Gas Chromatography-Mass Spectrometry Coupled with Multivariate Statistical Analysis to Identify the Alpha Glucosidase Inhibitors from Flesh of Salacca zalacca Fruits and Their Molecular Docking Studies

Fruit of salak (Salaaca zalacca) is traditionally used and commercialized as an antidiabetic agent. However, scientific evidence to prove this folk claim is quite lacking. Therefore, this research was aimed to evaluate the α-glucosidase inhibition activity of S. zalacca fruit and identify the bioactive compounds. The fruits were extracted by different ratios of ethanol and water (0, 20, 40, 60, 80, 100%, v/v) to get E0 (100% water), E20 (20% ethanol), E40 (40% ethanol), E60 (60% ethanol), E80 (80% ethanol), and E100 (100% ethanol) extracts. The extracts obtained were subjected to the α-glucosidase inhibitory assay. Gas chromatography-mass spectrometry- (GC-MS-) based metabolomics approach was used in profiling the bioactive metabolites present in the extracts. Orthogonal partial least square (OPLS) was used to correlate GC-MS data and α-glucosidase assay results to identify the possible chemical markers. All active compounds identified were subjected to molecular docking. The extracts from the S. zalacca fruit showed potent inhibition activity against α-glucosidase. The IC50 values from the α-glucosidase inhibitory assay ranged between 16 and 275 µg/ml. Overall, E60 displayed significantly higher α-glucosidase inhibition activity, while E0 showed the lowest α-glucosidase inhibition activity. Major compounds detected in S. zalacca fruits were sugars, fatty acids, and sterols, including myo-inositol, palmitic acid, stearic acid, and β-sitosterol. Moreover, the results obtained from molecular docking indicated that palmitic acid and β-sitosterol were close to the active side of the enzyme. Some of the residues that interacted include HID295, ASN259, LEU313, LYS125, PHE159, VAL216, PHE178, TYR72, TYR158, HIE315, ARG315, and PHE303. The bioassay result strongly suggests that E60 extract from S. zalacca fruits has potential α-glucosidase inhibitory activity. The hydrophobic compounds, including palmitic acid and β-sitosterol, were found to induce the α-glucosidase inhibition activity.


Introduction
Diabetes mellitus (DM) is a chronic metabolic disorder that may lead to other macro-and microvascular complications. e highest number of DM cases that have been reported to date was recorded in Asian countries, which cover about 60% of the world's DM patients [1]. World populations majorly practice conventional medicines relying on plantbased medicines due to the availability [2]. Various plants have been utilized as a traditional remedy for multiples diseases and conditions for centuries [3]. e effect and mechanism of most plants for the treatment of diabetes can be related to their inhibitory activities against α-glucosidase enzyme. is intestinal enzyme is found in the brush border of the small intestine, responsible for catalysing the breakdown of polysaccharides into monosaccharides, thus reducing the postprandial glucose level, thereby decreasing the hyperglycaemia in DM patients [4]. In recent times, various plants and herbs have been extensively studied, providing scientific proof that the phytoconstituents present in plant matrix have various pharmacological effects on the human body system, such as fermented Chinese Ge-Gen-Qin-Lian decoction and Radix Salvia Miltiorrhiza [5][6][7]. Salaaca zalacca is one example of a plant that is traditionally used as a hypoglycemic remedy, more commonly popular in Asian countries, including Malaysia, Myanmar, Indonesia, and ailand [8,9].
Salacca zalacca Gaertn Voss. is one of the palm species belonging to the family Arecaceae and is ubiquitously found all around Southeast Asia. S. zalacca fruits are a good source of vitamins, fibre, minerals, and carbohydrates [10]. Moreover, S. zalacca possesses antioxidant, anticancer, and antidiabetic effects [11][12][13][14], attributable to their high levels of vitamin C, Phenolics acids, and flavonoids [10]. In recent times, it has been claimed to possess antidiabetic activity [12,14]. erefore, this study intends to initially analyse the α-glucosidase (EC 3.2.1.20, α-D-glucoside glucohydrolase) inhibitory activity of the S. zalacca fruit extracts. It is one of the pathways involved in the management of DM. One of the most appropriate methods to analyse these constituents are by using the metabolomics approach.
Typically, the analysis of metabolites can be achieved by herbal fingerprinting that requires the use of different chromatography and spectroscopy techniques, such as gas chromatography-mass spectrometry (GC-MS), and nuclear magnetic resonance (NMR) [15][16][17][18]. erefore, the aim of the present study is to correlate the metabolic variation between S. zalacca extracts and α-glucosidase inhibitory activity using the GC-MS-based metabolomics approach.
is study is planned to identify the related bioactive compounds in different ratios of ethanol-water and molecular docking were also applied to predict the inhibitory activity.

