Computational Investigation of the Interaction of Novel Indene Methylene Analogues with Acetylcholinesterase from Both Dynamic and Thermodynamic Perspectives

Page: [1911 - 1921] Pages: 11

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Abstract

Background: Torpedo californica acetylcholinesterase (TcAChE) is an important drug development target for Alzheimer's disease (AD) therapeutics. The current in silico study aims to recognise indene methylene-derived compounds acting against TcAChE to gain insight into the molecular interactions.

Objective: The current study focused on identifying novel inhibitors for Torpedo californica acetylcholinesterase (TcAChE) by virtual screening, molecular docking, drug-likeness, molecular simulation, and DFT profile for anti-Alzheimer's activity.

Methods: Molecular docking, ADMET screening, molecular simulation, and DFT were performed for drug development having anti-Alzheimer's activity related to Torpedo californica acetylcholinesterase (TcAChE).

Results: On the AutoDock Vina algorithms, ligands SD-24 [-12.6, -13.1 kcal/mol], SD-30 [-12.5, -12.6 kcal/mol], SD-42 [-11.8, -12.5kcal/mol] showed promising docking and confirmatory redocking scores compared to Donepezil [-8, -10.9 kcal/mol], followed by ADMET screening. The best three complexes were subjected to molecular dynamics simulations (MDSs) over 30 ns to understand the TcAChE dynamics and behavior in a complex with the ligand. MEP and NBO analysis was performed for the DFT/B3LYP theory and 6-311G [d,p] base set and Gaussian 09 package program. For MDSs, the root means square (RMSD) parameter remained stable for 30 ns at 0.25 nm. The ligand-AChE complex formed 2 to 4 satisfactory intermolecular H bonds, which substantiated the stability of the three compounds in the protein binding cluster as potent binders. The LUMO (owest unoccupied molecular orbital)- HOMO (highest occupied molecular orbital) energy gap of the SD24, SD30, and SD42 compounds was 4.0943, 4.2489, and 4.2489 eV, respectively, and stability was ordered as SD24>SD30=SD42.

Conclusion: The outcome of in silico studies suggests that SD24, SD30, and SD42 compounds have promising drug-likeness, simulation, and DFT profiles for anti-Alzheimer's activity. However, in vitro and in vivo studies are required to confirm their biological activities.

Keywords: Alzheimer's disease (AD), acetylcholine esterase (AChE), virtual screening, ADME/T screening, molecular dynamics simulations (MDSs), Torpedo californica AChE (TcAChE)

