Synergistic effect of potential alpha-amylase inhibitors from Egyptian propolis with acarbose using in silico and in vitro combination analysis

3,5-dinitrosalicylic acid (DNS), acarbose, chlorogenic acid, N,O-bis(trimethylsilyl)trifluoroacetamide (BSTFA), potato starch, pyridine, quercetin, quercetin-7-methyl ether, rosmarinic acid, and α-amylase from porcine pancreas (A3176) were procured from Sigma-Aldrich (Germany). Aucubin, catechin, kaempferol, luteolin, myricetin, and quercetin-3-methyl ether were obtained from Indofine Chemical Company, Inc. (USA). Analytical purity grade solvents were used throughout the study.

A raw propolis sample (Fig. S1) was collected from an apiary located in Kafr El Sheikh, Egypt according to relevant guidelines and regulations.

Prior to analysis, the propolis sample was stored in the dark at ambient temperature. An aliquot of (100g) of finely pulverized Egyptian propolis sample was extracted using 1 L of 95% (v/v) ethanol by sonication for 1 h at 40 °C followed by overnight maceration. The extract was subsequently filtered, and the resulting filtrate was evaporated under reduced pressure using a rotary evaporator and kept refrigerated at 5 ℃ until further use.

An in-house database comprised of 378 phytoconstituents (Sheet S1) was constructed from the sample under investigation, in addition to an exhaustive literature review of Egyptian propolis. The review included publications spanning a 25 year period (from 1997 to date). It is considered as the largest in-house database of Egyptian propolis.

Molecular docking studies of α-amylase inhibitors were performed using both Schrödinger Maestro molecular modeling Suite (Schrodinger, LLC, New York) and AutoDock Vina [34] via the CB-Dock2 server [35] (https://​cadd.​labshare.​cn/​cb-dock2/​php/​index.​php) on the in-house propolis library.

To determine the three-dimensional crystal structure that would be used in the in silico study of this work, three pancreatic α-amylase crystalline structures were downloaded from the RCSB protein data bank (PDB) and were comparatively evaluated. The crystal structures chosen included human pancreatic α-amylase co-crystallized with the flavonoid, myricetin (PDB ID: 4GQR) and with the pseudo-hexasaccharide, acarviostatin I03 (PDB ID: 3OLD), respectively. Additionally, porcine pancreatic α-amylase co-crystallized with the pseudo-tetrasaccharide, acarbose (PDB ID: 1OSE) was also included.

With reference to the established technique [37], the assay was carried out quantitatively with minor adjustments. Initially, 500µL test solution was preincubated with 500µL α-amylase solution (0.55 unit/mL) at 25°C for 10 min. The α-amylase was prepared in a buffer solution (0.02M sodium phosphate pH 6.9 with 0.006M NaCl). Afterwards, 500µL of 1% w/v starch in buffer solution was mixed in and left to incubate for 10 min at 25°C. To bring the reaction to a halt, 1mL dinitrosalicylic acid (DNS) was introduced and the mixture was placed in a boiling water bath for 3.5 min. After cooling to room temperature, the mixture was then diluted using 10mL distilled water and absorbance was measured using a Laxco spectrophotometer (α1502, Laxco Inc., USA) at λ 540nm. Blanks used throughout the experiment were made by adding pure buffer instead of α-amylase solution. Acarbose was used as positive control. To calculate the pancreatic α-amylase inhibitory activity, the following equation was used:

CompuSyn software (www.​combosyn.​com) was employed to assess the nature of the test substance-acarbose interaction. Propolis and kaempferol were each individually paired with acarbose and investigation was carried out using four different analysis methodologies. These include: median effect, isobolographic, combination index, and lastly, dose reduction index analyses.

Data input included each inhibitors’ independent activity and the summation of both inhibitors’ activity when used in conjunction of each other. For combination assay, the inhibitors were mixed at five different concentration levels to give a final concentration equal to the concentration required to achieve 10, 30, 50, 70, and 90% inhibition of α-amylase when used independently. α-amylase inhibitory activity assay was conducted as per Sect. 2.6.

