INTRODUCTION:

Euryale ferox salisb, a member of Nympheaceae species, an aquatic plant with rooted macro-hydrophyte and gigantic floating leaves grown as a crop in mithilanchal area of North Bihar¹. The common name given to the plant is Prickly water lilly or Gorgon Nut. The gorgon nut's popped extended kernel is known as Makhana or fox nut².

Makhana has many bioactive constituents which perform different activities such as anti-oxidant, anti-diabetic, anti-hyperlipidemic, hepatoprotective, anti-cytotoxic, anti-depressant and cardioprotective³. Makhana is considered a super food as it contains a variety of phytochemicals such as polyphenols, triterpeniods, alkaloids, flavonoids, essential oils, glycosides and polysaccharides. Research in modern pharmacology has documented many of makhana's bioactive properties, including its effects against inflammation, viruses, tumors, hypertension, hyperglycemia, hyperlipidaemic, renal disorders, oxidation, and, damage caused by free radicals⁴. Makhana is nutrient dense full of proteins, fiber, carbohydrates, vitamins, minerals and polyphenols. The nutritive value of Makhana per 100gram of edible portion is 12.8g moisture, 9.7g protein, 0.1g fat, 76.9g carbohydrate, 0.5g minerals, 20mg Calcium, 90mg phosphorus and 1.45g iron. They are rich in amino acids, magnesium, potassium and phosphorus⁵. With a rich nutritional profile and abundant health benefits, it is regarded as a functional food.

Islet autoantibodies are the principal indicators of the autoimmunity caused in pancreas. They are also the biomarkers used to diagnose islet autoimmunity (AID). Insulin (IAA), IA-2 (IA-2A), GAD 65 (GADA), ZnT8 transporter (ZnT8A), and Tetraspanin-7 (TSPAN7A) are some of these biomarkers. Presence of islet autoantibody biomarkers indicate increased sugar levels in the blood, a condition known as hyperglycemia, which occurs due to insulin deficiency as a consequence of loss of islet β-cells of the pancreas⁶. Autoantibodies against islet cell antigens are helpful in detecting patients who are at a greater risk of developing type 1 diabetes. The presence of multiple autoantibodies is a strong predictor of future T1D⁷. They can be detected before the beta cells are completely destroyed. As the disease progresses, 80%–90% of patients may already have detectable autoantibodies. Most of the patients have clinical manifestations of ketoacidosis, weight loss, exhaustion, and characteristic osmotic symptoms⁸. T1DM-associated autoimmunity leads to the formation of type 1 diabtes -associated auto-antibodies and death of beta cells in around 70–90% of patients with autoimmune T1DM. Islet auto-antibodies are considered indicators of the onset of autoimmunity rather than being pathogenetic⁹. A crucial component of the aetiology of IDDM is the presence of diabetogenic substances in the diet. In addition to being a significant predictor of diabetes, diet has a significant role in defining the course of the disease. Previous studies have demonstrated that the dietary regulation of diabetogenesis is a process that builds over time and is not trigger-like in the early stages of infancy. Food's ability to either increase or decrease the manifestation of diabetes is thus a time and dose-dependent interaction¹⁰. In this study, we found the bioactivity of the compound Ferotocotrimer E of Euryale ferox as a potential compound to inhibit the development of islet autoantibodies among Type 1 diabetes mellitus based on the in silico study.

MATERIALS AND METHODS:

Preparation of Ligand:

The phytochemicals described on Euryale ferox were obtained from plant ethanobotanical databases, such as Dr. Duke's Ethanobotanical Database, and research papers^11,12. Some of the information was obtained from Phytochemistry and Therapeutics of Indian Medicinal Plants (IMPPAT).Makhana contains 49 phytochemicals, some of which are utilized in various kinds of clinical research. The Canonical SMILES of the Euryale Ferox compounds are retrieved from Pubchem and Marvin sketch. The SMILES are then uploaded on SwissADME database to predict the drug likeness.

Preparation of Target Protein:

The SMILES are fed into Stitch database to predict the target proteins that are found to be interacting with the 49 compounds of Euryale Ferox. The Protein targets and its family details were predicted from string database.

Disease specific Target prediction:

The disease specific targets were predicted using Genecards and compared with the compound specific targets to find the hits which resulted into 56 common targets.

Common Targets:

The common targets were discovered to be shared by both disease targets and compound targets. Out of which Peroxisome proliferator-activated receptor gamma (PPARG) was found to be the most associated with Type 1 diabetes as per KEGG database. Hence initial molecular docking screening for all the 49 compounds was conducted against PPARG (Uniprot ID: A0A0S2Z4K5) using Autodock vina. The target protein sequence was retrieved from Uniprot and modeling was done using Swiss model.

