Int J App Pharm, Vol 13, Issue 5, 2021, 280-286Original Article
BIOCOMPUTATIONAL AND PHARMACOLOGICAL ANALYSIS OF PHYTOCHEMICALS FROM ZINGIBER OFFICINALE (GINGER), ALLIUM SATIVUM (GARLIC), AND MURRAYAKOENIGII (CURRY LEAF) IN CONTRAST TO TYPE 2-DIABETES
RATUL BHOWMIK1, SHUBHAM ROY1, SOUNOK SENGUPTA2, SAMEER SHARMA3*
1Department of Pharmaceutical Chemistry, SPER, Jamia Hamdard, New Delhi, India, 2Department of Pharmacology, NSHM Knowledge Campus, Kolkata-Group of Institutions, Kolkata, West Bengal, India, 3Department of Bioinformatics, BioNome Private Limited, Bengaluru, India
Email: sameer21.97@gmail.com
Received: 03 Jun 2021, Revised and Accepted: 03 Jul 2021
ABSTRACT
Objective: This study was aimed to analyze the inhibitory effect of the flavonoid class of phytochemicals present in ginger (Zingiber Officinale), garlic (Allium sativum), and curry leaf (Murrayakoenigii) against some receptors of type-2 diabetes such as human aldose reductase receptor, mitogen synthase kinase receptor, as well as dipeptidyl peptidase receptor by implementing several in silico analysis techniques.
Methods: The 3D structures of the flavonoid class of phytochemicals of all the three plants were retrieved from the PubChem database in 3D SDF format and were converted to PDB format using PyMol software. These phytochemicals were subjected to in silico tools such as SwissADME, Pre-ADMET, and iMODS web server. The PDB-IDs of the targeted receptors human aldose reductase, dipeptidyl peptidase-IV, and mitogen synthase kinase were retrieved from Protein Data Bank in PDB format. All these receptors were then prepared for docking procedure using Autodock Tools. Now, both the prepared proteins and ligands were subjected to docking analysis using Pyrex (AutodockVina).
Results: Naringenin and kaempferol showed excellent docking results with the aldose reductase receptor. On the other hand, rutin showed the best docking score with dipeptidyl peptidase receptor-IV, whereas, epigallocatechin showed the best docking results with mitogen synthase kinase receptor. The ADME analysis showed that resveratrol had the best gastrointestinal absorption as well as high blood-brain barrier permeability.
Conclusion: Overall, the molecular docking results when analyzed showed a good binding affinity with the targeted receptors of diabetes. The ADME analysis and molecular docking results of the phytochemicals concluded that these compounds can be used as a potential cure for treating diabetes.
Keywords: Diabetes, Phytochemicals, ADME, Molecular docking
© 2021 The Authors. Published by Innovare Academic Sciences Pvt Ltd. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/)
DOI: https://dx.doi.org/10.22159/ijap.2021v13i5.42294. Journal homepage: https://innovareacademics.in/journals/index.php/ijap
INTRODUCTION
Ginger, scientific name Zingiberofficinale, family Zingiberaceae, is a flowering plant. Its rhizome is commonly consumed as folk medicine or as a spice in food. Based on scientific evidence ginger can be used as an Antiplatelet, antimicrobial, anticancer agent [1]. The ginger extract can be used to prevent autoxidation of fat at an earlier stage i.e. as an antioxidant agent [2]. Garlic, scientific name Allium sativum, Family Amaryllidaceae is a bulbous type flowering plant. It is commonly used as a flavoring agent in food as well as in traditional medicine. Meta-analysis Animal studies and epidemiological studies have proved that garlic consumption can reduce the expression of cancer, for example, stomach, colon, esophagus, cervix, skin, lung, breast, and uterine cancers. Clinical data shows that along with cancer garlic is also effective as an antifungal, antimicrobial, antioxidant, antidiabetic, and antihypertensive drug [3, 4]. The curry tree, scientific name Murrayakoenigii, family Rutaceae is a tropical and sub-tropical plant. Due to its characteristic aroma and chemical constituents, it is used as