Preparation of Extracts for Metabolomics Studies.
e fruits of S. zalaaca were cleaned with tap water and then peeled into peel and flesh. Immediately, the separated fruits flesh was cut into small cups to remove the seeds. Initially, the fruits flesh was weighed and dried using a freeze dryer. e freeze-dried samples were ground into a fine powder using a laboratory blender and then kept at −80°C prior to extraction. Five grams of dried powdered fruit was suspended in different ratios of ethanol and water (0, 20, 40, 60, 80, 100%, v/v). Approximately 150 mL of the desired solvent was mixed with powdered and shaken before being transferred to sonicator at 25°C for 1 h. A total of 36 extracts were obtained and filtered through a Whatman no. 1 filter paper. Finally, the filtered solutions were evaporated using a rotary evaporator at 40 ± 1°C. e stock concentrations of extracts were dried using a freeze dryer. e obtained amount of the extracts was weighed before keeping at −80°C prior to further analysis [4].

α-Glucosidase Inhibitory Activity.
e α-glucosidase inhibitory activity was measured using a method described by Collins et al. [19] with slight modifications. Different dilutions of the S. zalacca extracts were prepared in triplicate (i.e., 320, 160, 80, 40, 20, 10, and 5 µg/mL). en, 10 µL of each dilution was added to 100 µL of 30 mM phosphate buffer pH 6.5. After that. 15 µL of α-glucosidase enzyme solution (0.02 U/well) was added. e mixture was allowed to stand for 5 min before adding 75 µL of the substrate (PNPG) (6 mg in 20 mL of 50 mM phosphate buffer pH 6.5). A total of 50 µL of (2 M) glycine of pH 10 was added to the mixture to stop the reaction. e mixture was immediately measuring the absorbance at 405 nm. e α-glucosidase inhibition was decreased using the equation: IC 50 of the extract was determined from the graph of α-glucosidase inhibitory activity (%) against the amount of extract (µg). e samples of extracts were derivatized using N-Methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA) and methoxyamine HCl for GC-MS analysis following the method described by Robinson et al. (2005) [20]. Briefly, about 12.5 mg S. zalacca fruits ethanolic extract was dissolved in 25 μL pyridine in a 1 mL Eppendorf microcentrifuge tube. e mixture was initially vortexed for 2 min then further sonicated for 20 min at 30°C. About 50 μL (20 mg/mL in pyridine) was added to the sample solution and incubated for 2 h at 60°C. About 150 μL of MSTFA was added and incubated again for 30 min at 60°C. e derived sample was filtered using a microsyringe filter and equilibrated to room temperature overnight.