Molecular Docking:

Preparation of Ligand And Target Protein:

The docking procedure made use of generated protein models. The three-dimensional structures of Model Proteins were examined for errors using the Pymol program. Similarly, the Pymol Visualization Tool was also used to eliminate the ion molecules and conventional inhibitors, if any were present. The software Chem3D ultra 11.0 was utilised to create the three-dimensional structures of the ligands under investigation. The protein was given Kollmann charges and polar hydrogen bonds while all bound water and ligands were eliminated at the same time. Molecular docking study was done using the most recent Auto Dock Vina v.1.2.0. software version. Depending on macromolecule target of the docking study, the auto grid which is a component of auto-dock was employed to compute the grid maps along with the interaction energies. The region of enzyme's active target site served as grid centre.

With a use of automated docking experiments, the inhibitors' binding free energy was assessed. The genetic algorithm with local search (GA-LS) approach yielded best results for the conformations search. Using the program ADT (Auto-Dock Tool Kit), 100 independent docking runs were performed with the docking parameters set to default values. The visualisation application Chimaera from UCSF was used for molecular graphics and visualisation. For every ligand, 100 assessments were conducted with all protein targets and the posed. The Type 1 Diabetes target proteins were obtained from Protein data bank and were cleaned in pymol to remove additional ligands, inhibitors, Ions and water molecules as they may hinder the interaction sites. The Type 1 Diabetes targets and their PDB ID are mentioned below in Table 1.

Table 1 : Type 1 Diabetes Targets and their PDB ID

S. No	Target Name	PDB ID
1	Human estrogen receptor alpha ligand-binding domain in complex with 4-hydroxytamoxifen	3ERT
2	Crystal Structure of the Kinase domain of Human HER2 (erbB2)	3PP0
3	Crystal structure of wild-type RAC1	3TH5
4	Human Hsp90-beta with PU3 (9-Butyl-8(3,4,5-trimethoxy-benzyl)-9H-purin-6-ylamine)	1UYM
5	1E6 TCR in Complex with HLA-A02 carrying AQWGPDPAAA	5HYJ

RESULTS:

Autodock vina:

The autodock vina results of compounds against Peroxisome proliferator-activated receptor gamma (PPARG) with the binding energy and bond length of each interaction was recorded between -2.8Kcal/mol and -8.9Kcal/mol. Based on the autodock vina results the top 10 compounds showing best binding energy was docked against the Type 1 Diabetes-related genes using autodock vina mentioned below in Table 2¹³.

Table 2: Autodock vina results of Top 10 compounds against Type 1 Diabetes

Ligand name	Binding energy (Kcal/mol)
Ligand name	3ERT	3PP0	3TH5	1UYM	5HYJ
Ferotocotrimer E	-11	-8.4	-8.2	-8.9	-11
Buddlenol E	-5.6	-6.7	-7.2	-5.8	-7.8
24-ethylcholesta-5 ,22 E-dienyl-3β-O-pyranoglucoside	-7.9	-7.4	-7.2	-6.3	-9.4
24-ethylcholest-5-enyl-3β-O-pyranoglucoside	-8.3	-7.4	-6.1	-7	-7.3
2β-hydroxybetulinic acid 3β-oleiate	-8.3	-6.7	-5.7	-5.2	-7.4
24-methylcholest-5-enyl-3β-O-pyranoglucoside	-7.5	-7.2	-6.9	-8.8	-7
2β-hydroxybetulinic acid 3β-caprylate	-7.8	-6.2	-5.9	-5.2	-8.7
Euryalin A	-6.9	-6	-5.7	-7.4	-8.1
5,7,4 trihydroxyflavanone	-7.5	-7.3	-7.3	-8.8	-8.1
Euryalin C	-7.5	-6.5	-5.8	-7.2	-7.1

Molecular Docking:

The results of molecular docking displayed the interaction between Ferotocotrimer E and the Type 1 diabetes related genes namely 3ERT, 3PPO, 3TH5, 1UYM and 5HYJ. The selected targets exhibited interactions between Ferotocotrimer E and 3ERT at the residue Cys530A with the bond length 3.825Å, Ferotocotrimer E and 3PPO at the residue Leu712A with the bond length 4.406Å, Ferotocotrimer E and 3TH5 at the residue Phe37A with the bond length 5.415 Å, Ferotocotrimer E and 1UYM at the residue Leu220 A with the bond length 5.150 Å and Ferotocotrimer E and 5HYJ at the residue Val 160 D with the bond length 3.877Å respectively mentioned below from Fig. 1 to Fig.5.

