Drug innovation studies targeting Diabetes: A computational docking approach on muti-drug targets including COVID Inhibitors

doi:10.21203/rs.3.rs-2457415/v1

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Drug innovation studies targeting Diabetes: A computational docking approach on muti-drug targets including COVID Inhibitors

https://doi.org/10.21203/rs.3.rs-2457415/v1

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Background: Diabetes mellitus cases are rising exponentially and promoting hyperglycemia with multifactorial disease conditions and also increases susceptibility to viral infection (Corona virus). Many antidiabetic drugs are available on the market but, still its control is a challenging task and the need of time is to develop a cost-effective, potent antidiabetic drug having no side effects. The objective of the study is concerned with lead preparation and insilico analysis of the mechanism of action of biomolecule on multiple targets of the diseases.

Methods: In the present study, lead was prepared from C. roseus alkaloids ligand library (21 compounds) then tested its molecular interaction with 4 drug targets (AMPK, DPP4, alpha glucosidase and PPARy) for antihyperglycemic effect. Insilico testing of lead compound vindoline (CID: 425978) with drug targets- AMPK, DPP4, alpha glucosidase and PPARy was by advanced computational docking studies and system biology approaches.

Result: Molecular docking studies of vindoline with multiple potential drug targets show strong non-covalent interactions. Docking results of 5'-AMP-activated protein kinase (AMPK) activator metformin with AMPK1 and AMPK2 targets were -4.0 Kcal/mol and -4.2 Kcal/mol, while vindoline docked score showed -6.2 and - 6.3 Kcal/mol respectively; Dipeptidyl peptidase 4 (DPP4) inhibitor vildagliptin with DPP4 target was -6.7 Kcal/mol and for the vindoline - 6.8 Kcal/mol. Alpha-glucosidase inhibitor acarbose with the target was -6.7 Kcal/mol, vindoline – 6.8 Kcal/mol and Peroxisome proliferator-activated receptor gamma (PPARy) activator pioglitazone with PPARy nuclear receptor was -6.4 Kcal/mol, while vindoline – 6.1 Kcal/mol. Drulito and Osiris explorer's result shows that the bioactive compounds had good solubility (Log S= -3.12 mol/lit), absorption (cLogP =1.32), permeation (Molecular weight=456), action (TPSA=88.54), drug likeness= +3.95, drug-score=0.74, non-toxic characteristics.

Conclusion: C. roseus alkaloid vindoline (CID: 425978) has the potential to act on multiple target sites, efficiently reduce blood glucose levels as well as control secondary pathological aspects of diabetes with few side effects. Also, it has antiviral and antimicrobial properties. Hence, it has a high probability of becoming a potent antihyperglycemic drug and can be used as a futuristic first line antidiabetic drug and the 4th line of drugs for chronic complicated cases of diabetes.

Diabetes

AMPK

DPP4

Alpha glucosidase

PPARy receptor

C. roseus alkaloids

Diabetes mellitus cases are increasing exponentially and doubling every decade from a predictable 30 million cases in 1985 [1] to 463 million in 2019, which belongs to the 20–79 year age group and predicated that by 2030, the affected person number may increase to 578 million and 700 million by 2045 [2]. The disease is a cluster of prevalent metabolic complaints that share hyperglycemia as a common phenotype [1].There is improper metabolism of carbohydrate, fat, and protein which is triggered by an insulin resistance state, diminished insulin secretion, reduced glucose utilization, or amplified glucose production. This impaired condition in carbohydrate metabolism and absence of well-organized uptake and consumption of glucose by most cells of the body are causing secondary pathophysiologic changes in multiple organ systems and promoting multifactorial disease conditions like abdominal obesity, fasting hyperglycemia, amplified blood triglycerides, reduced high density lipoprotein cholesterol, hypertension, glycosuria and tissue injury [1, 3].

Also, the IDF atlas 2019 reported that India had 88 million diabetic cases, whereas in Jharkhand (a state in India), the cases were about 1 million [4]. About 1.2 million deaths occur per year due to diabetes in South East Asia, which is the 2nd highest death in the IDF region. At present, diabetes is the 9th major cause of death. The 2nd largest case of the disease has been reported in India. In 2013, international expenses on diabetes were approximate $548 billion or 11% of international expenditures on health [1], whereas in 2019 it was about 12% [5, 6].

Diabetes mellitus is widely classified into 2 groups- Type I diabetes or insulin -dependent diabetes mellitus (IDDM), which is initiated by absence of insulin secretion and Type II diabetes or non-insulin-dependent diabetes mellitus (NIDDM), which is primarily triggered by insulin resistance /insensitivity of target tissues to insulin hormone. Type II diabetes is more recurrent than type I and about 90 to 95% of cases of diabetes mellitus related to type II diabetes [2, 7]. For treatment of type II diabetes, several approaches; oral drug remedies are taken care of which improves blood glucose control, reduces the toxic effect of glucose on beta cells, and increases endogenous insulin secretion. To fulfil the treatment aspect, many drug targets like AMPK, DPP4, Alpha glucosidase, PPARy etc are identified which are helpful in maintaining the normal blood glucose level and lifestyle of the patient [8, 9] as shown in Fig. 1.

AMP-activated protein kinase (AMPK) is a cellular energy sensing protein kinase and controls energy metabolism. It phosphorylates IRS1, PFKFB2 and PFKFB3 components and governs insulin-signalling and glycolysis pathway and glucose homeostasis in the liver by phosphorylating CRTC2/TORC2 [10].

Activator of AMPK enzyme plays a crucial role in suppression of hepatic gluconeogenesis and enhances insulin-induced glucose intake in skeletal muscle. Accumulation of glucose in skeletal muscle decreases break down of lipid in adipose tissue and improves fatty acid oxidation [8]. Metformin (N, N-dimethylbiguanide) is an AMPK activator and is used as a first-line drug for initial control of type 2 diabetes [11, 12]. But it has side effects too, like delayed absorption of glucose, other hexoses, amino acids and Vit B12 in the small intestine, metallic taste, pain in the abdomen, nausea, anorexia, bloating, mild diarrhoea and fatigue related problems [13].

