POTENTIAL OF SECONDARY METABOLITE OF Jasminum sambac AS DIABETES MELLITUS MEDICINE BY MOLECULAR DOCKING METHOD

Diabetes mellitus (DM) is a non-infectious disease with a high prevalence in Indonesia. The study explored the potential of the phytochemical component of Jasminum sambac to treat DM. The potential of Jasminum. sambac as a candidate for DM therapy was demonstrated through in silico analysis using several databases and computer-aided drug discovery tools. The bioactive compounds analyzed were obtained from KnapSACK databases. The screening was done to find compounds by estimating bioavailability prediction on the SwissADME. The SwissTargetPrediction tool connects predictions of target proteins from compounds that pass screening to various probable proteins and utilizes the String-DB to show the network between target proteins and associated diseases. After finding the target protein, continue docking the chemical compound to the target protein using PyRx with AutoDock 4.2.6. The search results for the compounds in Jasminum sambac found nine active substances with good bioavailability. The results of the pharmacological network found four proteins associated with Jasminum sambac , among others: GCGR, GSK3B, PPARA, and PPARG. In addition, in-depth analysis was done on the molecular interactions that occurred, how these compounds bind the enzymes found in humans, and their potential to be inhibitors of diabetes mellitus. Proteins such as GCGR, GSK3B, PPARA, and PPARG bind to the compounds found in Jasminum sambac , such as (-)-alpha-cadinol and linalyl benzoate. Thus, it can be said that Jasminum sambac can have anti-diabetic activity


Background
Diabetes mellitus (DM), also known in Indonesia as "kencing manis" is a long-lasting condition that can last a lifetime.An elevated blood sugar or hyperglycemic condition caused by a decrease in insulin in the pancreas is a sign of metabolic disorders that causes diabetes to develop (Lestari, Zulkarnain and Sijid, 2021).Indonesia is the fourth most diabetic country, behind the United States, China, and India.Moreover, the number of people suffering from DM in Indonesia is expected to increase by 2-3 times by 2030 compared to 2000 (Dwi Hardika et al., 2016).Developing new drugs, therapies, and appropriate lifestyle management strategies is usually the focus of research on diabetes treatment (Harreiter and Roden, 2019).Jasminum sambac, better known as the Arabian jasmine, is one of the plants well-known by the Indonesian people (Jamil and Alghifari, 2023).In Indonesia, this plant is used as a mixture in some beverages.However, these flowers have a high aesthetic and aromatic value (Khan et al., 2021).The claim that J. sambac has the potential to treat diabetes mellitus has not been supported by scientific research.This study focuses on how medicinal plants can cure or reduce the effects of diabetes mellitus.The trial uses the in silico method to test a drug candidate protein bound to a compound that will be produced as a drug that directly treats diabetes mellitus (Gaál and Balogh, 2019).In silico research is a type of research that uses computer simulation and specific software to predict interactions between drugs and pathogens in the body (Sutan Mulia Ananda and Gemah Nuripah, 2022).Network pharmacology, which combines pharmacologists and databases, gains momentum thanks to system biology, bioinformatics, and high throughput histology.Based on the low efficacy of highly selective single-target drugs, it combines biological networks with polypharmacology (Aihaiti et al., 2021).Molecular docking is in silico-based technique that is popular in drug discovery, allows the identification of new compounds that have therapeutic benefits, projecting ligan-target interactions at the molecular level or describing the relationship between structures and activities (SAR) (Pinzi and Rastelli, 2019).This study aims to discover how developing new drugs, therapies, and appropriate lifestyle management strategies is usually the focus of research on diabetes treatment using J. sambac.

Tools
This study was conducted using several online databases and software.

Research Methods
The secondary metabolite of J. sambac was obtained from KNApSAcK, and PubChem was used to see the compound configuration and SMILES code (Kim, 2021).The SMILES code was added to SwissADME using the Boiled-Egg method to predict bioavailability and SwissTargetPrediction to predict proteins which can interact with secondary metabolite compounds (Lena et al., 2023).Next, the list of emerging proteins inserted into StringDB enriches proteins by looking for predictions of proteins associated with the immune system (Ge et al., 2020).

Network Pharmacology Analysis
The secondary metabolite of J. sambac was obtained from the KNApSAcK online database.There are 18 compounds obtained Table 1.SwissADME made bioavailability predictions using the Boiled-Egg method to identify highly bioavailable compounds Figure 1.The physicochemical space that is most likely to absorb the brain is the yellow region, also known as egg yellow, while the white region represents the physical chemical space of the molecules most likely to be absorbed by the intestinal tract (Daina and Zoete, 2016).Nine compounds have good bioavailability Table 2  Of the nine secondary metabolite compounds that pass Boiled-Egg, further analysis using SwissTargetPrediction was used to predict the interaction of the compound with the protein targeted in the research (Daina, Michielin and Zoete, 2019).There are 226 proteins that are predicted to have interaction with compounds.All known and predicted inter-protein relationships, including physical and functional interactions, are included in the String-DB database (Szklarczyk et al., 2021).From network pharmacology analysis, it found four proteins that are closely related to diabetes mellitus, such as PPARG, PPARA, GCGR, and GSK3B.PPARA is a protein that has the ability to control the expression of insulin response proteins in liver cells that can control hyperglycaemia through regulation of 110 lipid metabolism (Azeem and Hakeem, 2023).GCGR is the G-protein-paired receptor (GPCR) most commonly found in island cells and liver cells.After glucagon binds to GCGR specifically, glucagons promote the breakdown of glycogen in the liver and increase blood glucose levels, which can stimulate the release of insulin so that it plays an important role in glucose metabolism and the formation of diabetes (Jia et al., 2022).GSK3B is an important glycogen synthesis enzyme involved in blood glucose regulation, insulin deficiency, and IR.In addition, GSK3B acts as a substrate of a negatively regulated insulin signal pathway, which, if genetically reduced, will enhance the homeostasis of insulin-resistant glycose (Liu et al., 2020).Of the four proteins taken by one of the Proceedings of International Pharmacy Ulul Albab Conference and Seminar (PLANAR), 3, 93-102 97 proteins, PPARG, no research has confirmed the association of the protein as an antidiabetic.These four are proteins that will be subsequently validated by the molecular docking method.

