In-Silico Modeling of Potential Molecules to target Diabetes Type-2

By and by, the world is in a battle with Diabetes and its variants with no prompt medicines accessible. The scourge brought about by the disease is expanding step by step. A ton of researchers are continuing for the potential medication up-and-comer that could help the medical care framework in this battle. We present a docking‐based screening using a quantum mechanical scoring of a library built from approved drugs and compounds that Curcumin, Delphinidin, Cyanidin-3,5-diglucoside, Diterpenoid Lactones, Glycosides, Alkaloids, with Proteins with PDB id’s 3K35 and 3A5J could display antiviral activity against Diabetes types-2. Clearly, these compounds should be further evaluated in experimental assays and clinical trials to confirm their actual activity against the disease. We hope that these findings may contribute to the rational drug design against Diabetes type-2.


Introduction
The World Health Organisation (WHO) estimated the global prevalence of diabetes among adults over 18 years of age as 8.5% in 2014. There are estimated 72.96 million cases of diabetes in the adult population of India. The prevalence in urban areas ranges between 10.9% and 14.2% and prevalence in rural India was 3.0-7.8% among population aged 20 years and above with a much higher prevalence among individuals aged over 50 years (INDIAB Study). More than 95% of people with diabetes have type 2 diabetes. It influences individuals worldwide and there is no immunization yet for this rapidly spreading as a major threat to worldwide general wellbeing. As of late, various endeavors have been made to plan novel inhibitors or utilize drug repurposing ways to deal with recognition hostile to medications. However no prominent drug has been discovered hitherto for treatment, and this paper intends to find a potential drug in-silico that can effectively act as a solution. Procedure:

Ligand Screening
For the initial Ligand screening purposes, a webbased tool named Swiss ADME (https ://www.swiss adme.ch/) was used to eliminate a few compounds according to Lipinski's rule of five parameters. For a compound to qualify as ligand it should Have < 500 Da molecular weight, a high lipophilicity i.e. value of Log P being less than 5, hydrogen bond acceptors being less than 10 and H-bond donors less than 5. Any compound with more than 2 violations was ruled out for further study (Lipinski2004).

Protein Preparation and Active site
Determination. • Ignore waters (true/false) • Ignore chains of non-standard residues (true/false) -ignore chains composed entirely of residues other than the 20 standard amino acids.
• Ignore all non-standard residues (true/false) -ignore all residues other than the 20 standard amino acids.

Residue Analysis
PyMOL was used for visualization of interactions of the docked structure at the ligand sites. Discovery Studio 2020 was used to study the ligand interactions and total number of residues. It was also used to plot the 2D structure of the interactions and residues.

Statistical Analysis:
Descriptive, estimation and Hypothesis testing with confidence interval of 95% was applied to data using formula 1 given belw.

Formula 1 used for calculation of confidence interval
Results and Discussion:

Molecular Docking:
The docking result was obtained from Auto dock vina in the form of Dock score for all the three proteins docked with above mentioned ligands.

PDB-ID 3A5J
For 3A5J, seven active sites were selected out of which the zeroth active site was selected with a Deep site score of 0.98, Table 1. The selection was made on the basis of the highest binding energy of the ligand-receptor. The docking results before statistics are shown in Table 1 and Table 2 shows the post statistical docking scores with Ligand Protein Interactions.   Cyanidin-3,5-diglucoside -8.1

PDB-ID 3K35
For 3K35, six active sites were selected out of which the 0th and 1st active sites were selected with a Deep site score of 0.99 and 0.98, Table 1. The selection was made on the basis of the highest binding energy of the ligand-receptor. The docking results before statistics are shown in Table 3 and Table 4 shows the post statistical docking scores with Ligand Protein Interactions.   Cyanidin-3,5diglucoside -8.8 Cyanidin-3,5-diglucoside -8.9 Cyanidin-3,5-diglucoside

Conclusion:
All six ligands were studied using bioavailability radar. Our results proposed Cyanidin-3,5diglucoside, Curcumin and Inumakilactone A Glycoside showed best docking result for diabetic, Proteins with PDB id's 3A5J and 3K35. For Diabetic protein with PDB id 3A5J, Cyanidin-3,5-diglucoside and Inumakilactone A Glycoside showed standardized results, whereas, other diabetic protein included in study with PDB id 3K35 showed best docking results with Cyanidin-3,5-diglucoside and Curcumin. To find the effectiveness and to propose the exact mechanism in-vitro studies can be encouraged on Curcumin, Cyanidin-3,5-diglucoside, Cembranoid Diterpene lactone and Delphinidin targeting respective diseases that are discussed above to understand the mechanism and a potential cure for Diabetes type-2.