Figure 1: Interaction between Ferotocotrimer E and 3ERT at the residue Cys530A with the bond length 3.825Å

Figure 2: Interaction between Ferotocotrimer E and 3PP0 at the residue Leu712A with the bond length 4.406Å

Figure 3: Interaction between Ferotocotrimer E and 3TH5 at the residue Phe37A with the bond length 5.415 Å

Figure 4: Interaction between Ferotocotrimer E and 1UYM at the residue Leu220 A with the bond length 5.150 Å

Figure 5: Interaction between Ferotocotrimer E and 5HYJ at the residue Val 160 D with the bond length 3.877Å

Molecular Simulation:

Molecular dynamics simulation using Desmond Schrodinger suite computes the atom movements over the period of time. Two complexes with best binding energy alongside Ferotocotrimer E were taken for Molecular simulation for the simulation time of 100ns. Using the ligand and protein RMSD (root mean square deviation) plot and interaction map analysis, complex stability was verified.

Protein –Ligand contact Histogram: Throughout the simulation, it is possible to watch how the protein interacts with the ligand. There are four different kinds of these exchanges, or "contacts": 1. Hydrogen Bonds; 2. Hydrophobic; 3. Ionic; and 4. Water Bridges. The 'Simulation Interactions Diagram' section allows one to see the more detailed subtypes that are contained within each type of interaction. Normalisation of the stacked bar charts occurs throughout the trajectory. Prominent interacting residues are mentioned in Table 3.

Table 3: Protein-Ligand contacts

S. No	Complexes	Type of interactions (interacting residues)
S. No	Complexes	H-Bonds	Hydrophobic	Ionic	Water bridges
1	3ERT- Ferotocotrimer E	Asn532, Val533	Trp383, Tyr526, Leu536	Nil	Asp351, Cys530, Leu536
2	5HYJ- Ferotocotrimer E	Ser 40 D	Tyr39D, Pro111D, Leu192E,Val157E	Nil	Arg41 D, Ser40D, Val109D,Asp155E, His169E

3ERT- Ferotocotrimer E:

The protein's RMSD provides information about the structural conformation during the simulation. Changes within the order of 1-3 A are reasonable for tiny molecules because the simulation system is more stable when the RMSD values are lower. It was found to be around 2.0- 3.5 Å in this study with slight fluctuations from the beginning which is of change of order 1.5 Å and it attained equilibration between 82- 95 ns (Fig.6). The prominent interactions for the complex 3ERT- Ferotocotrimer E with H bond was seen with ASN532 and VAL533, Hydrophobic interactions were found to be seen with TRP383, TYR526 and LEU536 and water bridges were more prominent with ASP351, CYS530, LEU536 (Fig.7).

Figure 6: Protein-Ligand RMSD

Figure 7: Protein-Ligand contacts Histogram

5HYJ- Ferotocotrimer E:

The RMSD values was found to be around 3.2-6.4 Å which is of change of order 3.4 Å and it attained equilibration between 80- 100 ns (Fig.8). The prominent interactions for the complex 5HYJ - Ferotocotrimer E with H bond was seen with SER 40 D, Hydrophobic interactions were found to be seen with TYR39, PRO111, VAL157E and LEU192E and water bridges were more prominent with ARG41 D, SER40D, VAL109D, ASP155E, HIS169E (Fig.9)

Figure 8: Protein-Ligand RMSD

Figure 9: Protein-Ligand contacts Histogram

DISCUSSION:

