Identifying the alpha-glucosidase inhibitory potential of dietary phytochemicals against diabetes mellitus type 2 via molecular interactions and dynamics simulation

Department of Biochemistry, College of Medicine, University of Ha’il, 2440, Ha’il, Saudi Arabia Department of Bioengineering, Integral University, Lucknow, 226026, Uttar Pradesh, India Department of Pathology, College of Medicine, University of Hail, 2440, Ha’il, Saudi Arabia Department of Otolaryngology Head and Neck Surgery, College of Medicine, University of Ha’il, 2440, Ha’il, Saudi Arabia Imam Muhammad Ibn Saud Islamic University, Riyadh 13318, Saudi Arabia Department of Biology, College of Science, University of Ha’il, 2440, Ha’il Saudi Arabia Laboratory of Genetics, Biodiversity and Valorisation of Bioressources, High Institute of Biotechnology-University of Monastir, Monastir 5-000, Tunisia Department of Biochemistry, College of Medicine, Shaqra University, Shaqra, 11961, Saudi Arabia


Introduction
Diabetes mellitus type 2 (DM2) is considered the deadliest form of diabetes wherein the pancreas does not secrete ample quantities of insulin due to impaired β-cells, and the body is reluctant to use it aptly, resulting in an increased glucose concentration in the blood. About >90% of older people are prone to be diagnosed with DM2 worldwide. However, the progression of DM2 has been seen slow in children and younger people (1). A child born by diabetic parents has a 50% chance of developing DM2. The propensity of developing the disease for identical twins is greater than 75%, irrespective of whether they have grown up in the same family or not. Environmental factors may render a genetically susceptible person more vulnerable to the disease, viz. rich calorie indexed dietary compounds, and sedentary habits might prompt the disease onset before.
Obesity, high-calorie diet, visceral fat accumulation, sedentary lifestyles, genetic susceptibility, physical inactivity, hypertension, dyslipidemia, gestational diabetic history, and ethnicity viz., Hispanics, African Americans, Native Americans, Asian Americans, and Pacific Islanders are major risk factors for DM2. Diabetes mellitus type 1 (DM1) or juvenile-onset diabetes mellitus is another non-communicable disease that accounts for about 5-10% of cases globally and is more common in children and the younger populace. It is insulindependent (IDDM), wherein the pancreatic cells of hereditarily vulnerable patients do not secrete insulin due to the autoimmune-facilitated selective beta-cell damage resulting in absolute insulin scarcity, hyperglycemia, metabolic complications, oxidative stress, and inflammations. Human leukocyte antigen DR3 (HLA-DR3) and DR4 (HLA-DR4) isotypes susceptible populations are prone to developing DM1 four to six folds more than normal individuals. Primary adrenal insufficiency, celiac disease, gastritis type A, and Hashimoto thyroiditis are also strongly associated with DM1 (2-4). On average, 10% of diabetic patients have another variant of diabetes referred to as latent autoimmune diabetes in adults (LADA) with a salient feature of the delayed arrival of DM1. Sometimes LADA is poorly diagnosed and misunderstood as a DM2. Data reveals that pregnant women are also susceptible to gestational diabetes mellitus (GDM) that may be diminished after the child's birth. However, such children may be affected by DM2 at the later stage of their lives (5). Moreover, a fraction of the population exhibits a moderate form of diabetes, better known as impaired fasting glycemia (IFG) and impaired glucose tolerance (IGT). People having IFG and IGT are more likely to be developed DM2 later (6).
The centenary commemoration of insulin did not make scientific communities so happy and relaxed because diabetes still ranks 7th among various threatening diseases showing exponential growth globally. More than 700 million cases can be seen in the coming 2-3 decades until the sincere implementation of the Sustainable Development Goals (SDGs), preventing strategies to control and manage diabetes in every nook and corner of the world. Among all types, DM2 is most prevalent in the population irrespective of developing or developed socioeconomic status and is the culprit for millions of deaths each year worldwide. Retinopathy, gastroparesis, nephropathy, erectile dysfunction, bladder dysfunction, peripheral neuropathy are the microvascular complications associated with DM2.
Moreover, cerebrovascular disease, coronary heart disease, Monckeberg arteriosclerosis, peripheral artery disease, including gangrene and ulceration, are grouped as the macrovascular disease often seen in DM2 patients. Chronic diabetic patients can also suffer from diabetic foot, limited joint mobility, hyporeninemic hypoaldosteronism, sialadenosis, diabetic cardiomyopathy, necrobiosis lipoidica, diabetic fatty liver disease, and hyperosmolar hyperglycemic state (7)(8)(9)(10)(11). American Diabetes Association (ADA), World Health Organization (WHO), International Diabetes Federation (IDF), and regional committees make efforts to implement the SDGs recommendations to cure and prevent noncommunicable diseases that assure drop down almost 30% of premature mortalities worldwide (12,13). The comparative data of diabetes occurrence in 2019 and estimated prevalence in 2030 and 2045 as per IDF Atlas 9th edition is shown in Table 1. Reports reveal that DM2 cases in the Middle East & North Africa are increasing steeply compared to other continents at a surprising rate in the past few years. For the most part, one-fourth of the population is impacted by type 2 diabetes, which is also expected to increment in the coming days significantly (14-16). According to IDF statistics 2019, the trend in DM2 (20-79 years) for the top five countries in the Middle East and North Africa is illustrated in Figure 1.
Many chronic disease risks are associated with DM2, so if left untreated over a long time, leading to damage of various vital organs, as mentioned before. As per the recommendation of IDF and ADA, minimizing postprandial glucose (PPG) concentration is the most crucial step to managing diabetes (17-20). Therefore, inhibiting molecular targets elevating PPG levels is a therapeutically promising strategy (19). Alpha-glucosidase (AGS) is an essential enzyme in the digestive tract's mucosal brush borders that increases PPG concentration by catalytic hydrolysis of the terminal (1→4)-linked glycosidic bonds in dietary polysaccharides, oligosaccharides, and glycans into αglucose and fructose. AGS plays a significant role in carbohydrate metabolism, lysosomal catabolism of glycans, and post-translational enzymatic changes of cellular glycoproteins in conjunction with alphaamylase located intestinal lumen transforming dietary starches to oligosaccharides (21). AGS inhibition retards the breakup of carbohydrates in the small intestine and lessens the PPG elevation. Thus, this phenomenon significantly affects polysaccharide digestion, glycoprotein dispensation, and cellular engagements honing the path of identifying new bioactive compounds against diabetes and other metabolic and cellular diseases (22). Acarbose (glucobay, precose) (DB00284), miglitol (glyset) (DB00491), voglibose (volix) (DB04878), 1deoxynojirimycin (duvoglustat) (DB03206), and emiglitate (BAY o 1248) commercial AGS inhibitors have been recommended to postprandial hyperglycemia along with healthy diets and active lifestyles. These inhibitors retard the metabolism of complex carbohydrates and glycans by inhibiting AGS and thus check gastrointestinal absorption, which lowers blood glucose after having meals (23)(24)(25)(26)(27). Even though scrupulous reports support therapeutic aids in curbing the post-meal glucose concentration, PPG decrease in broad coverage of population exhibiting significant disease risks decrement is still underway. Moreover, regular consumption of AGS inhibitors leads to cause side effects, viz., flatulence, diarrhoea, vomiting, abdominal pain, distension, and allergic issues (28). So, despite commercially available promising AGS drugs, we need to identify natural bioactive molecules having great inhibition potential and meagre side effects. Towards this direction, the proposed research goals to find promising inhibitors against DM2 via molecular interaction of AGS with sixteen small phytomolecules including 4,5-dimethyl-3-hydroxy-2(5H)-furanone, apigenin, bromelain, caffeic acid, cholecalciferol, dihydrokaempferol 7-oglucopyranoside, galactomannan, genkwanin, isoimperatorin, luteolin, luteolin 7-o-glucoside, neohesperidin, oleanoic acid, pelargonidin-3rutinoside, quercetin, and quinic acid using AutoDock Tools (ADT) (29). Post molecular interaction analyses, MD simulation, metabolism prediction, molecular reactivity, and biological activity spectrum investigation of phytomolecules and their comparison with AGS drug molecules reveal bromelain's strong inhibition potential and stability.

