|Muhamad Sahlan||Department of Chemical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, West Java 16424, Indonesia|
|Muhammad Nizar Hamzah Al Faris||Department of Chemical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, West Java 16424, Indonesia|
|Reza Aditama||Biochemistry Research Group, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung, West Java 40132, Indonesia|
|Kenny Lischer||Department of Chemical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, West Java 16424, Indonesia|
|Apriliana Cahya Khayrani|
|Diah Kartika Pratami||Lab of Pharmacognosy and Phytochemistry, Faculty of Pharmacy, Pancasila University, Jakarta, 12640, Indonesia|
Diabetes mellitus is one of the metabolic diseases, characterized by hyperglycemia, which is usually caused by endogenous glucose production through gluconeogenesis. Furthermore, fructose 1,6-bisphosphatase (FBPase), which is the last enzyme involved in gluconeogenesis, is used as inhibition target due to its relatively safe effect. In addition, It is known that propolis has shown antidiabetic activity through some sets of mechanisms due to its varied constituents. Therefore, this study aims to explore the antidiabetic activity of South Sulawesi propolis compounds against the allosteric site of FBPase (PDB ID: 3KC1) through molecular docking on Autodock Vina. The results show that 18 out of 30 propolis compounds outweigh AMP affinity. Furthermore, only two flavonoids showed 100% interaction similarity to the re-docked native ligand and AMP natural inhibition. These two compounds were Broussoflavonol F and Glyasperin A, which had docking score of -9 kcal/mol and -8.2 kcal/mol, respectively. This indicates that both compounds are capable of being used as FBPase inhibitors for the treatment of diabetes mellitus.
Allosteric inhibition; Diabetes mellitus; Fructose 1,6-Bisphosphatase; Molecular docking; Propolis
Diabetes mellitus is a world-wide metabolic disease that is characterized by hyperglycemia, which is usually caused by insulin secretion deficiency (Association, 2014; Abdillah and Suwarno, 2016). In severe hyperglycemia cases, the disease is worsened by the accompaniment of organ failures (Association, 2014; Seeberger and Rademacher, 2014). Among several classifications of the disease, type 2 diabetes mellitus (T2DM), for which insulin resistance is an additional symptom, is accounted for 90–95% of the total recorded cases. Most T2DM patients frequently go undiagnosed for many years, and the risk increases with age, obesity, and an unhealthy lifestyle (Moller, 2001; Association, 2014; Control, 2020). To date, several T2DM drugs have been developed and marketed, including thiazolidinediones and metformin groups. Unfortunately, the use of thiazolidinediones correlates with heart failure formation while metformin has the potential to produce lactic acidosis in its users (Singh et al., 2007; Lalau, 2010). Because hyperglycemia is a major characteristic of diabetes, recently administered therapies have worked to lower patients' blood sugar levels. Several drugs have been developed and marketed with different targets and mechanism of actions (Moller, 2001; Seeberger and Rademacher, 2014). One technique that shows a promising effect is to reducing the production of endogenous glucose in the gluconeogenesis pathway which is considered as the major contributor to high blood glucose levels (Seeberger and Rademacher, 2014).
Fructose 1,6-Bisphosphatase (FBPase) is known to be the penultimate enzyme in the gluconeogenesis pathway that catalyzes the hydrolysis of fructose 1,6-bisphosphate to fructose 6-phosphate by controlling the conversion of all substrates into glucose (Erion et al., 2005; Tsukada et al., 2009; Seeberger and Rademacher, 2014). Two reasons for choosing FBPase as an inhibition target are, that: (1) it does not directly involved in glycogenolysis, glycolysis, or the tricarboxylic acid cycle (Erion et al., 2005); and (2) the genetic deficiency of the compound in humans shows no severe anomaly in biochemical and clinical parameters (Matsuura et al., 2002; Seeberger and Rademacher, 2014). In regulating blood glucose levels, the inactive state of FBPase is naturally inhibited by AMP at the allosteric site, and by fructose 2,6-bisphosphate at the substrate part (Tsukada et al., 2009). This study focuses on the allosteric site, since its nature is not highly hydrophilic, unlike that of the substrate (Erion et al., 2005).
