873-885.
[http://dx.doi.org/10.1021/acschemneuro.8b00148] [PMID: 29664277]
[18]
Oset-Gasque, M.J.; Marco-Contelles, J. Alzheimer’s disease, the “one-molecule, one-target” paradigm, and the multitarget directed ligand approach. ACS Chem. Neurosci., 2018, 9(3), 401-403.
[http://dx.doi.org/10.1021/acschemneuro.8b00069] [PMID: 29465220]
[19]
Kwong, H.C.; Chidan Kumar, C.S.; Mah, S.H.; Mah, Y.L.; Chia, T.S.; Quah, C.K.; Lim, G.K.; Chandraju, S. Crystal correlation of heterocyclic imidazo[1,2-a]pyridine analogues and their anticholinesterase potential evaluation. Sci. Rep., 2019, 9(1), 926.
[http://dx.doi.org/10.1038/s41598-018-37486-7] [PMID: 30700752]
[20]
Khan, M.T.H. Molecular interactions of cholinesterases inhibitors using in silico methods: Current status and future prospects. N. Biotechnol., 2009, 25(5), 331-346.
[http://dx.doi.org/10.1016/j.nbt.2009.03.008] [PMID: 19491049]
[21]
Mariki, A.; Anaeigoudari, A.; Zahedifar, M.; Pouramiri, B.; Ayati, A.; Lotfi, S. Design, green synthesis, and biological evaluation of new substituted tetrahydropyrimidine derivatives as acetylcholinesterase inhibitors. Polycycl. Aromat. Compd., 2022, 42(8), 5231-5241.
[http://dx.doi.org/10.1080/10406638.2021.1933102]
[22]
Ahmad, S.; Iftikhar, F.; Ullah, F.; Sadiq, A.; Rashid, U. Rational design and synthesis of dihydropyrimidine based dual binding site acetylcholinesterase inhibitors. Bioorg. Chem., 2016, 69, 91-101.
[http://dx.doi.org/10.1016/j.bioorg.2016.10.002] [PMID: 27750058]
[23]
Akıncıoğlu, H.; Gülçin, İ. Potent acetylcholinesterase inhibitors: Potential drugs for alzheimer’s disease. Mini Rev. Med. Chem., 2020, 20(8), 703-715.
[http://dx.doi.org/10.2174/1389557520666200103100521] [PMID: 31902355]
[24]
BIOVIA Dassault Systèmes; BIOVIA Discovery Studio: San Diego, 2022.
[25]
Li, Z.; Wan, H.; Shi, Y.; Ouyang, P. Personal experience with four kinds of chemical structure drawing software: review on ChemDraw, ChemWindow, ISIS/Draw, and ChemSketch. J. Chem. Inf. Comput. Sci., 2004, 44(5), 1886-1890.
[http://dx.doi.org/10.1021/ci049794h] [PMID: 15446849]
[26]
Gao, H.; Jiang, Y.; Zhan, J.; Sun, Y. Pharmacophore-based drug design of AChE and BChE dual inhibitors as potential anti-Alzheimer’s disease agents. Bioorg. Chem., 2021, 114(March), 105149.
[http://dx.doi.org/10.1016/j.bioorg.2021.105149] [PMID: 34252860]
[27]
Opo, F.A.D.M.; Rahman, M.M.; Ahammad, F.; Ahmed, I.; Bhuiyan, M.A.; Asiri, A.M. Structure based pharmacophore modeling, virtual screening, molecular docking and ADMET approaches for identification of natural anti-cancer agents targeting XIAP protein. Sci. Rep., 2021, 11(1), 1-18.
[PMID: 33414495]
[28]
Hosseini, F.; Mohammadi-Khanaposhtani, M.; Azizian, H.; Ramazani, A.; Tehrani, M.B.; Nadri, H.; Larijani, B.; Biglar, M.; Adibi, H.; Mahdavi, M. 4-Oxobenzo[d]1,2,3-triazin-pyridinium-phenylacetamide derivatives as new anti-Alzheimer agents: Design, synthesis, in vitro evaluation, molecular modeling, and molecular dynamic study. Struct. Chem., 2020, 31(3), 999-1012.
[http://dx.doi.org/10.1007/s11224-019-01472-0]
[29]
Dileep, K.V.; Chiemi, K.I.; Mutsuko, M-T.; Mayumi, K-N.; Yonemochi, K.; Hanada, M.S. Discovery, Crystal structure of human acetylcholinesterase in complex with tacrine. Implic drug Macromol. Int. J. Biol., 2022, 210, 172-181.
[PMID: 35526766]
[30]
Wu, G.; Robertson, D.H.; Brooks, C.L., III; Vieth, M. Detailed analysis of grid-based molecular docking: A case study of CDOCKER—A CHARMm-based MD docking algorithm. J. Comput. Chem., 2003, 24(13), 1549-1562.
[http://dx.doi.org/10.1002/jcc.10306] [PMID: 12925999]
[31]
Gupta, A.; Müller, A.T.; Huisman, B.J.H.; Fuchs, J.A.; Schneider, P.; Schneider, G. Generative recurrent networks for de novo drug design. Mol. Inform., 2018, 37(1-2), 1700111.
[http://dx.doi.org/10.1002/minf.201700111] [PMID: 29095571]
[32]
Böhm, H.J. LUDI: Rule-based automatic design of new substituents for enzyme inhibitor leads. J. Comput. Aided Mol. Des., 1992, 6(6), 593-606.
