Int J App Pharm, Vol 17, Issue 1, 2025, 199-208Original Article
EXPLORING JACKFRUIT FLOUR POLYPHENOLS AS PROMISING SGLT-2 INHIBITORS FOR HYPERGLYCEMIA MANAGEMENT
SHASHANK M. PATIL1, MANU G.2, RAMYA C. M.3, RAJASHEKHARA S.4, SUDHANVA M. DESAI5, SARAVANAN PARAMESWARAN1, RAMITH RAMU1*
1Department of Biotechnology and Bioinformatics, JSS Academy of Higher Education and Research, Mysore–570015, Karnataka, India. 2Department of Pharmacology, JSS Medical College, JSS Academy of Higher Education and Research, Mysore-570015, Karnataka, India. 3Department of Physiology, JSS Medical College, JSS Academy of Higher Education and Research, Mysore-570015, Karnataka, India. 4Department of Chemical Engineering, Siddaganga Institute of Technology, BH Road, Tumakuru-572103, Karnataka, India. 5Department of Chemical Engineering, Dayananda Sagar College of Engineering, Bengaluru–560078, Karnataka, India
*Corresponding author: Ramith Ramu; *Email: ramith.gowda@gmail.com
Received: 04 Sep 2024, Revised and Accepted: 12 Nov 2024
ABSTRACT
Objective: This study explored the potential of dietary polyphenols from whole green jackfruit flour as natural Sodium-Glucose Co-Transporter-2 (SGLT-2) inhibitors for managing hyperglycemia in diabetes mellitus.
Methods: Advanced bio-computational techniques, including molecular docking, Molecular Dynamics (MD) simulations, and binding free energy calculations, were employed to identify and assess polyphenols from jackfruit flour. Caffeic and syringic acids were highlighted for their strong binding affinities to the SGLT-2 receptor. Additionally, a ligand-based pharmacophore model was developed using caffeic acid as a reference to screen for new lead compounds in commercial and natural product databases.
Results: The study found that caffeic acid and syringic acid exhibited stronger binding affinities and more stable interaction profiles with the SGLT-2 receptor than the standard drug empagliflozin. MD simulations demonstrated that these compounds provided greater stability in the binding site, indicating their potential efficacy as SGLT-2 inhibitors. The pharmacophore screening further supported these findings, identifying both compounds as promising lead candidates.
Among the 14 dietary polyphenols obtained from High-Performance Liquid Chromatography (HPLC), a molecular docking study suggested that caffeic acid (binding affinity:-9.0 kcal/mol) and syringic acid (binding affinity:-9.1 kcal/mol) exhibited stronger binding affinities and more stable interaction profiles with the SGLT-2 receptor compared to the standard drug empagliflozin (binding affinity:-10.4 kcal/mol). Further, molecular dynamics simulations demonstrated that these compounds provided greater stability in the binding site, indicating their potential efficacy as SGLT-2 inhibitors through Root mean Square Deviation (RMSD), Root mean Square Fluctuation (RMSF), Radius of Gyration (Rg), Solvent Accessible Surface Area (SASA), and ligand hydrogen bonds. The pharmacophore screening further supported these findings, identifying both compounds as promising lead candidates.
Conclusion: This study is the first to identify caffeic acid and syringic acid from whole green jackfruit flour as effective SGLT-2 inhibitors. These natural compounds show significant potential as novel agents for managing hyperglycemia and diabetes mellitus. The findings support further exploration of plant-derived therapies in diabetes treatment.
Keywords: SGLT-2 inhibitors, Caffeic acid, Syringic acid, Empagliflozin, Molecular docking, Molecular dynamics simulation, Binding free energy calculations
© 2025 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.2025v17i1.52573 Journal homepage: https://innovareacademics.in/journals/index.php/ijap
INTRODUCTION
Type-2 Diabetes Mellitus (T2DM) is a global chronic condition characterized by a range of metabolic disorders, including obesity, hypertension, heart failure, dyslipidemia, hyperuricemia, and renal failure. Additionally, individuals with T2DM are susceptible to complications such as diabetic neuropathy and retinopathy, which impact the nervous system and vision, respectively [1]. T2DM is also related to pulmonary diseases [2, 3]. Although various oral and intravenous glucose-lowering medications are widely used, the risk of cardiorenal complications in diabetic patients remains a significant concern [4].
Recent research has focused on identifying new molecular targets to mitigate the effects of diabetes mellitus. In this regard, Sodium-Glucose Co-Transporter (SGLT) proteins play a vital role in glucose reabsorption in the body. Sodium-glucose Co-Transporter-1 (SGLT-1), which has high affinity but low capacity, is mainly found in the kidneys' proximal tubules and the small intestine [5]. However, Sodium-Glucose Co-Transporter-2 (SGLT-2) proteins, responsible for over 90% of filtered glucose reabsorption, are considered prime targets for diabetes treatment. The standard renal glucose reabsorption threshold is around 180 mg/dl, but in T2DM patients, this threshold may increase due to SGLT-2 overexpression, exacerbating hyperglycemia. Selective SGLT-2 inhibitors can lower this threshold to between 40 and 120 mg/dl. SGLT-2 proteins are primarily located in the proximal convoluted tubules of the kidneys and are absent in the rare condition known as Familial Renal Glucosuria (FRG) [6, 7].