Propolis is a resinous material collected by honeybee from various plant, which has been preclinically proven for its variety of chemical constituent, exhibiting a wide range of biological activities, including antioxidant, antimicrobial, anti-inflammatory, and antidiabetic (Fuliang et al., 2005; Diva et al., 2019; Pratami et al., 2019). Propolis constituents include polyphenols, aromatic acids, terpenoids, steroids, and amino acids depending on its vegetation and geographical origin (Kumazawa et al., 2004; Miyata et al., 2020). Propolis has been shown to have antidiabetic properties in that it reduces the total cholesterol levels, decreases low and increases high-density lipoproteins, and regulates blood glucose levels (Fuliang et al., 2005). According to Miyata et al. (2020) there are several new compounds that have been obtained from South Sulawesi propolis through X-ray structure analysis (Miyata et al., 2020).
In modern drug discovery, virtual screening of constituents has become an important step in evaluating and reducing the number of compounds to be subjected to experimental testing (Seeliger and de Groot, 2010). There are two common methods of virtual screening in drug discovery: (1) molecular docking, which simulates small molecules to protein binding sites by assuming the receptor to be rigid and have a constant covalent length and angles, as well as a rotatable ligand bond (Trott and Olson, 2010); and (2) molecular dynamics, which evaluates every single atom during simulation. This technique, however, requires many processes and high-performance hardware (Suhartanto et al., 2018). In general, docking programs use a scoring function based on empirical free binding energies to measure conformation (Trott and Olson, 2010; Forli et al., 2016). Despite the fact that there is no scoring function that accurately measures binding affinity, due to its simplification and insufficient experimental data, fitness accuracy is reached by employing optimizers, such as those used in AutoDock (Trott and Olson, 2010; Seeberger and Rademacher, 2014).
This research aims to evaluate the antidiabetic activity of South Sulawesi propolis compounds from LC-MS/MS analysis and results published by Miyata et al. (2020) by inhibiting fructose 1,6-bisphosphatase at the allosteric site. Although there have been many molecular docking studies, the use of South Sulawesi propolis as a drug candidate for diabetes mellitus has not been carried out. Therefore, this study is recommended as a reference for further in vitro research.
In this study, the in silico antidiabetic activity of South Sulawesi propolis was investigated. Among 30 selected propolis compounds, only 18 showed promising docking scores compared to AMP (-6.7 kcal/mol). Meanwhile, Broussoflavonol F and Glyasperin A showed docking scores of -9 kcal/mol and -8.2 kcal/mol, respectively, indicating 100% residue similarity in its interaction compared to the two re-docked positive controls and the AMP reference. Thus, both compounds have the potential to act against T2DM by inhibiting FBPase. Furthermore, the flavonoid structure is recommended for designing FBPase inhibitors. Finally, to ensure the validity of this finding, further research should be conducted by employing in vitro studies.
We acknowledge the financial support from the Ministry of Research, Technology, and Higher Education Republic of Indonesia through the Grants Penelitian Tesis Magister (Nomor:8/E1/KP.PTNBH/2020 and Nomor:255/PKS/R/UI/2020).