a flavoring agent in Indian dishes. The essential oil present in Murrayakoenigii leaves shows mosquitocidal-Antibacterial activity, Antifungal activity, and Antiprotozoal activity [5]. This study is mainly based on the phytochemical screening of the three above-mentioned plants. Flavonoids are a group of phytochemicals with different types of phenolic structures. Their antioxidant effects of flavonoids can cure various diseases like Alzheimer’s disease (AD), cancer, atherosclerosis, etc. A significant amount of flavonoid intake can maintain coronary heart disease [6]. Chemically flavonoids contain A, B, and C ring systems. B ring when linked in position 3 of the ring C are called isoflavones similarly when B ring is linked in position 4 are called neoflavonoids. When the B ring is linked in position 2 of the C ring, it can be further subdivided into several subgroups like flavones, flavonols, flavanones, flavanonols, flavanols, or catechins and anthocyanins [7]. In this study, we have mainly selected the important flavonoid phytochemicals of the three plants i.e. Ginger, Garlic, and Curry leaves to carry out a comparative molecular docking analysis against some important receptors aldose reductase, dipeptidyl peptidase-IV, and mitogen synthase kinase. Moreover, this study also included pharmacological analysis of our selected phytochemicals to predict and analyze drug-likeness properties of the phytochemicals.
MATERIALS AND METHODS
Ligand preparation
All the important flavonoid phytochemicals of the plants Murrayakoenigii, Allium sativum, and Zingiberofficinale were retrieved from the PubChem database in the form of 3D Standard Data Format (3D SDF) [8, 9]. PyMol was then used for converting the ligand from 3D SDF to Protein Data Bank (PDB) format [10].
Protein preparation
The targeted receptor molecules were selected and downloaded in PDB format from the protein data bank database [11]. The protein molecules were then loaded in Autodock Tools software [12]. Firstly, the extraction of the co-crystallized ligand was done to validate the protein. Immediately, after this, protein preparation of the protein was started by removing water molecules, removing chains or heteroatoms not required, repairing missing atoms, the addition of hydrogen atoms, computing charges (Kollman charges) and finally converting it into pdbqt format. Finally, generation of the grid box was done keeping the co-crystallized ligand at the center. The dimension of the grid box was saved for docking using AutodockVina as a config. txt file. The co-crystallized was then removed from the prepared protein pdbqt file.
ADMET and drug-likeness analysis
SwissADME and Pre-ADMET web servers were used to predict drug-likeness and ADMET properties of our selected phytochemicals [13]. Lipinski’s rule was used to virtually screen the best hit compounds from our selected list of phytochemicals. According to Lipinski’s rule of five, a compound, to qualify as a ligand, should have less than 500 Da molecular weight, high lipophilicity i.e. value of Log P less than five, hydrogen bond donors less than 5, and hydrogen bond acceptors less than 10. Compounds violating any two rules of Lipinski’s were eliminated for further screening. Other than Lipinski’s rule, physicochemical analysis, as well as Drug-likeliness properties of all the ligand molecules, were also taken into consideration for the drug screening process.
Boiled-egg
For predicting blood-brain barrier permeability as well as gastrointestinal absorption of our selected phytochemicals, BOILED EGG was used. According to BOILED-Egg plot analysis, compounds found in the yellow region were considered to be having higher blood-brain barrier permeability, whereas compounds found in the white region of the plot were considered to be having higher gastrointestinal absorption properties. The BOILED-Egg plot analysis was performed using the SwissADME webserver.
