An in silico study on antidiabetic activity of bioactive compounds in Euphorbia thymifolia Linn.
© The Author(s) 2016
Received: 26 January 2016
Accepted: 20 June 2016
Published: 18 August 2016
Euphorbia thymifolia is a small prostrate herbaceous annual weed which is abundant in waste places and open grasslands and distributes in most Asian countries. This medicinal plant has been studied for many bioactivities and therapeutic effects such as anti-microbial effect (Killedar et al. 2011), bronchial asthma (Sharma and Tripathi 1984) and the anti-hyperglycemic effect of Euphorbiaceae family has been fully reviewed (Bnouham et al. 2006). Besides that, E. thymifolia has also been traditionally used for treatment of gastrointestinal disorder, inflammatory and respiratory diseases (Loi 2015).
Diabetes mellitus (DM) and its complications are main causes of deaths in most countries. Type 2 DM has also been known as another terms “Non-insulin dependent diabetes mellitus (NIDDM)” which accounted for more than 90 % of diagnosed cases of DM in adults (International Diabetes Federation (IDF) 2015). In accordance with Ford et al. (2002), the statistics of patients suffering Type 2 DM and metabolic syndromes were estimated about 50 million in the US and 314 million around the world and this number was predicted to increase dramatically in the next decades. The feature of Type 2 DM is the partial or complete failure in using insulin (insulin resistance) even though the functional insulin is available and then causes hyperglycemia. To overcome this resistance, the pancreatic β cells produce extra mount of insulin to maintain glucose in the normal range. However, this process is only effective in the short term as burnout β cell occurs. At this time, the patients have suffered Type 2 DM.
Many efforts to figure out the effective treatments for Type 2 DM have been increased. For many years, scientists have endeavored to apply not only pharmacological methods but also non-pharmacological approaches but none of them met all safety requirements in medication. Losing weight and doing exercise have been highly recommended as two major non-drug therapies to increase insulin sensitivity. In aspect of pharmaceutical science, although metformin and thiazolidinedione both have good effect in insulin resistance, they cannot be widely used because of their undesirable side effects. Currently, research on relationships between antioxidant compounds and Type 2 DM has been well published and documented. People revealed that an intake of antioxidant in diet has contributed to reduce the development of Type 2 DM (Montonen, et al. 2004; Evans 2007). Besides, in 2005, Fraga investigated that the intake of dark chocolate which was a rich source of flavonols could decrease blood pressure and improved insulin sensitivity in healthy persons (Fraga 2005).
In the light of these evidences, the objective of this research is to test the anti-hyperglycemic activity of antioxidant compounds in the ethanolic extracts of E. thymifolia by using them as ligands for four targeted proteins to determine which compound is effective binder. The chemical composition analyzed by GC–MS from areal part of E. thymifolia suggested three main families: tannin, flavonoid and terpenoid (Sandeep et al. 2009; Prasad and Bisht 2011) which are strong anti-oxidant compounds. Possessing polyphenol structure involving high number of hydroxyl group inside, tannin and flavonoid were, thus, predicted to be able to form hydrogen bonds with various reactive oxygen species, such as singlet oxygen, peroxynitrite and hydrogen peroxide which are major causes of cell damages. Due to this mechanism, tannin and flavonoid were considered to play potential roles in reducing the oxidative stress related to Type 2 DM (Evans 2007; Maiese et al. 2007). Terpenoid is an enormous class of organic compound in plant whose potential antioxidant activity has already studied (Gonzalez-Burgos and Gomez-Serranillos 2012). However, there are no research indicating their affinity for Type 2 DM. Four targeted proteins used in this study was previously investigated to serve as potential drug target for Type 2 DM (Nguyen and Le 2012; Shi 2009; Vogel 2002). 11β-HSD1 (11β-hydroxysteroid dehydrogenase type I) or “cortisone reductase” is an NADPH-dependent enzyme highly expressed in main metabolic tissues including liver, adipose tissue, and the central nervous system. In these tissues, HSD11B1 reduces cortisone to the active hormone cortisol that activates glucocorticoid receptors. 11βHSD1 inhibition is a tempting target for the treatment of glucortinoid-associated diseases, especially of Type 2 DM (Davani, et al. 2004; Andrews and Walker 1999). Glutamine-fructose-6-phosphate amidotransferase (GFAT or GFPT) is the first and rate-limiting enzyme of the hexosamine pathway. GFAT controls the flux of glucose into the hexosamine pathway and catalyzes the formation of glucosamine 6-phosphate. The majority of glucose will enter the glycolysis pathway, with a small percentage entering the hexosamine pathway. GFPT or GFAT regulate the hexosamine pathway products. Therefore, this enzyme involved in a therapeutic target against Type 2 DM (Chou 2004). Protein-tyrosine phosphatase 1B (PTP1B) is a negative regulator of the insulin signaling pathway and is considered a promising potential therapeutic target, in particular for treatment of Type 2 DM. It has also been implicated in the development of breast cancer and has been explored as a potential therapeutic target in that avenue as well. Sirtuin-6 or Mono-ADP ribosyltransferase-sirtuin-6 (SIRT6) is a stress responsive protein deacetylase and mono-ADP ribosyltransferase enzyme encoded by the SIRT6 gene. SIRT6 functions in multiple molecular pathways related to aging, including DNA repair, telomere maintenance, glycolysis and inflammation. Promisingly, the absence of enzyme SIRT6 may lead to dramatically induced of blood sugar level (Hasan et al. 2002). The objective of this study was to display a range of bioactive compounds from all three families and determine if and how they interact with proteins that is important to Type 2 DM (Muthumani et al. 2016, Prasad and Bisht 2011, PROTA 2008) (Table 2).