Molecular dynamics (MD) simulations of α-amylase with acarbose, α-amylase with kaempferol, and α-amylase with acarbose and kaempferol co-ligand complexes obtained by molecular docking were performed with Gromacs v2020.1 [42]. MD input files were created with the CHARMM-GUI [43] server's Solution Builder tool (https://​charmm-gui.​org/​?​doc=​input/​solution). Protein–ligand complexes were solvated with the TIP3 water model and neutralized by adding 0.15 M KCl using the Monte Carlo method. Protein–ligand topology files were created with AMBER FF99SB [44] force fields. For MD simulation, it was equilibrated with the Nose–Hoover thermostat and Parrinello-Rahman barostat methods at 303.15 K and 1.0 atm pressure. Bond constraints were made with hydrogen bonds according to the LINCS algorithm. MD simulation was performed under periodic boundary conditions for 150 ns. For MD trajectory analysis, root mean square deviation (RMSD) was created with gmx rms script, graphics were created with Grace-5.1.2, and MD animation videos were created with PyMOL Molecular Graphics System v2.4.1. Binding free energy Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) calculations were calculated from 1500 frames recorded for 150 ns with the gmx_MMPBSA [45] tool.

Owing to the chemical diversity between propolis samples, standardization of propolis is critical in order to guarantee its chemical consistency and thereafter, consistent efficacy. Therefore, it has been noted that for one to be ascertain of the biological activity, we must in turn first chemically characterize propolis. One of the most frequently employed methods in the chemical analyses of propolis is GC–MS [19, 46].

Silylation is a crucial step required prior to analysis of the relatively non-volatile propolis components to assist in their separation. Trimethylsilyl (TMS) derivatives are more volatile, more thermotolerant and less polar than their corresponding parent compound.

Although other analytical techniques not requiring prior extensive sample preparation can be employed in the separation of propolis constituents, the unequivocal separation capabilities and resolution provided by capillary GC and the invaluable structural data brought forth by EIMS make it worth the additional derivatization step. Numerous silylating agents can be used with propolis, each with their own set of advantages and disadvantages. In the current study, the examined propolis sample was subjected to BSTFA as the silylating agent as it shows high reactivity with all common polar functional groups.

The GC–MS analysis runtime lasted 21 min and the resulting TIC chromatogram (Fig. 1) revealed that the selected Egyptian propolis sample contained a total of 78 compounds. Of the separated derivatized compounds, 27 compounds (Table 1) were subsequently identified through comparison of their spectral data with Wiley and NIST mass spectral databases. The retention time (min.), compound name, phytochemical class, % TIC by normalization, molecular masses, and library used in identification of the compounds are described in Table 1.

The overall chemical composition of propolis varies greatly from one sample to another. Thus, it has been documented that a plethora of compounds belonging to a diverse list of classes may be found in each propolis sample. These classes include alcohols, aldehydes, aliphatic acids and their esters, amino acids, aromatic acids and their ester, ethers, fatty acids, flavonoids, hydrocarbon esters, ketones, steroids, sugars and terpenoids amongst others [17]. Of the 27 compounds identified by the aforementioned means, 12 of them are well-documented in Egyptian and worldwide propolis. These include d-glucopyranose [47‐49] (10.6%); chrysin [46‐48, 50‐59] (8.1%); sucrose [47, 48] (5.8%); 1-[4-hydroxyphenyl]-3-[2,4-dihydroxyphenyl]-2-propen-1-one [57] (4.7%); genistein [53, 54, 56‐58, 60] (4%); myo-inositol [50, 61] (2.5%); isoferulic acid [46, 50, 52, 58‐60, 62] (1.8%); palmitic acid [46‐48, 50‐52, 57, 58, 60, 62‐65] (0.5%); d-mannitol [47, 48] (0.4%); linoleic acid [46, 50, 52, 58] (0.3%); d-gluconic acid [50, 52, 63] (0.2%); and gallic acid [52, 58, 66] (0.1%).

On the other hand, 15 of the 27 identified compounds are reported for the first time in Egyptian propolis. Uniquely, aucubin, one of these 27 compounds is reported for the first time in propolis worldwide. To further confirm the presence of aucubin in the sample, the spectra and retention time was compared with that of standard reference aucubin.

The stereotypical chemical profile of Egyptian propolis is distinguishable by its rich content of flavonoids and phenolic acids and their esters [50].This, however, is not always the case, as Hegazi et al. previously reported on Egyptian propolis rich in triterpenoids which made up 17.3% of the total composition [63]. Hegazi et. al also characterized an Egyptian propolis sample by its benzofuran lignans content (13.5%) in 2007 [52]. Moreover, in 2014, Morsy et al. identified an Egyptian propolis sample rich in fatty acids [65]. In this study, it has been found that the sample's chemical profile bears a strong resemblance to that of Maltese propolis as reported by Popova et al. [33], being that the most abundant compounds present are sugars. The source of these sugars reiterate the long-standing hypothesis that plant mucilage could be another source bees rely on for propolis [33]. This is not the first time that carbohydrate and sugar rich Egyptian propolis samples were studied, as this has been noted by Christov et al. as early as 1998 [47].

