Structure-Based Design and Molecular Dynamics Simulations of Pentapeptide AEYTR as a Potential Acetylcholinesterase Inhibitor

https://doi.org/10.22146/ijc.46329

Vivitri Dewi Prasasty(1), Enade Perdana Istyastono(2*)

(1) Faculty of Biotechnology, Atma Jaya Catholic University of Indonesia, Jakarta 12930, Indonesia
(2) Faculty of Pharmacy, Sanata Dharma University, Paingan, Maguwoharjo, Depok, Sleman, Yogyakarta 55282
(*) Corresponding Author

Abstract


Structure-based virtual screening protocol to identify potent acetylcholinesterase inhibitors was retrospectively validated. The protocol could be employed to examine the potential of designed compounds as novel acetylcholinesterase inhibitors. In a research project designing short peptides as acetylcholinesterase inhibitors, peptide AEYTR emerged as one of the potential inhibitors. This article presents the design of AEYTR assisted by the validated protocol and guided by literature reviews followed by molecular dynamics studies to examine the interactions of the pentapeptide in the binding pocket of the acetylcholinesterase enzyme. The molecular dynamics simulations were performed using YASARA Structure in Google Cloud Platform. The peptide AEYTR was identified in silico as a potent acetylcholinesterase inhibitor with the average free energy of binding (DG) of -19.138 kcal/mol.

Keywords


Acetylcholinesterase; Pentapeptide; Molecular Dynamics; YASARA Structure; Google Cloud Platform

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References

[1] Mehta, M., Adem, A., and Sabbagh, M., 2012, New acetylcholinesterase inhibitors for Alzheimer’s disease, Int. J. Alzheimers Dis., 2012, 728983.

[2] Fillit, H., and Hill, J., 2005, Economics of dementia and pharmacoeconomics of dementia therapy, Am. J. Geriatr. Pharmacother., 3 (1), 39–49.

[3] Knapp, M., King, D., Romeo, R., Adams, J., Baldwin, A., Ballard, C., Banerjee, S., Barber, R., Bentham, P., Brown, R.G., Burns, A., Dening, T., Findlay, D., Holmes, C., Johnson, T., Jones, R., Katona, C., Lindesay, J., Macharouthu, A., McKeith, I., McShane, R., O’Brien, J.T., Phillips, P.P.J., Sheehan, B., and Howard, R., 2017, Cost-effectiveness of donepezil and memantine in moderate to severe Alzheimer’s disease (the DOMINO-AD trial), Int. J. Geriatr. Psychiatry, 32 (12), 1205–1216.

[4] Ahmadi-Abhari, S., Guzman-Castillo, M., Bandosz, P., Shipley, M.J., Muniz-Terrera, G., Singh-Manoux, A., Kivimäki, M., Steptoe, A., Capewell, S., O’Flaherty, M., and Brunner, E.J., 2017, Temporal trend in dementia incidence since 2002 and projections for prevalence in England and Wales to 2040: Modelling study, BMJ, 358, j2856.

[5] Murray, A.P., Faraoni, M.B., Castro, M.J., Alza, N.P., and Cavallaro, V., 2013, Natural AChE inhibitors from plants and their contribution to Alzheimer’s disease therapy, Curr. Neuropharmacol., 11 (4), 388–413.

[6] Macalino, S.J.Y., Gosu, V., Hong, S., and Choi, S., 2015, Role of computer-aided drug design in modern drug discovery, Arch. Pharmacal Res., 38 (9), 1686–1701.

[7] Tanrikulu, Y., Proschak, E., Werner, T., Geppert, T., Todoroff, N., Klenner, A., Kottke, T., Sander, K., Schneider, E., Seifert, R., Stark, H., Clark, T., and Schneider, G., 2009, Homology model adjustment and ligand screening with a pseudoreceptor of the human histamine H4 receptor, ChemMedChem, 4 (5), 820–827.

[8] Sirci, F., Istyastono, E.P., Vischer, H.F., Kooistra, A.J., Nijmeijer, S., Kuijer, M., Wijtmans, M., Mannhold, R., Leurs, R., de Esch, I.J.P., and de Graaf, C., 2012, Virtual fragment screening: discovery of histamine H(3) receptor ligands using ligand-based and protein-based molecular fingerprints, J. Chem. Inf. Model., 52 (12), 3308–3324.

[9] Istyastono, E.P., Nurrochmad, A., and Yuniarti, N., 2016, Structure-Based virtual screening campaigns on curcuminoids as potent ligands for histone deacetylase-2, Orient. J. Chem., 32 (1), 275–282.