[1]
Singh, S.P.; Gupta, D. Discovery of potential inhibitor against human acetylcholinesterase: A molecular docking and molecular dynamics investigation. Comput. Biol. Chem., 2017, 68, 224-230.
[http://dx.doi.org/10.1016/j.compbiolchem.2017.04.002] [PMID: 28432980]
[2]
Zhang, L.; Li, D.; Cao, F.; Xiao, W.; Zhao, L.; Ding, G. Identification of human acetylcholinesterase inhibitors from the constituents of EGb761 by modeling docking and molecular dynamics simulations. Comb. Chem. High Throughput Screen., 2017, 21, 41-49.
[3]
Chang, C.H.; Lin, C.H.; Lane, H.Y. Machine learning and novel biomarkers for the diagnosis of Alzheimer’s disease. Int. J. Mol. Sci., 2021, 22(5), 2761.
[http://dx.doi.org/10.3390/ijms22052761] [PMID: 33803217]
[4]
Li, Z.; Jiang, X.; Wang, Y.; Kim, Y. Applied machine learning in Alzheimer’s disease research: Omics, imaging, and clinical data. Emerg. Top. Life Sci., 2021, 5(6), 765-777.
[http://dx.doi.org/10.1042/ETLS20210249] [PMID: 34881778]
[5]
Vijayakumar, S.; Manogar, P.; Prabhu, S.; Sanjeevkumar Singh, R.A. Novel ligand-based docking; molecular dynamic simulations; and absorption, distribution, metabolism, and excretion approach to analyzing potential acetylcholinesterase inhibitors for Alzheimer’s disease. J. Pharm. Anal., 2018, 8(6), 413-420.
[http://dx.doi.org/10.1016/j.jpha.2017.07.006] [PMID: 30595949]
[6]
Kryger, G.; Silman, I.; Sussman, J.L. Structure of acetylcholinesterase complexed with E2020 (Aricept®): Implications for the design of new anti-Alzheimer drugs. Structure, 1999, 7(3), 297-307.
[http://dx.doi.org/10.1016/S0969-2126(99)80040-9] [PMID: 10368299]
[7]
Perry, R.H.; Blessed, G.; Perry, E.K.; Tomlinson, B. Histochemical observations on cholinesterase activities in the brains of elderly normal and demented (Alzheimer-type) patients. Age Ageing, 1980, 9(1), 9-16.
[http://dx.doi.org/10.1093/ageing/9.1.9] [PMID: 7370097]
[8]
Svansdottir, H.B.; Snaedal, J. Music therapy in moderate and severe dementia of Alzheimer’s type: A case–control study. Int. Psychogeriatr., 2006, 18(4), 613-621.
[http://dx.doi.org/10.1017/S1041610206003206] [PMID: 16618375]
[9]
Kressig, R.W. Dementia of the Alzheimer type: Non-drug and drug therapy. Ther. Umsch., 2015, 72(4), 233-238.
[http://dx.doi.org/10.1024/0040-5930/a000670] [PMID: 25791046]
[10]
Funicello, M.; Cerminara, I.; Chiummiento, L. heterocycles for alzheimer disease: 4-and 5-substituted benzothiophenes as starting scaffold in the construction of potential new inhibitors of bace 1. Med. Chem., 2016, 6(377), e384.
[11]
Goyal, D.; Kaur, A.; Goyal, B. Benzofuran and indole: Promising scaffolds for drug development in Alzheimer’s disease. ChemMedChem, 2018, 13(13), 1275-1299.
[http://dx.doi.org/10.1002/cmdc.201800156] [PMID: 29742314]
[12]
Sarıkaya, G.; Çoban, G.; Parlar, S.; Tarikogullari, A.H.; Armagan, G.; Erdoğan, M.A.; Alptüzün, V.; Alpan, A.S. Multifunctional cholinesterase inhibitors for Alzheimer’s disease: Synthesis, biological evaluations, and docking studies of o/p -propoxyphenylsubstituted-1 H -benzimidazole derivatives. Arch. Pharm. (Weinheim), 2018, 351(8), 1800076.
[http://dx.doi.org/10.1002/ardp.201800076] [PMID: 29984517]
[13]
Li, X.; Yu, Y.; Tu, Z. Pyrazole scaffold synthesis, functionalization, and applications in Alzheimer’s disease and Parkinson’s disease treatment. Molecules, 2021, 26(5), 1202.