Dipeptidyl Peptidase-4 (DPP4) enzyme is related to rapid degradation of endogenous GLP-1, signal transduction, apoptosis, and immune regulation (positive regulator of T-cell coactivation) [14]. Also, it acts as a sensory receptor for corona virus MERS-CoV2 in humans [10]. DPP4 inhibitors bind competitively and selectively with DPP4 and inhibit incretin GLP-1 and GIP degradation [15]. An increase in the level of GLP-1 and GIP indirectly influences insulin secretagogues and stimulates postprandial insulin discharge, and reduces glucagon secretion. As a result, it lowers fasting blood glucose levels in diabetics. DPP-4 inhibitors are an alternative antidiabetic treatment [16] and can be recommended as 2nd line therapy after failure of a first line drug (eg; metformin) but can also clinically used as 3rd, 4th line therapy as per condition and seriousness of case. Vildagliptin binds covalently in the DPP4 active site and inhibits it [15]. But the drug is less selective in nature because it also inhibits DPP8 and DPP9 and causes side effects like vomiting, loose stools, headache, rashes, sensitive reactions, edema and hepatotoxicity [8]. Alpha glucosidase is a small intestine mucosal enzyme, crucial for catalyzing the final stage of carbohydrate digestion [6] and responsible for conversion of oligosaccharide into monosaccharide and increases blood glucose level [5, 14, 17]. The competitive inhibition of alpha-glucosidase takes place by an inhibitor which competes with the oligosaccharides and also interferes in its cleavage into monosaccharides from the carbohydrate-binding region. In the late postprandial period, its inhibitor raises plasma GLP-1, decreases GIP levels and plasma insulin [7]. α-glucosidases inhibitors (Acarbose, miglitol, and voglibose) are used as a third-line antidiabetic drug [13]. Acarbose (complex oligosaccharide) reversibly inhibits α-glucosidases, declines postprandial hyperglycemia, and lowers HbA1c moderately (by 0.4–0.8%). Regular use of Acarbose in the initial phase of the disease reduces the incidence of type 2 diabetes in addition to hypertension and cardiac disease. Side effects of alpha glucosidase inhibitor are related to gastrointestinal problems like flatulence, intestinal discomfort and loose stool due to fermentation of unutilized carbohydrates [8] and hepatitis [18].

Peroxisome Proliferator-Activated Receptor Gamma (PPARᵧ) is a nuclear receptor. Its expression is mainly observed in fat tissues, but up to some extent in muscle and some other cells too. Its agonist pioglitazon promotes the RNA synthesis of several insulin responsive genes [8], activates the genes that are involved in adipogenesis and lipid metabolism of adipose tissues [14]. Its agonist like pioglitazone reverses insulin resistance and improves GLUT4 expression followed by glucose uptake into muscle and fat tissues. It suppresses gluconeogenesis in the liver and activates genes involved in fatty acid metabolism, lipogenesis and insulin sensitizing action. It enhances glycaemic control and, as a result, decreases HbA1C and insulin levels in diabetic patients [19]. PPARy agonist pioglitazon has some side effects like elevation in plasma volume, declination in haemoglobin and haematocrit, oedema, weight gain and probability of hypoglycaemia etc [20]. With time, many new approaches have been explored to combat diabetes and its complications, but it is still a challenge for researchers and medical science to cure the disease [21–23].

In the Ayurvedic literature, Sushruta Samhita and Charaka Samhita literature reported more than 500 species of antidiabetic plants [24, 25] and in some literature more than 1200 plant species are reported having hypoglycaemic ability [21]. Catharanthus roseus (Vinca rosea) is a member of the Apocynaceae family and commonly known as sadabahar and Periwinkle. It is recognised for its action against tumours, microbes, and hyperglycaemia. This plant is rich in alkaloids, flavonoids and phenolic compounds. The C. roseus alkaloids (Catharanthine, leurosinesulfate, lochnerine, vindoline, vindolinine and tetrahydroalstonine) have the capacity to decrease blood sugar level [26] and delay diabetes-induced cataract formation [27–29]. Literatures also supported its positive effect on other enzymes such as succinate dehydrogenase, glycogen synthase, glucose-6-phosphate-dehydrogenase and malate dehydrogenase [30]. C. roseus therapy works on blood glucose homeostasis, protein, and lipid metabolisms, stimulating glycogen synthesis and boosting glucose utilization in the liver [31]. Cell line (pancreatic β-TC6 or myoblast C2C12 cells) study of vindoline, vindolidine, vindolicine and vindolinine alkaloids reported that the alkaloid inhibit protein tyrosine phosphatase-1B (PTP-1B) activity and stimulate insulin sensitizer and antioxidant potential [32]. A drug development process is very expensive and time-consuming [33].

Currently, in this study we are addressing scientific issue of recent treatment approach in pharmacology for diabetes mellitus is to discover biomolecules having fewer or no side effects with multiple therapeutic drug target sites to adequately reduce glucose levels and to control secondary pathological aspects of diabetes effectively [22]. Natural compounds are used to treat a range of diseases and infections, including diabetes, and have a wide structural range of pharmacological actions, along with a reduced amount of toxicity in contrast to synthetic drugs [6, 34]. Further, using advance computational and docking approach we identified certain natural compounds with significant potential to decrease blood sugar levels may result in the development of a successful drug candidate for diabetes syndrome [23]. Alkaloids, flavonoids and phenolic compounds are showing antidiabetic activities without side effects and are widely distributed in the C. roseus plant [5].

1) Selection Of Drug Target

The structure of target proteins like α-glucosidase (UniprotID- O43451), DPP-4 (UniprotID- P27487), AMPK (UniprotID- Q13131, P54646) PPARy (UniprotID-P37231) of human were identified and downloaded from Uniprot [10]. Homology modelling of proteins was performed by SWISS MODEL server [35].

2) Identification Of Physiochemical Properties Of The Protein

The sequence analysis of the target proteins, physical and chemical parameters was determined by using ProtParam [36]. Secondary structures of target proteins were predicted by a self-optimized prediction method (SOPMA) [37]. SOSUI (engine ver. 1.11) [38] was used to determine whether the drug target is a soluble or a transmembrane protein. The crystal three-dimensional (3-D) structure of proteins was downloaded from the website Swiss-Model-Server [35]. SWISS-MODEL is a computerized homology modelling server, was used to superimpose proteins in order to deduce structural alignments and compare their active sites. The selected proteins- Model Quality Estimation (QMEAN Scores), sequence identity, coverage, method, resolution details was determined (Table-4). The protein structure against a background of phi-psi probabilities was validated by Ramachandran Z-score and Ramachandran plot – Zlab [39]. The active site and its functional domain were identified by CASTp 3.0 software [40].

3) Preparation Of Target Proteins

Energy of the target proteins was minimized by Swiss-Pdb Viewer (aka DeepView) v 4.1.1 [41]. It is used to superimpose proteins in order to construct structural alignments and judge their active sites or any other related parts and to obtain H-bonds, mutations in amino acids, angles and distances connecting atoms. With the help of UCSF Chimera software [42] hydrogen and charges were added, its molecular torsion, degree of freedom and stereo-chemical variation were adjusted, followed by computation of gasteiger charges were done and file saved in PDB-Format. Chimera is an extensible molecular modelling system. The target protein grid was developed by using Chimera and Auto Dock Vina tool [43] with grid dimensions 20A°, 20A°, 20A° and the grid box was set at 717,-23.55, 34.849 for alpha glucosidase; -76.407, -36.793, 13.714 for AMPK1; -36.303,-82.337, -22.555 for AMPK2; -7.759, 55.202, 45.807 for DPP4; -2.406, 32.019,17.164 for PPARy in X, Y and Z coordinates [40].

4) Selection Of Ligands And Library Preparation

Identification of C. roseus metabolites having antihyperglycemic properties was selected with the help of a literature survey. Vindoline alkaloids (CID: 260535) and their similar structures were selected for the study (27, 28, 32). Compound libraries were prepared for 21 compounds (see Table-6). As a control, antidiabetic drugs Acarbose (CID: 445421), Vildagliptin (CID: 6918537), Metformin (CID: 4091), Pioglitazon (CID: 4829) were selected. 21 components identified and 4 controls SDF files were downloaded from PubChem [44] data base. Their SDF file format was converted into PDB file by Open Babel software [45].