Molecular docking analysis
The molecular docking analysis was mainly performed to predict the interaction as well as inhibitory activity of our selected phytochemicals against our targeted protein receptors. The docking study was carried out using PyRx (AutoDockVina) [14, 15]. The prepared ligands were docked with the prepared protein receptors. The results of docking were displayed in the terms of binding affinity. The binding affinity represents the binding energy. The binding energy exhibits the extent of binding of a particular compound. Furthermore, the best type of conformation would be the one that would bind with its target. The molecule with the best binding affinity along with good ADMET properties was chosen as the best hit compounds. The structural analysis of the compounds was done by using Discovery Studio Visualizer 2021 [16].
Molecular dynamic simulation
It is a computer-based simulation technique for the analysis of atoms or molecules' physical movements. A few crucial hydrogen bond interactions can be identified using MD simulations. MD simulations aid in advancements in protein docking or virtual screening. In this study, the iMODS server was used for the molecular dynamic simulation. the iMODS server helps in exploring the normal mode analysis and produces accessible information of pathways which may be with macromolecules or between homologous structures.
RESULTS AND DISCUSSION
Ligands
Naringenin, catechin, epigallocatechin, epicatechin, resveratrol, quercetin, apigenin, quercetrin, rutin, kaempferol, morin, and myricetin were the flavonoid phytochemical chosen for the study. The structures of these phytochemicals were retrieved from the PubChem database in the form of 3D SDF format.
Proteins
Glycogen Synthase Kinase, Dipeptidyl Peptidase-IV, and Aldose Reductase were the receptors chosen for the study. The PDB-Ids of the receptors Glycogen Synthase Kinase (PDB-Id: 3F7Z), Dipeptidyl Peptidase-IV (PDB-Id: 3F8S), an Aldose Reductase (1US0) were all retrieved from protein data bank (PDB) database in PDB format. These proteins were then prepared for docking using Autodock Tools as mentioned in the protein preparation process above.
ADMET and drug-likeliness analysis
Lipinski's rule of five analysis was carried out to predict the rigidity of the compounds for structure-based drug design. The physicochemical analysis, as well as the drug-likeness analysis of all our selected phytochemicals, are listed below.
Table 1: Physiochemical analysis
| Ligand | Molecular formula | Molecular weight (g/mol) | Monoisotropic mass (g/mol) | Heavy atom count | Tropological polar surface area |
| Naringenin | C15H12O5 | 272.25 | 272.068473 | 20 | 86.99 |
| Catechin | C15H16O7 | 308.28 | 308.0896029 | 22 | 111 |
| Epigallocatechin | C15H14O7 | 306.27 | 306.0739528 | 22 | 131 |
| Epicatechin | C15H14O6 | 290.27 | 290.079038 | 12 | 110.38 |
| Resveratrol | C14H12O3 | 228.24 | 228.0786442 | 17 | 60.7 |
| Quercetin | C15H10O7 | 302.04 | 302.042653 | 16 | 131.36 |
| Apigenin | C15H10O5 | 270.24 | 270.052823 | 20 | 90.9 |
| Quercetrin | C21H20O11 | 448.4 | 448.1005615 | 32 | 186 |
| Rutin | C27H30O16 | 610.5 | 610.1533849 | 43 | 266 |
| Kaempferol | C15H10O6 | 286.24 | 286.047738 | 21 | 111.13 |
| Morin | C15H10O7 | 302.23 | 302.0426527 | 22 | 127 |
| Myricetin | C15H10O8 | 318.23 | 318.0375673 | 23 | 148 |
Table 2: Lipinski’s analysis
| Ligand | Molecular formula | H-Bond donar | H-Bond acceptor | CLogP | Molar refractivity |
| Narigenin | C15H12O5 | 3 | 5 | 2.16 | 71.57 |
| Catechin | C15H16O7 | 6 | 7 | 1.51 | 77.38 |
| Epigallocatechin | C15H14O7 | 6 | 7 | 1.16 | 76.36 |
| Epicatechin | C15H14O6 | 5 | 6 | 1.51 | 74.33 |
| Resveratrol | C14H12O3 | 3 | 3 | 2.83 | 67.88 |
| Quercetin | C15H10O7 | 5 | 7 | 1.49 | 78.03 |
| Apigenin | C15H10O5 | 3 | 5 | 2.34 | 73.99 |
| Quercetrin | C21H20O11 | 7 | 11 | 0.58 | 109 |
| Rutin | C27H30O16 | 10 | 16 | -1.26 | 141.38 |
| Kaempferol | C15H10O6 | 4 | 6 | 1.84 | 76.01 |
| Morin | C15H10O7 | 5 | 7 | 1.49 | 78.03 |
| Myricetin | C15H10O8 | 6 | 8 | 1.14 | 80.06 |
BOILED-egg analysis
The BOILED-Egg analysis showed that resveratrol was the only compound showing both high blood barrier permeability property as well as good gastrointestinal absorption properties. The other compounds showing high gastrointestinal absorption other than resveratrol were apigenin, naringenin, kaempferol, morin, quercetin, epicatechin, epigallocatechin, catechin. The least gastrointestinal absorption ability was shown by morin, quercetrin, and rutin.