As the inhibition of SGLT-2 proves to be a proficient target for antidiabetic studies, several SGLT-2 inhibitor drugs have been proposed to date. SGLT-2 inhibitors allow glucose excretion in urine, preventing glucose reabsorption by inhibiting SGLT-2 in the Proximal Convoluted Tubule (PCT) [8]—elimination of glucose from the body, results in decreased plasma levels, thereby improving all glycemic markers. In 1835, the then-French chemists discovered phloridzin, a natural compound derived from the root bark of apple trees, initially used to treat malaria. Bioactive compounds isolated from banana flower and pseudostem extracts are proven to be cytoprotective, Deoxyribonucleic Acid (DNA) protective, antimicrobial, and thrombolytic [9, 10]. Today, drugs like empagliflozin, canagliflozin, and dapagliflozin, which target SGLT-2 inhibition, have received approval from the Food and Drug Administration (FDA) for managing diabetes mellitus. However, these SGLT-2 inhibitors come with several side effects. One of the most common issues is an increased risk of vaginal infections, with a fourfold rise observed during clinical trials. Patients experiencing severe hyperglycemia and glycosuria are more susceptible to mycotic infections due to the presence of glucose in the urine. Additionally, SGLT-2 inhibition can lead to osmotic diuresis, causing volume depletion (hypovolemia), which manifests as frequent urination, excessive thirst, and orthostatic hypotension. Although urinary tract infections, genital mycotic infections, and osmotic diuresis-related symptoms were more frequently reported in clinical studies, they were generally mild to moderate severity [11, 12].
The phytochemical intervention of diabetes mellitus has been the centre of attraction in the current scenario. Several plants have been proven with their antidiabetic effects in various dimensions including in vitro, in vivo, and in silico approaches. Jackfruit (Artocarpus heterophyllus), also referred to as the jack tree, is a species of tree belonging to the mulberry, breadfruit, and fig families (Moraceae), and it is native to Southeast Asia, originating from India [13, 14]. Asthma, wounds, dermatosis, anemia, and diarrhea have all been treated using leaves and roots in the past. The phytochemical and pharmacological characteristics of the pulp of jackfruit, leaf, bark, and root have been studied [15]. By decreasing the generation of Prostaglandin E2 (PGE2) and Nitric Oxide (NO), the pulp extract of jackfruit has been shown to exhibit anti-inflammatory characteristics. Jackfruit leaf extracts, on the other hand, have antioxidant qualities and can help to lower hyperglycemia and hyperlipidemia [16].
However, the antidiabetic effect of jackfruit has yet to be explored in different aspects. Since the computational tools offer a great opportunity to virtually examine and validate the pharmacological experiments, we aim to find a suitable inhibitor for SGLT-2 using the same. Through this study, we aim to virtually screen the whole jackfruit compounds obtained from High-Performance Liquid Chromatography (HPLC) analysis as the SGLT-2 inhibitors. With the outcomes from computational studies, this study becomes a pavement for the discovery of jackfruit phytochemicals as site-specific inhibitors of SGLT-2, which can be adopted for biological evaluation in the near future.
MATERIALS AND METHODS
Molecular docking simulations
The X-ray crystallographic structure of the human Sodium-Glucose Co-Transporter-2– Membrane-Associated Protein 17 (SGLT-2-MAP17) complex bound to empagliflozin (PDB ID: 7VSI) was retrieved, along with the associated ligand structures, from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) and PubChem databases, building on previous work by the researchers [17]. Empagliflozin served as the control in this study. To prepare the protein and ligand structures for molecular docking simulations, AutoDock Tools 1.5.6 was employed, following methodologies outlined in earlier studies by the authors [18-20]. A thorough literature review was undertaken to accurately predict the binding sites of the target protein [21]. The protein's binding pocket was defined within a grid box measuring 16.84 Å × 16.84 Å × 16.84 Å, with coordinates set at x = 38.30 Å, y = 50.24 Å, and z = 46.38 Å using AutoDock Tools 1.5.6.
The phytocompounds were subjected to virtual screening using AutoDockVina 1.1.2, a command-line software tool. The Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm was employed to manipulate and correctly position the ligands within the target site and to evaluate the scoring function for each ligand's conformation [22, 23]. Due to the high number of torsional angles generated during ligand formation, the ligands were treated as flexible, while the protein remained rigid throughout the docking process. However, 10 degrees of freedom were allowed for the ligands. Among the ten binding poses produced, the first one, showing a Root-Mean-Square Deviation (RMSD) of zero, was deemed the most precise and demonstrated the highest binding affinity, indicating a more effective binding interaction. The molecular docking simulations were visualized using Biovia Discovery Studio Visualizer 2021, an open-source graphical user interface software. The interaction strength of the ligands was assessed based on the total number of bonds, specific hydrogen bonds, and binding affinity [24, 25].