Figure 3. Proteins associated with diabetes mellitus with more specific relationship pathways
The PPARG gene encodes the PPAR-gamma protein, a transcription factor that controls genes required for lipid and glucose homeostasis.PPAR-gamma activation in adipocytes supports the correct and balanced secretion of adipocytokines such as adiponectin and leptin, which aid in mediating insulin action in peripheral tissues and maintaining insulin sensitivity (Jang et al., 2023).While, in PPARA or PPAR-alpha, The creation of PPAR-alpha agonists has attracted a lot of attention, partly because of its possible benefits for maintaining lipid homeostasis.Triglyceride and VLDL levels decrease as a result of lipoprotein remodeling, which is facilitated by lipoprotein lipase activation (Nicholls and Uno, 2012).The human glucagon receptor (GCGR) is a seven transmembrane-spanning class B G protein-coupled receptor (GPCR) that plays an important role in glucose homeostasis and type 2 diabetes pathogenesis (Gao et al., 2023).The human glucagon receptor (GCGR) is a seven transmembrane-spanning class B G protein-coupled receptor (GPCR) that plays an important role in glucose homeostasis and type 2 diabetes pathogenesis (Gao et al., 2023).GSK-3 beta regulates glycogen metabolism by controlling glycogen synthesis, and it modulates mitochondrial permeability and cytochrome C release to regulate apoptosis.GSK-3beta also activates the transcription factor NF-B and is involved in a variety of biological processes such as embryonic development, cell differentiation, and insulin response (Lin et al., 2020).

Molecular Docking Analysis
The molecular docking method is widely used in modern drug design by studying the conformation of ligands adopted in macromolecular target binding sites.This molecule docking technique also counts the free energy that binds ligands-receptors by looking at essential phenomena in the Proceedings of International Pharmacy Ulul Albab Conference and Seminar (PLANAR), 3, 93-102 process of intermolecular identification (Hui et al., 2022).This method also calculates the free energy that binds the ligands-receptors by examining the critical phenomena involved in intermolecular identification (Mun et al., 2022).
The four proteins that resulted from network pharmacology analysis: PPRA (PDB ID = 3KDT ), PPARG (PDB ID = 8HUP), GCGR (PDB ID = 5XEZ), and GSK3B (PDB ID = 5F94), will be docked with nine compounds that pass Boiled-Egg.Based on the data obtained (Table 4), it is seen that the four ligands that have a role as comparators have a binding energy of -8.53 kcal/mol with PPARA, -7.49 kcal/mol with PPARG, 7 kcal/mol with GCGR, and -5.92 kcal per mol with GSK3B.The two compounds, Molecule 9 and Molecule 13 have lower energy binding and pKi compared to ligand from protein PPARG and GSK3B.The interaction between the compound and the target protein will be stronger when both are at the lowest energy conditions.Thus, the lower the energy of binding between the compound and the target protein, the stronger the interaction.
The predictive value of the inhibition constant (pKi), in addition to the magnitude of the binding energy, also affects the strength of the interaction; the smaller the pKi value, the stronger the interactions (Muchlisin et al., 2022).The interaction can be seen from the binding and amino acid residues Table 5.It is possible to see from the comparative ligands and nine compounds found in J. sambac that there is one ligan that does not have amino acids residue in the protein GCGR, so the ligands can not be compared with another compound.If we look at the PPARG protein, there is one compound (molecule 10) that has the same hydrogen bond and hydrophobic interaction residue (Arg288) as the ligand.Molecule 2 and Molecule 13 have two similar hydrophobic interactions with ligand (Phe 264 and His266), and so are molecule 10 but with different residues (Phe266 and Arg 288).In the GSK3B protein, Molecule 3, Molecule 4, Molecule 6, and Molecule 9 also have similar hydrophobic interaction residue with comparator ligands (Leu130).However, PPRA and GCGR proteins do not have similar hydrogen bond and hydrophobic interaction between secondary metabolite compounds and ligands.To determine a specific mechanism of action, an interaction between ligands and proteins is required.The analysis results suggest that the (-)-alpha-cadinol and linalyl benzoate indicate that these secondary metabolite compounds can be potential as antidiabetics activity.

Conclusion
The results of the research showed that two secondary metabolite compounds ((-)-alpha-cadinol and linalyl benzoate) in the plant of Jasminum sambac can be potential as antidiabetics activity when it have intercatin with GSK3B and PPARG.

FFigure 1 .
Figure 1.Bioavailability prediction of the secondary metabolite of J. sambac using BOILED-Egg

Table 3 .Figure 2 .
Figure 2. Network pharmacology prediction results using StringDB.The red color indicates a protein associated with diabetes mellitus.

Table 1 . A list secondary metabolite of J. sambac obtained from KNApSAcK online database No. Name of Compound
.