Computer-Aided Discovery of Pentapeptide AEYTR as a Potent Acetylcholinesterase Inhibitor
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
Keywords
Full Text:
Full Text PDFReferences
[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
Article Metrics
Abstract views : 1642 | views : 1796Copyright (c) 2020 Indonesian Journal of Chemistry
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Indonesian Journal of Chemistry (ISSN 1411-9420 /e-ISSN 2460-1578) - Chemistry Department, Universitas Gadjah Mada, Indonesia.
View The Statistics of Indones. J. Chem.