Molecular dynamics simulations
To conduct the Molecular Dynamics (MD) simulation, SGLT-2 protein complexes bound to empagliflozin, syringic acid, and caffeic acid were chosen based on their strongest negative binding affinities. The simulations were performed using GROningen MAchine for Chemical Simulations 2018.1 (GROMACS 2018.1), a comprehensive biomolecular software package as cited in the study by Patil et al. (2021) [26]. GROMACS 2018.1 is a robust software suite designed for molecular dynamics, facilitating the simulation of Newtonian equations of motion for systems that range from hundreds to millions of particles. This software is particularly adept at handling biological compounds like nucleic acids, proteins, and lipids, and is especially efficient at calculating nonbonded interactions, which frequently occur and are critical in simulations [27, 28]. The MD simulations included not only the protein complexes with empagliflozin, syringic acid, and caffeic acid but also a simulation of the protein's bare backbone. Four separate simulations were conducted, each lasting 100 nanoseconds at a controlled temperature of 310K and a pressure of 1 bar. The resulting data, including RMSD, Root-Mean-Square Fluctuation (RMSF), Radius of Gyration (Rg), Solvent-Accessible Surface Area (SASA), and ligand hydrogen bond trajectories, were analyzed using GRaphing, Advanced Computation and Exploration of data (GRACE), a specialized graphical user interface for visualizing MD simulation results [29, 30].
Free energy landscape (FEL) analysis
Besides MD simulation, Principal Component Analysis (PCA) was conducted to assess the conformational changes during the protein-ligand complex simulation, following the methodology outlined by Patil et al. (2023) [31]. PCA serves as a powerful tool for identifying motion directions and statistically characterizing collective movements. The g_covar and g_anaeig utilities were employed to derive the two primary components – Primary Component 1 (PC1) and Primary Component 2 (PC2) by diagonalizing the matrix, using the essential dynamics protocol to protect these components. Moreover, the Gibbs FEL was constructed using PC1 and PC2 as reaction coordinates, offering valuable insights into the protein's conformational shifts and the dynamic behavior of the ligands.
Binding free energy (BFE) calculations
Following MD simulations, the binding free energy was assessed for the target proteins employing the Molecular Mechanics/Poisson-Boltzmann Surface Area (MM-PBSA) method. This approach integrates MD simulations with thermodynamic principles to evaluate the ligand's binding interaction with the protein. For each ligand-protein complex, the g_mmpbsa tool was employed along with the MmPbSaStat. py script, processing trajectory data generated by GROMACS 2018.1 as input. The g_mmpbsa tool calculates binding free energy by considering three components: molecular mechanical energy, polar solvation energy, and apolar solvation energy. The MD trajectories from the final 50 ns of the simulation were used to calculate free energy (ΔG), within a frame interval of 1000. The assessment of ΔG involves the combination of Molecular Mechanical Energy (MME) with polar and apolar solvation energies. The equations used for the binding free energy calculation are detailed in the following [32, 33].
ΔG (Binding) = G (Complex) − [G (Protein) + G (Ligand)] -------- (I)
ΔG = ΔE (MM) + ΔG (Solvation) − TΔS = ΔE(Bonded+non-bonded) + ΔG(Polar+non-polar) – TΔS ------- (II)
GBinding: refers to the binding free energy, GComplex: Represents the overall free energy of the protein-ligand complex, GProtein, and GLigand: Denote the total free energies of the isolated protein and ligand within the solvent, respectively, ΔG: The standard free energy change, ΔEMM: The average molecular mechanics potential energy in a vacuum, GSolvation: The energy associated with solvation, ΔE: Encompasses the total energy from both bonded and non-bonded interactions, ΔS. The entropy change in the system due to ligand binding, T: Temperature measured in Kelvin [34, 35].
Pharmacophore-based virtual screening (PBVS)
Pharmacophoric features were identified by analyzing the binding poses of the screened hits using a ligand-based 3D pharmacophore. The pharmacophore model was constructed with the help of the Pharma Gist web server [32]. The selection of the appropriate pharmacophore model was guided by the critical intermolecular interactions between the screened hits and the SGLT-2 protein, using caffeic acid as the reference. This chosen pharmacophore model was then employed for virtual screening against the ZINC natural product database via ZINCPharmer, aiming to identify new inhibitors for human SGLT-2 [33]. The molecules identified from the ZINC purchasable compounds and natural product database were further subjected to Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) analysis using the Swiss ADME tool in Simplified Molecular Input Line Entry System (SMILES) format.