[http://dx.doi.org/10.3390/molecules26051202] [PMID: 33668128]
[14]
Parlar, S.; Bayraktar, G.; Tarikogullari, A.H.; Alptüzün, V.; Erciyas, E. Synthesis, biological evaluation and molecular docking study of hydrazone-containing pyridinium salts as cholinesterase inhibitors. Chem. Pharm. Bull., 2016, 64(9), 1281-1287.
[http://dx.doi.org/10.1248/cpb.c16-00221] [PMID: 27581632]
[15]
Sastry, G.M.; Adzhigirey, M.; Day, T.; Annabhimoju, R.; Sherman, W. Protein and ligand preparation: Parameters, protocols, and influence on virtual screening enrichments. J. Comput. Aided Mol. Des., 2013, 27(3), 221-234.
[http://dx.doi.org/10.1007/s10822-013-9644-8] [PMID: 23579614]
[16]
Schumacher, M.; Camp, S.; Maulet, Y.; Newton, M.; MacPhee-Quigley, K.; Taylor, S.S.; Friedmann, T.; Taylor, P. Primary structure of Torpedo californica acetylcholinesterase deduced from its cDNA sequence. Nature, 1986, 319(6052), 407-409.
[http://dx.doi.org/10.1038/319407a0] [PMID: 3753747]
[17]
Pang, Y.P.; Kozikowski, A.P. Prediction of the binding site of 1-benzyl-4-[[5,6-dimethoxy-1-indanon-2-yl]methyl]piperidine in acetylcholinesterase by docking studies with the SYSDOC program. J. Comput. Aided Mol. Des., 1994, 8(6), 683-693.
[http://dx.doi.org/10.1007/BF00124015] [PMID: 7738604]
[18]
Kryger, G.; Giles, K.; Harel, M.; Toker, L.; Velan, B.; Lazar, A. 3D structure of a complex of human acetylcholinesterase with fasciculin-II at 2.7 Å resolution BT - structure and function of cholinesterases and related proteins. Springer US: Boston, MA, 1998; p. 370.
[http://dx.doi.org/10.1007/978-1-4899-1540-5_102]
[19]
Burley, S.K.; Petsko, G.A. Amino-aromatic interactions in proteins. FEBS Lett., 1986, 203(2), 139-143.
[http://dx.doi.org/10.1016/0014-5793(86)80730-X] [PMID: 3089835]
[20]
Lanka, G.; Bathula, R.; Dasari, M.; Nakkala, S.; Bhargavi, M.; Somadi, G.; Potlapally, S.R. Structure-based identification of potential novel inhibitors targeting FAM3B (PANDER) causing type 2 diabetes mellitus through virtual screening. J. Recept. Signal Transduct. Res., 2019, 39(3), 253-263.
[http://dx.doi.org/10.1080/10799893.2019.1660897] [PMID: 31517548]
[21]
Jacob, R.B.; Andersen, T.; McDougal, O.M. Accessible high-throughput virtual screening molecular docking software for students and educators. PLoS Comput. Biol., 2012, 8, e1002499-e1002499.
[PMID: 22693435]
[22]
Muhammad, S.; Fatima, N. In silico analysis and molecular docking studies of potential angiotensin-converting enzyme inhibitor using quercetin glycosides. Pharmacogn. Mag., 2015, 11(42), 123.
[http://dx.doi.org/10.4103/0973-1296.157712] [PMID: 26109757]
[23]
Jamkhande, P.G.; Ghante, M.H.; Ajgunde, B.R. Software based approaches for drug designing and development: A systematic review on commonly used software and its applications. Bull. Fac. Pharm. Cairo Univ., 2017, 55(2), 203-210.
[http://dx.doi.org/10.1016/j.bfopcu.2017.10.001]
[24]
Daina, A.; Michielin, O.; Zoete, V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep., 2017, 7(1), 42717.
[http://dx.doi.org/10.1038/srep42717] [PMID: 28256516]
[25]
Shaik, N.A.; Al-Kreathy, H.M.; Ajabnoor, G.M.; Verma, P.K.; Banaganapalli, B. Molecular designing, virtual screening and docking study of novel curcumin analogue as mutation (S769L and K846R) selective inhibitor for EGFR. Saudi J. Biol. Sci., 2019, 26(3), 439-448.