GC-MS-Based
Metabolomics. GC-MS analysis of the S. zalacca fruits ethanolic extracts was carried out following the method described by Saleh et al. [11]. GC-MS analysis was performed on an Agilent 6890 gas chromatograph equipped with a 5973-mass selective detector using an electron impact ionization. A DB-5MS, 5% phenyl methyl siloxane column with an inner diameter of 250 μm, and a film thickness of 250 μm attached with an autosampler (Agilent, Santa Clara, United States) was used for the separation. A volume of 1 μL sample was injected in splitless mode into GC-MS. e initial oven temperature was set at 180°C and the hold time 10 min and then increased to 220°C at a rate of 20°C/min with the hold time of 5 min. e final oven temperature was 315°C at a rate of 30°C/min with a hold time of 10 min, and the total run time was 30.16 min. Helium gas was used as carrier gas at a flow rate of 1.5 mL/ min. e injector and detector were set to 330°C and 250°C, respectively. Mass spectra were acquired using a full scan and a monitoring mode in a range of 50-550 m/z after a solvent delay of 6 min. Identification of compounds was carried out by matching their peak spectra with the NIST14 library.
For GC-MS based metabolomics, raw chromatograms were converted into CDF format. is preprocessing of all the raw data into a matrix data includes systematic noise filtration, data binning, automatic peak detection, and chromatographic alignment. Baseline correction of the total ion chromatogram (TIC), integration, and peak picking were done using ACD/Spec Manager v.12.00 (Advanced Chemistry Development, Inc., ACD/Labs Toronto, Canada). Identification of metabolites was performed by matching their respective spectra with the NIST library. e XCMS package in R version 2.15.1 was applied to align the GC-MS chromatograms using the following parameters; xcms set (fwhm � 30, step � 0.1, method � bin) and group (bw � 10). e information on α-glucosidase activity was added to the converted excel format [16].

Statistical Analysis.
e results were calculated as the mean ± SD from six separate experiments (n � 6). e data were analyzed by one-way ANOVA test followed by Tukey's test using Minitab version 17. 0. Table 1 displays the trend of the extraction yield of the S. zalacca fruits extracts. e yield of extraction varied with the ratio of the water and ethanol of the solvents used, with the highest yield obtained from 80% hydroethanolic solvent (E80) as 75.5% and the least yield obtained from the ethanolic (100%, E100) solvent as 20.2% only. e trend of this result can be simplified in the order as

Metabolite Profiling of S. zalacca Fruits Based on
Metabolomics Approach

GC-MS-Based Metabolites
Profiling. When chemical profiling using GC-MS, derivatization is required for the metabolites to be converted into volatile compounds such as acetyl trimethyl silane (TMS) ethers or oxime-TMS ethers [21]. Flame ionization detectors or mass spectrometers are mostly used for detection. Confirmation of the identified metabolites was achieved by carefully examining the spectral pattern and comparison with the GC-MS NIST14 database library. e metabolites identified mostly belonged to sugars, sterols, as well as fatty acids that include D-(-)-fructofuranose, D-(-)-fructose, D-psicofuranose, D-glucose, sucrose, D-(+)-trehalose, myo-inositol, β-D-glucopyranose, D-(+)xylose, D-(+)-talofuranose, α-D-glucopyranose, D-psicopyranose, β-sitosterol, stearic acid, palmitic acid, and citric acid (Supplementary Figure S1). All these metabolites have been labelled and indicated in each of the extracts' chromatogram analyzed. Specifically, three metabolites were found to be abundant in E60, E80, and E100 extracts, namely stearic acid and palmitic acid, β-sitosterol when compared to E0, E20, and E40. It is pertinent to note that there was a decrease in the intensity of the metabolites in the extract as the polarity of the solvent increased by increasing the amount of water in ethanol. In the meantime, sucrose and D-glucose were found to be dominant in the highly polar E0, E20, and E40 extracts. It is observable that a consecutive decrease in water ratio resulted in a decrease in the polarity. is results in metabolites with less polarity abundantly found in E60, E80, and E100. ese include palmitic acid, stearic acid, and β-sitosterol which might be attributed to the α-glucosidase inhibitory activity of S. zalacca fruits hydroethanolic extracts.

