Computer-Aided Discovery of Pentapeptide AEYTR as a Potent Acetylcholinesterase Inhibitor

https://doi.org/10.22146/ijc.55447

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

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

Abstract


One of the key targets in the drug development for potential Alzheimer’s disease (AD) therapeutics is the search for acetylcholinesterase enzyme (AChE) inhibitors. Very recently, a pentapeptide AEYTR was reported as a potential inhibitor for AChE. The peptide was identified in a retrospectively validated virtual screening campaign, which was subsequently followed by 10 ns molecular dynamics (MD) simulations. The study aimed to characterize the structure and identify in vitro of AEYTR peptide as a potent acetylcholinesterase inhibitor. This article presents the structure characterization and the in vitro examination of the peptide as an AChE inhibitor, followed by MD simulations for 100 ns. The results show that the pentapeptide is a potent AChE inhibitor with an IC50 value in the picomolar range and stabilizes the enzyme during MD simulations.

Keywords


acetylcholinesterase; short peptide; computer-aided discovery

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References

[1] Bharti, D.R., Hemrom, A.J., and Lynn, A.M., 2019, GCAC: Galaxy workflow system for predictive model building for virtual screening, BMC Bioinf., 19 (13), 550.

[2] Istyastono, E.P., Kooistra, A.J., Vischer, H.H., Kuijer, M., Roumen, L., Nijmeijer, S., Smits, R.A., 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.

[3] 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 H1 receptor, J. Med. Chem., 54 (23), 8195–8206.

[4] 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 H3 receptor ligands using ligand-based and protein-based molecular fingerprints, J. Chem. Inf. Model., 52 (12), 3308–3324.

[5] Schultes, S., Kooistra, A.J., Vischer, H.F., Nijmeijer, S., Haaksma, E.E.J., Leurs, R., de Esch, I.J.P., and de Graaf, C., 2015, Combinatorial consensus scoring for ligand-based virtual fragment screening: A comparative case study for serotonin 5-HT3A, histamine H1 and histamine H4 Receptors, J. Chem. Inf. Model., 55 (5), 1030–1044.

[6] 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.

[7] Lo, Y.C., Rensi, S.E., Torng, W., and Altman, R.B., 2018, Machine learning in chemoinformatics and drug discovery, Drug Discovery Today, 23 (8), 1538–1546.

[8] Sterling, T., and Irwin, J.J., 2015, ZINC 15 - Ligand discovery for everyone, J. Chem. Inf. Model., 55 (11), 2324–2337.

[9] 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.

[10] Moitessier, N., Englebienne, P., Lee, D., Lawandi, J., and Corbeil, C.R., 2008, Towards the development of universal, fast and highly accurate docking/scoring methods: A long way to go, Br. J. Pharmacol., 153 (Suppl. 1), S7–S26.

[11] Chen, Y.C., 2015, Beware of docking!, Trends Pharmacol. Sci., 36 (2), 78–95.

[12] 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.

[13] de Graaf, C., and Rognan, D., 2008, Selective structure-based virtual screening for full and partial agonists of the β2 adrenergic receptor, J. Med. Chem., 51 (16), 4978–4985.

[14] Kooistra, A.J., Leurs, R., de Esch, I.J.P., and de Graaf, C., 2015, Structure-based prediction of G-protein-coupled receptor ligand function: A β-adrenoceptor case study, J. Chem. Inf. Model., 55 (5), 1045–1061.

[15] de Graaf, C., Rein, C., Piwnica, D., Giordanetto, F., and Rognan, D., 2011, Structure-based discovery of allosteric modulators of two related class B G-protein-coupled receptors, ChemMedChem, 6 (12), 2159–2169.

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

[17] 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.

[18] Therneau, T., Atkinson, B., and Ripley, B., 2015, rpart: Recursive Partitioning and Regression Trees, R package version 4.1-9, http://CRAN.R-project.org/ package=rpart.

[19] Istyastono, E.P., 2015, Employing recursive partition and regression tree method to increase the quality of structure-based virtual screening in the estrogen receptor alpha ligands identification, Asian J. Pharm. Clin. Res., 8 (6), 207–210.

[20] 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, Indones. J. Pharm., 28 (2), 100–111.

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

[22] Prasasty, V.D., and Istyastono, E.P., 2019, Data of small peptides in SMILES and three-dimensional formats for virtual screening campaigns, Data Brief, 27, 104607.

[23] Prasasty, V.D., and Istyastono, E.P., 2020, Structure-based design and molecular dynamics simulations of pentapeptide AEYTR as a potential acetylcholinesterase inhibitor, Indones. J. Chem., 20 (4), 953–959.

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

[25] Lill, M.A., and Danielson, M.L., 2011, Computer-aided drug design platform using PyMOL, J. Comput.-Aided Mol. Des., 25 (1), 13–19.

[26] Korb, O., Stützle, T., and Exner, T.E., 2007, An ant colony optimization approach to flexible protein–ligand docking, Swarm Intell., 1 (2), 115–134.

[27] Korb, O., Stützle, T., and Exner, T.E., 2009, Empirical scoring functions for advanced protein-ligand docking with PLANTS, J. Chem. Inf. Model., 49 (1), 84–96.

[28] ten Brink, T., and Exner, T.E., 2009, Influence of protonation, tautomeric, and stereoisomeric states on protein-ligand docking results, J. Chem. Inf. Model., 49 (6), 1535–1546.

[29] 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.

[30] Walsh, R., 2018, Comparing enzyme activity modifier equations through the development of global data fitting templates in Excel, PeerJ, 6, e6082.

[31] Park, K., 2017, Emergence of hydrogen bonds from molecular dynamics simulation of substituted N-phenylthiourea–catechol oxidase complex, Arch. Pharmacal Res., 40 (1), 57–68.

[32] Wang, M., Wang, Y., Kong, D., Jiang, H., Wang, J., and Cheng, M., 2018, In silico exploration of aryl sulfonamide analogs as voltage-gated sodium channel 1.7 inhibitors by using 3D-QSAR, molecular docking study, and molecular dynamics simulations, Comput. Biol. Chem., 77, 214–225.

[33] Riswanto, F.D.O., Murugaiyah, M.S.A., Rawa, V., Salin, N.H., Istyastono, E.P., Hariono, M., and Wahab, H.A., 2019, Anti-cholinesterase activity of chalcone derivatives: Synthesis, in vitro assay and molecular docking study, Med. Chem., 15, 1–11.



DOI: https://doi.org/10.22146/ijc.55447

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