[http://dx.doi.org/10.1016/j.sjbs.2018.05.026] [PMID: 30899155]
[26]
Yadav, R.; Imran, M.; Dhamija, P.; Chaurasia, D.K.; Handu, S. Virtual screening, ADMET prediction and dynamics simulation of potential compounds targeting the main protease of SARS-CoV-2. J. Biomol. Struct. Dyn., 2021, 39(17), 6617-32.
[http://dx.doi.org/10.1080/07391102.2020.1796812] [PMID: 32715956]
[27]
Lipinski, C.A. Lead- and drug-like compounds: The rule-of-five revolution. Drug Discov. Today. Technol., 2004, 1(4), 337-341.
[http://dx.doi.org/10.1016/j.ddtec.2004.11.007] [PMID: 24981612]
[28]
Muegge, I. Selection criteria for drug-like compounds. Med. Res. Rev., 2003, 23(3), 302-321.
[http://dx.doi.org/10.1002/med.10041] [PMID: 12647312]
[29]
Van Der Spoel, D.; Lindahl, E.; Hess, B.; Groenhof, G.; Mark, A.E.; Berendsen, H.J.C. GROMACS: Fast, flexible, and free. J. Comput. Chem., 2005, 26(16), 1701-1718.
[http://dx.doi.org/10.1002/jcc.20291] [PMID: 16211538]
[30]
Berendsen, H.J.C.; van der Spoel, D.; van Drunen, R. GROMACS: A message-passing parallel molecular dynamics implementation. Comput. Phys. Commun., 1995, 91(1-3), 43-56.
[http://dx.doi.org/10.1016/0010-4655(95)00042-E]
[31]
Vanommeslaeghe, K.; MacKerell, A.D., Jr. Automation of the CHARMM General Force Field (CGenFF) I: Bond perception and atom typing. J. Chem. Inf. Model., 2012, 52(12), 3144-3154.
[http://dx.doi.org/10.1021/ci300363c] [PMID: 23146088]
[32]
Vermaas, J.V.; Hardy, D.J.; Stone, J.E.; Tajkhorshid, E.; Kohlmeyer, A. TopoGromacs: Automated topology conversion from CHARMM to GROMACS within VMD. J. Chem. Inf. Model., 2016, 56(6), 1112-1116.
[http://dx.doi.org/10.1021/acs.jcim.6b00103] [PMID: 27196035]
[33]
Mary, Y.S.; Mary, Y.S.; Armaković, S.; Armaković, S.J.; Yadav, R.; Celik, I.; Mane, P.; Chakraborty, B. Stability and reactivity study of bio-molecules brucine and colchicine towards electrophile and nucleophile attacks: Insight from DFT and MD simulations. J. Mol. Liq., 2021, 335, 116192.
[http://dx.doi.org/10.1016/j.molliq.2021.116192]
[34]
Junejo, J.A.; Zaman, K.; Rudrapal, M.; Celik, I.; Attah, E.I. Antidiabetic bioactive compounds from Tetrastigma angustifolia (Roxb.) Deb and Oxalis debilis Kunth.: Validation of ethnomedicinal claim by in vitro and in silico studies. S. Afr. J. Bot., 2021, 143, 164-175.
[http://dx.doi.org/10.1016/j.sajb.2021.07.023]
[35]
Cheng, S.; Song, W.; Yuan, X.; Xu, Y. Gorge motions of acetylcholinesterase revealed by microsecond molecular dynamics simulations. Sci. Rep., 2017, 7(1), 3219.
[http://dx.doi.org/10.1038/s41598-017-03088-y] [PMID: 28607438]
[36]
Levitt, M.; Perutz, M.F. Aromatic rings act as hydrogen bond acceptors. J. Mol. Biol., 1988, 201(4), 751-754.
[http://dx.doi.org/10.1016/0022-2836(88)90471-8] [PMID: 3172202]
[37]
Chadha, N.; Tiwari, A.K.; Kumar, V.; Lal, S.; Milton, M.D.; Mishra, A.K. Oxime-dipeptides as anticholinesterase, reactivator of phosphonylated-serine of AChE catalytic triad: Probing the mechanistic insight by MM-GBSA, dynamics simulations and DFT analysis. J. Biomol. Struct. Dyn., 2015, 33(5), 978-990.
[http://dx.doi.org/10.1080/07391102.2014.921793] [PMID: 24805972]
[38]
Frisch, M.J. Gaussian09, Revision C. 01wis4; Gaussian, Inc.: Wallingford, CT 2009. Available from: http://www.gaussian.com
