Pharmacophore Modeling, Docking, and Molecular Dynamics Simulation of Flavonoids as Inhibitors of Urokinase-type Plasminogen Activator

Authors

  • Bina Lohita Sari Pharmacy Study Program, Pakuan University, Jalan Pakuan PO BOX 452, Bogor 16143, Indonesia
  • Slamet Ibrahim School of Pharmacy, Institut Teknologi Bandung, Jalan Ganesha 10, Bandung 40132, Indonesia
  • Daryono Hadi Tjahjono School of Pharmacy, Institut Teknologi Bandung, Jalan Ganesha 10, Bandung, 40132, Indonesia

DOI:

https://doi.org/10.5614/j.math.fund.sci.2021.53.3.8

Keywords:

anti-cancer, flavonoids, in-silico study, isorhamnetin, kaempferol, quercetin, rhamnetin

Abstract

The urokinase-type plasminogen activator (uPA) system plays a significant role in the invasion and metastasis of cancer cells. The present study was conducted to investigate natural product compounds as inhibitors and hit molecules of uPA using in-silico analysis. A pharmacophore model was built to screen the Indonesian Herbal Database (HerbalDB) to obtain inhibitors of different scaffolds. Based on the molecular docking score, four ligands were selected as potential uPA inhibitors. Subsequently, the stability of the ligand-uPA complex was analyzed using molecular dynamics (MD) simulation. An RMSD graph of the backbone protein and the RMSF values of the amino acid residues were also determined. In addition, the MM-PBSA method was applied to calculate the free binding energy. According to the results, Model_3, characterized by aromatic rings 23 (F1 and F2), cationic H-bond donor (F3), and metal ligator (F4) features, had an adequate goodness-of-hit score (GH). The four top-ranked ligands, isorhamnetin, rhamnetin, quercetin, and kaempferol, showed higher docking scores compared to the others. This study confirmed that isorhamnetin, rhamnetin, and kaempferol build stable complexes with uPA with lower binding energy than quercetin.

References

Tang, L., Han. X., The Urokinase Plasminogen Activator System in Breast Cancer Invasion and Metastasis, Biomedicine and Pharmacotherapy, 67(2), pp. 179-182, 2013. DOI:10.1016/j.biopha.2012.10.003.

Al-Sha?er, M. A., Khanfar, M.A., Taha, M. O., Discovery of Novel Urokinase Plasminogen Activator (uPA) Inhibitors Using Ligand-based Modeling and Virtual Screening Followed by In vitro Analysis, Journal of Molecular Modelling, 20(1), pp. 1-15, 2014. DOI:10.1007/s00894-014-2080-4.

Skeldal, S., Larsen, J.V., Pedersen, K.E., Petersen, H.H., Egelund, R., Christensen, A., Jensen, J.K., Gliemann, J., Andreasen, P.A., Binding Areas of Urokinase-type Plasminogen Activator-plasminogen Activator Inhibitor-1 Complex for Endocytosis Receptors of the Low-density Lipoprotein Receptor Family, Determined by Site-directed Mutagenesis, The FEBS Journal, 273, pp. 5143-5159, 2006. DOI:10.1111/j.1742-4658.2006.05511.x.

Sulimov, V.B., Katkova, E.V., Oferkin, I.V., Sulimov, A.V., Romanov, A.N., Roschin, A.I., Beloglazova, I.B., Plekhanova, O.S., Tkachuk, V.A., Sadovnichiy, V.A., Application of Molecular Modeling to Urokinase Inhibitors Development, Biomed Research International, pp. 1-15, 2014, DOI:10.1155/2014/625176.

Nisar, B., Sultan, A., Rubab, L., Comparison of Medicinally Important Natural Products Versus Synthetic Drugs-A Short Commentary, Natural Products Chemistry & Research, 6(2), pp. 1-2, 2017. DOI:10.4172/2329-6836.1000308.

Mitra, S., Dash, R., Natural Products for the Management and Prevention of Breast Cancer, Evidence-based Complementary and Alternative Medicine, pp. 1-17, 2018. DOI:10.1155/2018/8324696.

Xiao, J., Dietary Flavonoid Aglycones and Their Glycosides: Which Show Better Biological Significance, Critical Reviews in Food Science and Nutrition, 57(9), pp. 1874-1905, 2017. DOI:10.1080/10408398.2015.1032400.

Yanuar, A., Mun?im, A., Lagho, A.B.A., Syahdi, R.R., Rahmat, M., and Suhartanto, H., Medicinal Plants Database and Three-dimensional Structure of the Chemical Compounds from Medicinal Plants in Indonesia, International Journal of Computer Science Issues, 8(5), pp. 180-183, 2011.

