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. 2023 Nov 10;8(46):43617–43631. doi: 10.1021/acsomega.3c05082

In Silico and In Vitro Analyses to Repurpose Quercetin as a Human Pancreatic α-Amylase Inhibitor

Bimal K Raut 1, Siddha Raj Upadhyaya 1, Jyoti Bashyal 1, Niranjan Parajuli 1,*
PMCID: PMC10666247  PMID: 38027372

Abstract

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Human pancreatic α-amylase (HPA), situated at the apex of the starch digestion hierarchy, is an attractive therapeutic approach to precisely regulate blood glucose levels, thereby efficiently managing diabetes. Polyphenols offer a natural and multifaceted approach to moderate postprandial sugar spikes, with their slight modulation in carbohydrate digestion and potential secondary benefits, such as antioxidant and anti-inflammatory effects. Taking into consideration the unfavorable side effects of currently available commercial medications, we aimed to study a library of polyphenols attributed to their remarkable antidiabetic properties and screened the most potent HPA inhibitor via a comprehensive in silico study encompassing molecular docking, molecular mechanics with generalized Born and surface area solvation (MM/GBSA) calculation, molecular dynamics (MD) simulation, density functional theory (DFT) study, and pharmacokinetic properties followed by an in vitro assay. Significant hydrogen bonding with the catalytic triad residues of HPA, prominent MM/GBSA binding energy of −27.03 kcal/mol, and the stable nature of the protein–ligand complex with regard to 100 ns MD simulation screened quercetin as the best HPA inhibitor. Additionally, quercetin showed strong reactivity in the substrate-binding pocket of HPA and exhibited favorable pharmacokinetic properties with a considerable inhibitory concentration (IC50) of 57.37 ± 0.9 μg/mL against α-amylase. This study holds prospects for HPA inhibition and suggests quercetin as an approach to therapy for diabetes; however, it is imperative to conduct further research.

1. Introduction

α-Amylase (α-1,4-glucan-4-glucanohydrolase) (E.C. 3.2.1.1), belonging to the glycosyl hydrolase family 13, serves a significant role in the catalytic breakdown of α-(1,4)-glycosidic bonds in starch through hydrolysis.1,2 The mechanism of metabolizing starch in humans commences with the activity of human salivary α-amylase (HSA), an enzyme that hydrolyzes polymeric starch into shorter oligomers. The majority of starch is subjected to digestion by abundant pancreatic amylase, which is secreted into the duodenal portion of the small intestine. The suppression of the HPA enzymatic function presents straightforward avenues to mitigate postprandial hyperglycemia by regulating starch breakdown.

Type 2 diabetes mellitus (T2D), the most commonly encountered form of diabetes, is associated with postprandial hyperglycemia. The global prevalence of diabetes mellitus currently impacts more than 536.6 million individuals, with projections indicating a potential increase to 643 million by 2030 and 783.2 million by 2045.3 To reduce the prevalence of T2D-related pathophysiologies, nearly 65 medicines have been clinically authorized.4 Due to the unfortunate reciprocal interplay between gut microbiota and antidiabetic medications, frequent utilization of these medications induces gastrointestinal complications caused by various factors including anaerobic fermentation in the lower gut, ultimately resulting in suboptimal patient adherence.5−7 The HPA, situated at the apex of the starch digestion hierarchy, has the potential to effectively regulate blood glucose levels quantitatively while minimizing the specificity issues that currently exist with α-glucosidase inhibitors.

HPA catalyzes starch hydrolysis by a double displacement method involving two distinct steps; the initial step, known as the glycosylation step, entails the cleavage of the glycosidic bond, ultimately leading to covalent intermediate formation. The addition of a water molecule within the subsequent stage (deglycosylation) completes the hydrolysis of the carbohydrate by releasing the covalent intermediate between the enzyme and the substrate.8,9 The conserved catalytic residues ASP197, GLU233, and ASP300 responsible for facilitating carbohydrate hydrolysis are of paramount importance in the process of hydrolyzing glycosidic bonds present in carbohydrates. Numerous studies have elucidated diverse inhibitory mechanisms that are responsible for the actions of well-established α-amylase inhibitors. These mechanisms encompass the establishment of hydrogen bonds with the catalytic triad (ASP197, GLU233, and ASP300) and other catalytic residues, interacting at sites distinct from the active site, and the establishment of covalent bonds with enzymes.10 The regulation of acid–base catalysis is facilitated by the residue GLU233, while coordination concerning the optimal substrate orientation in the binding site is carried out by ASP300. Additionally, ASP197 plays an important role in intervening in the nucleophilic process.11

The molecular frameworks of plant-based secondary metabolites have an extensive record of serving as viable medicinal chemistry and drug development bases. Incorporating these chemotypes into chemical collections for screening and attaining selective target activation, inhibition, and modulation has recently seen a resurgence in research.12 Polyphenols’ ability to inhibit α-amylase is greatly influenced by their molecular structure, specifically by the substituent (namely, hydroxyl and methoxy) of the aromatic rings imparting unique molecular characteristics, including but not limited to polarity, stability, and binding. Research findings from both in vivo and in vitro studies provide substantial evidence supporting the assertion that flavonoids, the most prominent subclasses of polyphenols, have a notable impact on the management of T2D through various mechanisms, improving the glucose metabolism,13 disrupting the amyloid assembly pathway, and thereby impeding the formation of amylin aggregates,14 resulting in pancreatic β-cell apoptosis.15 A longitudinal link between increased flavonoid intake and a lower incidence of diabetes supports the intriguing idea that adding bioactive ingredients with inhibitory activity against digestive enzymes to food products can delay the digestion of carbohydrates.16

Computational methodologies can be employed to unveil the mechanism of action of potential pharmaceuticals in the human organism, encompassing predicting outcomes of drug–protein interactions, and scrutiny of biological pathways and functions,17−19 thereby identifying potent metabolites that could be transformed into promising HPA inhibitors. Herein, to infer the aforementioned potentially novel target sites, we devised a computational framework study that encompasses docking, molecular mechanics with generalized Born and surface area solvation (MM/GBSA), molecular dynamics (MD) simulation, density functional theory (DFT), pharmacokinetic, and toxicity analyses to predict the effective roles of polyphenols in the inhibition of α-amylase. The inhibitors that exhibit efficacious binding and interaction poses with the catalytic triad residue and possess formidable stability with HPA concerning root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), solvent-accessible surface area (SASA), and hydrogen (H)-bond will proficiently bind with the aimed enzyme with potent reactivity and kinetic stability, thus efficaciously regulating hyperglycemia. To better understand the credibility of the computational analysis outcomes, an analysis was undertaken to explore the inhibitory effect along with the mechanism of action against porcine pancreatic α-amylase (PPA) through an inhibition assay. Employing enhanced comprehension of the inhibitory mechanism, the findings of this investigation will clarify an approach for the strategic suppression of α-amylase inhibition by specific metabolites.