3D structure of 11-β HSD1, GFAT, PTP1B, SIRT6 were taken from Protein Data Bank as following 11β-HSD1 (PDB code 1XU7), GFAT (PDB code 2ZJ3), PTP1B (PDB code 4Y14) and SIRT6 (PDB code 3K35). To verify the capacity of the model in reproducing experimental observation with new ligand, all these structures were tested again at the binding site. Following this way, 11β-HSD1 (PDB code 1XU7) was tested again with molecule: NADPH dihydro-nicotinamide-adenine-dinucleotide phosphate (NDP), GFAT (PDB code 2ZJ4) was tested with 2-deoxy-2-amino glucitol-6-phosphate (AGP), SIRT6 (PDB code 3K35) with adenosine-5-diphosphoribose (APR) and PTP1B (PDB code 4Y14) with 3-bromo-4-[difluoro(phosphono)methyl]-N-methyl-Nalpha-(methylsulfonyl)-l-phenylalaninamide. This work was done by Autodock Vina (Trott and Olson 2009) and VMD was used for visualization (Humphrey et al. 1996).
Bioactive compound preparation
Autodock Vina (Trott and Olson 2009) was employed for binding affinity measurement. The content of configure file was determined as position of receptor file, ligand file, data of Grid-box’s three coordinates X, Y, Z were 18.125, −27.72, −0.34 respectively in case of 11β-HSD1, 8.82, 5.31, −7.903 for GFAT, −11.21, −22.77, −6.75 in PTP1B, 14.5, −18.02 and 17.04 in SIRT6, the size of Grid box which was set up in 30 × 30 × 30 points, number of modes which were 10 and the energy range which was set up at 9 kcal/mol. Docking process in AutoDock Vina has been performed with 1000 of exhaustiveness for enhancing accuracy.
This part of process was carried out by using the pharmacophore tool included in LigandScout (Wolber and Langer 2005). The program showed us the 2D and 3D structure with the position and interaction of ligand in the binding pocket of the receptor. From these 2D figures, some types of bond were identified by color and symbol. Four features namely hydrogen bond acceptor (HBA), hydrogen bond donor (HBD), negative ionizable area (NIA), hydrophobic interaction were labeled as red arrow, green arrow, red star and orange bubble (supporting information) respectively.
Results and discussion
Free energy binding of bioactive compound to targeted proteins
In addition, Fig. 1 also indicated the best receptor for these bioactive compounds in E. thymifolia. Following this chart, the line for 11β-HSD1 (1XU7) stayed at the upper level, followed by GFAT1 and SIRT6 at middle, and then the line of protein PTP1B (4Y14) located at bottom of chart. This proves that the 11β-HSD1 was the best receptor for binding of tannin and flavonoid family. In term of terpenoid family, 12 compounds have 3D structure on NCBI website, and their absolute value of binding energy was illustrated in Fig. 1. The good binding energy (>|−8| kcal/mol) belonged to line of 11β-HSD1 (1XU7). This line has half of result which was larger 10 kcal/mol in term of absolute value. For this reason, the 11β-HSD1 line located at top of chart. Followed by SIRT6 protein line which had 6 molecules in range of 9 and 11.5 kcal/mol, the next position is GFAT1 line and then in the bottom of chart, the PTP1B owned 10 compounds which had low results (<|−8| kcal/mol). Terpenoid family had a highest in number of ligands in this study, but there were only two compounds β-amyrine and taraxerol were chosen for pharmacophore analysis step. Half of them, 6 compounds were rejected because of low result. Those were 2-(4-methyl-3-cyclohexene-1-yl)-2-propanol, limonene, phytol, piperiterone, safranal, caryophyllene oxide. Their absolute value of binding energy to all four proteins ranged from 4.7 to 6.5 kcal/mol. They all shared a simple structure with only one ring and few hydroxyl groups outside which may explain their low binding affinity. Thus, these molecules appear to have a low capacity to form a complex with the four target proteins.