Molecular docking analysis
Most of the phytochemicals showed good docking results for our three targeted receptors human aldose reductase, glycogen synthase kinase, and dipeptidyl peptidase-IV. For the aldose reductase receptor, the highest dock scores of about-10 and-9.9 with naringenin and kaempferol respectively. For the dipeptidyl peptidase-IV receptor, the highest dock score of about-9.7 was observed with rutin. Lastly, with mitogen synthase kinase receptor, the highest dock score of about-9.0 was observed with epigallocatechin. Thus, the compounds that showed the best dock score indicate good binding affinity with their respective receptor.
Table 3: Drug-likeness analysis
| Ligands | Blood-brain barrier | GI absorption | Permeability glycoprotein substrate | LogS (scale insoluble<-10<poorly<-6<moderately<-4<soluble<-2very<0<highly) [Water solubility] |
| Naringenin | No | High | Yes | -3.49 |
| Catechin | No | High | No | -2.02 |
| Epigallocatechin | No | High | No | -2.08 |
| Epicatechin | No | High | Yes | -2.22 |
| Resveratrol | Yes | High | No | -3.62 |
| Quercetin | No | High | No | -3.16 |
| Apigenin | No | High | No | -3.94 |
| Quercetrin | No | Low | No | -3.33 |
| Rutin | No | Low | Yes | -3.3 |
| Kaempferol | No | High | No | -3.31 |
| Morin | No | High | No | -3.16 |
| Myricetin | No | Low | No | -3.01 |
Table 4: Molecular docking results with aldose reductase receptor
| Ligand | PDB-ID | dock score |
| Narigenin | 1US0 | -10 |
| Catechin | 1US0 | -9.2 |
| Epigallocatechin | 1US0 | -9.6 |
| Epicatechin | 1US0 | -9.4 |
| Resveratrol | 1US0 | -8.8 |
| Quercetin | 1US0 | -9.7 |
| Apigenin | 1US0 | -9.7 |
| Quercetrin | 1US0 | -8.3 |
| Rutin | 1US0 | -10 |
| Kaempferol | 1US0 | -9.9 |
| Morin | 1US0 | -9.7 |
| Myricetin | 1US0 | -8.7 |
Fig. 1: The left-sided diagram shows the 2D amino acid interactions of Naringenin with human aldose reductase receptors. The right-sided diagram shows the binding analysis of Naringenin (light blue) at the active site of the co-crystallized/native ligand (deep green) of the receptor human aldose reductase. In the right-sided diagram, protein is represented in light violet color, whereas, the amino acid residues are represented in deep blue color
Table 5: Molecular docking results with mitogen synthase kinase receptor
| Ligand | Pdbid | Dock score |
| Narigenin | 3F7Z | -8.2 |
| Catechin | 3F7Z | -7.7 |
| Epigallocatechin | 3F7Z | -9 |
| Epicatechin | 3F7Z | -7.8 |
| Resveratrol | 3F7Z | -7.3 |
| Quercetin | 3F7Z | -8 |
| Apigenin | 3F7Z | -8 |
| Quercetrin | 3F7Z | -8.7 |
| Rutin | 3F7Z | -8.1 |
| Kaempferol | 3F7Z | -7.5 |
| Morin | 3F7Z | -7.8 |
| Myricetin | 3F7Z | -8 |
Fig. 2: The left-sided diagram shows the 2D amino acid interactions of epigallocatechin with mitogen synthase kinase receptor. The right-sided diagram shows the binding analysis of Epigallocatechin (light blue) at the active site of the co-crystallized/native ligand (deep green) of the receptor mitogen synthase kinase. In the right-sided diagram, protein is represented in light violet color, whereas, the amino acid residues are represented in deep blue color
Table 6: Molecular docking results with dipeptidyl peptidase-IV receptor
| Ligand | Pdbid | Dock score |
| Narigenin | 3F8S | -7.1 |
| Catechin | 3F8S | -7.7 |
| Epigallocatechin | 3F8S | -8.1 |
| Epicatechin | 3F8S | -7.5 |
| Resveratrol | 3F8S | -6.9 |
| Quercetin | 3F8S | -7.8 |
| Apigenin | 3F8S | -7.8 |
| Quercetrin | 3F8S | -8.8 |
| Rutin | 3F8S | -9.7 |
| Kaempferol | 3F8S | -7.7 |
| Morin | 3F8S | -7.7 |
| Myricetin | 3F8S | -8 |
