DOCKING STUDY OF ALLICIN WITH SULFONYLUREA RECEPTOR 1, COMPLEX 1 AND PPARγ RECEPTOR ON INSULIN RESISTANCE

  • Muhammad Andre Reynaldi Department of Pharmaceutics, Tanjungpura University, Pontianak, Indonesia
  • Hafrizal Riza Department of Pharmaceutics, Tanjungpura University, Pontianak, Indonesia
  • Sri Luliana Department of Pharmaceutics, Tanjungpura University, Pontianak, Indonesia

Abstract

Objective: Allicin is a potential type 2 antidiabetic. Sulfonylurea receptor 1 (SUR1), nikotinamida adina dinukleotida dehydrogenase (Complex 1) and peroxisome proliferator-activated receptors gamma (PPARγ) are known as important receptors responsible in insulin resistance This study aimed to determine the physicochemical properties, and the affinity of allicin on SUR1, Complex 1 and PPARγ receptors based on the binding energy and the type of interaction.

Methods: The physicochemical properties of allicin were analyzed using ChemOffice, and the binding energy and type of interaction were analyzed using the docking method with Autodock Vina.

Results: The results from the analysis showed allicin has log p (logarithmic partition) 1.35, massa relativity (mr) 162.26 g/mol, and the binding energy of allicin on SUR1, Complex 1 and PPARγ are respectively-4.0;-3.0; and-4.1 kcal/mol. The type of interaction between allicin and receptors is van der waals.

Conclusion: Allicin has good permeability and has the potential to bind to SUR1, Complex 1 and PPARγ receptors contributing to the activity of allicin as antidiabetic.

Keywords: Allicin, SUR1, Complex 1, PPARγ, Autodock Vina

Downloads

Download data is not yet available.

References

1. Thomson K, Al-Amin ZM, Al-Qattan KK, Shaban LH, Ali M. Anti-diabetic and hypolipidaemic properties of garlic (Allium sativum) in streptozotocin-induced diabetic rats. Int J Diabetic Metabolism 2007;15:108-15.
2. World Health Organization, Global Report on Diabetes; 2016. Available from: http://apps.who.int/iris/bitstream/10665/ 204871/1/9789241565257_eng.pdf. [Last accessed on 28 Nov 2017]
3. Kementerian Kesehatan Republik Indonesia, Situasi dan Analisis Diabetes; 2014. Available from: http://www.depkes.go.id/resources/download/pusdatin/infodatin/infodatin-diabetes.pdf. [Last accessed on 28 Nov 2017]
4. Gilbert B, Alves LF. Synergy in plant medicine. Curr Med Chem 2003;10:13-20.
5. Ojo RJ, Memudu AE, Akintayo CO, Akpan IS. Preventive effect of allium sativum on alloxan induced diabetic rat. ARPN J Agric Bio Sci 2012;7:609-12.
6. Block JH. Drug design strategies. In: Beale JM, Block JH. Eds. Wilson and Gisvold’s textbook of organic medicinal and pharmaceutical chemistry. 12th Ed. Philadelphia: Lippincott Williams and Wilkins; 2011. p. 38-9.
7. Jensen F. Introduction to computational chemistry. 2nd Ed. Hoboken: United States: John Wiley and Sons Ltd; 2006. p. 69-416.
8. Krovat EM, Steindl T, Langer T. Recent advances in docking and scoring. Curr Computer-Aided Drug Design 2005;1:93-102.
9. Kapetanovic IM. Computer-aided drug discovery and development: in silico-chemico-biological approach. Chem Biol Interact 2008;171:165–76.
10. Saini V. Moleculer mechanism of insulin resistance in type 2 diabetic mellitus. World J Diabetic 2010;1:68-75.
11. Ruderman NB, Carling D, Prentki M, Cacicedo JM. AMPK, insulin resistance, and the metabolic syndrome. J Clin Invest 2013;123:2764-72.
12. Coman C, Socaciu C. Docking of phytochemicals to the peroxisome proliferator-activated receptor gamma. Bull UASVM Agric 2012;69:236-42.
13. ChemOffice protocol; 2017. Available from: http://www. cambridgesoft.com/Ensemble_for_Chemistry/details/Default.aspx?fid=16andpid=735. [Last accessed on 20 Jan 2017]
14. Yanuar A. Penambatan molekuler. Praktek dan aplikasi pada virtual screening. Depok: Fakultas Farmasi Universitas Indonesia; 2012. p. 43-93.
15. Zaitseva J, Oswald C, Jumpertz T, Janewein S, Wiedenann A, Holland IB, et al. A structural analysis of asymmetry required for catalytic activity of an ABC-ATPase domain dimer. EMBO J 2006;25:3432-43.
16. National Centre for Biotechnology Information; 2016. Available from: https://www.ncbi.nlm.nih.gov/protein/341941160?log$=activity. [Last accessed on 23 Dec 2016]
17. Li Y, Wang Z, Furukawa N, Escaron P, Weiszmann J, Lee G, et al. T2384, a novel antidiabetic agent with unique peroxisome proliferator-activated receptor γ binding properties. J Bio Chem 2008;283:9168-76.
18. Chan SL, Labute P. Training a scoring function for the alignment of small molecules. J Chem Inf Model 2010;50:1724–35.
19. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Delivery Rev 2000;46:3-26.
20. Trott O, Olson JA. AutoDockVina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comp Chem 2010;31:455-61.
21. Vaiyapuri S, Shruthi SD. In vitro and in silico antidiabetic activity of pyran ester derivative isolated from tragia cannabina. Asian Pac J Trop Biomed 2014;4:S454-9.
22. Vineet M, Arun S, Pallavi K, Udayabanu M. Antioxidant, anti-inflammatory, and antidiabetic activity of hydroalcoholic extract of ocimum sanctum: an in vitro and in silico study. Asian J Pharm Clin Res 2016;9:44-9.
23. Jensen F. Introduction to computational chemistry. 2nd Ed. Denmark: John Wiley and Sons, Ltd; 2007. p. 1-21.
Statistics
171 Views | 351 Downloads
Citatons
How to Cite
Reynaldi, M. A., H. Riza, and S. Luliana. “DOCKING STUDY OF ALLICIN WITH SULFONYLUREA RECEPTOR 1, COMPLEX 1 AND PPARγ RECEPTOR ON INSULIN RESISTANCE”. International Journal of Pharmacy and Pharmaceutical Sciences, Vol. 10, no. 10, Oct. 2018, pp. 130-3, doi:10.22159/ijpps.2018v10i10.28105.
Section
Original Article(s)