2. Results and Discussion

2.1. Quality of Protein and Its Validation

The parameter known as the “overall quality factor” for nonbonded atomic interactions, ERRAT, quantifies the level of quality, with higher values denoting better quality. For high-quality models, the acceptable range is > 50; for high-resolution structures, the acceptable range is 95% or higher; and for low-resolution structures, the acceptable range is an average quality factor of around 91%.20,21 The current HPA was predicted to have an overall quality factor of 97.7178 according to the ERRAT server, demonstrating the protein to be of high quality (Supporting Information, Figure S1).

2.2. Identification of the Binding Site

Utilizing SiteMap, the authenticity of the catalytic site of the receptor protein was verified. It displayed the amino acid residues TRP58, TRP59, TRP62, GLN63, HIS101, TYR151, LEU162, LEU165, ARG195, ASP197, ALA198, LYS200, HIS201, GLU233, VAL234, ILE235, PHE265, ASN298, HIE299, ASP300, HIE305, and ALA307 in the binding site region (Supporting Information, Figure S2 and Table S1), thereby verifying the catalytic sites. Furthermore, these binding sites were found to be in accordance with the literature predicted by the same tool SiteMap with regard to the HPA protein.22 The SiteScore utilized to assess the ability of a drug to bind to a target site and to identify specific binding sites23 was found to be 0.992 (∼1). The Dscore value of 1.012, which is >0.7, manifested the binding site to be druggable.24 Similarly, the catalytic region was assessed in terms of volume, balance, and donor/acceptor value in the catalytic region, which aligns with the established threshold.25

2.3. Molecular Docking, MM/GBSA Calculation, and Molecular Dynamics Simulation

Employing molecular docking and MD simulation, a powerful approach in drug discovery, the prediction of enzyme inhibitors gains significant traction as it enables a detailed understanding of their interaction and potential efficacy.26,27 Polyphenols are structurally varied molecules with the ability to develop a variety of interactions with enzymes and are one of the most extensively researched constituents to moderate carbohydrate digestion, leading to controlled blood sugar levels, making them valuable for diabetes control and general metabolic health. Specifically, the multiple hydroxyl groups attributed to antioxidant properties maintaining insulin sensitivity,28 inhibition of the enzyme involved in carbohydrate digestion and metabolism,29 receptor modulation, broad mechanism of action via multiple pathways,30,31 pleiotropic effects,32 and prebiotic nature33 make polyphenols a viable metabolite to study comprehensively. Initially, the selected polyphenols were screened based on molecular docking encompassing a binding score with competitive interaction and MD simulation to explore the optimal HPA inhibitor.

With regard to molecular docking, precision and firmness were ensured initially using redocking approaches. The RMSD of less than 2 Å (0.1766 Å) of the cognate ligands verified the protocol.34 The superimposition of the cognate ligand is displayed in Figure S3 (Supporting Information). Following the successful validation of the docking protocol, the chosen secondary metabolites were subjected to docking within the active site of HPA. Consequently, an assessment of the ligand interactions, docking score, and MM/GBSA value was evaluated, as presented in Table 1. These compounds exhibited docking scores of −0.67 to −7.54 kcal/mol, glide scores from −0.67 up to −7.62 kcal/mol, and MM/GBSA binding energies in the range of 7.42 to −27.03 as compared to −7.84 kcal/mol docking score, −8.17 glide score kcal/mol, and −26.69 kcal/mol MM/GBSA binding energy of the standard compound acarbose.

Table 1. Binding Scores of Selected Metabolites.

compounds docking score (kcal/mol) glide score (kcal/mol) MM/GBSA dG bind
apigenin –5.80 –6.15 –17.15
caffeic acid –4.30 –4.30 7.91
catechin –7.43 –7.43 –13.79
chlorogenic acid –0.67 –0.67 7.42
ellagic acid –7.54 –7.62 –23.89
epicatechin –7.16 –7.16 –16.95
isovitexin –3.86 –3.92 –17.47
kaempferol –4.69 –6.45 –17.59
protocatechuic acid –3.59 –3.59 11.70
quercetin –6.63 –7.00 –27.03
scutellarin –4.09 –4.09 –8.89
vitexin –7.05 –7.13 –21.99
acarbose –7.84 –8.17 –26.69
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Based on the binding score, compounds like caffeic acid, chlorogenic acid, and protocatechuic acid showed poor results, signifying a weak interaction between the protein and the ligand, while compounds such as apigenin, catechin, epicatechin, isovitexin, kaempferol, and scutellarin exhibited moderate binding scores. Meanwhile, with a promising docking score, glide score, and MM/GBSA, the three metabolites—ellagic acid, quercetin, and vitexin—showed the highest score, thereby indicating a strong interaction between HPA and these aforementioned metabolites. The result was correlated with the findings of other research demonstrating active inhibition by ellagic acid,35 quercetin,36 and vitexin37 against the α-amylase study. This was further supported via the binding pose, as displayed in Table S2 and Figures S4–S15 (Supporting Information). To gain an additional understanding of the behavior of bounded protein–ligand complexes, all of these compounds were run up to 100 ns MD simulation.

The MD simulation holds significant importance in the realm of pharmaceutical research due to its ability to offer valuable insights into the intricate behavior of molecules at both the atomic and molecular scales. This, in turn, enables researchers to acquire an in-depth comprehension of the interactions between these molecules and drug targets, thereby facilitating the optimization process.38 Additionally, MD simulations can be used to estimate how long a ligand will remain attached to its target protein. Extended ligand-target residence durations are commonly linked with enhanced therapeutic effectiveness. Consequently, comprehending the dynamics of ligand association and dissociation is critical.39 The structural behavior, molecular flexibility, and stability of α-amylase with various phytochemicals were accessed by a 100 ns MD simulation using GROMACS 2021.4. To comprehend the molecular dynamic results, several built-in GROMACS techniques such as RMSD, RMSF, SASA, radius of gyration, and hydrogen-bond analysis were performed.