Molecular docking is a method that predicts the orientation a molecule will prefer when joined to form a stable complex¹⁴. It is one of the most effective methods for predicting and examining the atomic-level binding interactions between receptors and tiny compounds¹⁵. The phytochemicals found in Eurayale ferox were found to be interacting with 65 protein targets out of which 56 protein targets were common between Euryale ferox and Islet autoantibodies causing Type 1 Diabetes¹⁶. The protein Peroxisome proliferator-activated receptor gamma (PPARG) was found to be most associated with Type 1 Diabetes. Research points to a substantial involvement for the PPARG gene in the onset of type 2 diabetic mellitus (T2DM) also. The primary function of PPARG is in the development, maintenance, and function of adipocytes. Recent data suggests that PPARG is also critical for the maturation and operation of several immune system-related cell types which includes monocytes or macrophages, lymphocytes and dendritic cells ^[17]. Molecular docking results observed that all the 49 compounds showed binding interactions with different levels of binding energy ranging from -2.8 to -8.9 kcal/mol. The top ten compounds docked against Type 1 diabetes targets were found to have higher binding energies ranging from -5.2 to -11 kcal/ mol^[18]. The NCBI reports that FDA-approved medications have binding energies between -5.63 and -6.85 kcal/mol^[19]. Based on the results, potential inhibitors of Type 1 Diabetes Mellitus were found to be Ferotocotrimer E, Buddlenol E, 24-ethylcholesta-5, 22 E-dienyl-3β-O-pyranoglucoside, 24-ethylcholest-5-enyl-3β-O-pyranoglucoside, 24-methylcholest-5-enyl-3β-O-pyranoglucoside, 2β-hydroxybetulinic acid 3β-oleiate, 2β-hydroxybetulinic acid 3β-caprylate, Euryalin A, 5,7,4 trihydroxyflavanone and Euryalin C. With 3ERT and 5HYJ having a binding energy of -11kcal/mol, it was discovered that ferotocotrimer E was best docked²⁰. The binding energy of Ferotocotrimer E with 1UYM, 3PPO, 3TH5 was -8.9, -8.4 and -8.2kcal/mol respectively. Similar docking studies showed that the highest binding affinity was demonstrated by camptothecin towards the PPAR gamma protein, which had the lowest binding energy. This was followed by the insulin receptor and glucokinase, both of which had binding energies of > - 8.5 kcal/mol.²¹, Momordica charantia's component nerolidol had the best binding relationship with the diabetes protein, according to another docking investigatio²². In order to ascertain synthesis methods, spectrum analysis, docking simulation, photochemical activities, medicinal impacts, and toxicological investigations, AUTODOCK and its Tools are more effective²³. To understand the atoms' motions over time, a molecular dynamics simulation was run using the Desmond Schrodinger package²⁴. Ferotocotrimer E and the two complexes, 3ERT and 5HYJ2, with the best binding energies were selected for molecular simulation during a 100 ns simulation period²⁵. The interaction map and RMSD plot of the ligand and protein were examined in order to verify complex stability²⁶. ASN532 and VAL533 were the main objects of interaction for the complex 3ERT-Ferotocotrimer E with H bond. Water bridges were more noticeable with ASP351, CYS530, and LEU536 while hydrophobic interactions were observed with TRP383, TYR526, and LEU536. Significant interactions were observed between SER 40 D and the complex 5HYJ - Ferotocotrimer E with H bond ²⁷. It was discovered that TYR39, PRO111, VAL157E, and LEU192E displayed hydrophobic interactions, while ARG41 D, SER40D, VAL109D, ASP155E, and HIS169E showed higher evidence of water bridges ²⁸. The docking result of Ocimum sanctum against Lanosterol 14-alpha demethylase in one study indicated that Bornyl acetate had the best binding interaction of -13.9783Kcal/mol with binding site of the fungal protein by hydrogen bonding²⁹. There have been recent insilico studies on hepatoprotective properties of the phytoconstituents present in Gentianaceae family mainly the flavones³⁰, herbal compound Allicin from Allium sativum as potential constituents as a prevention for TB³¹, active compounds of Zingiber officinale in the management and treatment of gout as well as in the oxidative stress conditions³², Ocimum basilicum L. having anticancer properties that inhibit human colorectal cancer genes³³, Momordica charantia's luteolin for dementia in Alzheimer's disease³⁴, Bioactivity of Annona reticulate as an antibacterial agent ³⁵ and many more like these. Thus, the Ferotocotrimer compound of Euryale ferox could be considered as a better lead molecule as a growth inhibitor of Islet autoantibodies.

CONCLUSION:

In the present study 10 potential growth inhibitors of Islet autoantibodies were obtained including Ferotocotrimer E, Buddlenol E, 24-ethylcholesta-5, 22 E-dienyl-3β-O-pyranoglucoside, 24-ethylcholest-5-enyl-3β-O-pyranoglucoside, 2β-hydroxybetulinic acid 3β-oleiate, 24-methylcholest-5-enyl-3β-O-pyranoglucoside, 2β-hydroxybetulinic acid 3β-caprylate, Euryalin A, 5,7,4 trihydroxyflavanone and Euryalin C with different range of binding energies from -5.2 to -11kcal/mol. The results of the computational investigations showed that these phytochemicals create hydrogen bonds with ASN532, VAL533, and SER 40 D amino acids in the enzyme's active region, as well as hydrophobic interactions with TRP383, TYR526, LEU536, TYR39, PRO111, VAL157E, and LEU192E.Therefore we provide the top 10 phytochemicals as potentially potent, orally accessible, nontoxic leads for further consideration based on the ADMET screening and effective binding analysis at the reverse transcriptase active site. Thus Makhana, a rich source of phytochemicals can be considered to inhibit growth of islet autoantibodies and preserve the health of pancreatic islet beta cells.

ACKNOWLEDGEMENTS:

I thank the department of Clinical Nutrition and Dietetics, SRM Medical College Hospital and Research Centre for their support extended.

CONFLICT OF INTEREST:

The author(s) declares no conflict of interest.

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Received on 04.05.2024 Revised on 21.09.2024

Accepted on 26.11.2024 Published on 02.05.2025

Available online from May 07, 2025

Research J. Pharmacy and Technology. 2025;18(5):2017-2022.

DOI: 10.52711/0974-360X.2025.00288

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.