Abdillah, A.A., Suwarno., 2016. Diagnosis of Diabetes using Support Vector Machines with Radial Basis Function Kernels. International Journal of Technology, Volume (7)5, pp. 849–858
Association, A.D., 2014. Diagnosis and Classification of Diabetes Mellitus. Diabetes Care, Volume 37(Supplement 1), pp. S81–S90
Control, C.F.D.P., 2020. National Diabetes Statistics Report. Atlanta, GA: Centers for Disease Control and Prevention, US Department of Health and Human Services
Daina, A., Michielin, O., Zoete, V., 2017. SwissADME: A Free Web Tool to Evaluate Pharmacokinetics, Drug-Likeness and Medicinal Chemistry Friendliness of Small Molecules. Scientific Reports, Volume 7(42717), pp. 1–13
Diva, A.N., Pratami, D.K., Wijanarko, A., Hermansyah, H., Sahlan, M., 2019. Effect of Ethanolic Propolis Extract from Tetragonula Biroi Bees on the Growth of Human Cancer Cell Lines Hela and MCF-7. In: AIP Conference Proceedings, Volume 2092(1), p. 030002
Erion, M.D., Van Poelje, P.D., Dang, Q., Kasibhatla, S.R., Potter, S.C., Reddy, M.R., Reddy, K.R., Jiang, T., Lipscomb, W.N., 2005. MB06322 (CS-917): A Potent and Selective Inhibitor of Fructose 1, 6-Bisphosphatase for Controlling Gluconeogenesis in Type 2 Diabetes. In: Proceedings of the National Academy of Sciences, Volume 102(22), pp. 7970–7975
Forli, S., Huey, R., Pique, M.E., Sanner, M.F., Goodsell, D.S., Olson, A.J., 2016. Computational Protein–Ligand Docking and Virtual Drug Screening with the Autodock Suite. Nature Protocols, Volume 11(5), pp. 905–919
Fuliang, H., Hepburn, H., Xuan, H., Chen, M., Daya, S., Radloff, S., 2005. Effects of Propolis on Blood Glucose, Blood Lipid and Free Radicals in Rats with Diabetes Mellitus. Pharmacological Research, Volume 51(2), pp. 147–152
Ghorbani, A., 2017. Mechanisms of Antidiabetic Effects of Flavonoid Rutin. Biomedicine & Pharmacotherapy, Volume 96, pp. 305–312
Harborne, S.P., Ruprecht, J.J., Kunji, E.R., 2015. Calcium-Induced Conformational Changes in the Regulatory Domain of the Human Mitochondrial ATP-Mg/Pi Carrier. Biochimica et Biophysica Acta (BBA)-Bioenergetics, Volume 1847(10), pp. 1245–1253
Kaur, R., Dahiya, L., Kumar, M., 2017. Fructose-1, 6-Bisphosphatase Inhibitors: A New Valid Approach for Management of Type 2 Diabetes Mellitus. European Journal of Medicinal Chemistry, Volume 141, pp. 473–505
Kumazawa, S., Hamasaka, T., Nakayama, T., 2004. Antioxidant Activity of Propolis of Various Geographic Origins. Food chemistry, Volume 84(3), pp. 329–339
Lalau, J.D., 2010. Lactic Acidosis Induced by Metformin: Incidence, Management and Prevention. Drug safety, Volume 33(9), pp. 727–740
Lipinski, C.A., 2004. Lead-and Drug-Like Compounds: The Rule-of-Five Revolution. Drug Discovery Today: Technologies, Volume 1(4), pp. 337–341
Lipinski, C.A., Lombardo, F., Dominy, B.W., Feeney, P.J., 1997. Experimental and Computational Approaches to Estimate Solubility and Permeability in Drug Discovery and Development Settings. Advanced Drug Delivery Reviews, Volume 23(1-3), pp. 3–25
Marcou, G., Rognan, D., 2007. Optimizing Fragment and Scaffold Docking by Use of Molecular Interaction Fingerprints. Journal of Chemical Information and Modeling, Volume 47(1), pp. 195–207