RESULTS AND DISCUSSION
Molecular docking simulation
Molecular docking analyses of phytocompounds identified via Reverse Phase High-Performance Liquid Chromatography (RP-HPLC) reveal that these phenolic compounds successfully bind to the active site of the human SGLT-2 protein, occupying the same position as the co-crystallized ligand 7R3703 in the SGLT-2 protein structure (PDB ID: 7VSI). Among the compounds examined for their affinity for binding and interaction, caffeic acid (-9.0 kcal/mol) and syringic acid (-9.1 kcal/mol) (fig. 1) demonstrated superior binding affinities, closely matching those of the control drug empagliflozin, which had a binding affinity of-10.4 kcal/mol. The results of the virtual screening are provided in (table 1). Given their promising binding affinities and significant intermolecular relations with human SGLT-2, the phenolic substances of syringic acid and caffeic acid were selected for further study. Although other compounds also exhibited a good tendency for binding, their physicochemical properties were not suitable for use as lead molecules. In particular, kaempferol and quercetin were excluded from further analysis due to their known adverse metabolic effects [36].
Fig. 1: Structures of key phenolic compounds A) caffeic acid and B) syringic acid from our earlier work [14]
Table 1: Overview of inter-molecular interaction data of ethanol extract of Jackfruit flour (EJF) phenolic compounds against human SGLT-2
| S. No. | Name of the compound | Binding affinity (kcal/mol) | Total no. of intermolecular bonds | Total no. of hydrogen bonds |
| 1 | Ascorbic acid | -5.4 | 7 | 3 |
| 2 | Gallic acid | -6.2 | 8 | 4 |
| 3 | Catechin | -8.9 | 7 | 5 |
| 4 | Rutin | -6.8 | 6 | 4 |
| 5 | Methyl gallate | -6.4 | 5 | 2 |
| 6 | Caffeic acid* | -9.0 | 6 | 5 |
| 7 | Syringic acid* | -9.1 | 9 | 5 |
| 8 | p-Coumaric acid | -6.6 | 7 | 3 |
| 9 | Sinapic acid | -6.6 | 7 | 4 |
| 10 | Ferulic acid | -6.9 | 8 | 4 |
| 11 | Myricetin | -7.2 | 9 | 3 |
| 12 | Apigenin | -7.8 | 6 | 3 |
| 13 | Kaempferol | -9.2 | 7 | 4 |
| 14 | Quercetin | -9.0 | 6 | 3 |
| 15 | Empagliflozin* | -10.4 | 8 | 5 |
*The molecules investigated in the current study that might serve as lead molecules for further investigation on the inhibition of human SGLT-2
The active site/inhibitor binding site of SGLT-2 (PDB ID: 7VSI) is distributed between the helices M1b, M1a, M2, M6b, M7, and M10. The inhibitor binding site, located between these helices, is occupied by the co-crystallized ligand 7R3703. In the course of our research, it was discovered that every molecule occupied the same site of binding as 7R3703 (fig. 2). Binding interactions of caffeic acid, syringic acid, and empagliflozin followed the previously reported studies by Niu et al., [37], where the compound LX2761 was found to be docked right at the inhibitory site of binding of the protein-SGLT-2 (PDB ID: 7VSI). Both caffeic and syringic acids were found to have the same binding interactions as reported by the study. It was also reported to correlate with a docking study by Kuswandi et al. (2022) [38], who reported Garcinia atroviridis-derived phytocompounds as the potential inhibitors of human SGLT-2 protein (PDB ID: 7VSI). With myricetin from G. atroviridis reported as the potential inhibitor of human SGLT-2 having binding interactions in the same vicinity, both caffeic acid and syringic acid can also be used as effective inhibitors of the same. However, within the inhibitory site, both studies have not reported to have found their compounds stable. Therefore, the stability of the reported compounds needs to be examined with molecular dynamics simulations for reliability to proceed with further investigations.
Important residues of the inhibitor binding site, such as Trp291, Lys321, Phe98, Glu99, Leu283, and Tyr290, which are situated between the helices M1, M2, M6, M7, and M10, have been attached to both caffeic acid and syringic acid. These interactions were also observed in previous studies that used the same PDB (7VSI) [38, 39]. Detailed interactions of caffeic acid and syringic acid, along with the positive control molecule, empagliflozin, with the human SGLT-2 residues are tabulated in table 2. Also, a visualization of the bound sites and their interaction with the compounds has been portrayed in fig. 3.