Docking simulation
Molecular docking of natural ligands and reference drug molecules with AGS was achieved using ADT to find their most plausible binding interactions. PDBQT files of AGS, ligands and drug molecules, grid parameter file (.gpf), and a docking parameter file (.dpf) were prepared to perform docking experiments. The grid box around the protein molecule was drawn with variable grid points in x, y, z axes and maximum spacing (1.00 Å) between two consecutive grids. Ten runs for each ligand were executed. Minimum free energy of binding (ΔG) and inhibition constant (Ki) was chosen as selective parameters towards getting one of the best-docked conformations of ligands into the binding pocket of AGS (35)(36)(37)(38)(39).

Molecular dynamics simulation
MD simulation of 10 ns duration was performed on docked complexes of AGS with bromelain, luteolin, and acarbose at 300K at the MM level using GROMACS 5.1.2 (40). The ligands were extracted from the docked complexes utilizing the gmx grep module. The CGENFF server obtained the topology and forcefield parameter files of the ligand. The topologies were generated for AGS utilizing pdb2gmx modules of gromacs, and bromelain, luteolin, and acarbose using the CGENFF server were merged (41). All docked complexes were soaked in a dodecahedron box of water molecules with a margin of 10Å. The gmx editconf module was used for creating boundary conditions. The charges on the docked complexes were neutralized by adding Na + and Clions using the gmx genion module to maintain neutrality, preserving the physiological concentration of 0.15 M. The system was then minimized for 500000 steps using the steepest descent algorithm. Finally, the system temperature was raised from 0-300K during their equilibration of 100 ps at constant NVT and NPT. After the equilibration phase, the particle mesh was applied following the Ewald method (42,43). Finally, the protein-ligand system was introduced to 10 ns of MD simulation under identical conditions at 1 bar and temperature of 300K. The gmx rms, gmx rmsf, and gmx sasa modules of GROMACS were used to obtain RMSD, RMSF, and SASA of ligand-protein bound molecules (40,41).