2.3.1. Root-Mean-Square Deviation (RMSD)

An assessment of RMSD evaluation was conducted to monitor the conformational and structural alterations of the atoms in the backbone of the enzyme–substrate and α-amylase complexes. The RMSD plot of α-amylase and all protein–ligand complexes was analyzed based on a 100 ns trajectory (Figure 1). It is noteworthy to mention that, except for ellagic acid, epicatechin, and protocatechuic acid, all protein–ligand complexes, as well as the apoprotein, achieved stability within a simulation time of 20 ns. The apoprotein was stable up to 80 ns, after which it showed slight fluctuation along with an average RMSD of 0.243 nm. The higher stability of the apigenin and caffeic acid complexes can be attributed to the fact that their RMSD values were within acceptable bounds (below 0.24 nm) compared to the apoprotein. The compounds kaempferol, quercetin, scutellarin, and vitexin achieved stability within 10 ns, exhibiting average RMSDs of 0.263, 0.270, 0.254, and 0.26 nm, respectively (Table 2). The acarbose–protein complex exhibited the highest level of stability, as evidenced by the RMSD value of 0.168 nm, also supported by prior studies.40 This finding implies that the protein experienced less deviation upon formation of the complex with acarbose. Except for protocatechuic acid, all other protein–ligand complexes showed stability under a 100 ns simulation.

Figure 1.

Figure 1

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RMSD plot of the protein (α-amylase) and ligand (screened metabolites) complex.

Table 2. Average RMSD, Rg, RMSF, and SASA Value of Protein and Screened Ligands Complexes.
complex average RMSD (nm) average Rg (nm) average RMSF (nm) average SASA (nm2)
apoprotein 0.243 2.278 0.115 248.909
α-amylase–apigenin 0.235 2.256 0.102 250.017
α-amylase–caffeic acid 0.228 2.278 0.111 251.856
α-amylase–catechin 0.297 2.247 0.129 247.583
α-amylase–chlorogenic acid 0.272 2.263 0.111 246.769
α-amylase–ellagic acid 0.263 2.260 0.109 251.130
α-amylase–epicatechin 0.268 2.270 0.121 248.927
α-amylase–isovitexin 0.294 2.288 0.121 246.366
α-amylase–kaempferol 0.263 2.262 0.115 251.947
α-amylase–protocatechuic acid 0.247 2.266 0.128 251.945
α-amylase–quercetin 0.270 2.268 0.122 246.973
α-amylase–scutellarin 0.254 2.269 0.118 248.046
α-amylase–acarbose 0.168 2.369 0.0771 269.877
α-amylase–vitexin 0.26 2.275 0.129 248.433
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2.3.2. Radius of Gyration (Rg)

The parameter radius of gyration (Rg) is utilized to assess the stability of both apoproteins and protein–ligand complexes during a 100 ns simulation. To assess the structural compactness, stability, and conformational states of the biomolecules, the Rg value was analyzed through the utilization of a molecular dynamics (MD) simulation trajectory.41 The graph illustrating the variation of Rg over time for both apoprotein and protein–ligand complexes within a 100 ns time frame is presented in Figure 2.

Figure 2.

Figure 2

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Rg plot of the protein (α-amylase) and ligand (screened metabolites) complex.

The apoprotein α-amylase exhibited an average Rg of 2.278 nm, slightly lower than the previously reported value.42 Except for the complexes involving α-amylase–acarbose, α-amylase-isovitexin, and α-amylase–caffeic acid, the remaining complexes exhibited reduced Rg values in comparison to the apoprotein. This suggests a more condensed structure in these complexes. The complex formed between α-amylase and catechin exhibited the lowest Rg value, specifically measuring 2.247 nm, whereas the α-amylase–acarbose complex displayed the highest Rg value, i.e., 2.369 nm. When compared to apoprotein, all of the compounds displayed relatively similar and consistent Rg values (Table 2). Throughout the 100 ns simulation, the acquired Rg value indicates the fact that all of the protein–ligand complexes attained a comparatively stable folded conformation. The α-amylase–acarbose complex displayed the highest radius of gyration, indicating that acarbose does not effectively fit within the protein’s binding pocket. All of the ligands form a stable interaction with the protein compared to the acarbose and have a more compact structure. The average Rg value of ellagic acid, epicatechin, quercetin, and acarbose is comparable with previously reported values.41 The results demonstrated improved compactness and rigidity of protein–ligand complexes, indicating overall complex stability.

2.3.3. Root-Mean-Square Fluctuation (RMSF)

The RMSF value is a quantitative measure employed to assess the variations occurring in the native protein residues and ligand atoms under specific temperature and pressure conditions. A greater RMSF value denotes a more flexible part of the structure such as turns and loops and, conversely, a lower RMSF value suggests the presence of a more stable secondary structure, such as helices and sheets.41 The fluctuations noted in the residues of the apoprotein and protein–ligand complexes during 100 ns simulation have been plotted as a function of time and are displayed in Figure 3. Consistent variations and RMSF values were observed within the identical residues across all complexes. The average RMSF values for various complexes, including apoprotein, α-amylase–apigenin, α-amylase–caffeic acid, α-amylase–chlorogenic acid, α-amylase–ellagic acid, and α-amylase–epicatechin, were determined to be 0.115, 0.102, 0.111, 0.129, 0.111, 0.109, and 0.121 nm, respectively. The average RMSF values for the complexes of α-amylase with isovitexin, kaempferol, protocatechuic acid, quercetin, scutellarin, acarbose, and vitexin were observed to be 0.121, 0.115, 0.128, 0.122, 0.118, 0.071, and 0.129 nm, respectively (Table 2).

Figure 3.

Figure 3

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RMSF plot of the protein (α-amylase) and ligand (screened metabolites) complex.