Multivariate Data Analysis.
One of the ways of identifying bioactive metabolites is through the GC-MSbased metabolomics approach using MVDA to develop an orthogonal partial least-squares (OPLS) model. Two results are usually automatically generated, namely OPLS components 1 and 2. e m/z (mass-to-charge ratio) of certain retention time was generated along with 1/IC 50 (µg/mL) values of the analytes that were taken as variables x and y, respectively. e major aim of performing OPLS is to determine differences in the constituents among the analytes and to identify the m/z values responsible for the pharmacological activity.
e OPLS model can be assessed through different parameters, namely, the permutation test, the goodness of fit, and the capacity of this model in predicting the y-value via the predicted plot. Meanwhile, the cumulative values R 2 Y explains more on goodness of fit which indicates the variation percentage of the response as explained by the model, whereas the Q 2 Y, the cumulative value, represents variation percentage of response based on model prediction in accordance with the cross validation. If the cumulative values of R 2 Y and Q 2 Y are observed greater than 0.5, then the fitness of the model and predictive ability are considered satisfactory [20]. e R 2 Y and Q 2 Y values obtained in this study were 0.856 and 0.925, respectively. In addition to the above-mentioned parameters, other ones that were also considered significant in determining the model accuracy and performance are the root-mean-square error of crossvalidation (RMSE CV ) and root-mean-square errors of estimation (RMSE E ). RMSE CV simply measures the quality of the model in terms of new sample prediction. Low RMSE CV values indicate higher predictive accuracy of the sample. RMSE E, on the other hand, measures the average deviation of the model from the data. e RMSEE and RMSE CV values obtained in this study were 0.356078 and 0.376476, respectively. e score scatter plot of the OPLS model is shown in Figure 1(a). It indicates good separation of the samples in terms of the chemical profile and pharmacological activity (IC 50 value) of the samples. e positive OPLS component 1 consists of samples with higher activity (E60, E100, and E80) having IC 50 value less than 30 µg/mL, whereas the less active samples (E40, E20, and E0) were found at the negative OPLS component 1.
e loading plot of different extracts of S. zalacca fruits hydroethanolic extracts is shown in Figure 1(b). e metabolites in each sample are represented by each peak. e compounds identification and details are shown in Table 2. ey are observed opposite to the 1/IC 50 value. ese compounds are assumed to decrease α-glucosidase inhibitory activity. ey were found to be abundant in sample, E0, E20, and E40, with the IC 50 value greater than 100 µg/mL, which indicate that these compounds possess less activity. e remaining peaks found in the same position aligned with the IC 50 value were efficient in inhibiting α-glucosidase activity. ese metabolites were identified as plant fatty acids, carbocyclic sugar, and sterol, such as palmitic acid, stearic acid, myo-inositol, and β-sitosterol.
e permutation test plot shown in Figure 2 describes the validation of the developed OPLS model. e plot displayed the fraction intercepts of the sum of the squares intercept Y-value and the predictive ability of the model intercept Y-value. In this study, the R 2 Y and Q 2 Y values obtained were 0.507 and −0.408, respectively. ese values are acceptable because R 2 and Q 2 are less than or equal to -0.5 and 0.5, respectively. Meanwhile, the plot showing the observed versus predicted plot predicts the OPLS model accuracy along its validation using R 2 value obtained from the regression. is plot is valid if the R 2 value is greater than 0.9. erefore, the result obtained is valid with the R 2 value of 0.9455.

Docking of Bioactive Compounds.
All identified compounds responsible for the α-glucosidase inhibition activity were docked to the Saccharomyces cerevisiae isomaltase (SCI) crystal structure (PDB ID: 3A4A). e conformations showing the lowest binding energy for the compounds along with the interacting residues are summarised in Table 3. Based on the AutoDock 1.5.6 simulation result displayed in Table 3, the inhibitor complex of α-glucosidase-quercetin, which was used as positive control showed −8.25 kcal/mol binding energy having six hydrogen bonds with the interacting residues LYS156, ASN415, GLU411, THR310, LEU313, and PRO312 along with hydrophobic interactions involving ARG315 and PHE314. e stearic acid binding affinity in complex with α-glucosidase was observed to be −3.80 kcal/mol. One H-bond was displayed in the complex including CYS342 with hydrophobic interactions with LYS16 and AGR263. Meanwhile, for