[10] Istyastono, E.P., Kooistra, A.J., Vischer, H., Kuijer, M., Roumen, L., Nijmeijer, S., Smits, R., de Esch, I.J.P., Leurs, R., and de Graaf, C., 2015, Structure-based virtual screening for fragment-like ligands of the G protein-coupled histamine H4 receptor, Med. Chem. Commun., 6 (6), 1003–1017.

[11] Kiss, R., Kiss, B., Könczöl, A., Szalai, F., Jelinek, I., László, V., Noszál, B., Falus, A., and Keseru, G.M., 2008, Discovery of novel human histamine H4 receptor ligands by large-scale structure-based virtual screening, J. Med. Chem., 51 (11), 3145–3153.

[12] de Graaf, C., Kooistra, A.J., Vischer, H.F., Katritch, V., Kuijer, M., Shiroishi, M., Iwata, S., Shimamura, T., Stevens, R.C., de Esch, I.J.P., and Leurs, R., 2011, Crystal structure-based virtual screening for fragment-like ligands of the human histamine H(1) receptor, J. Med. Chem., 54 (23), 8195–8206.

[13] Marcou, G., and Rognan, D., 2007, Optimizing fragment and scaffold docking by use of molecular interaction fingerprints, J. Chem. Inf. Model., 47 (1), 195–207.

[14] Radifar, M., Yuniarti, N., and Istyastono, E.P., 2013, PyPLIF-assisted redocking indomethacin-(R)-alpha-ethyl-ethanolamide into cyclooxygenase-1, Indones. J. Chem., 13 (3), 283–286.

[15] Radifar, M., Yuniarti, N., and Istyastono, E.P., 2013, PyPLIF: Python-based protein-ligand interaction fingerprinting, Bioinformation, 9 (6), 325–328.

[16] Schultes, S., Nijmeijer, S., Engelhardt, H., Kooistra, A.J., Vischer, H.F., de Esch, I.J.P., Haaksma, E.E.J., Leurs, R., and de Graaf, C., 2013, Mapping histamine H4 receptor–ligand binding modes, Med. Chem. Commun., 4 (1), 193–204.

[17] Istyastono, E.P., Yuniarti, N., Hariono, M., Yuliani, S.H., and Riswanto, F.D.O., 2017, Binary quantitative structure-activity relationship analysis in retrospective structure based virtual screening campaigns targeting estrogen receptor alpha, Asian J. Pharm. Clin. Res., 10 (12), 206–211.

[18] Riswanto, F.D.O., Hariono, M., Yuliani, S.H., and Istyastono, E.P., 2017, Computer-aided design of chalcone derivatives as lead compounds targeting acetylcholinesterase, Indonesian J. Pharm., 28 (2), 100–111.

[19] Mysinger, M.M., Carchia, M., Irwin, J.J., and Shoichet, B.K., 2012, Directory of useful decoys, enhanced (DUD-E): Better ligands and decoys for better benchmarking, J. Med. Chem., 55 (14), 6582–6594.

[20] Prasasty, V., Radifar, M., and Istyastono, E.P., 2018, Natural peptides in drug discovery targeting acetylcholinesterase, Molecules, 23 (9), 2344.

[21] Krieger, E., and Vriend, G., 2015, New ways to boost molecular dynamics simulations, J. Comput. Chem., 36 (13), 996–1007.

[22] Chen, H., Zhu, X., Guo, H., Zhu, J., Qin, X., and Wu, J., 2015, Towards energy-efficient scheduling for real-time tasks under uncertain cloud computing environment, J. Syst. Software, 99, 20–35.

[23] Liu, K., Watanabe, E., and Kokubo, H., 2017, Exploring the stability of ligand binding modes to proteins by molecular dynamics simulations, J. Comput.-Aided Mol. Des., 31 (2), 201–211.

[24] Trott, O., and Olson, A.J., 2010, AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading, J. Comput. Chem., 31 (2), 455–461.

[25] Dvir, H., Wong, D.M., Harel, M., Barril, X., Orozco, M., Luque, F.J., Munoz-Torrero, D., Camps, P., Rosenberry, T.L., Silman, I., and Sussman, J.L., 2002, 3D structure of Torpedo californica acetylcholinesterase complexed with huprine X at 2.1 Å resolution: Kinetic and molecular dynamic correlates, Biochemistry, 41 (9), 2970–2981.

[26] Yuniarti, N., Mungkasi, S., Yuliani, S.H., and Istyastono, E.P., 2019, Development of a graphical user interface application to identify marginal and potent ligands for estrogen receptor alpha, Indones. J. Chem., 19 (2), 531–537.



DOI: https://doi.org/10.22146/ijc.46329

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