RMSF results showed fluctuations of around 0.445, 0.438, 0.317, 0.369, 0.373, and 0.323 nm in residues ASN350, GLY351, ASN352, PRO374, ASP375, and GLY460, respectively, in apoprotein, consistent with a prior study.42 Out of the selected protein–ligand complexes, α-amylase–protocatechuic acid showed the highest fluctuations compared to other complexes. Generally, the highest fluctuations were observed in residues LYS142, THR143, GLY144, GLU239, HIS305, GLY306, ALA307, GLY308, GLY309, ALA310, SER311, GLN349, ASN350, GLY351, PRO374, ASP375, and GLY460, which are not part of the active site. Both isovitexin and vitexin complexes showed almost similar types of fluctuations, which is also supported by a previous study.43 From this result, we can conclude that except for α-amylase–protocatechuic acid complexes, all other protein–ligand complexes exhibited acceptable fluctuations and hence act as potent inhibitors of α-amylase.

2.3.4. Solvent-Accessible Surface Area (SASA)

Solvent-accessible surface area (SASA) quantifies the proportions that comprise a biomolecule’s surface area available for solvent interaction. Nevertheless, it also predicts the conformational changes during the interaction. The study determined the average SASA value of the apoprotein as 248.909 nm2, while that of protein–ligand complexes ranged from 246 to 269 nm2 (Table 2). Out of the selected complexes, catechin, chlorogenic acid, isovitexin, quercetin, scutellarin, and vitexin exhibited lower SASA values compared to that of apoprotein, implying effective binding interaction of these compounds within the binding pocket of the enzyme and thus resulting in a more compact complex (Figure 4). The α-amylase–isovitexin complex showed the lowest SASA value (246.366 nm2), whereas the α-amylase–acarbose complex exhibited the highest SASA value (269.877 nm2). The average SASA values of α-amylase–ellagic acid, α-amylase–epicatechin, and α-amylase–quercetin were found to be 251.130 nm2, 248.927 nm2, and 246.973 nm2, respectively, lower than the previously reported value with HPA (PDB ID: 3BAJ).41 The lower the SASA value, the more strongly the ligand can be accommodated within the catalytic site of the protein, and a more compact structure will be formed.44 Since the SASA values of all of the complexes were analogous to those of the apoprotein, we can conclude that the selected phytochemicals act as potential inhibitors.

Figure 4.

Figure 4

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SASA plot of the protein (α-amylase) and ligand (screened metabolites) complex.

2.3.5. Hydrogen Bonds

Considering the pivotal role of hydrogen bonds in facilitating substrate binding to proteins and their impact on various biological processes, such as metabolism, adsorption, drug affinity, and specificity, it becomes imperative to ascertain the hydrogen-bonding patterns by monitoring the dynamic fluctuations of hydrogen bonds across all protein–substrate complexes. It was noted that all of the complexes consistently maintained hydrogen bonds throughout the entire 100 ns simulation trajectory. In the complexes, α-amylase–epicatechin, α-amylase–isovitexin, α-amylase–quercetin, and α-amylase–vitexin complexes exhibited the six strongest hydrogen bonds (Figure 5). These observed hydrogen-bond results showed that all of the substrates were successfully and tightly bound to enzymes via hydrogen bonding.

Figure 5.

Figure 5

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H-bond plot of the protein (α-amylase) and ligand (screened metabolites) complex.

The compounds protocatechuic acid, chlorogenic acid, and caffeic acid that showed weak interaction and low binding score exhibited high fluctuations, deviation, and SASA value along with less hydrogen-bonding interaction. Moderate simulation results were reported for apigenin, ellagic acid, kaempferol, scutellarin, and catechin. Although ellagic acid exhibited a prominent MM/GBSA value (−23.89 kcal/mol), it has the highest Rg and SASA value compared to other ligands, which suggests ellagic acid does not bind effectively in the binding pocket of the protein and forms less hydrogen bonding. Similarly, MD simulation results revealed epicatechin, isovitexin, quercetin, and vitexin exhibited up to six hydrogen bonds during 100 ns simulation. However, epicatechin, isovitexin, and vitexin were found to be bonded with only one catalytic site: GLU233, ASP197, and ASP300, respectively, and exhibited lower MM/GBSA binding scores as compared to the standard acarbose.

In context to both molecular docking and MD simulation, quercetin, the flavonoid metabolite, showed the most prominent binding interaction with the catalytic site residue as well as exhibited a prominent binding score and stable simulation results too. Using MM/GBSA, the most prominent binding score,45,46 it was determined that quercetin (the only compound from the selected library of metabolites) showed a greater MM/GBSA of −27.03 kcal/mol in contrast to the standard drug acarbose (−26.69 kcal/mol). With regard to the binding pose, GLN63, ASP197, HIE299, and ASP300 amino acid residues, which are some of the binding site residues predicted by SiteMap, were found to be bonded with the quercetin metabolite via H-bonding, TRP59 by Pi–Pi stacking. Some similar binding interactions of quercetin were observed with the α-amylase enzyme (PDB ID: 3BAJ).41 ASP197 and ASP300, being the two catalytic triad residues involved in the hydrolysis of dietary starch, are bonded with quercetin (Figure 6). According to the results of fluorescence quenching and enzyme activity, quercetin showed similar inhibitory actions against α-amylase activity through hydrogen and hydrophobic binding, further supported by fluorescence spectroscopy.47 These findings suggest that quercetin has a greater affinity for binding than acarbose and might be potentially explored as a therapeutic agent. Furthermore, the molecular dynamics simulation supports the docking results. The RMSD, a crucial parameter used to determine the deviation in the skeleton of the protein after forming the complex, showed that the quercetin–α-amylase complex and apoprotein are almost equally stable. Similarly, the Rg and SASA values showed that quercetin–α-amylase is more compact compared to the apoprotein, indicating that quercetin binds strongly within the binding region (active site) of the protein and forms a compact structure. On the contrary, quercetin also maintains hydrogen bonding with the protein under 100 ns simulation. Overall, simulation results showed the stable quercetin–HPA complex in the context of the biological system and therefore illustrated its efficacy as a therapeutic inhibitor. This finding is consistent with the study that remarked quercetin exhibits stable 100 ns MD simulation results against HPA.36,48

Figure 6.

Figure 6

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Molecular interaction and binding pose of quercetin in the binding site of HPA. (A) Two-dimensional (2D) ligand interaction, (B) three-dimensional (3D) interaction, and (C) molecular surface diagram of the quercetin–HPA complex.