Binding energy (kcal/mol) of bio-molecules in E. thymifolia to 11β-HSD1, PTP1B, GFAT and SIRT6
Binding energy (kcal/mol)
24 methylen cycloartenol
11β-HSD1 and GFAT1
Along with hydrogen bond, hydrophobic interactions were also displayed. Β-amyrine and taraxerol seemed to be rich on hydrophobic contact at position of the methyl group which was non-polar [Fig. 2(6, 7)]. These two compounds were also in contact with this receptor because of the presence of the benzene ring. The residue Thr 124, Thr 220 and Thr 222 were three residues which could form not only hydrophobic interaction with terpenoid family but also hydrogen bond with 1-O-galloyl-β-d-glucose, quercetin-3-galactoside, quercitrin, members of tannin, and flavonoid group. Furthermore, in Fig. 2(2), the residues Thr 220, Thr 222, Ala 223, Ile 121, Leu 217 were frequently observed in ligand-receptor interactions between, so they could be a critical part in binding pocket. One important thing that Ser 261 and Arg 269 was shown as largely hydrophobic residues in previous study involving crystal structure analysis (Hult et al. 2006) but in the figures from our study, these hydrophobic interactions were not present.
SIRT6 and PTP1B
In addition, the hydrophobic interactions also played an important role in docking result. The good illustration was the difference in one methyl group at carbon number 6 of rhamnoside ring (IUPAC name) of quercitrin compared to quercetin-3-galactoside structure [Fig. 4(2, 3)]. This conduct to 9.3 kcal/mol binding affinity of quercitrin compared to 8.8 kcal/mol of quercetin-3-galactoside. For this reason, this kind of bond between five of seven ligands and SIRT6 was also considerable point; these compounds form hydrophobic interaction with Ile 217, Trp186, Phe 62 at two hydrophore groups: benzene ring in flavonoid family and methyl group in terpenoid family [Fig. 4(1, 3, 6, 7)].
In summary, from the list of 20 compounds, seven compounds were chosen due to high absolute value of binding energy to all four receptors (>8 kcal/mol). They are β-amyrine, taraxerol, 1-O-galloyl-β-d-glucose, corilagin, cosmosiin, quercetin-3-galactoside and quercitrin. Polyphenol, the frame of tannin and flavonoid family had high binding affinity to all four receptors. Besides that, the binding affinity of two of the terpenoid compounds also suggested that this family is also a good prospect for the treatment of Type 2 DM.
Although the basic concepts of interaction between 20 ligands of E. thymifolia and 4 receptors had been already defined, many questions still remained unclear for relationship between docking result in autodock step and number of bonds in 2D structure of pharmacophore analysis step. Therefore, further research is required using, the molecular dynamic (MD) and hydrogen bond analysis to clearly determined the stability of the hydrogen bonds and hydrophobic interactions between ligands and receptors.
THNV, NT and DN have been responsible for the all technical matters, scientific issues/values and the manuscript preparation. LL has been responsible for data analysis, reading and approving the final manuscript. All authors read and approved the final manuscript.
This project is funded by Vietnam National University at Ho Chi Minh City under grant number C2016-28-01. The authors would like to appreciate passionate support from Computational Biology Center at IU and Institute of Computational Science and Technology for supporting us to complete this project.