Fig. 3: The left-sided diagram shows the 2D amino acid interactions of Rutin with the dipeptidyl peptidase-IV receptor. The right-sided diagram shows the binding analysis of Rutin (light blue) at the active site of the co-crystallized/native ligand (deep green) of the receptor dipeptidyl peptidase-IV. In the right-sided diagram, protein is represented in light violet color, whereas, the amino acid residues are represented in deep blue color
Fig. 4: B-factor or mobility (The main-chain deformability is a measure of the capability of a given molecule to deform at each of its residues)
Fig. 5: Eigenvalues (The eigenvalue associated to each normal mode represents the motion stiffness. Its value is directly related to the energy required to deform the structure. The lower the eigenvalue, the easier the deformation)
Fig. 6: Variance (individual (red) and cumulative (green) variances)
Fig. 7: Covariance map (correlated (red), uncorrelated (white) or anti-correlated (blue) motions of coupled residues)
Fig. 8: Elastic network (Each dot denotes one spring within the respective atoms pair. The dots are colored based on the stiffness where the dark grey dots indicate the stiffer springs and vice versa)
DISCUSSION
In this study, we have targeted three receptors. Glycogen Synthase Kinase (PDB-ID: 3F7Z); DPP4 (PDB-ID: 3F8S) and Aldose Reductase (PDB-ID: 1US0). Glycogen Synthase kinase or (GSK)-3 is a serine/threonine kinase that provides negative feedback in the hormonal regulation of glucose homeostasis, by phosphorylating and thus inhibits glycogen synthase (GS). Human GSK-3 presents in two isoforms, alpha and beta, encoded by two distinct genes. Insulin is responsible for the inactivation of GSK-3, both in vitro and in vivo. Phosphorylation of the specific serine residues is responsible for the inactivation mechanism. In addition, it has been reported that insulin receptor substrate (IRS)-1, a key molecule participating in insulin-signaling cascades can also be phosphorylated by GSK-3. In some experiments, it has been found that Lithium-ion (Li) can cause specific inhibition of GSK-3 [15, 17] and has been reported to have insulin-like effects on glucose metabolism, including increases in glucose uptake hence activation of GS activity. As a result, it stimulates glycogen synthesis in muscle, skin, and fat cells [17]. In skeletal muscle, insulin regulation is correlated by phosphorylating and inhibiting GYS1 activity and hence glycogen synthesis. Selective cell-permeable reversible inhibitors (INH) of GSK-3 were used to evaluate the role of GSK-3 in controlling glucose metabolism. Dipeptidyl-peptidase 4 (DPP4) is a type of glycoprotein of 110 kDa, which can be found on the surface of various cells. DPP4 is highly correlated with glucose metabolism. It is responsible for the degradation of increments such as GLP-1. Incretin hormones, glucagon-like peptide-1 (GLP-1), and glucose-dependent insulinotropic polypeptide (GIP) are some of the major regulators of insulin secretion. Thus inhibition of DPP4 by the gliptin family of drugs gains high attention for the therapy of patients suffering from type 2 diabetes [18, 19]. Aldose reductase inhibitors are a class of drugs that can block the breakdown of glucose by inhibiting a specific metabolic pathway called the polyol pathway. The polyol pathway becomes hyperactive during the state of hyperglycemia. This pathway is responsible for the reduction of glucose to sorbitol via the enzyme aldose reductase (AR), utilizing the cofactor nicotinamide adenine dinucleotide