[39]
Tajammal, A.; Siddiqa, A.; Irfan, A.; Azam, M.; Hafeez, H.; Munawar, M.A.; Basra, M.A.R. Antioxidant, molecular docking and computational investigation of new flavonoids. J. Mol. Struct., 2022, 1254, 132189.
[http://dx.doi.org/10.1016/j.molstruc.2021.132189]
[40]
Dennington, R.; Keith, T.; Millam, J. GaussView, Version 5; Semichem Inc.: Shawnee Mission, KS, 2013.
[41]
Celik, I.; Erol, M.; Temiz Arpaci, O.; Sezer Senol, F.; Erdogan Orhan, I. Evaluation of activity of some 2, 5-disubstituted benzoxazole derivatives against acetylcholinesterase, butyrylcholinesterase and tyrosinase: ADME prediction, DFT and comparative molecular docking studies. Polycycl. Aromat. Compd., 2020, 42, 412-423.
[42]
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. Cryst., 1993, 26(2), 283-291.
[http://dx.doi.org/10.1107/S0021889892009944]
[43]
Celik, I.; Erol, M.; Kuyucuklu, G. Molecular modeling, density functional theory, ADME prediction and antimicrobial activity studies of 2-(substituted)oxazolo[4,5- b]pyridine derivatives. New J. Chem., 2021, 45(25), 11108-11118.
[http://dx.doi.org/10.1039/D1NJ00701G]
[44]
Gogoi, B.; Chowdhury, P.; Goswami, N.; Gogoi, N.; Naiya, T.; Chetia, P.; Mahanta, S.; Chetia, D.; Tanti, B.; Borah, P.; Handique, P.J. Identification of potential plant-based inhibitor against viral proteases of SARS-CoV-2 through molecular docking, MM-PBSA binding energy calculations and molecular dynamics simulation. Mol. Divers., 2021, 25(3), 1963-1977.
[http://dx.doi.org/10.1007/s11030-021-10211-9] [PMID: 33856591]
[45]
Hakiri, R.; Ameur, I.; Abid, S.; Derbel, N. Synthesis, X-ray structural, Hirshfeld surface analysis, FTIR, MEP and NBO analysis using DFT study of a 4-chlorobenzylammonium nitrate (C 7 ClH 9 N) + (NO 3) –. J. Mol. Struct., 2018, 1164, 486-492.
[http://dx.doi.org/10.1016/j.molstruc.2018.03.068]
[46]
Mary, Y.S.; Yalcin, G.; Mary, Y.S.; Resmi, K.S.; Thomas, R.; Önkol, T.; Kasap, E.N.; Yildiz, I. Spectroscopic, quantum mechanical studies, ligand protein interactions and photovoltaic efficiency modeling of some bioactive benzothiazolinone acetamide analogs. Chem. Pap., 2020, 74(6), 1957-1964.
[http://dx.doi.org/10.1007/s11696-019-01047-7]
[47]
Erol, M.; Celik, I.; Kuyucuklu, G. Synthesis, molecular docking, molecular dynamics, DFT and antimicrobial activity studies of 5-substituted-2-(p-methylphenyl)benzoxazole Derivatives. J. Mol. Struct., 2021, 1234, 130151.
[http://dx.doi.org/10.1016/j.molstruc.2021.130151]
[48]
Mumit, M.A.; Pal, T.K.; Alam, M.A.; Islam, M.A-A-A-A.; Paul, S.; Sheikh, M.C. DFT studies on vibrational and electronic spectra, HOMO–LUMO, MEP, HOMA, NBO and molecular docking analysis of benzyl-3-N-[2, 4, 5-trimethoxyphenylmethylene] hydrazinecarbodithioate. J. Mol. Struct., 2020, 1220, 128715.
[http://dx.doi.org/10.1016/j.molstruc.2020.128715] [PMID: 32834109]
[49]
Saral, A.; Sudha, P.; Muthu, S.; Irfan, A. Computational, spectroscopic and molecular docking investigation on a bioactive anti-cancer drug: 2-Methyl-8-nitro quinoline. J. Mol. Struct., 2022, 1247, 131414.
[http://dx.doi.org/10.1016/j.molstruc.2021.131414]
[50]
Pearson, R.G. Absolute electronegativity and hardness correlated with molecular orbital theory. Proc. Natl. Acad. Sci. USA, 1986, 83(22), 8440-8441.
[http://dx.doi.org/10.1073/pnas.83.22.8440] [PMID: 16578791]