Salentin, S., Schreiber, S., Haupt, V.J., Adasme, M.F., Schroeder, M., PLIP: Fully Automated Protein-ligand Interaction Profiler, Nucleic Acids Research Advance, 43, pp. 1-5, 2015. DOI:10.1093/nar/gkv315.

Edwardson, D.W., Narendrula, R., Chewchuk, S., Mispel-Beyer, K., Mapletoft, J.P.J., Parissenti., A.M., Role of Drug Metabolism in the Cytotoxicity and Clinical Efficacy of Anthracyclines, Current Drug Metabolism, 16(6), pp. 412-426, 2015. DOI:10.2174/1389200216888150915112039.

Schuliga, M., The Inflammatory Actions of Coagulant and Fibrinolytic Proteases in Disease, Mediators of Inflammation, pp. 1-9, 2015:437695. DOI:10.1155/2015/437695.

Agarwal, R., Shrestha, U.R., Chu, X-D., Petridis, L., Smith, J.C., Mesophilic Pyrophosphatase Function at high Temperature: A Molecular dynamics Simulation Study, Biophysical Society, 119(1), pp. 142-150, 2020. DOI:10.1016/j.bpj.2020.05.021.

Genheden, S., Ryde, U., The MM/PBSA and MM/GBSA Methods to estimate Ligand-binding Affinities, Expert Opinion on Drug Discovery, 10(5), pp. 449-61, 2015. DOI:10.1517/17460441.2015.1032936.

Zhu, M., Gokhale, V.M., Szabo, L., Munoz, R.M., Baek, H., Bashyam, S., Hurley, L.H., Von Hoff, D.D., Han, H., Identification of a novel Inhibitor of Urokinase-type Plasminogen Activator, Molecular Cancer Therapeutics, 6(4), pp. 1348-1354, 2007. DOI: 10.1158/1535-7163.MCT-06-0520.

Kumar, G., Banerjee, T., Kapoor, N., Surolia, N., Surolia, A., SAR and pharmacophore Models for the rhodanine Inhibitors of Plasmodium falciparum Enoyl-acyl carrier Protein Reductase, IUBMB Life, 62(3), pp. 204-213, 2010. DOI:10.1002/iub.306.

Sunseri, J., Koes, D.R., Pharmit: interactive Exploration of chemical Space, Nucleic Acids Research, 44, pp. 442-448, 2016. DOI:10.1093/nar/gkw287.

Fernandes, T.B., Segretti, M.C.F., Polli, M.C., Parise-Filho, R., Analysis of the applicability and Use of Lipinski`s Rule for central Nervous System Drugs, Lett. Drug Des. Discov., 13(10), pp. 999-1006, 2016. DOI: 10.2174/1570180813666160622092839.

Fei, J., Zhou, L., Liu, T., Tang, X.Y., Pharmacophore modeling, Virtual Screening, and molecular Docking Studies for Discovery of Novel Akt2 Inhibitors, International Journal Medical Sciences, 10 (3), pp. 265-274, 2013. DOI:10.7150/ijms.5344.

Mysinger, M.M., Carchia, M., Irwin, J.J., Shoichet, B.K., Directory of useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking, Journal Med. Chem., 55(14), pp. 6582-6594, 2012. DOI: 10.1021/jm300687e.

Empereur-Mot, C., Zagury, J.F., Montes, M., Screening Explorer - An interactive Tool for the Analysis of Screening Results, J. Chem. Inf. Model., 56(12), pp. 2281-2286, 2016. DOI:10.1021/acs.jcim.6b00283.

Wang, C., Greene, D., Xiao, L., Qi, R., Luo, R., Recent Developments and Applications of the MMPBSA Method, Front. Mol. Biosci., 4, pp. 1-18, 2018. DOI: 10.3389/fmolb.2017.00087.

Katz, B.A., Mackman, R., Luong, C., Radika, K., Martelli, A., Sprengeler, P.A., Wang, J., Chan, H., Wong, L.., Structural Basis for Selectivity of a small Molecule, S1-binding, Submicromolar Inhibitor of Urokinase-type plasminogen Activator, Chemistry Biology, 7 (4), pp. 299-312, 2000. DOI:10.1016/S1074-5521(00)00104-6.

Chen, C., Wang, T., Wu, F., Huang, W., He, G., Ouyang, L., Xiang, M., Peng, C., Jiang, Q., Combining Structure-based pharmacophore Modeling, virtual Screening, and In Silico ADMET Analysis to discover Novel Tetrahydro-quinoline based Pyruvate Kinase Isozyme M2 Activators with antitumor Activity, Drug Design Development Therapy, 8, pp. 1195-1210, 2014. DOI:10.2147/DDDT.S62921.

