Virtual Screening Natural Compounds from Plants as Inhibitor of Estrogen Receptor Alpha I (ESR1)

Andi Trihadi Kusuma; Daryono Hadi

doi:10.24198/ijpst.v1i1.19149

Virtual Screening Natural Compounds from Plants as Inhibitor of Estrogen Receptor Alpha I (ESR1)

Andi Trihadi Kusuma, Daryono Hadi

Abstrak

Flavanoid is one of the chemical compounds found in plants. Isoflavones (1.2-diarylpropane) are the largest group in the flavanoid that can be used to study the anticancer activity. 2IOK is one oftarget inhibitors which is obtainedfrom natural product compounds.The purpose of the research is to design new compounds have anti-cancer activity, especially for breast cancer based on the evaluation of the affinity of the compound against Estrogen Receptor Alpha (ESR1) usingthecomputational method. The parent compound is the test structure of isoflavones which has been reported to have anticancer activity, particularly for breast cancer. Based on the results of the validation method of docking with several combinations, the best method found was Triangle matcher-Affinity dG with anRMSDof 1.0452. Furthermore, this model providesROC graph value of 0.863. Therefore, the method was used to screen compounds in the UI database. Three compounds wereobtained from the process, which are potentially active against Estrogen Receptor Alpha (ESR1), namely C00010051, C00026048, C00025295. The MD simulations of protein-ligand complexes indicated exchanges process, namely the absence of interaction between the ligand with the Phe404, insteadofthe ligand formed hydrogen bonding with Glu353. Meanwhile, the C00025295 compound formed hydrogen bonding with the Leu346 residue.

Keywords: virtual screening, molecular docking, molecular dynamic, ESR1

Teks Lengkap:

PDF

Referensi

World Health Organization dalam World Cancer Burden (2012) Data of Cancer, WHO Press, UK

American Cancer Society (ACS), (2014): Cancer Facts & Figures 2014, www.cancer.org. diakses pada tanggal 16 Desember 2014.

American Cancer Society (ACS), (2015): Liver Cancer, http://www.cancer.org /acs/ groups/cid/documents/webcontent/003114-pdf diakses pada tanggal 20 Februari 2015

Muhammad Usman Mirza, Saba Ferdous., (2013) : Docking Studies reveal Phytochemicals as the longsearched anticancer drugs for Breast Cancer.,Center for Research in Molecular Medicine (CRiMM),Department of Computer Sciences City University London,UK

Liu, dkk (2013) The Potent Oxidant Anticancer Activity of Organoiridium Catalysts.

Kroemer et.al (2007) Tumor Cell Death and ATP Release Prime Dendritic Cells and Efficient Anticancer Immunity.

Kroemer et.al (2007) Cell Death Signaling and Anticancer Therapy

Sunseri, J., dan Koes, D.R. (2016): Pharmit: Interactive Exploration of Chemical Space, Nucleic Acids Research, 44, W442-W448.

Cereto-Massagué, A., Guasch, L., Valls, C., Mulero, M., Pujadas, G., dan Garcia-Vallvé, S. (2012): DecoyFinder: An Easy-to-Use Python GUI Application for Building Target-Specific Decoy Sets,Bioinformatics,28(12), 1661–1662.

Lagorce, D., Sperandio, O., Baell, J.B., Miteva, M.A., dan Villoutreix, B.O.(2015): FAF-Drugs3: A Web Server for Compound Property Calculation and Chemical Library Design,Nucleic Acids Research,43, W200–W207.

Molecular Operating Environment (MOE), v.2014.09.01, Chemical Computing Group Inc., 2014.

Marvin Suite, v.16.8.1, ChemAxon, 2016.

Hanwell, M.D., Curtis D.E., Lonie D.C., Vandermeersch T., Zurek E., dan Hutchison, G.R.(2012): Avogadro: An Advanced Semantic Chemical Editor, Visualisation, and Analysis Platform, Journal of Cheminformatics, 4(17), 1–17.

Sud, M.,(2016): MayaChemTools: An Open Source Package for Computational Drug Discovery,Journal of Chemical Information and Modeling, 56(12), 2292-2297.

Empereur-Mot, C.,Zagury, J-F., dan Montes, M.(2016): Screening Explorer – An Interactive Tool for the Analysis of Screening Results,Journal of Chemical Information and Modeling, 56(12), 2281-2286.

Abraham, M.J., Murtola, T., Schulz, R., Páll, S., Smith, J.C., Hess, B., dan Lindahl, E.(2015): Gromacs: High Performance Molecular Simulations through Multi-Level Parallelism from Laptops to Supercomputers, SoftwareX,1–2, 19–25.

Blunt et.al (2012) ,Marine natural products

Blunt, dkk (2015) Synthesis and Intracellular Redox Cycling of Natural Quinones and Their Analogues and Identification of Indoleamine‐2,3‐dioxygenase (IDO) as Potential Target for Anticancer Activity.

Lipinski, C.A., Lombardo, F., Dominy, B.W., dan Feeney, P.J. (1997): Experimental and Computational Approaches to Estimate Solubility and Permeability in Drug Discovery and Development Settings, Advanced Drug Delivery Reviews, 23, 3-25.

Weber, dkk (2002) The Neuroendocrine-Derived Peptide Parathyroid Hormone–Related Protein Promotes Prostate Cancer Cell Growth by Stabilizing the Androgen Receptor

Ritchie, T.J., dan Macdonald, S.J.F. (2009): The Impact of Aromatic Ring Count on Compound Developability – Are Too Many Aromatic Rings a Liability in Drug Design?,Drug Discovery Today,14(21), 1011–1020.