phosphate (NADPH). This reduction reaction is responsible for increased sorbitol accumulation in the cells. Increases in sorbitol concentrations result in cellular and organ injury [20, 21]. So via inhibiting this enzyme we can prevent those complications. So basically all of these three receptors can be targeted as antidiabetic therapeutics. We have selected 3 plants Zingiber Officinale(Ginger), Allium sativum (Garlic) as well asMurrayakoenigi (Curry leaf) as lots of pharmacological evaluations of phytochemicals are already done on those plants.
More than 3000 publications in the past support the efficacy of garlic for the cure, prevention, and treatment of various types of diseases. Garlic has historically been used to treat various aches and pains, leprosy, deafness, diarrhea, constipation, parasitic infection, and fever and to relieve stomachache. It has also been used to lower food poisoning, tumors, blood pressure and as a mild anticoagulant [3]. In Ayurvedic medicine, garlic is used to treat respiratory problems, colic, and flatulence. Earache can be treated by using Garlic oil drops. Supplementation of garlic oil at 5 mg/kg body weight has been shown to have an anticoagulation effect in an animal study [20]. An aqueous extract obtained from 1 mg of a garlic preparation was as effective as an antioxidant, like 30 nmol ascorbic acids. In rat hepatocytes, garlic paste, garlic oil, allicin, and ajoene were found to significantly reduce cholesterol biosynthesis by inhibiting 3-hydroxy-3-methylglutaryl (HMG)-CoA reductase and 14-a-demethylase. Garlic also lowers hyperlipidemia in animal studies [20]. In a randomized placebo-controlled trial in ten healthy adults, there was a significant improvement in plasma viscosity and capillary blood flow within 5 h of taking 900 mg of standardized garlic powder [22]. Aged garlic and garlic’s diallyl sulfur compounds protected against acute chemically-induced hepatotoxicity in rats. Garlic cloves have shown antimicrobial properties against streptococci and anti-cariogenic properties against oral microorganisms with minor adverse effects [23].
Evidence for the benefit of ginger as an antiemetic in pregnancy is some of the strongest. Preliminary studies suggest that ginger may be effective for mild to moderate nausea and vomiting of pregnancy when used at a recommended dose of 1-g dried ginger per day or its equivalent in the form of ginger syrup [24]. There are few investigations into ginger’s capacity to alter blood clotting. One human study suggested that intake of 1-g ginger powder may have a synergistic effect on antiplatelet aggregation in hypertensive patients when used in combination with nifedipine. Findings from several animal studies suggest that ginger may have beneficial effects on blood pressure [25]. Streptozotocin (STZ) induced diabetic rats, specific extracts of ginger lowered blood glucose, cholesterol, and triglyceride levels and increased high-density lipoprotein cholesterol concentration [26]. In animals, ginger exhibited mixed results as an inhibitor of tumor formation in models of colon, bladder, lung, and skin cancer. In vitro studies of Ginger extracts and their chemical constituents indicate suppression of the growth of various common infectious bacteria including Staphylococcus aureus and Listeria monocytogenes [27]. The CO2 extract from ginger has high polyphenol content. It manifested very good scavenging of DPPH and reduced its reducing capacity. The extract can be used as an antioxidant at an earlier stage of fat oxidation [2].