Evidence-Based Complementary and Alternative Medicine
α-glucosidase-myo-inositol complex, the binding affinity value of −5.68 kcal/mol was observed. ree residues including PRO456, AGR467, and LYS406 were found to interact with myo-inositol through H-bonds, whereas TYR470 was found to interact via π-bond. It is interesting to know that β-sitosterol, despite interacting with just two residues by H-bonding with its O-H group, LEU313, and LYS125, it comparably displayed better binding energy of −9.55 kcal/mol than the quercetin (positive control). e hydrophobic contacts appeared to dominate in α-glucosidase-β-sitosterol complex, perhaps as a result of its alkyl group and cyclic skeleton that is preferably bound to PHE159, VAL216, PHE178, TYR72, TYR158, HIE315, ARG315, and PHE303. e crystallized ligand, α-D-glucose (ADG) demonstrated lower binding energy of 6.02 kcal/mol as compared to β-sitosterol and quercetin (positive control). However, the H-bond existing between ADG and the protein showed a stable interaction with nine amino acid residues, including ASH69, HIE112, ARG213, ASH215, GLH277, HIE351, ASP352, and ARG442 having hydrophobic interaction with TYR72. Unlike the docked ADG, results obtained from docking indicated that the binding of tested compounds was mostly supported through hydrophobic contacts with α-glucosidase. Our result corroborates with those reported by Seong et al. [21] in which the isoflavones, i.e., daidzein, genistein, and calycosin, exhibited more hydrophobic contacts with comparable residues, such as ILE262, ARG263,  Evidence-Based Complementary and Alternative Medicine ILE272, and VAL266. e 2D diagram of the interaction between the protein residues and ADG, positive control, stearic acid, palmitic acid, myo-inositol, and β-sitosterol is shown in Figure 3. Moreover, the 3D superimposed diagram of the compounds and ADG is shown in Figure 4(a). It was also observed that the binding site for palmitic acid and β-sitosterol was close to the active site pocket (Figures 4(b) and 4(c)).