[http://dx.doi.org/10.1007/BF00126217] [PMID: 1291628]
[33]
Muegge, I. PMF scoring revisited. J. Med. Chem., 2006, 49(20), 5895-5902.
[http://dx.doi.org/10.1021/jm050038s] [PMID: 17004705]
[34]
Brown, T. Design thinking. Harv. Bus. Rev., 2008, 86(6), 84-92, 141.
[PMID: 18605031]
[35]
Krammer, A.; Kirchhoff, P.D.; Jiang, X.; Venkatachalam, C.M.; Waldman, M. LigScore: A novel scoring function for predicting binding affinities. J. Mol. Graph. Model., 2005, 23(5), 395-407.
[http://dx.doi.org/10.1016/j.jmgm.2004.11.007] [PMID: 15781182]
[36]
Phillips, J.C.; Braun, R.; Wang, W.; Gumbart, J.; Tajkhorshid, E.; Villa, E.; Chipot, C.; Skeel, R.D.; Kalé, L.; Schulten, K. Scalable molecular dynamics with NAMD. J. Comput. Chem., 2005, 26(16), 1781-1802.
[http://dx.doi.org/10.1002/jcc.20289] [PMID: 16222654]
[37]
BindingDB. Available from: https://www.bindingdb.org/rwd/bind/index.jsp
[38]
Schuler, J.; Hudson, M.; Schwartz, D.; Samudrala, R. A systematic review of computational drug discovery, development, and repurposing for ebola virus disease treatment. Molecules, 2017, 22(10), 1777.
[http://dx.doi.org/10.3390/molecules22101777] [PMID: 29053626]
[39]
Hussein, R.K.; Elkhair, H.M. Molecular docking identification for the efficacy of some zinc complexes with chloroquine and hydroxychloroquine against main protease of COVID-19. J. Mol. Struct., 2021, 1231, 129979.
[http://dx.doi.org/10.1016/j.molstruc.2021.129979] [PMID: 33518801]
[40]
Yan, G.; Li, D.; Zhong, X.; Liu, G.; Wang, X.; Lu, Y.; Qin, F.; Guo, Y.; Duan, S.; Li, D. Identification of HDAC6 selective inhibitors: Pharmacophore based virtual screening, molecular docking and molecular dynamics simulation. J. Biomol. Struct. Dyn., 2021, 39(6), 1928-1939.
[http://dx.doi.org/10.1080/07391102.2020.1743760] [PMID: 32178584]
[41]
Kalita, J.; Chetia, D.; Rudrapal, M. Molecular docking, drug-likeness studies and ADMET prediction of quinoline imines for antimalarial activity. J Med Chem Drug Des., 2019, 2(1), 1-7.
[42]
Fernandes, P.A.; Passos, Ó.; Ramos, M.J. Necessity is the mother of invention: A remote molecular bioinformatics practical course in the COVID-19 era. J. Chem. Educ., 2022, 99(5), 2147-2153.
[http://dx.doi.org/10.1021/acs.jchemed.1c01195] [PMID: 35529516]
[43]
Hartshorn, M.J.; Verdonk, M.L.; Chessari, G.; Brewerton, S.C.; Mooij, W.T.M.; Mortenson, P.N.; Murray, C.W. Diverse, high-quality test set for the validation of protein-ligand docking performance. J. Med. Chem., 2007, 50(4), 726-741.
[http://dx.doi.org/10.1021/jm061277y] [PMID: 17300160]
[44]
Al-Khafaji, K.; Taskin Tok, T. Amygdalin as multi-target anticancer drug against targets of cell division cycle: Double docking and molecular dynamics simulation. J. Biomol. Struct. Dyn., 2021, 39(6), 1965-1974.
[http://dx.doi.org/10.1080/07391102.2020.1742792] [PMID: 32174270]
[45]
Böhm, H-J. The computer program LUDI: A new method for the de novo design of enzyme inhibitors. J. Comput. Aided Mol. Des., 1992, 6(1), 61-78.
[http://dx.doi.org/10.1007/BF00124387] [PMID: 1583540]
[46]
Muegge, I.; Martin, Y.C. A general and fast scoring function for protein-ligand interactions: A simplified potential approach. J. Med. Chem., 1999, 42(5), 791-804.
[http://dx.doi.org/10.1021/jm980536j] [PMID: 10072678]
[47]
Jabir, N.R.; Rehman, M.T.; Alsolami, K.; Shakil, S.; Zughaibi, T.A.; Alserihi, R.F.; Khan, M.S.; AlAjmi, M.F.; Tabrez, S. Concatenation of molecular docking and molecular simulation of BACE-1, γ-secretase targeted ligands: In pursuit of Alzheimer’s treatment. Ann. Med., 2021, 53(1), 2332-2344.
[http://dx.doi.org/10.1080/07853890.2021.2009124] [PMID: 34889159]
[48]
Shahwan, M.; Khan, M.S.; Husain, F.M.; Shamsi, A. Understanding binding between donepezil and human ferritin: Molecular docking and molecular dynamics simulation approach. J. Biomol. Struct. Dyn., 2022, 40(9), 3871-3879.
[http://dx.doi.org/10.1080/07391102.2020.1851302] [PMID: 33228460]

Rights & Permissions Print Cite
Article Metrics
25
2
© 2024 Bentham Science Publishers | Privacy Policy