Curry leaves have also been found to have a variety of health benefits. The ethanolic extract of the leaves showed fungi toxicity against Colletotrichumfalcatum and Rhizoctoniasolani. The acetone extract of the fresh leaves of Murrayakoenigii on fractionation gives three bioactive carbazole alkaloids named mahanimbine, murrayanol, and mahanine, which has shown mosquitocidal, antimicrobial activity[5]. Curry leaves powder supplementation (12 g providing 2.5 g fiber) was given for one month in 30 non-insulin-dependent diabetes mellitus patients. The results indicated a significant reduction in fasting and postprandial blood sugar levels at 15-day [28].
Due to the variety of health benefits of these three plants, we have chosen them for our drug repurposing study against some important diabetes type 2 receptors namely dipeptidyl peptidase-IV, aldose reductase as well as mitogen synthase receptor. The most important phytochemicals i.e. the flavonoid class of phytochemicals of these three plants was chosen as ligands for this study. Flavonoids are a group of phytochemicals with different types of phenolic structures. Their antioxidant effects of flavonoids can cure various diseases like Alzheimer’s disease (AD), cancer, atherosclerosis, etc. A significant amount of flavonoid intake can maintain coronary heart disease [6]. These phytochemicals were subjected to various in silico techniques such as molecular docking and ADME-based pharmacological tools analysis. Through this study, we were finally able to screen and find out our hit compounds for each receptor, which showed a good binding affinity with our selected type two diabetes receptors. In short, we can conclude by saying that our hit phytochemicals, can be considered as lead candidates in binding with our targeted receptors, and thus can help to treat type 2 diabetes. Results obtained from this research study will serve as an insight for future preclinical as well as in vivo studies.
CONCLUSION
Zingier Officinale(Ginger), Allium sativum (Garlic) as well as Murrayakoenigi (Curry leaf) have been previously used as a cure to many diseases. The flavonoids phytochemicals obtained from these three plants have overall good showed a binding affinity with the receptors dipeptidyl peptidase-IV, aldose reductase, and mitogen synthase kinase. Naringenin and kaempferol showed excellent docking results with the aldose reductase receptor. On the other hand, rutin showed the best docking score with dipeptidyl peptidase receptor-IV, whereas, epigallocatechin showed the best docking results with mitogen synthase kinase receptor. The ADME analysis showed that resveratrol had the best gastrointestinal absorption as well as high blood-brain barrier permeability. Hence, we can conclude by saying that these phytochemicals can provide a cure to diabetic disorders. To find the effectiveness as well as to propose the exact mechanism, in vitro studies can be encouraged on these phytochemicals to understand the exact mechanism and potential cure for diabetes.
FUNDING
Nil
AUTHORS CONTRIBUTIONS
All authors have contributed equally.
CONFLICTS OF INTERESTS
All authors have none to declare.
REFERENCES
Singletary K. Ginger: an overview of health benefits. Nutr Today 2010;45:171-83.
Stoilova I, Krastanov A, Stoyanova A, Denev P, Gargova S. Antioxidant activity of a ginger extract (Zingiber officinale). Food Chem 2007;102:764-70.
Rana SV, Pal R, Vaiphei K, Sharma SK, Ola RP. Garlic in health and disease. Nutr Res Rev 2011;24:60-71.
Zhou Y, Zhuang W, Hu W, Liu G, Wu T, Wu X. Consumption of large amounts of Allium vegetables reduces the risk for gastric cancer in a meta-analysis. Gastroenterology 2011;141:80-9.