2.4. DFT and Band-Gap Energy

An in-depth study using the DFT method was conducted to evaluate the reactivity of phytochemicals within the binding pocket of HPA. The energy levels of frontier molecular orbitals (FMOs) and band-gap energy (ELUMO – EHOMO) are key quantum parameters that significantly contribute to characterizing a molecule’s interactions with other species, such as protein–ligand or ligand–ligand interactions.49 Molecules with a lower band-gap energy tend to exhibit greater polarizability and higher chemical reactivity.50 In addition, quantum molecular descriptors such as chemical potential (μ), global hardness (η), softness (s), and electrophilicity index (ω) are employed to elucidate the relationship between the protein–ligand and drug efficacy in systems used for drug delivery. The Pearson principle of maximum hardness states that the chemical hardness inversely affects reactivity. Compounds with reduced chemical hardness and higher softness are considered to be more reactive.51 A higher electronegativity (χ), electrophilicity indices, and chemical potentials correspond to a greater chemical reactivity with catalytic proteins as they serve as good electrophiles rather than nucleophiles.52 Similarly, the electrophilicity index (ω) quantifies a molecule’s electron-accepting capacity and the chemical potential (μ) indicates the path of electron transfer.

Following molecular docking, MD simulation, and MM/GBSA binding energy calculations, quercetin was selected for the DFT analysis. The standard compound, acarbose, was also considered to compare the reactivity with that of quercetin. The optimal geometric structures of the hit molecule under study are shown in Figure S16 (Supporting Information).

The result demonstrated that quercetin has effective reactivity due to the low band gaps of HOMO and LUMO (0.14 kcal/mol) relative to the standard compound acarbose (0.20 kcal/mol) (Figure 7). Due to the C2=C3 double bond in the C ring, quercetin, the greatest antioxidant, exhibited the highest HOMO energy value, a planar structure, and the best electron orbital delocalization on all phenolic rings.53 Moreover, the quantum mechanical parameters such as η, S, χ, μ, and ω that are concerned with the reactivity suggest the high potency of quercetin toward HPA (Supporting Information, Table S3).

Figure 7.

Figure 7

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Band-gap energy illustration of quercetin and acarbose (standard).

The study generated MEP maps of the analyzed compounds to ascertain the electron-rich and -deficient regions within the compound. The MEP map is presented with a chromatic scheme as displayed in Figure 8. The blue coloration in the diagram represents the positive region, which is associated with a nucleophilic attack. Conversely, the red coloration indicates a negative region, which is indicative of an electrophilic attack. The green coloration, on the other hand, represents areas of zero potential within the molecule.54

Figure 8.

Figure 8

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MEP map of quercetin.

The study revealed that oxygen atoms exhibited negative charges, whereas hydrogen atoms that formed bonds with oxygen or nitrogen atoms exhibited positive charges. The negative and positive centers played a crucial role in the establishment of noncovalent interactions between ligands and receptors throughout molecular docking and MD simulations.55 Accordingly, the blue region in the MEP map, which depicts the electron-deficient center, entails hydrogen bonding between two hydrogens of hydroxyl groups of ring B on quercetin (hydrogen donor) and ASP300, ASP197, and HIS299 residues of HPA (hydrogen acceptor) as illustrated in Figures 6 and 9. Similarly, the red region of the diagram, which denotes the negative electrostatic potential, acts as a hydrogen-bond acceptor where the GLN63 HPA residue interacts with the electron-rich carbonyl group of quercetin. This study, therefore, encloses the molecular interactions and binding patterns between quercetin and HPA aiding in the docking and simulation results.

Figure 9.

Figure 9

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Mechanism of quercetin in the inhibition of HPA.

2.5. Pharmacokinetic Analysis

To be a drug, a candidate needs to be extremely selective and possess low undesirable effects.56 Drug candidates often experience a notable attrition rate during the later stages of development, primarily due to their efficacy and the unavoidable occurrence of adverse effects, which are largely attributed to ADMET issues.57 However, the potential failure of a promising candidate can be minimized by employing an in silico technique. As a result, the drug-likeness and ADMET properties of the potential hit (quercetin) were thoroughly examined employing several models, including QikProp, SwissADME, admetSAR, and pkCSM (Table 3, Supporting Information S4–S6).

Table 3. Drug-Likeness Properties of Quercetin and Acarbose via the SwissADME Server.

  no. of rotatable bonds no. of H-bond acceptors no. of H-bond donors TPSA (Å) MLOGP molar refractivity Lipinski’s rule of five Ghose filter Veber rule Egan rule drug-likeness
quercetin 1 7 5 131.36 – 0.56 78.03 yes; 0 violation yes yes yes accepted
acarbose 9 19 14 321.17 – 6.94 136.69 no; 3 violations no; 4 violations no; 1 violations no; 1 violations rejected
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For any compound to be a potent drug candidate, it should be easily absorbed in the body. The hit compound showed that 77.207% intestinal absorption surpasses the minimal absorption criteria of 30%58 and is much greater than the acarbose value (4.172%). The log VDss and blood–brain barrier (BBB) permeability signifying tissue distribution and accumulation into the brain leading to neurotoxicity,59 respectively, have revealed the hit compound quercetin to be safer in this perspective. Quercetin’s neuroprotective benefits have been corroborated by several investigations conducted in vitro, in vivo, and in humans. Low concentrations of quercetin (from picomolar to nanomolar) are discovered in the brain tissue of rats and pigs after quercetin is administered in vivo successfully attempting to improve quercetin’s bioavailability.60,61 Cytochrome P450 enzymes (key CYP3A4) contribute significantly to the detoxification of foreign chemicals and help in the metabolism of drugs.62 The hit compound does not inhibit CYP3A4, indicating it is easily metabolized in the liver and has no side effects. The organic-anion-transporting polypeptide 1B1 (OATP1B1/SLCO1B1) transporter is a prominently expressed uptake transporter in the human liver. Its significance lies in its crucial role in drug disposition. OATP1B1, OATP1B3, and the less widely recognized OATP2B1 (SLCO2B1) are all expressed in the basolateral membrane of hepatocytes and play a crucial role in mediating the transport of xenobiotics and endogenous compounds from the portal circulation.63 The hit compound showed the OATP1B1 substrate of the phosphate–OATP1B1 complex, leading to a decrease in potential side effects and toxicity. As of a previous study conducted on Chinese-Han males, quercetin inhibits OATP1B1 substrates with a modest inhibitory pravastatin pharmacokinetic impact on healthy volunteers.64 Hepatotoxicity and AMES toxicity are key factors determining liver injury and mutagenicity caused by the drugs.65 Quercetin showed both negative AMES toxicity tests and hepatotoxicity. It thus aligns with the conclusion that quercetin is not a CYP3A4 inhibitor, is not toxic to the liver, and has no AMES toxicity.66