5) Admet And Drug Likeness Property Prediction

All library compounds drug likeness property were determined by the software OSIRIS Property Explorer [46] and DruLiTo (Drug-likeness rules) tool [47]. The drug's likeness properties of compounds were tested by parameters like solubility (Log S) estimation, Total polar surface area (TPSA), clog P calculation (Lipinski rule), molecular mass and active fragment of compound, and drug score. Pharmacokinetic properties of compounds are predicted by their oral and gastrointestinal absorption, distribution, metabolism, and excretion (ADME) possibilities in the body. The pharmacodynamics activities of a compound were predicted by its bioavailability at the target side, blood brain barrier crossing possibility, toxicity, carcinogenicity etc.

6) Preparation Of Ligands

The ligands were prepared by using Avogadro 1.2.on-win 32 [48]. The tool is used for force field and geometry optimization. The UCSF Chimera [45] tool was used for hydrogen addition, charge addition, ionization of lead compound followed by adjustment of torsion, degree of freedom and stereo-chemical variation of ligand and then gasteiger charges were calculated and finally the file was saved in PDB file format.

7) Docking Studies

In chimera software, the prepared target protein (pdb file) and prepared ligand (pdb file) were opened. Surface binding analysis of them was performed with the help of the Grid, having sizes of 20, 20, 20 Å. The Vina result score (energy value) was recorded and the docked file was saved in pdbqt format. Receptor-ligand interactions were identified and visualised by BIOVIA Discovery Studio Visualizer 4.5 [49]. It provides insilico techniques related to molecular mechanics, free energy calculations, biotherapeutics development ability and more in the general environment. Bond details were recorded and docking surfaces were analysed. 2D and 3D structures of receptor-ligand interactions were generated.

1) Metabolites compound library analysis and active ligand identification of C. roseus

With extensive bibliographic research, vindoline alkaloids and their similar structures were selected for the study. At present, about 21 compounds have been identified and downloaded from the PubChem [44] data base (see table-6). All the compounds were tested for ADMET and drug likeness property. Among all compounds, only Vindoline alkaloid (CID: 425978) passed the screening test and was selected for lead preparation.

2) Sequence Analysis And Active Site Identification Of Protein

Selected drug targets are listed in Table-1. 5'-AMP-activated protein kinase catalytic subunit alpha-1 (AMPK1, Uniprot ID Q13131·AAPK1_HUMAN) is an enzyme (EC: 2.7.11.1) contains 559 amino acids and 5'-AMP-activated protein kinase catalytic subunit alpha-2 (AMPK2, Uniprot ID P54646·AAPK2_ HUMAN) having EC: 2.7.11.1 contains 552 amino acids. Dipeptidyl peptidase 4 (DPP4, Uniprot ID P27487 DPP4_HUMAN) is an enzyme (EC: 3.4.14.5) made up of 766 amino acids. The α-Glucosidase (Uniprot ID-O43451 MGA_HUMAN) belongs to Maltase-glucoamylase (EC:3.2.1.20) of human and made up of 2753 amino acids. Peroxisome proliferator-activated receptor gamma (PPARy, Uniprot ID P37231 · PPARG_HUMAN) is a nuclear receptor, made up of 505 amino acids. Sequence analysis of AMPK, DPP-4, α-glucosidase, PPARƴ have revealed physiochemical properties of the proteins like the stability index, theoretical PI value, the aliphatic index, Grand average of hydropathicity (GRAVY), extinction coefficient, and estimated half-live computed as shown in Table-1. All these results show that the target proteins were stable macromolecules. The secondary structure of the target protein uncovered the maximum presence of the alpha helix, random coil, extended and the beta strand on it (see Table-2). AMPK2, DPP-4, and α-Glucosidase have transmembrane helices whereas AMPK1 and PPARƴ are soluble proteins (see Table-3). The distribution of the torsion angles (phi (φ) and psi (ψ)) of the protein backbone of the target protein validates their structure (see its Table-5). Castp web server result shows about Surface features and functional regions on three-dimensional structures of proteins and presence of amino acids in active site of protein. In the target protein active site contains acidic, basic and neutral types of amino acids. Arg¹⁹⁹ of AMPK1, Glu²⁶⁴ of AMPK2, Lys⁵⁵⁴ of DPP4, Asp²¹⁷⁵ of α-glucosidase, Asn⁴⁰³ of PPARy were randomly selected at active site of target protein because they play vital role in catalytic activity and its coordinate have low binding potential energy. Followed by molecular docking analysis was performed between control drugs/ C. roseus (Vindoline, CID: 425978) active compounds with the target protein.

3) Admet And Drug Likeness Prediction

All the selected library compounds were passed Lipinski rule and most of the compounds passed Ghose filter, BBB likeness, veber filter, MDDR test but only one compound was qualify in toxicity test and then it was taken for testing Drug likeness and Drug-score. The best ligand (Table-7) and controls result of the OSIRIS Property Explorer is shown in Table-8. The tool generates values related to cLogP (O/W): Logarithm of partition coefficient between n-octanol and water; logS: logarithmic value of aqueous solubility; TPSA: Topological polar surface area. Values obtained by using software for qualified biomolecule CID: 425978 are within standard criteria, whereas rest 20 compounds had ambiguous configuration at stereo centre because of 2 parallel bonds/ unbalanced atom charge. ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) features were identified by DruLiTo tools and OSIRIS Property Explorer. Toxicity risk was computed for mutagenic, carcinogenic, irritation and reproductive property and best result were indicated by Green colour. For Vindoline alkaloid (CID: 425978) toxicity test result is green and indicating that the compound qualifies as nonmutagenic, noncarcinogenic, nonirritation and not reproductive in nature or within standard criteria. Noncarcingenic and nonmutagenic profile shows that there would no accumulation of biomolecules in the human body and it is not causing cancer or mutation in future if used for long duration treatment. Also non- irritating property shows that it might have no gastrointestinal problem, vomiting, diarrhoea etc. Value of cLogP = 1.32, means, the compound has great hydrophilicity and can absorbed effortlessly by the cells, that is has good possibility of absorption in gastrointestinal tract upon oral administration. Log S (solubility) = -3.12 mol/lit means compound has good absorption and distribution characteristics. Molecular weight = 456 shows that biomolecule has high activity on a biological target and high possibility to reach the place of action. TPSA = 88.54 means have high topological polar surface area for activity; Drug likeness = + 3.95, positive value of drug likeness represent molecules contains predominant fragment having biological activity or drug likeness property; Drug-score = 0.74 shows that compound have higher possibility to qualify for a drug; and blood brain barrier (BBB) likeness test by DruLiTo tool indicated by red colour that means it has possibility to cross BBB. Overall DruLiTo and Osiris software result is showing that compound have good drug efficacy and become a potential drug candidate and can be used for ligand preparation.