Saini SC, Bala G, Reddy S. A review on curry leaves (Murraya koenigii): versatile multi-potential medicinal plant. Am J Phytomed Clin Ther 2015;3:363-8.
Pietta PG. Flavonoids as antioxidants. J Nat Prod 2000;63:1035-42.
Panche AN, Diwan AD, Chandra SR. Flavonoids: an overview. J Nutr Sci 2016;5:e47.
Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, et al. PubChem 2019 update: improved access to chemical data. Nucleic Acids Res 2019;47. DOI:10.1093/nar/gky1033
Kim S, Thiessen PA, Bolton EE, Chen J, Fu G, Gindulyte A, et al. PubChem substance and compound databases. Nucleic Acids Res 2016;44:D1202–D1213.
DeLano WL. The PyMOL molecular graphics system. Version 2.3. Schrodinger LLC; 2020.
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al. The protein data bank. Nucleic Acids Res 2000;28:235-42.
Forli W, Halliday S, Belew R, Olson A. AutoDock Version 4.2. Citeseer; 2012.
Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 2017;7:42717.
Trott O, Olson AJ. AutoDock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 2010;31:455-61.
Dallakyan S, Olson AJ. Small-molecule library screening by docking with PyRx. Methods Mol Biol 2015;1263:1.
DS Visualizer. v4. 0.100. 13345, Accelrys Software Inc; 2013.
Nikoulina SE, Ciaraldi TP, Mudaliar S, Carter L, Johnson K, Henry RR. Inhibition of glycogen synthase kinase 3 improves insulin action and glucose metabolism in human skeletal muscle. Diabetes 2002;51:2190-8.
Deacon CF. Corrigendum: physiology and pharmacology of DPP-4 in glucose homeostasis and the treatment of type 2 diabetes frontiers in endocrinology. Frontiers Endocrinol 2019;10:275.
Rohrborn D, Wronkowitz N, Eckel J. DPP4 in diabetes. Front Immunol 2015;6:1-20.
Alexiou P, Pegklidou K, Chatzopoulou M, Nicolaou I, Demopoulos V. Aldose reductase enzyme and its implication to major health problems of the 21st century. Curr Med Chem 2009;16:734-52.
Schemmel KE, Padiyara RS, D’Souza JJ. Aldose reductase inhibitors in the treatment of diabetic peripheral neuropathy: a review. J Diabetes Its Complications 2010;24:354-60.
Gebhardt R, Beck H. Differential inhibitory effects of garlic-derived organosulfur compounds on cholesterol biosynthesis in primary rat hepatocyte cultures. Lipids 1996;3:1269-76.
Salehi I, Vahidinia A, Bakhtiari A, Mohammadi P, Sukumaran A, Hosseini-Zijoud SM. The effect of fresh garlic on the lipid profile and atherosclerosis development in male rats fed with a high ghee diet. Int J Pharm Pharm Sci 2015;7:486-90.
Chrubasik S, Pittler MH, Roufogalis BD. Zingiberis rhizoma: a comprehensive review on the ginger effect and efficacy profiles. Phytomedicine 2005;12:684-701.
Kemkar K, LS, Sathiyanarayanan A, Mahadik K. 6-Shogaol rich ginger oleoresin loaded mixed micelles enhances in vitro cytotoxicity on mcf-7 cells and in vivo anticancer activity against dal cells. Int J Pharm Pharm Sci 2018;10:160-8.
Bhandari U, Kanojia R, Pillai KK. Effect of ethanolic extract of Zingiber officinale on dyslipidaemia in diabetic rats. J Ethnopharmacol 2005;97:227-30.
Norajit K, Laohakunjit N, Kerdchoechuen O. Antibacterial effect of five zingiberaceae essential oils. Molecules 2007;12:2047-60.
Iyer UM, Mani UV. Studies on the effect of curry leaves supplementation (Murraya koenigi) on lipid profile, glycated proteins and amino acids in non-insulin-dependent diabetic patients. Plant Foods Hum Nutr 1990;40:275-82.