Discussion
e α-glucosidase enzyme is one of the key enzymes involved in the digestion of carbohydrates in humans. Its main function is to hydrolyse the carbohydrate to release glucose, which may lead to an increase in postprandial blood glucose level. Inhibition of this enzyme is one of the effective ways of controlling the postprandial blood glucose level, which is considered beneficial for diabetic patients. ere are several synthetic drugs available in the market for the management and treatment of diabetes, such as migitol, acarbose, and vogibose. However, they are usually accompanied by serious side effects such as hepatic disorders and other negative gastrointestinal symptoms [22]. erefore, plant-derived α-glucosidase inhibitors are envisaged as preferable and safer as a means of management and prevention of diabetes. Metabolites obtained from plants have provided a wide range of pharmacological benefits to human health [23]. In many bioactivity analyses of plants, in vitro techniques are applied to determine the pharmacological effectiveness of plant metabolites. In determining the in vitro antidiabetic activity, various procedures are employed. e most important of it is the α-glucosidase inhibitory assay. e pharmacological activity of plant metabolites is dependent on the type and numbers of functional groups present, the chemical structure, as well as the nature of substitution on the ring [24]. Currently, research on the use of medicinal plants has become a continuous effort due to the fact that numerous numbers of bioactive metabolites are distributed in plants with various pharmacological benefits. Water, a mixture of water and organic solvent and different organic solvents are employed to extract the potential metabolites from plants through different methods [4,11]. e polarity of the extracting solvent determines the type of metabolite and hence the bioactivities found in the extract. Due to this fact, different solvents were used in this study to obtain S. zalacca fruits extracts so as to determine different metabolites having α-glucosidase inhibition potential.
e results obtained showed a decrease in the ratio of water, with a moderate decrease in polarity, suggesting that the metabolites responsible for the inhibitory activity are the less polar ones such as sterols and fatty acids. is outcome signifies that the extracting solvent has a greater influence on   Evidence-Based Complementary and Alternative Medicine the bioactivity of the extract. Some of the compounds identified have previously been reported in the literature to exhibit in vitro α-glucosidase inhibitory effect. e investigation conducted on α-glucosidase inhibitory activity of palmitic acid and other fatty acids by Liu et al. [25] showed that palmitic acid exhibited inhibitory activity with the IC 50 value > 400 µg/mL which was in contradiction with the value earlier reported in other studies for the same compound, i.e., IC 50 11.53 µg/mL and 24.09 µg/mL [26,27]. Aside palmitic acid, other compound that was found active against α-glucosidase was stearic acid 5.3% inhibition at 10 µg/mL [28].
In another study, myo-inositol and β-sitosterol with other compounds were purified from the methanol extract of Tournefortia hartwegiana (Boraginaceae) (ME ). An oral glucose tolerance test (OGTT) was conducted using a different substrate such as glucose, maltose, and sucrose through intragastric administration. Results obtained   Evidence-Based Complementary and Alternative Medicine showed that the increased plasma glucose level was significantly (P < 0.05) suppressed by the extract containing myoinositol and β-sitosterol after the administration of the substrates. Moreover, ME also exhibited an in vitro α-glucosidase inhibitory activity in a concentration-dependent manner (IC 50 : 3.16 mg/mL). Another investigation was conducted by Kim et al. [29] on myo-inositol to determine its effect on the differentiation, lipolysis of adipocytes, insulin-stimulated glucose uptake and to test whether the antidiabetic activity of myo-inositol via is mediated through adipocytes. In the study, 3T3-L1 cells were treated with varying concentrations of myo-inositol. Lipid accumulation was increased after exposure to myo-inositol dosedependently (P < 0.01). An increase in adipogenesis was observed along with insulin-stimulated glucose uptake in adipocytes through activation of insulin signalling. Furthermore, lipolysis in matured adipocytes was also inhibited. erefore, myo-inositol possesses a promising therapeutic property for improving insulin sensitivities in adipocytes through the increase in lipid storage capacity, glucose uptake, and inhibition of free fatty acid formation.
Chemical and structural derivatives of plant sterols are phytochemicals that are found ubiquitously in many plant parts. Many of them have been previously identified from plant extracts; these include β-sitosterol. Research conducted on the antidiabetic and antioxidant potentials of β-sitosterol in STZ-induced rats showed that there was a significant decrease in glycated haemoglobin, nitric oxide, and serum glucose and with a simultaneous increase in serum insulin levels. In addition to that, pancreatic antioxidants levels were also increased with a drastic reduction in thiobarbituric acid-reactive substances [30]. In another study, β-sitosterol, which is an active phytosterol in the S. surattense stem bark, has also been reported to be useful in the treatment of coronary artery disease, breast cancer, prostate cancer, and hypercholesterolemia [31]. Apoptosis induced by caspase 8 activity, FAS receptor levels, and phosphorylation of extracellular signal-regulated kinase and mitogen-activated protein kinase has been suggested to be responsible for the chemopreventive property of β-sitosterol with no cytotoxicity to normal cells [32,33].