4) Molecular docking analysis of control drugs (Metformin) and C. roseus (Vindoline, CID: 425978) active compounds with AMPK target

Ampk1- Ligand Interaction

Metformin belongs to biguanides family and acts as an activator of AMPK [50, 51]. In computational drug discovery, intermolecular interaction in molecular docked pose of Metformin (control drug) confirmed its binding to the active site of catalytic subunit alpha-1of 5'-AMP-activated protein kinase (AMPK1) target of the human. The Figure-2 (A to D) shows the binding of metformin with AMPK1 chain A, catalytic pocket residue ARG199 and interactions between them. They interact with 2 Conventional H-bond interactions with Leu200, 6 attractive Charge interactions with GLU205, GLU194, one Salt Bridge: Attractive Charge interaction with GLU205, 2 Pi-Cation interactions with PHE180 and one unfavourable Donor-Donor interaction with ALA202. The binding affinity energy of the metformin with AMPK1 is -4.0 Kcal/mol (Table-9). The Figure-2 (E to H) shows the binding of Vindoline (CID: 425978) with AMPK1 chain A, catalytic pocket residue ARG199 and interactions between them. The bioactive compound, vindoline (CID: 425978) molecular docked pose shows strong intermolecular interaction and is attach to the active site of the AMPK1 by noncovalent interaction and having minimum binding affinity energy is -6.3Kcal/mol. Among non-covalent bonds there are 2 conventional H-bond interactions with LEU462, ARG182; 3 Carbon H-bond interactions with GLY198, GLN461, one Alkyl interaction with LEU200 and one Pi-Alkyl interaction with TYR463 (Table-9). The binding affinity of the metformin with AMPK1 is -4.0 Kcal/mol whereas the binding affinity of vindoline (CID: 425978) is – 6.3 Kcal/mol. The lower free energy of binding of vindoline may be due to comparatively more strong noncovalent interaction at lower distance between the bioactive compound and AMPK1.

Ampk2-ligand Interaction

Intermolecular interaction in the molecular docked position of the Metformin (control drugs) confirmed that they interact with the active site of catalytic subunit alpha-2 of 5'-AMP-activated protein kinase (AMPK2) chain A, catalytic pocket residues GLU264. The binding of AMPK2 with metformin where 4 conventional H-bond interactions with ASP261, HIS247, THR243; one Carbon H-bond interaction with ASP261; 2 with Attractive Charge interactions with ASP261 as shown in (Figure-3). The binding affinity energy of the metformin with AMPK2 is -4.2 Kcal/mol (Table-10). The mode of action of metformin with AMPK2 is the same as AMPK1, but it also promotes internalization and recycling of the insulin receptor (INSR).

The minimum binding affinity conformer molecular docked pose (Figure-3) of the C roseus active compound, vindoline (CID: 425978) binding, has confirmed that it also definitely goes and binds to the active site of the AMPK2 chain A, around catalytic pocket residues GLU264 with two conventional H-bond interactions ARG263, two Carbon H-bond interactions with ARG263, LYS260, 2 Alkyl interactions with LEU272, ARG263; one Attractive Charge interaction with GLU279 and one unfavorable Acceptor-Acceptor Interaction with ASP280 (Table-10). On comparing the binding affinity of metformin (-4.2 Kcal/mol) with the binding affinity of vindoline (CID: 425978) on the AMPK2 target, we found that vindoline has the greater binding affinity energy than control one with – 6.2 Kcal/mol, it shows that the lead is more potent activator of AMPK2 so it may control and regulate hyperglycemia and its complication in better way as a first line of antidiabetic drug.

5) Molecular docking analysis of control drugs (Vildagliptin) and Vindoline (CID: 425978) with the DPP4

The Insilico result of minimum binding affinity conformer of the molecular docked pose of the vildagliptin and DPP4 target interaction verified that binding to vildagliptin in the active site of the target protein of human (See Figure-4.) and 2D (two dimensional) plot. Vildagliptin binds with DPP4 chain B, around catalytic pocket residues LYS554 and interactions between them through 5 conventional H-bonds with TYR547, TYR666, ASN710, SER630, HIS740; one Carbon H-bond interaction with GLY741; one Pi -Donar H-bond interaction with TRP629; 2 Pi-Alkyl interactions with TRP629; one Attractive charge interaction with GLU205, one unfavorable positive-positive interaction with ARG125 (Table-11).

The final minimal potential energy of molecular dock pose (Figure-4) the vindoline and DPP4 target protein is -6.8 Kcal/mol and without root mean square deviation. The docking result establishes that they also definitely go and bind to the active site of the DPP4 chain B, around catalytic pocket residues LYS554 through 2 Conventional H-bond interactions with LYS554; 2 Carbon H-bond interaction with ASP545, VAL546; one Pi-Alkyl interaction with TYR547; 2 Pi-Cation interaction with TRP629 (Table-11). On comparing the binding affinity of vildagliptin (-6.7 Kcal/mol) with the binding affinity of vindoline (CID: 425978) (– 6.8 Kcal/mol), we found that the biomolecules have a greater binding affinity with DPP4 target than control one so it may become a potent DPP-4 inhibitor and are a good therapeutic alternative for treatment of the disease.

6) Molecular Docking Analysis Of Acarbose (Control Drugs) And Vindoline (Cid: 425978) With The α-glucosidase

α-glucosidase participates in the breakdown of oligosaccharides (up to heptasaccharides) into monosaccharides in the small intestine. The intermolecular interaction of molecular docked pose of the Acarbose (control drugs) demonstrated that they bind to the active site of the α-glucosidase protein of the human (See Figure-5.) and 2D (two dimensional) plot. Insilico results show that acarbose binds with chain A of α-glucosidase around catalytic active site ASP2175 through 6 conventional H-bonds with TYR2147, ASP2177, MET2179, ARG2181, ASP2253, THR2482; one carbon H-bond with GLU2180; two Pi alkyl bond with TRP2251, TRP2265 and one Unfavorable Donor-Donor interaction with TYR2147. The binding affinity energy and interaction details of the acarbose drugs are mentioned in the Table-12. Acarbose, via competitive inhibition mechanism, inhibits oligosaccharide binding to an alpha-glucosidase enzyme. The final minimal potential energy of molecular dock poses the acarbose and the target protein is -6.7 Kcal/mol.

C. roseus alkaloid vindoline (CID: 425978) binds strongly to the active site of α-glucosidase target through several non-covalent interactions (Table-12, Figure-5). The interactions between vindoline and α-glucosidase are three conventional H-bonds with ARG2181, SER2188; and 2 carbon H-bonds with GLN2182, VAL2259; 3 Alkyl interactions with ARG2181, PRO2189, VAL2259 and one Attractive Charge interaction with ASP2253. Binding affinity of the acarbose (-6.7 Kcal/mol) is lower than the binding affinity of vindoline (CID: 425978) – 6.8 Kcal/mol. The chimera and vina score levels show that the alkaloid has higher negative binding affinity energy than acarbose with – 6.8 Kcal/mol, showing it has a strong α-gluosidase enzyme inhibitory ability and better capability to regulate the rise in post prandial blood glucose level than acarbose.