To assess the possibility of assay interference or toxicity, the quantification of reactive functional groups (#rtvFG) present in each molecule was also evaluated and found to be within the range. Further, it was observed that both quercetin and acarbose showed CNS inactive properties. SASA value, which indicates the free surface area of the molecule, was also within the acceptable range. Since cell membranes are lipoid, the hit compound’s lipophilicity (QPlog Po/w) of 0.362 (−2.0 to 6.5) suggests it is less hydrophilic and may be readily absorbed and dispersed inside cells. The presence of the hydroxyl group may account for the compound’s hydrophilic nature, while the presence of both the aromatic ring and the keto-functional group may account for its lipophilic nature.67 Similarly, the ADMET properties of the hit compound were also evaluated in terms of QPlogHERG (predicted IC50 values for the blockage of human ether-a-go-go-related gene potassium ion channels), QPlogBB (predicted brain–blood partition coefficient), QPlogkhsa (prediction of binding to human serum albumin), and #metab (number of likely metabolic reactions). Consistent with an earlier study,68 the hit compound possessed a blood–brain partition coefficient and QPlogHERG within the acceptable range alongside its binding with human serum albumin. The Qikprop ADMET results showed quercetin (hit compound) has better pharmacological properties compared to acarbose. The reported ADME properties of quercetin, as well as its tolerable oral toxicity, decreased rat oral toxicology, in addition to the noncarcinogenic or mutagenic potential,69 further supporting our result.

Quercetin was further subjected to Lipinski’s (Pfizer) rule of 5, the Ghose Filter, Veber’s rule, and the Egan rule to predict whether the compound under study has a drug-like property or not. With a molecular weight of 302.24 g/mol, −0.56 log P, five hydrogen-bond donors, and seven hydrogen-bond acceptors, quercetin showed zero violation in Lipinski’s rule, demonstrating a drug-likeness property. The Ghose Filter, another parameter sought to evaluate the drug-likeness of compounds, relies on the evaluation of various physicochemical properties, including log P values (−0.4 to 5.6), molar refractivity values (40–130), molecular weight (160–480), number of atoms (20–70), and topological polar surface area (TPSA) ≤ 140 Å2.70 Quercetin followed Ghose’s rule with its acceptable aforementioned range. Since the investigated compound has no more than 140 Å2 TPSA and 10 rotatable bonds, it meets the criteria of Veber’s rule.71 TPSA is the parameter to evaluate the competence of the drug to permeate cells: TPSA > 140 Å2 and rotatable bond > 10 denote poor permeability of drugs.72 The Egan rule states that a candidate drug has good oral absorption if – 1.0 ≤ log P ≤ 5.8 and TPSA ≤ 130 Å2;73 quercetin was found to follow this rule within acceptable log P and TPSA values. These results indicate that no violations were observed for quercetin in terms of the Lipinksi, Ghose, Veber, and Egan rules, whereas acarbose, the reference compound, demonstrated noncompliance with all of these drug-likeness parameters. The finding is further supported by earlier research showing quercetin to be a drug-like candidate that satisfies all of the aforementioned criteria.66 Additionally, the number of stars evaluated via Qikprop indicates the drug-likeness properties of molecules, where higher numbers of stars indicate less drug-likeness properties.74 The #stars result showed quercetin has better drug-likeness properties compared to acarbose.

2.6. α-Amylase Inhibition Assay

During the enzyme assay, quercetin was found to inhibit pancreatic α-amylase with an IC50 value of 57.37 ± 0.9 μg/mL, which is almost comparable with that of the standard compound, acarbose (53.9 ± 0.3 μg/mL) (Supporting Information, Figure S17 and Table S7). This observed inhibition result aligns with the findings reported in the existing literature, which indicate that quercetin can inhibit PPA.26,48,75 PPA and HPA are substantially comparable in terms of their three-dimensional structures. Molecular cloning and primary structural analysis of PPA revealed that it shared the highest similarity to the HPA sequence of all known amylases (87.1%).76 Consequently, it is postulated that quercetin may exert a beneficial inhibitory effect on HPA as well. Therefore, the findings suggest that quercetin, the compound anticipated through computational methodology, possesses the capacity to impede the pancreatic α-amylase, thereby diminishing the breakdown of carbohydrates into maltose.

It is worth noting that the exact mechanism of quercetin’s α-amylase inhibition may vary depending on the specific enzyme and experimental conditions. Quercetin exhibits competitive binding to the active site of α-amylase, effectively impeding the enzyme’s ability to engage with its substrate, starch. This inhibition occurs through the disruption of noncovalent interactions, including hydrogen bonding and hydrophobic interactions.77 In accordance with our study, depicted in Figure 9, quercetin, the hit flavonoid, forms hydrogen bonding with the catalytic triad (ASP197, GLU233, and ASP300) and inhibits the hydrolysis reaction. Some studies have suggested that quercetin may also inhibit α-amylase through noncompetitive or mixed inhibition, where it binds to sites other than the active site, thereby affecting the enzyme function indirectly.47 Besides these, quercetin has been found to enhance plasma insulin levels and reduce blood glucose levels in diabetes models induced by streptozotocin (STZ) via preservation of β-cell mass and function, ultimately leading to an increase in the serum insulin efficacy. Moreover, a study concluded that quercetin exhibited the ability to mitigate islet cell dysfunction, enhance β-cell insulin secretion, and forestall diabetes by diminishing oxidative stress in alloxan-induced diabetic animal models.78

Quercetin, a bioflavonoid, is a plant pigment that is found in over 20 different plant materials and is well-known for its therapeutic biological activities including antiobesity, antihypertensive, antihypercholesterolemic, anticancer, antibacterial, antiatherosclerotic activities, antiviral, and so on.79−81 Since quercetin is a dietary flavonoid distributed in various food sources such as fruits (particularly citrus, apples, and cherries), vegetables (onions, broccoli), and beverages (tea and red wine)82 and is granted Generally Recognized as Safe (GRAS) status to quercetin (QU995) by the Food and Drug Administration (FDA), its consumption might be beneficial in terms of diabetes and other aforementioned diseases. Moreover, oxidative stress (OS), together with insulin insufficiency, leads to T2D;83 OS arises from the production of free radicals and reactive oxygen species (ROS), which disrupt the body’s protective mechanisms. Consequently, T2D manifests as a consequence of elevated levels of ROS.84 Consuming dietary sources of quercetin, which are rich in antioxidants and can decrease carbohydrate consumption via α-amylase inhibition, would be an attractive strategy for combating hyperglycemia, particularly T2D.