Although from the structure-activity relationship studies, the presence of multiple hydroxyl groups in a compound has been reported to be responsible for the α-glucosidase inhibitory effect, yet, compounds identified using GC-MS in this study were mostly nonpolar compounds which include sterol, fatty acids, excluding myo-inositol. ese compounds containing a few numbers of hydroxyl groups except myoinositol. Despite the same fact, these compounds were found to exhibit a certain level of α-glucosidase inhibitory activity based on the literature. is could be as a result of interactions existing between protein and the inhibitors, thereby forming probable conformational complexes that may result in positive enzyme inhibitory effect of the identified inhibitors; myo-inositol, palmitic acid and stearic acid, and β-sitosterol.
With these results, it is deduced that the hydrophobic and π-interactions existing between the cyclic part of the compounds structure with hydrophobic side chains (TYR, VAL, and PHE) of the enzyme could be responsible for the much higher activities of these nonpolar compounds, which is agreeable to the finding reported by Nussinov [34] and Du et al. [35]. In addition, the strong hydrogen bond also occurs between the compounds' hydroxyl group with the residues having both the polar (GLN and SER) and electrically charged side chains (ARG, ASP, and HIE (protonated HIS). is has been observed in quercetin and other similar compounds. Interaction between the residues and that of ADG was observed as shown through its binding energy. As illustrated in Table 3, there was an interaction between ADG and ASH215 residue that acted as a catalytic nucleophile. Furthermore, a strong H-bond was also formed with GLU277 protonated GLU residue, which served as the general acid-base catalyst. Apart from this, ADG also showed interaction with ASP352, which stabilises the substrate during the catalytic reaction so as to strengthen the acid-base hydrolysis reaction [34,36]. e 3D diagram in Figure 4(a) also indicated that the protein might have more than one site for ligand binding. is could induce allosteric control for catalytic reaction, which may cause changes in the active site, thereby resulting in the enzyme inhibition. Allosteric inhibitors are capable of modifying protein activity [35,37]. In general view, the conjugated behaviour of a compound in which the conformational perturbations produced at one region of a macromolecule affects the active site and consequently, the enzyme activity confers the allosteric binding ability. In some occasions where the allosteric interaction is unstable, the binding residues stabilises it. is will eventually result in an altered functional site due to the equilibrium shifting toward this conformer or the allosteric ligand, thus causing the inhibition activity [38,39]. Additionally, Figure 4(a) (the superimposed 3D structure) also suggested that the identified inhibitors using GC-MS displayed noncompetitive inhibition mode when compared to the control ligand, ADG, the known competitor. For instance, β-sitosterol, previously reported as a potential inhibitor binds away from the active site indicating the noncompetitive and allosteric binding mode. e binding of β-sitosterol was assisted through hydrophobic interactions that contributed to its binding energy, hence presumably responsible for its inhibitory activity. In this phenomenon, the conformational changes will be observed in the active site of the enzyme and therefore slows down the enzyme activity [39][40][41]. e predicted binding area in myo-inositol could be capable of inhibiting protein activity via a noncompetitive mode and those compounds with similar nature. Hydrophobic and π-interactions play a crucial role in the enzyme inhibitory activity of compounds with a lack of hydroxyl groups. e control ligand's binding site on α-glucosidase within the active site, whereas the compounds such as quercetin and palmitic acid were observed to occupy the entrance of the pocket and not directly interacting with the residues within the active site. is suggests the existence of an allosteric binding site, which could probably cause alterations in the enzyme activity. Cleft closure may be induced in this arrangement to avoid the entrance of substrate, 8 Evidence-Based Complementary and Alternative Medicine which then acts as noncompetitive inhibitors. In conclusion, molecular docking conducted in this study has enabled the prediction and provided supportive data for α-glucosidase inhibitory activity of the bioactive compounds.

Conclusion
e study showed that E60 S. zalacca fruit revealed potent α-glucosidase inhibitory activity. By GC-MS analysis, many compounds were detected in S. zalacca fruits, and the molecular docking results showed that palmitic acid and β-sitosterol were close to the active side of the enzyme. is study has provided the appropriate information on the potential antidiabetic action of S. zalacca fruits via α-glucosidase inhibitory effect and the phytochemical constituents that may be responsible for this activity. is finding can be applied for further investigations of the S. zalacca fruits' beneficial uses and the development of the medicinal preparations for the management of diabetes.

Data Availability
e data used to support the findings of this study are included within the article and the supplementary information files. Any other data can be made available upon request.

Conflicts of Interest
e authors declare that they have no conflicts of interest.