7) Molecular docking analysis of control drugs (Pioglitazone) and C. roseus (Vindoline, CID: 425978) active compounds with the PPARy

Computational calculation of the minimum binding affinity molecular docked pose of pioglitazone and PPARy conformer established that pioglitazone binds with chain B, around catalytic site ASN403 of the PPARy target (See Figure-6). 3D (three dimensional ) and 2D (two dimensional) plot Showed that Pioglitazone binds through 2 conventional H-bond interactions with LYS364, ARG262; one Carbon H-bond interaction with GLU406; one Pi-Cation interaction with ARG262, one Pi-Anion interaction with GLU406; one Pi-Alkyl interaction with ARG262; two Alkyl interaction with ARG262, LYS258. The Chimera and Vina score result shows its potential energy of interaction is -6.4 Kcal/ mol (Table-13).

The molecular docking of vindoline (CID: 425978) with PPARy (Figure-6) has shown it’s minimum binding affinity conformer, noncovalent interactions and establish that vindoline firmly binds to the around catalytic site ASN403, chain B of the PPARy through 3 conventional H-bond interaction with ARG262; one Carbon H-bond interaction with GLU406 (Table-13). The best conformer minimal potential energy of molecular docked pose at a selected co-ordinate of vindoline and PPARy target protein is -6.1 Kcal/mol whereas PPARy-Pioglitazone is -6.4 Kcal/mol.

Moreover, Pioglitazon (-6.4 Kcal/mol) can bind tightly with PPARy [52] and stimulate the nuclear receptor, and a similar interaction is detected in vindoline (– 6.1 Kcal/mol) too. Thus, the alkaloid has good affinity with receptors and may control the PPARy activity. The computational result shows that the bimolecules may become a PPARy agonist drug and are effective as a 4th line of antidiabetic drug (patients having cardiac problems) too [53].

Our research with vindoline showing similar docking behaviour with respect to above mentioned standard drug, overall result in the hypothesis of following multilevel drug target mechanism against diabetes as shown in Figure-7

Since, diabetes promotes multifactorial disease condition in chronic cases. In this research we observed that a natural compound vindoline (CID: 425978) act as a potent stimulator of AMPK than 1st line drug metformin. The binding affinity of vindoline alkaloid with AMPK1 and AMPK2 are – 6.3 Kcal/mol and – 6.2 Kcal/mol whereas metformin are – 4.0 Kcal/mol and – 4.2 Kcal/mol respectively. The lower binding free energy of vindoline may be due to formation of more stable noncovalent interactions (like H-bond, carbon H-bond interactions etc.). Similarly, interaction between vindoline and α/β-tubulin (PDB 1Z2B) (docking result − 7.28 Kcal/mol) had showed that vindoline enhance defence mechanism in plant and had inhibitory effect on cancer cells [54].

The research highlighting that vindoline is more potent stimulator of AMPK than metformin. So, able to promote phosphorylation activity of metabolic enzymes and decreases intracellular ATP levels, inhibits mitochondrial complex I of the electron transport chain and encourages peripheral glucose consumption through anaerobic glycolysis [8], increases GLP-1 plasma levels, stimulate peroxisome proliferator-activated receptor (PPARγ) mechanism and overcome Insulin resistance. As a result it decrease hepatic glucose production, promotes intake of glucose in skeletal muscle, reduces blood glucose level [10–13]. As a result, it prevents biosynthesis of protein, carbohydrate and lipids and inhibits cell growth and proliferation [10].

Target DPP4 is involved in degradation of incretin and promote blood glucose level. Docking results shows that the binding affinity of vindoline (CID: 425978) – 6.8 Kcal/mol is greater than DPP4 inhibitor, vildagliptin (-6.7 Kcal/mol). So, it may become a potent DPP-4 inhibitor and may become a good 2nd line therapeutic alternative for treatment of diabetes. It is reported that MERS-CoV virus bind with DPP4 and penetrate inside cell [55] and vindoline binds strongly with beta-corona virus polymerase (docking energy score − 6.8 kcal/mol) by interacting its catalytic residues ASP644 and inhibit the enzymatic activity, replication and multiplication of virus [56].

Our research supports that vindoline acts as potent inhibitor of Dipeptidyl Peptidase-4 (DPP4) enzyme. Thus, binds competitively and selectively to target site and inhibit degradation of incretin GLP-1 and GIP [15], influences insulin secretagogues and stimulate postprandial insulin discharge, decreases glucagon secretion and lowers as fasting blood glucose in type 2 diabetics [16]. At the same time also support that by inhibiting DPP4 check corona virus infection and seriousness in diabetic patient too.

Target α-glucosidase promotes glucose absorption in intestine and increases blood glucose level. Current research shows that binding affinity of the acarbose (-6.7 Kcal/mol) is lower than the binding affinity of vindoline (CID: 425978) – 6.8 Kcal/mol. The outcome of the study supports that vindoline has a strong α-gluosidase inhibition ability and become a potent 3rd line drug for diabetes. So, it has better capability to decrease and regulate the plasma glucose concentration after meal. Similarly, substituted aryl aldehyde inhibits α-glucosidase by binding to its C-terminal (IC₅₀ values = 5.31 to 53.34 µM) [57]. As per our research finding α-glucosidase inhibitor vindoline can inhibits oligosaccharide binding to α-glucosidase enzyme and or delays their cleavage into monosaccharides from the active site of the α-glucosidase enzyme [7, 14]. So it slows down monosaccharide formation and its absorption and as a result checks the increase in postprandial glucose level.

As per the inslico docking score of agonist Pioglitazon with PPARy is -6.4 Kcal/mol and vindoline is – 6.1 Kcal/mol. This shows that the vindoline alkaloids have good affinity with nuclear receptors and efficiently stimulate PPARy activity. Moreover, PPARy agonist acts as a 4th line drug and good therapeutic effect in diabetes induced cardiac problem cases. Docking studies result (-5.75 Kcal/mol) of Quadrangularin A of Cissus quadrangularis Linn with PPARy was show that it has crucial role in management of chronic cases of diabetes [58]. Hence, binding of vindoline with peroxisome proliferators activate the nuclear receptor PPARy followed by activated PPARy binds with PPAR response elements (PPRE) of DNA and controls the transcription of genes of glucose and lipids metabolism (e.g. acyl-CoA oxidase). Signals generated from the adipose tissue, e.g adiponectin or leptin, may mediate the improvement in skeletal glucose disposal [20]. Also, PPARy agonist promotes insulin sensitivity and insulin stimulated glucose acceptance in peripheral tissues (hepatic, muscle, and adipose tissue) and differentiation of pre-adipocytes [19, 52]. As a result it may be able to controls the glucose homeostasis, beta-oxidation pathway of fatty acids of peroxisome and regulates adipocyte differentiation [10].

Also, in vitro study in wistar rats, the antihyperglycemic effect of vindoline was tested and the result confirmed that vindoline decreases fasting blood glucose level (FBG) (p < 0.05) and hyperglycemia-induced hepatic injury and enhanced the in vitro insulin secretion and improves both the hepatic and pancreatic tissues [59]. Also, in diabetes induced kidney disorder, the structure of the renal parenchyma is improved and significantly reduced caspase 9 expression, lipid peroxidation [60]. It reduces glucose induced toxicity and intracellular reactive oxygen species generation in cells [61]. Many researchers reported that C. roseus has the hypoglycemic effect better than existing alpha-glucosidase inhibitors and biguanides and also nullifies the possibility of diarrhoea-related toxicity [13]. A lot of research reported that ethanolic Catharanthus roseus extract significantly reduces the high glucose level as well as improves in different antioxidant defence systems (P ≤ 0.05) in both serum and RBC and histopathological studies supporting the healing of pancreas beta- cells with its regeneration possibilities [61]. Our prediction model studies supported with experimental validation and proved that the biomolecule has ability to become a potent multi-targeting antidiabetic drug which can treat primary stage to chronic cases of diabetes.