Fig. 2: Site of Inhibitor-binding of the human protein SGLT-2 with bound compounds. Cyan: caffeic acid, green: syringic acid, and pink: empagliflozin
Table 2: Binding interactions of caffeic acid, syringic acid, and empagliflozin with human SGLT-2 protein with distance in angstroms (Å)
| Compounds | Hydrogen bonds | Hydrophobic interactions | ||
| (Π-Π) interaction | (Alkyl) interaction | (Π-alkyl) interaction | ||
| Caffeic acid | Trp291 (3.30), Lys321 (3.28), Ser460 (3.10), Phe98 (2.13), Gln457 (3.62) | Tyr290 (4.18) | - | |
| Syringic acid | Trp 291 (3.32), Lys231 (3.34), Gln457 (2.73), Phe98 (2.40), Glu99 (2.60) | Tyr290 (3.75), | Leu283 (5.01), Val286 (3.99), His80 (4.73) | |
| Empagliflozin | Thr87 (3.12), Ser287 (3.14), Trp291 (3.38), Thr87 (2.25), Gln457 (3.44) | Tyr290 (4.80) | - | Tyr290 (4.58), Leu84 (5.37) |
Fig. 3: Visualization of binding interactions of the compounds with SGLT-2 protein. Cyan: caffeic acid, green: syringic acid, and pink: empagliflozin
MD simulation
MD simulation offers deep structural and energetic insights into protein-ligand interactions. To evaluate the kinetics, elasticity, and durability of protein-ligand complexes, we examined parameters such as RMSF, RMSD, Rg, and SASA along with ligand-hydrogen bonds over a 100 ns trajectory. The RMSD metric, which reflects the stable conformation of the complex during the simulation, was a focal point of our analysis. We specifically monitored the RMSD of the optimized protein along with its atoms of the backbone. Throughout the RMSD calculations, the protein backbone atoms along with the complexes demonstrated an identical stability pattern. None of the molecules left the protein's active site throughout the simulation. The protein backbone atoms stabilized after 30 ns, while the complexes of caffeic acid and syringic acid achieved stability after 10 ns and 20 ns, respectively. In contrast, the empagliflozin-protein complex stabilized only after 50 ns. The RMSD values for the caffeic acid and syringic acid complexes closely matched those atoms of the backbone of the protein.
Besides RMSD, RMSF is pivotal for predicting conformational stability because residual fluctuations indicate the binding pose and interaction of ligands [24]. The protein backbone atoms show fluctuation at N-and C-terminals. High fluctuations were observed in the loop region residues (150-200). Both the caffeic acid and syringic acid complexes evince lower deviations in the beginning (at N-terminals) and throughout the simulation compared to empagliflozin. Further, Rg has provided insights into the folding and unfolding of proteins onto binding ligands, along with the compactness of the binding [25]. A protein with a stable fold will likely maintain a relatively stable Rg during simulation. The analysis of the Rg plot manifests protein, empagliflozin, caffeic acid, and syringic acid complexes having similar compactness. These results indicate that the binding of each compound significantly stabilized the structure of the protein.
Computing SASA helps in examining modifications in the solvent reactivity of proteins [26]. This value could be used to study interactions between and within proteins, ligands, and solvents. All the SASA plots show decreasing values since the SASA is occupied by the ligands. Numerically similar values also indicate that the ligands’ binding is in a similar pattern. Bonds between ligands and hydrogens are crucial for determining the structure re-agreement in dynamic trajectory analysis [27]. During MD simulation, phenolic compounds from the caffeic and syringic acid present the highest nos. of H bonds, compared to empagliflozin. Based on the results of outcomes of the MD simulation, caffeic acid, along with syringic acid, may potentially stimulate biological activity in the binding site of the inhibitor by connecting to the key residues. The values of MD trajectories have been summarized in table 4 and fig. 4 represents the different trajectories of molecular dynamics simulation. The overall stability of both caffeic acid and syringic acid reported in our study is by Arif et al. (2021) [40], where a polypeptide from Momordica charantia was docked and simulated with SGLT-2 protein.
Table 4: Value of MD trajectories obtained from GROMACS 18.1 for different protein-ligand complexes
| MD trajectories | Apo-protein | Caffeic acid-SGLT-2 | Syringic acid-SGLT-2 | Empagliflozin-SGLT-2 |
| RMSD in (nm) | 0.35-0.40 | 0.35-0.40 | 0.30-0.35 | 0.30-0.35 |
| Maximum RMSF (nm); | (0.55-0.60) | (0.45-0.50) | (0.45-0.50) | (0.50-0.55) |
| Rg (nm) | 0.20-0.25 | 0.20-0.25 | 0.20-0.25 | 0.20-0.25 |
| SASA (nm2) | 180-185 | 175-180 | 175-180 | 180-185 |
| Ligand H-bonds | not applicable | 3 | 6 | 4 |
Fig. 4: Display illustrating MD trajectories for several protein-ligand complexes acquired from GROMACS 18.1. Protein-caffeic acid complex: cyan, protein-syringic acid complex: green, protein-empagliflozin complex: red, and apo-protein: blue
FEL analysis
The FEL of six systems were analyzed based on the top two principal components, PC1 and PC2, to assess the impact of drug binding on conformational shifts. Lower Gibbs free energy indicates effective ligand binding, stable complex formation, and favorable conformational changes (Pandey et al., 2021) [41]. The SGLT-2 protein which is bound to both caffeic acid (0.0-8.98 kJ/mol) and syringic acid (0.0-8.3 kJ/mol) exhibited lower free energy levels. In contrast, the protein-empagliflozin complex (0.0–9.08 kJ/mol) had higher Gibbs free energy values. As opposed to the unbound protein structure (0.0-9.88 kJ/mol), the complexes with caffeic acid and syringic acid did not show a significant increase in Gibbs free energy. Elevated Gibbs free energy typically suggests less favorable conformational changes and reduced stability in the protein structure. Our FEL analysis results are consistent with a previous study by Bisha et al. (2014) [42], which explored the binding of a Na+ion within the inhibitor binding site of SGLT-2. The Gibbs free energy in this study ranges between 0.0 to-9.0 kJ/mol, which is the same in our study. Since there is a decrease in the Gibbs free energy in both caffeic acid and syringic acid-bound protein complexes, the SGLT-2 protein conformation changes are energetically favorable [29]. Fig. 5 depicts the FEL plots of experimental molecules bound with the target protein as well as the free proteins.