Besides sugars, the remaining compounds in the studied sample exhibited great diversity in chemical nature, these compounds belonged to the following classes: aromatic hydrocarbons, fatty acids, phenolic compounds, aromatic esters, flavonoids, glycosides, and aromatic alcohols.

Contrastingly, the top four most highly abundant constituents did not all vary in chemical nature; with the most abundant constituent being d-fructose (13.77%), closely followed by d-glucopyranose (10.59%), hexopyranose, (10.08%) and lastly, chrysin (8.05%). Their EI-MS spectra are presented in Figure S2. The fragmentation patterns of the four major compounds are highlighted in Table S2, together with their detailed characterization and fragmentation schemes (Figures S3-S6).

Virtual screening (VS) is a computational strategy that is used in drug discovery [67]. Its main application is the identification of top-hit compounds and optimization of lead compounds. Compared with other traditional experimental screening techniques, VS has the main advantage of being fast and cost effective [68, 69]. VS can be classified according to the method of screening into structure-based and ligand-based methods.

Structure-based virtual screening includes molecular docking which is the most widely used method [70]. Molecular docking models the interaction occurring between the test molecule and the target protein at an atomic level [71]. As for Ligand-based virtual screening, this includes methods such as pharmacophore modelling and Quantitative Structure Activity Relationship (QSAR). This method of screening relies on the presence of a set of active ligand molecules and it correlates their activity to structural information [72].

Docking studies may be done without specifying the binding site within the protein, this is called blind docking. However, in order to increase the efficiency of docking, it is recommended to locate and specify the binding site. This can be done by analyzing the structure of the target protein crystallized with a known ligand.

For centuries, people have relied on products of natural origin in the treatment and prevention of a myriad of diseases. However, natural products are not suited for high throughput screening drug discovery due to several reasons. To start, natural compounds are found in minute amounts and following their extraction and purification, these compounds are obtained in very limited quantities. Additionally, natural compounds exhibit high structural complexity and therefore, their synthesis is an incredibly difficult task. Therefore, structure-based drug discovery can be implemented using a library of pure natural compounds. This will in turn decrease the time, resources and effort wasted that would otherwise be used in in vitro screening.

With the aim of developing alternative α-amylase inhibitors, lead compounds from Egyptian propolis were examined. Following the promising in silico docking results, the top 10 hits' antidiabetic activity was further investigated through in vitro assay. These hits, in descending order, included myricetin, aucubin, chlorogenic acid, quercetin-7-methyl ether, quercetin, rosmarinic acid, catechin, luteolin, quercetin-3-methyl ether, and kaempferol. Their inhibitory activity was spectrophotometrically assayed at 540 nm as reported under Sect. 2.6. Acarbose, a well-established α-amylase inhibitor, was included as positive control. The concentration of each compound required to produce 50% α-amylase inhibition was determined by plotting the concentration–response curve (Fig. 2).

As illustrated in Fig. 2, all compounds demonstrated dose-dependent inhibition of α-amylase. Aucubin, an iridoid glycoside, exhibited the highest inhibitory activity with an IC₅₀ of 2.367 mM. Although aucubin has been documented to possess antidiabetic activity [81], this is the first in vitro study to investigate its effect on α-amylase. It is worth noting that the only other study mentioning aucubin’s anti- α-amylase activity predicted it by employing multivariate analysis to correlate aucubin’s concentration to the crude extract overall activity without testing aucubin individually [82]. The scarce investigations could be due to the fact that despite its great antidiabetic potential, aucubin’s main drawbacks revolve around its instability, difficult isolation, and low yield [83]. At slightly greater than two-fold aucubin’s IC₅₀, Kaempferol was the second most potent α-amylase inhibitor with an IC₅₀ of 4.84 mM. In contrast to aucubin, Kaempferol’s in vitro anti-α-amylase activity has been previously reported with similar IC₅₀ value [84], as well as in crude extracts [85].