Baell, J.B., dan Holloway, G.A. (2010): New Substructure Filters for Removal of Pan Assay Interference Compounds ( PAINS ) from Screening Libraries and for Their Exclusion in Bioassays, Journal of Medicinal Chemistry, 53(7), 2719–2740.

Bussi, G., Donadio, D., dan Parrinello, M. (2007): Canonical Sampling through Velocity Rescaling,Journal of Chemical Physics,126(1).

Cronin, M. T. D. dan J. C. Madden, (2010): In Silico Toxicology: Principles and Applications, RSC Publishing, UK, 1, 12, 488.

Da Silva, A.W.S, dan Vranken, W.F. (2012): ACPYPE - AnteChamber PYthon Parser interfacE, BMC Research Notes, 5(1), 367.

Drwal, M.N., dan Griffith, R.. (2013): Combination of Ligand- and Structure-Based Methods in Virtual Screening,Drug Discovery Today: Technologies,10(3), e395–e401.

Feher, M. (2006). Consensus Scoring for Protein-Ligand Interactions,Drug Discovery Today,11(9–10), 421–428.

Güner, O.F. (2002): History and Evolution of the Pharmacophore Concept in Computer-Aided Drug Design,Current Topics in Medicinal Chemistry2(12): 1321–1332.

Halgren, T.A. (1996): Merck Molecular Force Field. I. Basis, Form, Scope, Parametrization, and Performance of MMFF94,Journal of Computational Chemistry,17(5–6): 490–519.

Irwin, J.J., dan Shoichet, B.K.(2005): ZINC - A Free Database of Commercially Available Compounds for Virtual Screening,Journal of Chemical Information and Modeling,45(1), 177–182.

Irwin, J.J. (2008): Community Benchmarks for Virtual Screening,Journal of Computer-Aided Molecular Design,22(3–4), 193–199.

Jorgensen, W.L., Chandrasekhar, J., Madura, J.D., Impey, R.W., dan Klein, M.L.(1983): Comparison of Simple Potential Functions for Simulating Liquid Water,The Journal of Chemical Physics,79(2), 926.

Labute, P., Williams, C., Feher, M., Sourial, E., dan Schmidt, J.M. (2001): Flexible Alignment of Small Molecules, Journal of Medicinal Chemistry, 44(10), 1483-1490.

Labute, P. (2009): Protonate3D: Assignment of Ionization States and Hydrogen Coordinates to Macromolecular Structures,Proteins: Structure, Function and Bioinformatics,75(1), 187–205.

Labute, P., Kossner, M., Ajamian, A., Santavy, M., dan Lin, A. (2014): Pharmacophore Annotation Using Extended Hückel Theory, Journal of Cheminformatics, 6(Suppl 1), 54.

Lin, A. (2004): Overview of Pharmacophore Applications in MOE, https://www.chemcomp.com/journal/ph4.htm. Diakses pada tanggal 8 Mei 2017.

Lindorff-Larsen, K., Piana, S., Palmo, K., Maragakis, P., Klepeis, J.L., Dror, R.O., dan Shaw, D.E.(2010): Improved Side-Chain Torsion Potentials for the Amber ff99SB Protein Force Field,Proteins: Structure, Function and Bioinformatics,78(8), 1950–1958.

Parrinello, M., dan Rahman, A. (1981): Polymorphic Transitions in Single Crystals: A New Molecular Dynamics Method, Journal of Applied Physics,52(12), 7182–7190.

Planesas, J.M., Claramunt, R.M., Teixidó, J., Borrell, J.I., dan Pérez-Nueno, V.I. (2011): Improving VEGFR-2 Docking-Based Screening by Pharmacophore Postfiltering and Similarity Search Postprocessing,Journal of Chemical Information and Modeling, 51, 777–787.

Pusat Data dan Informasi UI (2015): InfoDatin Tanaman Obat Indonesia.

Ryley, J.F, dan Wilson, R.G.(1975): Laboratory Studies with Some RecentAnticoccidials, Parasitology, 70, 203–222.

Sun, H. (2008): Pharmacophore-Based Virtual Screening, Current Medicinal Chemistry,15(10), 1018–1024.

Svensson, F., Karlén, A., dan Sköld, C. (2012): Virtual Screening Data Fusion Using Both Structure- and Ligand-Based Methods, Journal of Chemical Information and Modeling, 52, 225–232.

Triballeau, N., Acher, F., Brabet, I., Pin, J-P., dan Bertrand, H-O.(2005): Virtual Screening Workflow Development Guided by the ‘ Receiver Operating Characteristic ’ Curve Approach . Application to High-Throughput Docking on Metabotropic Glutamate Receptor Subtype 4, Journal of Medicinal Chemistry, 48(7), 2534–2547.

Wang, J., Wolf, R.M., Caldwell, J.W., Kollman, P.A., dan Case, D.A. (2004): Development and Testing of a General Amber Force Field,Journal of Computational Chemistry,25(9), 1157–1174.

Wang, R., dan Wang, S. (2001): How Does Consensus Scoring Work for Virtual Library Screening? An Idealized Computer Experiment, Journal of Chemical Information and Computer Sciences,41(5), 1422–1426.

Yang, S-Y. (2010): Pharmacophore Modeling and Applications in Drug Discovery: Challenges and Recent Advances,Drug Discovery Today15 (11–12),444–450

DOI: https://doi.org/10.24198/ijpst.v1i1.19149