Table 5: BFE estimations of different protein-ligand binding complexes
| Forms of BFEs | Caffeic acid-SGLT-2 complex (kJ/mol) | Syringic acid-SGLT-2 complex (kJ/mol) | Empagliflozin-SGLT-2 complex (kJ/mol) |
| Van der Waal energy | -153.748±12.328 | -304.715±11.985 | -145.927±12.231 |
| Electrostatic energy | -4.685±7.026 | -5.767±2.797 | -5.234±1.891 |
| Polar solvation energy | 61.922±9.129 | 94.404±11.816 | 87.156±10.241 |
| SASA energy | -11.092±7.144 | -21.827±0.860 | -20.457±2.201 |
| Binding energy | -107.603±12.170 | -237.905±15.905 | -295.981±10.918 |
Fig. 5: The Gibbs FEL of various SGLT-2 protein-ligand complexes were analyzed using PCA. The landscape includes A) the SGLT-2 unbound protein or apo-protein structure, B) the SGLT-2 protein complexed with caffeic acid, C) the SGLT-2 protein with syringic acid, and D) the SGLT-2 protein bound to empagliflozin. The colored bar illustrates the Gibbs free energy values in kJ/mol
BFE calculations
The most significant indicator of therapeutic drug potency for drug design based on structure is the single most important indicator of drug potency is the in silico protein-ligand binding affinity prediction. For accurate predictions in the context of drug development programs, BFE simulations rely on statistical mechanics and physics-based molecular simulations [21, 22]. In this calculation, various factors were considered, including Van der Waals forces, electrostatic interactions, SASA, polar solvation, and binding energy [26]. The analysis reveals that Van der Waals energy primarily drives the formation of all protein-ligand complexes. The results indicate that complexes involving protein with phenolic acids, such as caffeic acid and syringic acid, exhibit greater stability compared to the protein-empagliflozin complex. Although all complexes are energetically favorable, the protein-phenolic acid complexes are notably more stable and reliable. Table 5 provides an overview of the calculations made for the BFE of protein-ligand complexes.
PBVS
Though the PharmaGist webserver provided several pharmacophore models, the appropriate pharmacophore model was selected based on the key intermolecular interactions between the screened hits and human SGLT-2 with caffeic acid as a reference molecule to facilitate the screening of lead molecules, which was presented in fig. 6. The pharmacophoric feature, Hydrogen bond acceptor was chosen to facilitate interactions with Lys321, Ser287, and Thr153 of SGLT-2 protein while Hydrogen bond acceptor facilitated the interaction with Thr153 of SGLT-2 protein. The hydrophobic pharmacophoric feature enables the aromatic ring from the virtually screened hits to form Pi-Pi stacked hydrophobic interaction with Tyr290 of human SGLT-2. The PBVS yielded 7826 and 607 from ZINC purchasable compounds and natural products database, respectively. The RMSD measure from ZINCPharmer is applied to filter the hits with 0.25Å cutoff yield of 675 and 27 molecules, respectively, and the top three hits based on RMSD are presented in table 6.
Table 6: The top 10 screened ZINC purchasable compounds and natural products database from PBVS with caffeic acid as reference molecule
| ZINC purchasable compounds database from PBVS | ZINC natural products database from PBVS | ||||
| ZINC Id | 2D structure | RMSD# | ZINC Id | 2D structure | RMSD# |
| ZINC02015035* | 0.130089 | ZINC91968790 | 0.144708 | ||
| ZINC01564653* | 0.145571 | ZINC77285163 | 0.14493 | ||
| ZINC00895821* | 0.145639 | ZINC91993129 | 0.145506 | ||
| ZINC01592824 | 0.148831 | ZINC91961112 | 0.145507 | ||
| ZINC02005215 | 0.149301 | ZINC95038058 | 0.145619 | ||
| ZINC01482068 | 0.177355 | ZINC91962572 | 0.14563 | ||
| ZINC13458828 | 0.17978 | ZINC91365352 | 0.150721 | ||
| ZINC15043216 | 0.183071 | ZINC06170676 | 0.15138 | ||
| ZINC12649959 | 0.190866 | ZINC01585592 | 0.155522 | ||
| ZINC00388044 | 0.191226 | ZINC94610848 | 0.155691 |
#-ZINC Pharmer RMSD
The top ten hits from ZINC purchasable compounds and natural compounds database were subjected to ADMET analysis. It was discovered that syringic acid and caffeic acid both had a bioavailability score of 0.55 and did not violate Lipinski's rule of five. The same was shown in our earlier research, where the inhibition of α-glucosidase, α-amylase, and aldose reductase in silico and in vitro was assessed for caffeic acid and syringic acid [15]. Regarding ZINC purchasable compounds, no compound was discovered to violate Lipinski’s rule of five. Compounds ZINC13458828 (4.75), ZINC15043216 (4.75), and ZINC01564653 (1.92) were the top 3 molecules with the best bioavailability score. The ADME evaluation has been presented in detail in table 7.