Multidrug therapy has long been a lucrative option for therapeutic challenges. Through use of low-dose combinations of drugs with mutually exclusive toxicities, better treatment outcomes with lower adverse effects (as opposed to monodrug therapy) can be achieved. Insight into the nature of the drug-drug interaction (synergism, addition, antagonism) of combination therapy can be envisioned through computational analysis [32]. Going forward with the in vitro results, the inhibitory α-amylase activity of acarbose, alongside the most potent and available propolis-derived constituent, kaempferol, was investigated. Additionally, the binary combination of acarbose-propolis was also evaluated.

In this section, kaempferol; the top chosen in vitro hit and acarbose; the synthetic inhibitor both were docked with human pancreatic α-amylase to support in vitro studies and predict protein–ligand interactions. As shown in Table 5, acarbose gave -7.5 kcal/mol and kaempferol, -8.1 kcal/mol interaction energy indicating stable binding. As shown in Figure S8A, B, acarbose key residues formed hydrogen bonds with GLN63, ASP197, GLU233, and ASP300. Kaempferol, on the other hand, was engaged by two hydrogen bonds with GLN63 and a π-π T-shaped interaction with TRP59, as shown in Figure S8C, D, resembling that formed with the co-crystallized ligand myricetin found in the 4GQR structure (Figure S9). In addition, kaempferol formed an extra hydrogen bond with ASP300 and a π-π stacking interaction with TYR62. Details of other interactions of acarbose and kaempferol with α-amylase are depicted in Table 5.

Molecular dynamics (MD) simulations were performed to deeply understand and examine the stability of α-amylase with acarbose, kaempferol, and the combination of acarbose and kaempferol protein–ligand complexes obtained from molecular docking studies [86]. MD simulations allow us to visualize the physical interactions occurring in the docked complexes and provides us with the ability to observe the conformational and structural changes transpiring in the protein and the ligand throughout the duration of the simulation. Root mean square deviation (RMSD) is a frequently used method to numerically explain the changes in the protein active pocket of ligands in protein–ligand complexes. In this study, conformational changes and mobility, namely stability, of acarbose, kaempferol, and acarbose – kaempferol combination were investigated according to α-amylase active site residues during the 150 ns simulation. First, individual drug complexes with the enzyme were analyzed. As shown in Fig. 6A, kaempferol stabilized after the first 30 ns and remained stable at 0.2 nm with a mean RMSD value of 0.23 ± 0.09 nm. The protein–ligand binding poses at the end of the MD simulation were analyzed to examine the interaction changes. As given in Fig. 6B, the hydrogen bond of kaempferol with GLN63 did not persist, while the hydrogen bond with ASP197 remained stable. An animated video for the MD trajectory to visualize protein–ligand interactions between 0 and 150 ns was created. In this way, their interaction every 0.5 ns for 150 ns was visualized in the Electronic supplementary information. Kaempferol remained stable after the first 30 ns, as shown in Video S1 confirming the previous findings. Secondly, the stability of acarbose with α-amylase and the protein–ligand interaction changes were analyzed. As given in Fig. 6C, RMSD of its complex was found to be below 0.9 nm with a mean value of 0.80 ± 0.22 nm. The binding mode of acarbose at 150 ns is given in Fig. 6D. Compared to the molecular docking pose, it was observed that the hydrogen bonds with ASP300, ASP197, and GLU233 were broken; however, acarbose remained in the active pocket by forming new hydrogen bonds and interactions as shown in Video S2.

Finally, the stability and behavior of the combination of acarbose and kaempferol with α-amylase were analyzed by MD simulation. According to the RMSD data given in Fig. 6E, kaempferol was clearly stable at 0.2 nm and the mean RMSD value was 0.19 ± 0.01 nm, while acarbose was below 0.8 nm after the first 10 nm and the mean RMSD value was 0.69 ± 0.10 nm. Hence, the RMSD values of the combination were surprisingly low compared to the individual drugs indicating elevated stability. The interactions of co-ligand acarbose and kaempferol MD with α-amylase at the end of the simulation are given in Fig. 6F. Interestingly, it was understood that the protein–ligand molecular docking interactions given in Fig. 5 were completely preserved for kaempferol, especially the hydrogen bonds formed with GLN63 and ASP197 and remained potently stable, while acarbose also preserved the hydrogen bond with ASP300. It can be concluded that acarbose and kaempferol combination with α-amylase complex stabilized each other by forming synergism as given in Video S3.