The clinical utility of quercetin, classified as a BCS class II drug, is impeded by numerous factors, encompassing its insufficient solubility in aqueous solutions, restricted permeability, a vulnerability in physiological milieus such as the gastric and intestinal tracts, transient biological half-life, and a substantial hepatic metabolism prior to systemic dissemination.85 The combined influence of these factors contributes to the less-than-ideal oral bioavailability of quercetin, thus imposing restrictions on its use in pharmaceutical utilization. Nevertheless, novel methodologies have been previously documented in the literature to address this issue, encompassing the development of solid lipid nanoparticles, self-nano emulsifying systems, polymeric nanoparticles, liposomes, nanocrystals, and cationic nanocarriers to augment the bioavailability of quercetin, thereby exemplifying a favorable inclination toward the clinical phase of quercetin.86

3. Conclusions

Inhibition of HPA has been a strategic approach to suppress carbohydrate digestion, resulting in a reduced rate of glucose release into the bloodstream and thereby controlling T2D and its associated complications. Polyphenols, which make up a heterogeneous group of secondary metabolites present in diverse plant species and dietary sources, have emerged as the subject of considerable study in recent times attributable to their potential health-promoting properties, particularly their capacity to regulate hyperglycemia. Based on the molecular docking and MD simulation of 12 secondary metabolites, our findings suggest that quercetin exhibits potential as a viable candidate. This is attributed to its MM/GBSA value, which surpasses that of acarbose, as well as its ability to interact with catalytic site residues, thereby inhibiting the hydrolysis of glycosidic bonds present in carbohydrates and ultimately controlling hyperglycemia. Additionally, quercetin demonstrated significant stability with HPA, as evidenced by its RMSD, RMSF, Rg, SASA, and H-bonding properties, further highlighting its efficacy within biological systems. The DFT analysis indicates that quercetin, in comparison to the reference compound acarbose, has a high reactivity in the binding pocket of HPA. After this, an evaluation of the pharmacokinetic parameters, encompassing the ADMET analysis, and the drug-likeness properties provides additional substantiation for its potential as a pharmaceutical candidate, and to determine its efficacy, an in vitro pancreatic α-amylase inhibition assay was conducted. The computational study was corroborated by the IC50 value of 57.37 ± 0.9 μg/mL, thereby inferring quercetin as a potent HPA inhibitory agent. Although quercetin has been documented as a significant metabolite in diverse ailments, it has not yet been contemplated for clinical trials in the administration of diabetes. The recorded consequences and revelations of in vitro exploration produced advantageous outcomes in conjunction with our computational evaluation. Hence, it is crucial to emphasize the importance of quercetin in forthcoming research undertakings, which ought to encompass in vivo models and headway toward sophisticated phases of drug investigation plans.

4. Materials and Methodology

4.1. Selection and Preparation of Ligands

With the purpose of screening for potential HPA inhibitors, a library of 12 phenolic secondary metabolites based on their biological properties was generated (Supporting Information, Figure S18 and Table S8), along with acarbose, which is a standard compound employed for diabetes treatment. Using the PubChem database (https://pubchem.ncbi.nlm.nih.gov), the 3D conformers of each ligand were retrieved in SDF (structure data format). Each of the ligand structures was imported into Maestro’s workspace using Schrödinger’s LigPrep module, and subsequently, the energy was optimized employing the OPLS4 force field.87,88 Additionally, Epik effectively generated tautomeric and ionization states while maintaining the specified chiralities at pH 7.0 ± 2.0 and generated up to 32 conformers per ligand into the maestro output format.

4.2. Protein Preparation

The HPA protein (PDB ID: 1HNY) of 1.80 Å resolution and zero mutation was retrieved from the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (https://www.rcsb.org/), and the protein preparation workflow was conducted following Schrödinger’s methodology.89 The CCD database was used to assign bond order, hydrogens were replaced, and zero-order bonds were formed between metals and disulfides as part of the preprocessing. Using glide, processes like filling in missing loops and filling in missing chains were carried out. The het states were subsequently generated with Epik under controlled conditions at a pH of 7.0 ± 2.0.90,91 The hydrogen bonds were optimized through the application of PROPKA and subjected to minimization employing the OPLS4 force field24 alongside the deletion of water with a distance of 5 Å from the ligands. The SiteMap application evaluated and validated the generated protein’s quality and its ligand binding site.24 ASP197, GLU233, and ASP300, which are catalytic triad residues, were incorporated into the protein to generate a receptor grid via a receptor grid generation program.

4.3. Molecular Docking

With regard to the Schrödinger glide module for ligand docking, Schrodinger (Release 2023-02) was utilized. The molecular docking of secondary metabolites with HPA was accomplished using the Glide software in its extra precision mode (XP).92 The docking procedure was validated using redocking approaches and superimposition to ensure the precision and consistency of docking while also attempting to minimize misleading-positive outcomes.93

4.4. MM/GBSA Binding Energy Calculation

The MM/GBSA binding energy of protein–ligand complexes was assessed through the employment of Schrödinger’s prime module.90 An OPLS4 force field and the implicit VSGB solvation model94 were utilized to determine the binding energy.