Fig. 6: The chosen pharmacophore model with highlighted pharmacophoric features for the screening of lead molecules against human SGLT-2 with caffeic acid as a reference molecule. Three hydrogen bond acceptors, one hydrogen bond donor, and one hydrophobic feature are represented by orange, white, and magenta circles, respectively, in the pharmacophore model
Table 7: ADME properties of top 10 natural products and ZINC purchasable compounds
| S. No. | Compound | MW | NRB | NHBA | NHDA | TPSA | LogP | CYP inhibition | BBB | Bioavailability |
| Top 10 virtually screened compounds from the PubChem database based on our earlier work | ||||||||||
| 1 | Caffeic acid | 180.16 | 2 | 4 | 3 | 77.76 Ų | 0.93 | No | No | 0.56 |
| 2 | Syringic acid | 198.17 | 3 | 5 | 2 | 75.99 Ų | 0.99 | No | No | 0.56 |
| 3 | p-Coumaric acid | 164.16 | 2 | 3 | 2 | 57.53 Ų | 1.26 | No | No | 0.85 |
| 4 | Sinapic acid | 224.21 | 4 | 5 | 2 | 75.99 Ų | 1.31 | No | No | 0.56 |
| 5 | Ferulic acid | 194.18 | 3 | 4 | 2 | 66.76 Ų | 1.36 | No | No | 0.85 |
| 6 | Myricetin | 318.24 | 1 | 8 | 6 | 151.59 Ų | 0.79 | No | No | 0.55 |
| 7 | Apigenin | 270.24 | 1 | 5 | 3 | 90.90 Ų | 2.11 | No | No | 0.55 |
| 8 | Kaempferol | 286.24 | 1 | 6 | 4 | 111.13 Ų | 1.58 | No | No | 0.55 |
| 9 | Quercetin | 302.24 | 1 | 7 | 5 | 131.36 Ų | 1.23 | No | No | 0.55 |
| 10 | Catechin | 290.27 | 1 | 6 | 5 | 110.38 Ų | 0.85 | No | No | 0.55 |
| 11 | Empagliflozin | 450.91 | 6 | 7 | 4 | 108.61 Ų | 2.01 | No | No | 0.55 |
| ZINC Purchasable Compounds and Natural Products Database from PBVS | ||||||||||
| 1 | ZINC02015035 | 213.19 | 3 | 6 | 5 | 124.01 Ų | -1.58 | No | No | 0.55 |
| 2 | ZINC01564653 | 196.16 | 2 | 5 | 4 | 97.99 Ų | 0.60 | No | No | 1.92 |
| 3 | ZINC00895821 | 210.18 | 3 | 5 | 3 | 86.99 Ų | 1.01 | No | No | 0.56 |
| 4 | ZINC01592824 | 215.18 | 3 | 6 | 4 | 103.78 Ų | -0.53 | No | No | 0.55 |
| 5 | ZINC02005215 | 211.21 | 4 | 5 | 4 | 89.79 Ų | -0.49 | No | No | 0.55 |
| 6 | ZINC01482068 | 215.18 | 3 | 6 | 4 | 103.78 Ų | -0.44 | No | No | 0.55 |
| 7 | ZINC13458828 | 484.36 | 7 | 14 | 9 | 243.90 Ų | -1.00 | No | No | 4.75 |
| 8 | ZINC15043216 | 484.36 | 7 | 14 | 9 | 243.90 Ų | -1.00 | No | No | 4.75 |
| 9 | ZINC12649959 | 211.21 | 4 | 5 | 3 | 92.78 Ų | 0.04 | No | No | 0.55 |
| 10 | ZINC00388044 | 182.17 | 3 | 4 | 3 | 77.76 Ų | 0.63 | No | No | 0.56 |
| 11 | ZINC91968790 | 290.31 | 6 | 4 | 2 | 78.87 Ų | 1.72 | No | No | 0.56 |
| 12 | ZINC77285163 | 356.28 | 5 | 10 | 6 | 173.98 Ų | -1.16 | No | No | 0.11 |
| 13 | ZINC91993129 | 278.30 | 7 | 4 | 2 | 78.87 Ų | 1.61 | No | No | 0.56 |
| 14 | ZINC91961112 | 264.28 | 6 | 4 | 2 | 78.87 Ų | 1.29 | No | No | 0.56 |
| 15 | ZINC95038058 | 293.32 | 9 | 5 | 2 | 84.86 Ų | 1.73 | No | No | 0.56 |
| 16 | ZINC91962572 | 292.33 | 8 | 4 | 2 | 78.87 Ų | 2.02 | No | No | 0.56 |
| 17 | ZINC91365352 | 211.22 | 3 | 4 | 5 | 135.59 Ų | -1.45 | No | No | 0.55 |
| 18 | ZINC06170676 | 309.32 | 4 | 5 | 3 | 91.26 Ų | 2.61 | No | No | 0.55 |
| 19 | ZINC01585592 | 445.38 | 5 | 10 | 6 | 257.74 Ų | -2.24 | No | No | 0.11 |
| 20 | ZINC94610848 | 268.31 | 7 | 5 | 3 | 86.99 Ų | 1.94 | No | No | 0.56 |
Note: Molecular Weight (MW), Number of Rotatable Bonds (NRB), Number of Hydrogen Bonds Acceptor (NHBA), Number of Hydrogen Bonds Donor (NHDA), Topological Polar Surface Area (TPSA), The base-10 logarithm of the partition coefficient P (LogP), Cytochrome P (CYP), and Blood-Brain Barrier (BBB).