The authors declare that they have no competing interest.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- Andrews RC, Walker BR (1999) Glucocorticoids and insulin resistance: old hormones, new targets. Clin Sci 96(5):513–523View ArticleGoogle Scholar
- Bnouham M, Ziyyat A, Mekhfi H, Tahri A, Legssyer A (2006) Medicinal plants with potential antidiabetic activity—a review of ten years of herbal medicine research (1990–2000). Int J Diabetes Metab 14(1):1–25Google Scholar
- Chou K-C (2004) Molecular therapeutic target for type-2 diabetes. J Proteome Res 3(6):1284–1288View ArticleGoogle Scholar
- Davani B et al (2004) Aged transgenic mice with increased glucocorticoid sensitivity in pancreatic β-cells develop diabetes. Diabetes (American Diabetes Association) 53(suppl 1):S51–S59Google Scholar
- Dennington R, Keith T, Millam J (2009) GaussView, version 5. Prod. Shawnee Mission Semichem Inc., ShawneeGoogle Scholar
- Evans JL (2007) Antioxidants: do they have a role in the treatment of insulin resistance? Indian J Med Res 125(3):355Google Scholar
- Ford ES, Giles WH, Dietz WH (2002) Prevalence of the metabolic syndrome among US adults: findings from the third National Health and Nutrition Examination Survey. JAMA 287(3):356–359View ArticleGoogle Scholar
- Fraga CG (2005) Cocoa, diabetes, and hypertension: should we eat more chocolate? Am J Clin Nutr 81(3):541–542Google Scholar
- Gonzalez-Burgos E, Gomez-Serranillos MP (2012) Terpene compounds in nature: a review of their potential antioxidant activity. Curr Med Chem 19(31):5319–5341View ArticleGoogle Scholar
- Hasan S et al (2002) Acetylation regulates the DNA end-trimming activity of DNA polymerase β. Mol Cell 10(5):1213–1222View ArticleGoogle Scholar
- Hosfield DJ et al (2005) Conformational flexibility in crystal structures of human 11β-hydroxysteroid dehydrogenase type I provide insights into glucocorticoid interconversion and enzyme regulation. J Biol Chem 280(6):4639–4648View ArticleGoogle Scholar
- Hult M et al (2006) Active site variability of type 1 11β-hydroxysteroid dehydrogenase revealed by selective inhibitors and cross-species comparisons. Mol Cell Endocrinol 248(1–2):26–33View ArticleGoogle Scholar
- Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14(1):33–38View ArticleGoogle Scholar
- International Diabetes Federation (IDF) (2015) What is Diabetes. In: Cavan D, da Rocha Fernandes J, Makaroff L, Ogurtsova K, Webber S (eds) IDF diabetes atlas, 7th edn. International Diabetes Federation, BrusselsGoogle Scholar
- Killedar SG, Desai RG, Kashid UT, Bhore NV, Mahamuni SS (2011) Antimicrobial activity and phytochemical screening of fresh latex of Euphorbia thymifolia Linn. Int J Res Ayurveda Pharm 2(5)Google Scholar
- Loi DT (2015) Cac cay thuoc va vi thuoc chua ly, Part B. Chua ly truc trung. In: Loi DT (ed) Cay Thuoc va Vi Thuoc Vietnam (Vietnamese medicinal plants and herbal formulations), chap IV, Part B. Hong Duc, Ha Noi, pp 199–200Google Scholar
- Maiese K, Daniela Morhan S, Zhong Chong Z (2007) Oxidative stress biology and cell injury during type 1 and type 2 diabetes mellitus. Curr Neurovasc Res 4(1):63–71View ArticleGoogle Scholar
- Montonen J, Knekt P, Järvinen R, Reunanen A (2004) Dietary antioxidant intake and risk of type 2 diabetes. Diabetes Care (American Diabetes Association) 27(2):362–366View ArticleGoogle Scholar
- Muthumani D, Hedina A, Kausar J, Anand V, Pushpa (2016) Phytopharmacological activities of Euphorbia thymifolia Linn. Syst Rev Pharmacy 7(1):30–34View ArticleGoogle Scholar
- Nguyen NDT, Le LT (2012) Targeted proteins for diabetes drug design. Adv Nat Sci Nanosci Nanotechnol 3:013001View ArticleGoogle Scholar
- Pan PW, Feldman JL, Devries MK, Dong A, Edwards AM, Denu JM (2011) Structure and biochemical functions of SIRT6. J Biol Chem 286(16):14575–14587View ArticleGoogle Scholar
- Prasad K, Bisht G (2011) Evaluation of nutritive minerals and antioxidants values of Euphorbia thymifolia Linn. Curr Res Chem 3:98–105View ArticleGoogle Scholar
- PROTA (2008) Plant resources of tropical Africa. In: Schmelzer GH, Gurib-Fakim A (eds) Medicinal plants, vol 11(1). PROTA Foundation - Backhuys - CTA, Wageningen, pp 294–296Google Scholar
- Sandeep K, Rahul A, Vishvesh A, Chandrakant M (2009) Laxative and anti-helmintic activity of aqueous extract of Euphorbia thymifolia Linn. Res J Pharmacogn Phytochem 1(3):182–184Google Scholar
- Sharma GD, Tripathi SN (1984) Experimental evaluation of Dugdhika (Euphorbia prostrata W. Ait) for the treatment of ‘Tamaka Svasa’ (bronchial asthma). Anc Sci Life 3(3):143Google Scholar
- Shi Yigong (2009) Serine/threonine phosphatases: mechanism through structure. Cell 139(3):468–484View ArticleGoogle Scholar
- Trott O, Olson AJ (2009) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455–461Google Scholar
- Vogel GH (ed) (2002) Drug discovery and evaluation: pharmacological assays. Springer, Berlin, HeidelbergGoogle Scholar
- Wolber G, Langer T (2005) LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J Chem Inf Model 45(1):160–169View ArticleGoogle Scholar