4.5. Molecular Dynamic Simulation

A molecular dynamic (MD) simulation of 100 ns was performed to study the stability, H-bonding between the ligand and the protein, and the dynamic protein–ligand complex behavior.95 Gromacs software, version 2021.4, was used for performing MD simulations.96 Initially, the protein’s topology file was created using the GROMOS43a1 force field, and the PRODRG web server (https://www.sites.google.com/site/vanaaltenlab/prodrg) was used to create the ligand topology file.97−99 Utilizing a rudimentary water model, the solvation of each protein–ligand complex was achieved within a triclinic box endowed with a stoichiometric intermolecular potential, thereby facilitating a spatial separation of at least 1.0 nm between the complex and each side of the three-dimensional enclosure.100 The ionization state of the protein residues was determined according to their typical behavior under physiological conditions (pH 7.0) while periodic boundary conditions. The Monte-Carlo ion-placing method was employed to introduce the appropriate quantities of Na+ and Cl– ions to attain the overall neutrality of the entire complex.101 Three steps were involved in the MD simulation. The first step comprised the initial optimization of each system’s geometry using the steepest descent method over 5,000 iterations (5 ps). The subsequent procedure entailed employing the Berendsen temperature coupling technique to facilitate the initial equilibrium phase while maintaining constant particle number, volume, and temperature (NVT) conditions. The final equilibration step was conducted employing the Parrinello–Rahman barostat, which ensured the preservation of a consistent number of particles, pressure, and temperature throughout the entirety of the procedure.102 Ultimately, molecular dynamics (MD) simulations were executed for a duration of 100 ns, employing the constant pressure (NPT ensemble) methodology.

4.6. Density Functional Theory (DFT) Analysis

The reactivity and effectiveness of phytochemicals against α-amylase were investigated using Gaussian 09 and Gaussview 6.0 software programs, employing density functional theory (DFT)-based analysis. The energy levels of the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) were computed utilizing the 6-31G(d,p) basis set and the Becke, 3-parameter, Lee–Yang–Parr (B3LYP) correlation function of density functional theory (DFT).103 The energy gap was determined by the subsequent equation104

4.6. 1

The given equation was employed to determine the ionization potential (IP), electron affinity (EA), as well as quantum mechanical parameters such as global hardness (η), global softness (S), electronegativity (χ), electrophilicity index (ω), and chemical potential (μ).51,105

4.6. 2
4.6. 3
4.6. 4
4.6. 5
4.6. 6
4.6. 7

The molecular electrostatic potential (MEP) diagram was used to explore the responsiveness of drug delivery systems to electrophilic or nucleophilic assaults and to depict the distribution of electron density.

4.7. Pharmacokinetic Analysis

The pharmacokinetic parameters of the hit molecule were assessed using ADMET analysis (absorption, distribution, metabolism, excretion, and toxicity) and drug-likeness properties. From the PubChem database, the canonical SMILES (Simplified Molecular Input Line Entry System) of the chosen compounds were extracted directly by copying and submitted to the appropriate servers for their respective analysis. The aforementioned parameters were accomplished using the admetSAR 2.0,106 pkCSM web server,107 and the SwissADME bioinformatics tool.108 Similarly, Schrödinger’s QikProp model was also considered, concerning ADME pharmacokinetic profiles of the optimal compound.

4.8. In Vitro Assays with α-Amylase

4.8.1. Chemical and Materials

Acarbose, porcine pancreatic α-amylase (PPA), 2-chloro-4-nitrophenyl-α-d-maltotrioside (CNPG3), and quercetin were purchased from the esteemed supplier Sigma-Aldrich (Germany). The acquisition of dimethyl sulfoxide and other required chemicals was facilitated by Fisher Scientific (India).

4.8.2. α-Amylase Inhibition Assay

The assessment of α-amylase inhibition was performed using a widely recognized methodology as outlined in the literature, with slight modifications.109 A solution of quercetin (20 μL) in 5% DMSO was mixed with 80 μL of enzyme PPA (1.5 U/mL) in 50 mM phosphate-buffered saline (pH 7.0, 0.9% NaCl) and subjected to preincubation at 37 °C for 15 min. The enzymatic reaction was initiated through the addition of CNPG3 as the substrate at a concentration of 375 μM, followed by a 15 min incubation period at the same temperature, 37 °C. The absorbance measurement was performed using a microplate reader, specifically at a wavelength of 405 nm. The evaluation of inhibitory activity was ascertained using the following formula

4.8.2.

where A is the absorbance of the sample and the control.

Acknowledgments

The authors would like to acknowledge Schrodinger Inc. for the software license support.

Glossary

Abbreviations

ADMET

Absorption, distribution, metabolism, excretion, and toxicity

B3LYP

Becke, 3-parameter, Lee–Yang–Parr

CNPG3

2-chloro-4-nitrophenyl-α-d-maltotrioside

DFT

density functional theory

DM

diabetes mellitus

DMSO

dimethyl sulfoxide

HOMO

highest occupied molecular orbital

HPA

human pancreatic α-amylase

IC50

half-maximal inhibitory concentration

LUMO

lowest unoccupied molecular orbital

MD

molecular dynamics

MM/GBSA

molecular mechanics with generalized Born and surface area solvation

PPA

porcine pancreatic lipase

Rg

radius of gyration

RMSD

root-mean-square deviation

RMSF

root-mean-square fluctuation

SASA

solvent-accessible surface area

T2D

type II diabetes

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.3c05082.

  • Binding site result predicted by SiteMap (Table S1); binding interaction and types of interaction of selected metabolites with HPA (Table S2); DFT result of selected metabolites against HPA (Table S3); ADMET properties of quercetin and acarbose obtained via QikProp (Table S4); ADMET properties of quercetin and acarbose via admetSAR 2.0 server (Table S5); ADMET properties obtained via the pkCSM web server (Table S6); IC50 value of quercetin and acarbose (Table S7); list of selected polyphenols and flavonoids (Table S8); ERRAT of HPA (PDB ID: 1HNY) (Figure S1); binding site of the protein predicted by SiteMap (Figure S2); superimposition of the cognate ligand into the active site of HPA (Figure S3); molecular interaction and binding pose of 11 selected metabolites and acarbose (standard) (Figures S4–S15); optimized structure of quercetin (Figure S16); α-amylase inhibition of quercetin and acarbose (standard) (Figure S17); structure of the selected metabolites (Figure S18) (PDF)

Author Contributions

B.K.R and S.R.U. performed in silico and in vitro works and were involved in data analysis, J.B. drafted the manuscript, and N.P. revised the manuscript and supervised the project.

The authors declare no competing financial interest.

Supplementary Material

ao3c05082_si_001.pdf (1.9MB, pdf)

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