Kalyaanamoorthy, S., Chen, Y.P.P., Structure-based Drug Design to Augment Hit Discovery, Drug Discovery Today, 16(17/18), PP. 831-839, 2011. DOI: 10.1016/j.drudis.2011.07.006.

Koes, D.R., Camacho, C.J., Shape-based Virtual Screening with Volumetric Aligned Molecular Shapes, Journal of Computational Chemistry, 35(25), pp. 1821-1834, 2014. DOI: 10.1002/jcc.23690.

Rao, S.N., Head, M.S., Kulkarni, A., LaLonde, J.M., Validation studies of the Site-directed Docking Program LibDock, Journal Chemistry Inf. Model, 47(6), pp. 2159-2171, 2007. DOI: 10.1021/ci6004299.

Vogel, S.M., Bauer, M.R., Boeckler, F.M., DEKOIS: Demanding Evaluation Kits for Objective In Silico Screening - A Versatile Tool for Benchmarking Docking Programs and Scoring Functions, Journal Chemistry Inf. Model, 51(10), pp. 2650-2665, 2011. DOI:10.1021/ci2001549.

Fawcett, T., An Introduction to ROC analysis, Pattern Recognit. Lett., 27(8), pp. 861-874, 2005. DOI:10.1016/j.patrec.2005.10.010.

Guedes, I.A., de Magalhs, C.S., Dardenne, L.E., Receptor-ligand Molecular Docking, Biophys. Rev., 6(1), pp. 75-87, 2014. DOI:10.1007/s12551-013-0130-2.

Meng, M., Zhang, X., Mezei, M., & Cui, M., Molecular Docking: a powerful Approach for Structure-based Drug Discovery, Curr. Comput. Aided Drug Des., 7(2), pp. 146-157, 2011.

Weston, L.A., Mathesius, U., Flavonoids: Their Structure, Biosynthesis and Role in the Rhizosphere, Including Allelopathy, Journal Chemical Ecology, 39(2), pp. 283-297, 2013. DOI: 10.1007/s10886-013-0248-5.

Kandakumar, S., Manju, D.V., Pharmacological Applications of Isorhamnetin: A Short Review, International Journal Trend Sciences Research Development, 1(4), pp. 672-678, 2017. DOI:10.31142/ijtsrd2202.

Xue, G., Gong, L. Yuan, C. Xu, M., Wang, X., Juang, L., Huang, M., A Structural Mechanism of Flavonoids in Inhibiting Serine Proteases, Food and Function, 8(7), pp. 2437-2443, 2017. DOI:10.1039/c6fo01825d.

Kumar, S., Pandey, A. K., Chemistry and Biological Activities of Flavonoids: An Overview, The Scientific World Journal, 2013, pp. 1-16, 2013. DOI:10.1155/2013/162750.

Kitchen, D. B., Decornez, H., Furr, J. R., Bajorath, J., Docking and Scoring in Virtual Screening for Drug Discovery: Methods fad Applications, Nature Reviews Drug Discovery, 3(11), pp. 935-947, 2004. DOI:10.1038/nrd1549.

Torrens-Fontanals, M., Stepniewski, T. M., Aranda-Garc, D., Morales-Pastor, A., Medel-Lacruz, B., Selent, J., How Do Molecular Dynamics Data Complement Static Structural Data of GPCRs, International Journal of Molecular Sciences, 21(16), pp. 5933, 2020. DOI:10.3390/ijms21165933.

Setiawan, M. T., Yanuar, A., Virtual Screening and Molecular Dynamics Simulation of Compounds from the Herbal Database of Indonesia Against Histone Deacetylase 2, International Journal of Applied Pharmaceutics, 10(1), pp. 235-239, 2018. DOI:10.22159/ijap.2018.v10s1.52.

Yanuar, A., Chavarina, K. K., Syahdi, R. R., Molecular Dynamic Simulation Analysis on Marine Fungi Compounds Against EGFR and VEGFR-2 Inhibitory Activity in Non-small Cell Lung Cancer, Journal of Young Pharmacists, 10(2), 2018. DOI:10.5530/jyp.2018.2s.6.

Gao, Y., Mei, Y., Zhang, J. Z. H., Treatment of Hydrogen Bonds in Protein Simulations, in Advanced Materials for Renewable Hydrogen Production, Storage and Utilization, 2015. DOI:10.5772/61049.

Kumari, R., Kumar, R., Open-Source Drug Discovery Consortium, Lynn, A., g _ mmpbsa - A GROMACS Tool for MM-PBSA and Its Optimization for High-throughput Binding Energy Calculations, Journal of Chemical Information and Modeling, 54, pp. 1951-1962, 2014. DOI:10.1021/ci500020m

Downloads

Published

2022-01-10

Issue

Section

Articles