Biological activity identification
The biological activity of bromelain, luteolin, and acarbose against various molecular targets was predicted using the online computation tool PASS (prediction of activity spectra for substances). PASS uses Pa and Pi symbols for a subclass of active and inactive compounds having values in the range of 0.000-1.000, respectively (49,50).

Results and discussion Molecular interactions
All ligands and reference drug molecules were docked to AGS, getting one of their respective conformers' most energetically favourable binding interactions. Natural ligands exhibit plausible binding having ΔG values between -5.83 to -9.54 kcal/mol and inhibition constant (Ki) in the range of 3.53 to 336.29 μM. Drug molecules depict molecular interactions with ΔG values in the range of -6.19 to -7.93 kcal/mol, and Ki between 324.64 to 88.25 μM. Among drug molecules, acarbose (ΔG: -7.93 kcal/mol, Ki: 88.25 μM) was found one of the best molecules interacting efficiently with AGS. Four ligands, namely bromelain, cholecalciferol, luteolin, and neohesperidin, showed better binding interactions than acarbose, the most efficient drug molecule. Bromelain and luteolin molecules were portrayed as the top two better binders having free energy of binding -9.54 and -9.02 kcal/mol and inhibition constant 3.53 and 4.88 μM, respectively. Furthermore, bromelain, luteolin, and acarbose were carried forward for MD simulation.

MD simulation
Molecular dynamics simulation of 10 ns duration for bound complexes of bromelain, luteolin, and acarbose with AGS was executed using the GROMACS package. MD plots for root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), solvent-accessible surface area (SASA), and free energy of solvation during SASA were created to evaluate the molecular interaction stability of ligands and protein complexes. The molecular interaction of ligands into the binding pocket of AGS acquires conformational changes to attain stability (53)(54)(55).

Root-mean-square deviation
The protein's stability and likeness to its native structure were measured by RMSD. The average value of RMSD for acarbose (black), bromelain (red), and luteolin (green) complexed with AGS was found 0.17 nm, 0.15 nm, and 0.16 nm, respectively. Minimum and maximum deviation of drug and ligand molecules were depicted as 0.09-0.25 nm, 0.09-0.20 nm, and 0.09-0.23 nm, respectively (Figure 5a). The RMSD plot reveals that the bound complex of bromelain with target protein is more stable than acarbose and luteolin.

Root-mean-square fluctuation
The RMSF illustrates the mean fluctuation of residues during entire periods of MD simulation. The pictorial graph ensures the stability of AGS bound with bromelain, luteolin and acarbose. Residues fluctuations at a different position in the RMSF plot are due to the molecular interaction of ligand and drug molecules. The plot reveals that residues fluctuation upon binding with luteolin and acarbose is exhibited more than bromelain (Figure 5b), dictating the impact of both phytoligands and reference molecule with AGS is not portrayed in similar patterns during simulation. However, luteolin and acarbose depict almost similar trends of residues fluctuation.

Solvent-accessible surface area and free energy of solvation
The illustration of SASA exposes protein's interactable surface to the solvent molecules. The average value of SASA for acarbose, bromelain, and luteolin interacted with AGS was depicted as 33.37 nm2, 34.05 nm2, and 32.45 nm2 (Figure 5c). The SASA findings exhibit that internal residue of AGS upon binding of bromelain and acarbose are less accessible by the solvent as compared to luteolin. The average free energy of solvation (ΔG solv ) of AGSacarbose, -bromelain, and -luteolin was predicted as -43.38 kJ/mol/nm2, -44.93 kJ/mol/nm2, and -43.48 kJ/mol/nm2, respectively (Figure 5d). RMSD, RMSF, SASA, and free energy of solvation plots comparatively favour the potency of bromelain as the most plausible inhibitor of AGS.

CYP450 metabolism prediction
Identifying the sites of a chemical compound most likely to be metabolized is imperative to facilitate the combinatorial design of small chemical molecules, thereby curtailing their attrition rate in different phases of clinical trials. Therefore, CYP450 metabolism of bromelain and luteolin was compared with drug molecule acarbose based on different scores, energy, COO-dist, Span2end, and 2D-SASA. The most probable CYP3A4, 2D6, and 2C9 sites of metabolism and their attribute depictions are shown in Figures 6a-c, 7a-c, and 8a-c, respectively. Table 3-5 shows the attributed-values for CYP3A4, 2C9, and 2D6 respectively (56,57).