Present studies, provide evidence for 3D confirmation of vindoline having pubchem CID: 425978 have potential to act on multiple target of glucose regulation pathway and show more efficient binding energy potential than standard antidiabetic drug. Also, vindoline has antiviral property and able to inhibit binding and replication of human corona virus. On the basis of above outcome we can say that the biomolecules can become a potent antidiabetic drug which is able to control blood glucose level as well as its secondary complication. Furthermore, it addresses the futuristic vital pharmacological issues of diabetes mellitus and will open multiple therapeutic drug target disease related research possibilities.

AMPK- AMP-activated protein kinase

DPP4 - Dipeptidyl Peptidase-4

PPARᵧ- Peroxisome Proliferator-Activated Receptor Gamma

GLUT4- Glucose transporter 4

UNL-Ligand

LEU- Leucine

GLU- Glutamic acid

PHE- Phenylalanine

ALA- Alanine

ARG: Arginine

GLY: Glycine

GLN: Glutamine

TYR: Tyrosine

ASP- Aspartic acid

HIS- Histidine

THR- Threonine

LYS- Lysine

TYR- Tyrosine

ASN- Asparagine

SER- Serine

GLY- Glycine

TRP- Tryptophan

ASP- Aspartic acid

VAL- Valine

MET- Methionine

PRO- Proline

Ethical statements: Current research article work was performed by computational tool, without using any scientific lab animals, human samples or any human patients. Thus, no official permission was taken from our research team against the research article from the ethical committee of our university and government.

Acknowledgment: Richa Goyal is thankful to Dr. Mukesh Nitin (Head, Department of Tech. Data Sci. & Biosciences, Digianalix, Ranchi, Jharkhand, INDIA) for guidance and feedback.

Funding: The authors have received no external financial support for the research and publication of this article

Availability of data and materials: In this research article, the data was generated during the research, extensively studied and screened from various research and review articles related directly and indirectly to our concerned topics and this is fully documented and cited in detail in our references.

Authors’ contributions: Richa Goyal contributed study, compiling, analysis, writing of the manuscript; Professor M Anwar Mallick helped with proof reading of the research article. Also, Assistant Professor Manoj Kumar helped with proof reading of the research article.

Conflicting Interest: All the authors declared no conflict of interests. Also, there is no conflict of interest associated with any of the senior authors or others contributing their efforts to this manuscript.

Consent for publication and Ethical approval: As per PhD university guidelines, the review article is included as a part of my PhD research work. So, officially, there is consent of publication approval from my Supervisor Professor M Anwar Mallick and co-supervisor Assistant Professor Manoj Kumar, who are already co-authors, on this publication. However, no ethical approval is required for this review article. Thus, no official permission was taken from our research team against the review article from the ethical committee of our university and government because in this review no scientific lab animals, human samples or any human patients were used in the study.

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Table 1: Physiochemical property of target proteins (Uniprot and ProtParam value).

Proteins	Uniprot/ Pubmed ID	Length (Amino acid)	Molecular weight	pI	Instability index	Aliphatic index	Grand average of hydropathicity (GRAVY)	Extinction coefficients (M-1 cm-1, at 280 nm)	Estimated half-life (mammalian reticulocytes, in vitro)
AMPK	AAPK1_HUMAN (Q13131)	559AA	64009.23	8.32	49.44	84.03	-0.461	60905	30 hours
	AAPK2_HUMAN (P54646)	552AA	62319.61	7.65	49.20	85.11	-0.295	53290	30 hours
DPP-4	DPP4_HUMAN (P27487)	766 AA	88278.63	5.67	43.35	80.29	-0.340	198940	30 hours
α-glucosid -ase	MGA_HUMAN (O43451)	2,753 AA	312022.39	5.21	38.54	78.08	0.336	590100	30 hour
PPARƴ	PPARG_HUMAN (P37231)	505AA	57620.15	5.61	50.20	86.32	-0.308	32320	30 hours

Table 2: Secondary structure detail of target protein (SOPMA result).

Proteins	Sequence Length	Alpha helix(Hh)	310 helix(Gg)	Pi helix(Ii)	Beta bridge(Bb)	Extended strand(Ee)	Beta turn(Tt)	Bend region(Ss)	Random coil(Cc)	Ambiguo--us states	Other states
AMPK1	559	30.95%	0.00%	0.00%	0.00%	17.35%	6.80%	0.00%	44.90%	0.00%	0.00%
AMPK2	552	27.72%	0.00%	0.00%	0.00%	17.39%	8.15%	0.00%	46.74%	0.00%	0.00%
DPP-4 inhibitors	766	18.15%	0.00%	0.00%	0.00%	29.63%	6.53%	0.00%	45.69%	0.00%	0.00%
α-Glucosidase	2753	18.99%	0.00%	0.00%	0.00%	25.67%	6.85%	0.00%	48.49%	0.00%	0.00%
PPARƴ activator	505	45.94%	0.00%	0.00%	0.00%	11.29%	5.54%	0.00%	37.23%	0.00%	0.00%

Table 3: SOSUI prediction about nature of target protein.

Receptor	Protein nature	No	Region	Transmembrane seq	Type
AMPK1	Soluble protein
AMPK2	Membrane Protein	1	6-28	KVLLGLLGAAALVTIITVPVVLL	Primary
DPP4	Membrane Protein	1	6-28	KVLLGLLGAAALVTIITVPVVLL	Primary
α-glucosidase	Membrane Protein	1	12-34	LEIVLSVLLLVLFIISIVLIVLL	Primary
PPARγ	Soluble protein

Table 4: QMEAN Scores for the model protein generated by using SWISS- MODEL server.

Receptor	Template	Model Quality Estimation (QMEAN Scores)	Sequence Identity	Coverage	Method	Resolution
AMPK activator- 1	4rer.1.A	0.68 ± 0.05	99.07	0.97	X-ray	4.05Å
AMPK activator- 2	7myj.2.A	0.73 ± 0.05	99.82	1.00	X-ray	2.95Å
DPP-4 inhibitors	2qt9.1.A	0.91 ± 0.05	99.87	1.00	X-ray	2.10Å
α-Glucosidase	3ton.1.A	0.83 ± 0.05	98.99	0.32	X-ray	2.95Å
PPARy receptor	3e00.1.B	0.73 ± 0.05	100.00	0.80	X-ray	3.10Å

Table1 6: Library of vindoline alkaloid their similar structures and control drug (Pubchem CID).