Matsuura, T., Chinen, Y., Arashiro, R., Katsuren, K., Tamura, T., Hyakuna, N., Ohta, T., 2002. Two Newly Identified Genomic Mutations in a Japanese Female Patient with Fructose-1, 6-Bisphosphatase (Fbpase) Deficiency. Molecular Genetics and Metabolism, Volume 76(3), pp. 207–210
Miyata, R., Sahlan, M., Ishikawa, Y., Hashimoto, H., Honda, S., Kumazawa, S., 2020. Propolis Components and Biological Activities from Stingless Bees Collected on South Sulawesi, Indonesia. HAYATI Journal of Biosciences, Volume 27(1), pp. 82–82
Moller, D.E., 2001. New Drug Targets For Type 2 Diabetes and The Metabolic Syndrome. Nature, Volume 414, pp. 821–827
Pasaribu, A.P., Siddiq, M.F., Fadhila, M.I., Hilman, M.H., Yanuar, A., Suhartanto, H., 2017. A Preliminary Study on Shifting from Virtual Machine to Docker Container for Insilico Drug Discovery in the Cloud. International Journal of Technology, Volume 8(4), pp. 611–621
Pratami, D.K., Mun’im, A., Yohda, M., Hermansyah, H., Gozan, M., Putri, Y.R.P., Sahlan, M., 2019. Total Phenolic Content and Antioxidant Activity of Spray-Dried Microcapsules Propolis from Tetragonula Species. In: AIP Conference Proceedings. Volume 2085(1), p. 020040
Sarian, M.N., Ahmed, Q.U., So’ad, M., Zaiton, S., Alhassan, A.M., Murugesu, S., Perumal, V., Syed Mohamad, S.N.A., Khatib, A., Latip, J., 2017. Antioxidant and Antidiabetic Effects of Flavonoids: A Structure-Activity Relationship Based Study. BioMed Research International, Volume 2017, pp. 1–14
Seeberger, P.H., Rademacher, C., 2014. Carbohydrates as Drugs. Springer International Publishing
Seeliger, D., De Groot, B.L., 2010. Ligand Docking and Binding Site Analysis with PyMOL and Autodock/Vina. Journal of Computer-Aided Molecular Design, Volume 24(5), pp. 417–422
Singh, S., Loke, Y.K., Furberg, C.D., 2007. Thiazolidinediones and Heart Failure: A Teleo-Analysis. Diabetes Care, Volume 30(8), pp. 2148–2153
Suhartanto, H., Yanuar, A., Wibisono, A., Hermawan, D., Bustamam, A., 2018. 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. International Journal of Technology, Volume 9(1), pp. 150–158
Trott, O., Olson, A.J., 2010. AutoDock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization, and Multithreading. Journal of Computational Chemistry, Volume 31(2), pp. 455–461
Tsukada, T., Takahashi, M., Takemoto, T., Kanno, O., Yamane, T., Kawamura, S., Nishi, T., 2009. Synthesis, SAR, and X-ray Structure of Tricyclic Compounds as Potent FBPase Inhibitors. Bioorganic & Medicinal Chemistry Letters, Volume 19(20), pp. 5909–5912
Tsukada, T., Takahashi, M., Takemoto, T., Kanno, O., Yamane, T., Kawamura, S., Nishi, T., 2010. Structure-based Drug Design of Tricyclic 8H-indeno [1, 2-d][1, 3] Thiazoles as Potent FBPase Inhibitors. Bioorganic & Medicinal Chemistry Letters, Volume 20(3), pp. 1004–1007
Vinayagam, R., Xu, B., 2015. Antidiabetic Properties of Dietary Flavonoids: A Cellular Mechanism Review. Nutrition & Metabolism, Volume 12(1), pp. 1–60
Warren, G.L., Do, T.D., Kelley, B.P., Nicholls, A., Warren, S.D., 2012. Essential Considerations for using Protein–Ligand Structures in Drug Discovery. Drug Discovery Today, Volume 17(23-24), pp. 1270–1281
Wishart, D.S., Knox, C., Guo, A.C., Shrivastava, S., Hassanali, M., Stothard, P., Chang, Z., Woolsey, J., 2006. DrugBank: A Comprehensive Resource for in Silico Drug Discovery and Exploration. Nucleic Acids Research, Volume 34, pp. D668-72