CONCLUSION
The SGLT-2 inhibitors are a promising family of medications that can effectively treat hyperglycemia and undoubtedly assist a huge number of diabetic people in a safe and tolerable manner. Their distinct mode of action, blood pressure management, and pleiotropic effects make them suitable as alternative strategies for people who are not well-controlled by conventional treatments. In this study, the potent phenolic compounds from whole green jackfruit flour were investigated as potential inhibitors against human SGLT-2 protein. Among the examined compounds, both caffeic acid and syringic acid showed higher binding efficiency through molecular docking, binding with key residues of human SGLT-2 that could induce inhibition of the protein activity. Additionally, these compounds also demonstrated favorable stability and binding affinity during MD simulations and BFE estimations, suggesting their potential to act similarly to the conventional SGLT-2 inhibitor empagliflozin. Moreover, pharmacophore modeling suggests that the caffeic acid can serve as a lead molecule with 5 features and subsequent virtual screening against ZINC purchasable compounds and natural products database yielded 675 and 27 molecules, respectively for further study. While pharmacological investigations are needed to confirm their biological efficacy, these findings suggest that both caffeic acid and syringic acid could show potential leads for developing SGLT-2 inhibitors for the treatment of hyperglycemia in diabetes mellitus.
ACKNOWLEDGMENT
The authors express their gratitude to the JSS Academy of Higher Education and Research in Mysore, Karnataka, India, for their generous support, encouragement, and provision of essential facilities.
FUNDING
This research received no external funding.
ABBREVIATIONS
Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET), Binding Free Energy (BFE), Blood-Brain Barrier (BBB), Broyden-Fletcher-Goldfarb-Shanno (BFGS), Cytochrome P (CYP), Deoxyribonucleic acid (DNA), Ethanol Extract of Jackfruit Flour (EJF), Familial Renal Glucosuria (FRG), Food and Drug Administration (FDA), Free Energy Landscapes (FEL), High-Performance Liquid Chromatography (HPLC), GRaphing, Advanced Computation and Exploration of data (GRACE), GROningen MAchine for Chemical Simulations 2018.1 (GROMACS 2018.1), Molecular Dynamics (MD), Molecular Mechanical Energy (MME), Molecular Weight (MW), Nitric Oxide (NO), Number of Hydrogen Bonds Acceptor (NHBA), Number of Hydrogen Bonds Donor (NHDA), Number of Rotatable Bonds (NRB), Pharmacophore-Based Virtual Screening (PBVS), Primary Component 1 (PC1), Primary Component 2 (PC2), Principal Component Analysis (PCA), Prostaglandin E2 (PGE2), Proximal Convoluted Tubule (PCT), Radius of Gyration (Rg), Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), Reverse Phase High-Performance Liquid Chromatography (RP-HPLC), Root mean Square Deviation (RMSD), Root mean Square Fluctuation (RMSF), Sodium-Glucose Co-Transporter (SGLT), Sodium-Glucose Co-Transporter-1 (SGLT-1), Sodium-Glucose Co-Transporter-2 (SGLT-2), Sodium-Glucose Co-Transporter-2– Membrane-Associated Protein 17 (SGLT-2-MAP17), Solvent Accessible Surface Area (SASA), Simplified Molecular Input Line Entry System (SMILES), The base-10 logarithm of the partition coefficient P (LogP), Topological Polar Surface Area (TPSA), Type 2 Diabetes Mellitus (T2DM)
AUTHOR CONTRIBUTIONS
The manuscript was planned and conceptualized by R. R. Data analysis and method development were conducted by S. M. P., M. G, and R. C. M. Supervision and editing were carried out by S. P., R. S., and S. M. D. All authors have reviewed and approved the final version of the manuscript.
CONFLICTS OF INTERESTS
All authors declare no conflict of interest.
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