Vindoline alkaloid their similar structures (Pubchem CID)				Control
CID: 260535	CID: 25203293	CID: 129316872	CID: 11969850	Metformin(CID:4091 )
CID: 16667702	CID: 24728631	CID: 124040752	CID: 5459818	Vidagliptin (CID:6918537)
CID: 16596	CID: 11953805	CID: 40473139	CID: 3081522	Acarbose (CID:445421)
CID: 129320270	CID: 425978	CID: 25203244	CID: 439783	Pioglitazon (CID:4829 )
CID: 25203805	CID: 260534	CID: 12444852	CID: 179551
			CID: 132599668

Table 8: Drug likeness prediction by using OSIRIS Property Explorer, clog P (O/W): Logarithm of partition coefficient between n-octanol and water, logS: aqueous solubility, TPSA: Topological polar surface area.

Compound name	Title (Pubchem CID	Toxicity Risk	clogP	Log S (Solubility)	Molecular weight	TPSA	Drug likeness	Drug score	BBB
Vindoline	CID: 425978	G	1.32	-3.12	456	88.54	3.95	0.74	Fail
Metformin	CID: 4091	G	-1.54	-0.13	129	91.49	1.21	0.88	Pass
Vildagliptin	CID: 6918537	G	0.56	-2.67	303	76.36	0.66	0.75	Pass
Acarbose	CID:445421	G	-7.18	0.59	645	321.1	-7.4	0.29	Fail
Pioglitazone	CID: 4829	G	3.08	-3.83	356	93.59	5.02	0.76	Pass

Table 9: Molecular docking analysis: Binding energy and the type of molecular interaction between Metformin (control drugs) and active compound of C. roseus Vindoline (CID: 425978) against target AMPK1. UNL: C. roseus active compounds, LEU: Leucine, GLU: Glutamic acid, PHE: Phenylalanine, ALA: Alanine, ARG: Arginine, GLY: Glycine, GLN: Glutamine, TYR: Tyrosine.

Compounds	Vina score /Binding affinity (kcal/mol)	ligand	Distance(Å)	Target protein	Category	Type of interaction
AMPK1-Metformin (Control)	-4.0	UNL1:H	2.45	A: LEU200: O	Hydrogen bond	Conventional H-bond interaction
		UNL1:HN2	2.20	A: LEU200: O	Hydrogen bond	Conventional H-bond interaction
		UNL1:N	5.57	A:GLU205:OE2	Electrostatic attraction	Attractive Charge interaction
		UNL1:N	4.27	A:GLU205:OE2	Electrostatic attraction	Attractive Charge interaction
		UNL1:N	3.95	A:GLU205:OE2	Electrostatic attraction	Attractive Charge interaction
		UNL1:N	3.06	A:GLU194:OE2	Electrostatic attraction	Attractive Charge interaction
		UNL1:N	4.79	A:GLU194:OE2	Electrostatic attraction	Attractive Charge interaction
		UNL1:N	2.96	A:GLU194:OE2	Electrostatic attraction	Attractive Charge interaction
		UNL1:H	1.84	A:GLU205:OE2	Electrostatic attraction	Salt Bridge; Attractive Charge interaction
		UNL1:N	4.16	A:PHE180	Non covalent molecular interaction	Pi-Cation interaction
		UNL1:N	4.91	A:PHE180	Non covalent molecular interaction	Pi-Cation interaction
		UNL1:H	2.55	A:ALA202:HN	Intrinsic interactions	Unfavourable Donor-Donor interaction
AMPK1-Vindoline (Test)	-6.3	UNL1:NH	2.30	A:LEU462:O	Hydrogen bond	Conventional H-bond interaction
		UNL1:O	2.31	A:ARG 182:HH22	Hydrogen bond	Conventional H-bond interaction
		UNL1:C	3.62	A: GLY198:O	Hydrogen bond	Carbon H-bond interaction
		UNL1:C	3.59	A: GLY198:O	Hydrogen bond	Carbon H-bond interaction
		UNL1:C	3.56	A:GLN461:OE1	Hydrogen bond	Carbon H-bond interaction
		UNL1	5.20	A:LEU200	Hydrophobic	Alkyl interaction
		UNL1	5.47	A:TYR463	Hydrophobic	Pi-Alkyl interaction

Table 10: Molecular docking analysis: Binding energy and the type of molecular interaction of control drugs and active compound of C. roseus against target AMPK2. UNL: C. roseus active compounds, ASP: Aspartic acid, HIS: Histidine, THR: Threonine, ARG: Arginine, LYS: Lysine, LEU: Leucine, GLU: Glutamic acid.

Compounds	Vina score /Binding affinity (kcal/mol)	ligand	Distance(Å)	Target protein	Category	Type of interaction
AMPK2-Metformin (Control)	-4.2	UNL1:HN	2.33	A: ASP261:O	Hydrogen bond	Conventional H-bond interaction
		UNL1:H	2.11	A: ASP261:O	Hydrogen bond	Conventional H-bond interaction
		UNL1:HN2	2.40	A:HIS247:ND1	Hydrogen bond	Conventional H-bond interaction
		UNL1:H	2.40	A: THR243:O	Hydrogen bond	Conventional H-bond interaction
		UNL1:C	3.54	A:ASP261:OD1	Hydrogen bond	Carbon H-bond interaction
		UNL1:N	4.45	A:ASP261:OD1	Electrostatic attraction	Attractive Charge interaction
		UNL1:N	5.18	A:ASP261:OD1	Electrostatic attraction	Attractive Charge interaction
AMPK2-Vindoline (Test)	-6.2	UNL1:O	2.21	A:ARG263:HH21	Hydrogen bond	Conventional H-bond interaction
		UNL1:O	2.29	A:ARG263:HH21	Hydrogen bond	Conventional H-bond interaction
		UNL1:O	2.80	A:ARG263:HD2	Hydrogen bond	Carbon H-bond interaction
		UNL1:O	2.62	A:LYS260:HA	Hydrogen bond	Carbon H-bond interaction
		UNL1:C	5.22	A:LEU272	Hydrophobic	Alkyl interaction
		UNL1:C	3.87	A:ARG263	Hydrophobic	Alkyl interaction
		UNL1:N	5.36	A:GLU279:OE2	Electrostatic attraction	Attractive Charge interaction
		UNL1:O	2.97	A:ASP280:OD1	Intrinsic interactions	Unfavourable Acceptor-Acceptor Interaction

Table 11: Molecular docking analysis: Binding energy and the type of molecular interaction of control drugs and active compound of C. roseus against DPP4. UNL: C. roseus active compounds. UNL: C. roseus active compounds, TYR: Tyrosine, ASN: Asparagine, SER: Serine, HIS: Histidine, GLY: Glycine, TRP: Tryptophan, GLU: Glutamic acid, ARG: Arginine, LYS: Lysine, ASP: Aspartic acid, VAL: Valine.

