VIRTUAL SCREENING CAMPAIGNS ON ISOFLAVONES TO DISCOVER POTENT CYCLOOXYGENASE-2 INHIBITORS

Enade Perdana Istyastono

Abstract


By employing recently published structure-based virtual screening (SBVS) to identify potent cyclooxygenase-2 (COX-2) inhibitors, all isoflavones collected by ZINC15 database were virtually screened. There were 3371 isoflavones in ZINC15 database and 1356 compounds out of them met the Lipinski’s rule of 5. Notably, only 3 isoflavones out of those 1356 compounds were identified as potent COX-2 inhibitors.

Keywords


virtual screening; isoflavone; ZINC15 database; cyclooxygenase-2

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Bento, A.P., Gaulton, A., Hersey, A., Bellis, L.J., Chambers, J., Davies, M., Krüger, F.A., Light, Y., Mak, L., McGlinchey, S., Nowotka, M., Papadatos, G., Santos, R., and Overington, J.P., 2014. The ChEMBL bioactivity database: An update. Nucl. Acids Res., 42 (D1), 1083–1090.

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

Dixon, R.A., 2004. Phytoestrogens. Annu. Rev. Plant Biol., 55 (1), 225–261.

Helferich, W.G., Andrade, J.E., and Hoagland, M.S., 2008. Phytoestrogens and breast cancer: a complex story. Inflammopharmacology, 16 (5), 219–226.

Huang, N., Shoichet, B.K., and Irwin, J.J., 2006. Benchmarking sets for molecular docking. J. Med. Chem., 49 (23), 6789–6801.

Istyastono, E.P., 2016. Optimizing structure-based virtual screening protocol to identify phytochemicals as cyclooxygenase-2 inhibitors. Indones. J. Pharm., 27 (3), 163–173.

Istyastono, E.P., 2017. Binary quantitative structure-activity relationship analysis to increase the predictive ability of structure-based virtual screening campaigns targeting cyclooxygenase-2. Indones. J. Chem., 17 (2), 322–329.

Istyastono, E.P., Riswanto, F.D.O., and Yuliani, S.H., 2015. Computer-aided drug repurposing: a cyclooxygenase-2 inhibitor celecoxib as a ligand for estrogen receptor alpha. Indones. J. Chem., 15 (3), 274–280.

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

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.

Krüger, D.M. and Evers, A., 2010. Comparison of structure- and ligand-based virtual screening protocols considering hit list complementarity and enrichment factors. ChemMedChem, 5 (1), 148–158.

Lipinski, C.A., Lombardo, F., Dominy, B.W., and Feeney, P.J., 2001. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 46 (1-3), 3–26.

Mitra, I., Saha, A., and Roy, K., 2010. Chemometric modeling of free radical scavenging activity of flavone derivatives. Eur. J. Med. Chem., 45 (11), 5071–5079.

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.

O’Boyle, N.M., Banck, M., James, C.A., Morley, C., Vandermeersch, T., and Hutchison, G.R., 2011. Open Babel: An open chemical toolbox. J. Cheminform. 3 (1), 33–47.

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

Radifar, M., Yuniarti, N., and Istyastono, E.P., 2013b. PyPLIF: Python-based Protein-Ligand Interaction Fingerprinting. Bioinformation, 9 (6), 325–328.

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

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.

Varinska, L., Gal, P., Mojzisova, G., Mirossay, L., and Mojzis, J., 2015. Soy and breast cancer: Focus on angiogenesis. Int. J. Mol. Sci., 16 (5), 11728–11749.

Weiss, D.R., Bortolato, A., Tehan, B., and Mason, J.S., 2016. GPCR-Bench: A benchmarking set and practitioners’ guide for G protein-coupled receptor docking. J. Chem. Inf. Model., 56 (4), 642–651.

Yuniarti, N., Ikawati, Z., and Istyastono, E.P., 2011. The importance of ARG513 as a hydrogen bond anchor to discover COX-2 inhibitors in a virtual screening campaign. Bioinformation, 6 (4), 164–166.




DOI: http://dx.doi.org/10.24071/jpsc.142698

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