ANALYSIS OF THE INTERACTION OF ALICIN BINDINGS ON PPARG USING MOLECULAR DYNAMICS SIMULATION

Authors

  • Muhammad Andre Reynaldi STIKES ARJUNA http://orcid.org/0000-0002-7827-5261
  • Aulia Faradilla Universitas Tanjungpura
  • Siti Nani Nurbaeti Universitas Tanjungpura
  • Hariyanto IH Universitas Tanjungpura
  • Inarah Fajriaty Universitas Tanjungpura
  • Hafrizal Riza Universitas Tanjungpura

Abstract

Allicin is one of the compounds contained in garlic which previous research predicted could be obtained using PPARG (Peroxisome Proliferatr Activated Receptor Gamma). However, short research requires further analysis, namely molecular dynamics. This research aims to analyze the interaction of allicin binding to PPARG used molecular dynamics. The dynamic molecular method used Gromac 2023 software with the chemical structure of allicin obtained from Pubchem data, while PPARG data was obtained from RCSB website. The results obtained from this study indicate that allicin interacts with similar amino acids as pioglitazone. However, its energy affinity is not as great as pioglitazoe. Based on the results of this research, it shows that allicin is predicted to be able to interact with PPARG in a molecular dynamic manner

Keywords: Allicin, PPARG, Molecular dynamics.

References

Nakamoto, M.; Kunimura, K.; Suzuki, J.-I.; Kodera, Y. Antimicrobial Properties of Hydrophobic Compounds in Garlic: Allicin, Vinyldithiin, Ajoene and Diallyl Polysulfides. Exp. Ther. Med. 2020, 19, 1550–1553, doi:10.3892/etm.2019.8388.

Reynaldi, M.A.; Riza, H.; Luliana, S. DOCKING STUDY OF ALLICIN WITH SULFONYLUREA RECEPTOR 1, COMPLEX 1 AND PPARγ RECEPTOR ON INSULIN RESISTANCE. Int. J. Pharm. Pharm. Sci. 2018.

Irudayaraj, S.S.; Stalin, A.; Sunil, C.; Duraipandiyan, V.; Al-Dhabi, N.A.; Ignacimuthu, S. Antioxidant, Antilipidemic and Antidiabetic Effects of Ficusin with Their Effects on GLUT4 Translocation and PPARγ Expression in Type 2 Diabetic Rats. Chem. Biol. Interact. 2016, 256, 85–93, doi:10.1016/j.cbi.2016.06.023.

Mazumder, M.; Ponnan, P.; Das, U.; Gourinath, S.; Khan, H.A.; Yang, J.; Sakharkar, M.K. Investigations on Binding Pattern of Kinase Inhibitors with PPARγ: Molecular Docking, Molecular Dynamic Simulations, and Free Energy Calculation Studies. PPAR Res. 2017, 2017, 6397836, doi:10.1155/2017/6397836.

Nickavar, B. Effect of Organosulfur Compounds from Different Garlic Preparations on Hyperlipidemia: An in-Silico Approach. Biointerface Res. Appl. Chem. 2022, 12, 4048–4061, doi:10.33263/BRIAC123.40484061.

Ramos, M.C.; Quoika, P.K.; Horta, V.A.C.; Dias, D.M.; Costa, E.G.; do Amaral, J.L.M.; Ribeiro, L.M.; Liedl, K.R.; Horta, B.A.C. PyPolyBuilder: Automated Preparation of Molecular Topologies and Initial Configurations for Molecular Dynamics Simulations of Arbitrary Supramolecules. J. Chem. Inf. Model. 2021, 61, 1539–1544, doi:10.1021/acs.jcim.0c01438.

Kagami, L.; Wilter, A.; Diaz, A.; Vranken, W. The ACPYPE Web Server for Small-Molecule MD Topology Generation. Bioinformatics 2023, 39, doi:10.1093/bioinformatics/btad350.

Childers, M.C.; Daggett, V. Validating Molecular Dynamics Simulations against Experimental Observables in Light of Underlying Conformational Ensembles. J. Phys. Chem. B 2018, 122, 6673–6689, doi:10.1021/acs.jpcb.8b02144.

Ke, Q.; Gong, X.; Liao, S.; Duan, C.; Li, L. Effects of Thermostats/Barostats on Physical Properties of Liquids by Molecular Dynamics Simulations. J. Mol. Liq. 2022, 365, 120116, doi:https://doi.org/10.1016/j.molliq.2022.120116.

Jorgensen, W.L.; Chandrasekhar, J.; Madura, J.D.; Impey, R.W.; Klein, M.L. Comparison of Simple Potential Functions for Simulating Liquid Water. J. Chem. Phys. 1983, 79, 926–935, doi:10.1063/1.445869.

Case, D.A.; Betz, R.M.; Cerutti, D.S.; T.E., C.I.; Darden, T.A.; Duke, R.E.; Giese, T.J.; Gohlke, H.; Goetz, A.W.; Homeyer, N.; et al. Amber 2016. Univ. California, San Fr. 2016.

Suhartanto, H.; Yanuar, A.; Wibisono, A.; Hermawan, D.; Bustamam, A. The Performance of a Molecular Dynamics Simulation for the Plasmodium Falciparum Enoyl-Acyl Carrier-Protein Reductase Enzyme Using Amber and GTX 780 and 970 Double Graphical Processing Units. Int. J. Technol. 2018, 9, 150–158, doi:10.14716/ijtech.v9i1.1186.

Aho, N.; Buslaev, P.; Jansen, A.; Bauer, P.; Groenhof, G.; Hess, B. Scalable Constant PH Molecular Dynamics in GROMACS. J. Chem. Theory Comput. 2022, 18, 6148–6160, doi:10.1021/acs.jctc.2c00516.

Rashid, H.U.; Ahmad, N.; Abdalla, M.; Khan, K.; Martines, M.A.U.; Shabana, S. Molecular Docking and Dynamic Simulations of Cefixime, Etoposide and Nebrodenside A against the Pathogenic Proteins of SARS-CoV-2. J. Mol. Struct. 2022, 1247, 131296, doi:10.1016/j.molstruc.2021.131296.

Fatriansyah, J.F.; Boanerges, A.G.; Kurnianto, S.R.; Pradana, A.F.; Fadilah; Surip, S.N. Molecular Dynamics Simulation of Ligands from Anredera Cordifolia (Binahong) to the Main Protease (Mpro) of SARS-CoV-2. J. Trop. Med. 2022, 2022, 1178228, doi:10.1155/2022/1178228.

John M. Beale, J.H.B. Wilson and Giswold’s Organic Medicinal and Pharmaceutical Chemistry. 2011, 12th edition.

Yanti, S.; Chien, W.-J.; Agrawal, D.C. Profiling of Insulin and Resveratrol Interaction Using Multi-Spectroscopy and Molecular Docking Study. Beni-Suef Univ. J. Basic Appl. Sci. 2022, 11, 90, doi:10.1186/s43088-022-00269-1.

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Published

2024-04-25

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