Compounds	Vina score /Binding affinity (kcal/mol)	ligand	Distance(Å)	Target protein	Category	Type of interaction
DPP4-Vidagliptin (Control)	-6.7	UNL1:N	2.45	B:TYR666:HH	Hydrogen bond	Conventional H-bond interaction
		UNL1:N	2.66	B:TYR547:HH	Hydrogen bond	Conventional H-bond interaction
		UNL1:O	2.87	B:ASN710:HD21	Hydrogen bond	Conventional H-bond interaction
		UNL1:O	2.53	B:SER630:HG	Hydrogen bond	Conventional H-bond interaction
		UNL1:H	2.73	B:HIS740:O	Hydrogen bond	Conventional H-bond interaction
		UNL1:O	2.45	B:GLY741:HA1	Hydrogen bond	Carbon H-bond interaction
		UNL1:H	2.52	B:TRP629	Hydrogen bond	Pi -Donor H-bond interaction
		UNL1	4.29	B:TRP629	Hydrophobic	Pi-Alkyl interaction
		UNL1	5.25	B:TRP629	Hydrophobic	Pi-Alkyl interaction
		UNL1:N	4.57	B:GLU205:OE2	Electrostatic attraction	Attractive charge interaction
		1:UNL1:N	4.77	B:ARG125:NH2	Intrinsic interaction	Unfavorable positive-positive interaction
DPP4-Vindoline (Test)	-6.8	UNL1:O	2.74	B:LYS554:HZ3	Hydrogen bond	Conventional H-bond interaction
		UNL1:O	2.94	B:LYS554:HZ1	Hydrogen bond	Conventional H-bond interaction
		UNL1:C	3.71	B:ASP545:OD2	Hydrogen bond	Carbon H-bond interaction
		UNL1:C	3.80	B: VAL546: O	Hydrogen bond	Carbon H-bond interaction
		UNL1	5.35	B:TYR547	Hydrophobic	Pi-Alkyl interaction
		UNL1:N	4.32	B:TRP629	Non covalent molecular interaction	Pi-Cation interaction
		UNL1:N	4.81	B:TRP629	Non covalent molecular interaction	Pi-Cation interaction

Table 12: Molecular docking analysis: Binding energy and the type of molecular interaction of control drugs and active compound of C. roseus against alpha glucosidase. UNL: C. roseus active compounds, THR: Threonine, TYR: Tyrosine, ASP: Aspartic acid, MET: Methionine, ARG: Arginine, GLU: Glutamic acid, TRP: Tryptophan, VAL: Valine, GLN: Glutamine, SER: Serine, PRO: Proline.

Compounds	Binding affinity (Kcal/mol)	ligand	Distance(A°)	Receptor	Category	Type of interaction
α- glucosidase Acarbose (Control)	-6.7	UNL1: O	3.02	A:THR2482:HG1	Hydrogen bond	Conventional Hydrogen bond interaction
		UNL1: H	1.97	UNL1: O	Hydrogen bond	Conventional Hydrogen bond interaction
		UNL1: O	2.03	A:TYR2147:HH	Hydrogen bond	Conventional Hydrogen bond interaction
		UNL1: H	2.53	A:ASP2177:OD1	Hydrogen bond	Conventional Hydrogen bond interaction
		UNL1: H	2.90	A:ASP2253:OD1	Hydrogen bond	Conventional Hydrogen bond interaction
		UNL1: H	2.53	A: MET2179: O	Hydrogen bond	Conventional Hydrogen bond interaction
		UNL1: O	2.03	A:ARG2181:HN	Hydrogen bond	Conventional Hydrogen bond interaction
		UNL1: O	2.67	A:GLU2180:HA	Hydrogen bond	Carbon Hydrogen bond interaction
		UNL1: C	5.03	A:TRP2265	Hydrophobic	Pi-Alkyl interaction
		UNL1: C	5.48	A:TRP2251	Hydrophobic	Pi-Alkyl interaction
		UNL1: H	1.55	A:TYR2147:HH	Intrinsic interactions	Unfavorable Donor-Donor interaction
α- glucosidase -Vindoline (Test)	-6.8	UNL1:C	3.61	A:VAL2259	Hydrogen bond	Carbon Hydrogen bond interaction
		UNL1:C	3.48	A:GLN2182:OE1	Hydrogen bond	Carbon-Hydrogen bond interaction
		UNL1: O	2.81	A:ARG2181:HN	Hydrogen bond	Conventional Hydrogen bond Interaction
		UNL1: O	2.07	A:SER2188:HG	Hydrogen bond	Conventional Hydrogen bond Interaction
		UNL1: O	2.07	A:SER2188:HG	Hydrogen bond	Conventional Hydrogen bond Interaction
		UNL1	4.81	A:VAL2259	Hydrophobic	Alkyl interaction
		UNL1:C	4.28	A:ARG2181	Hydrophobic	Alkyl interaction
		UNL1:C	5.12	A:PRO2189	Hydrophobic	Alkyl interaction
		UNL1:N	5.32	A:ASP2253:OD1	Electrostatic attraction	Attractive Charge interaction

Table 13: Molecular docking analysis: Binding energy and the type of molecular interaction of control drugs and active compound of C. roseus against PPARy. UNL: C. roseus active compounds, LYS: Lysine, ARG: Arginine, GLU: Glutamic acid.

Compounds	Vina score /Binding affinity (kcal/mol)	ligand	Distance(Å)	Target protein	Category	Type of interaction
PPARy –Pioglitazone (Control)	-6.4	UNL1:O	2.18	B:LYS364:HN	Hydrogen bond	Conventional H-bond interaction
		UNL1:O	2.82	B:ARG262:HH12	Hydrogen bond	Conventional H-bond interaction
		UNL1:C	3.29	B:GLU406:OE1	Hydrogen bond	Carbon H-bond interaction
		UNL1	3.92	B:ARG262:NH2	Non covalent molecular interaction	Pi-Cation interaction
		UNL1	4.95	B:GLU406:OE1	Non covalent molecular interaction	Pi-Anion interaction
		UNL1	5.49	B:ARG262	Hydrophobic	Pi-Alkyl interaction
		UNL1:C	4.21	B:ARG262	Hydrophobic	Alkyl interaction
		UNL1:C	4.09	B:LYS258	Hydrophobic	Alkyl interaction
PPARy-Vindoline (Test)	-6.1	UNL1:O	2.51	B:ARG262:HH11	Hydrogen bond	Conventional H-bond interaction
		UNL1:O	2.07	B:ARG262:HH11	Hydrogen bond	Conventional H-bond interaction
		UNL1:O	2.02	B:ARG262:HH12	Hydrogen bond	Conventional H-bond interaction
		UNL1:O	2.87	B:GLU406:HA	Hydrogen bond	Carbon H-bond interaction

Tables 5 and 7 is available in the Supplementary Files section.

No competing interests reported.

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Drug innovation studies targeting Diabetes: A computational docking approach on muti-drug targets including COVID Inhibitors

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Abstract

Figures

Background

Methods And Materials

1) Selection Of Drug Target

2) Identification Of Physiochemical Properties Of The Protein

3) Preparation Of Target Proteins

4) Selection Of Ligands And Library Preparation

5) Admet And Drug Likeness Property Prediction

6) Preparation Of Ligands

7) Docking Studies

Results

2) Sequence Analysis And Active Site Identification Of Protein

3) Admet And Drug Likeness Prediction

Ampk1- Ligand Interaction

Ampk2-ligand Interaction

6) Molecular Docking Analysis Of Acarbose (Control Drugs) And Vindoline (Cid: 425978) With The α-glucosidase

Discussion

Conclusion

Abbreviations

Declarations

References

Tables

Additional Declarations

Supplementary Files

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