Metabolites profiling, in-vitro and molecular docking studies of five legume seeds for Alzheimer’s disease

In-vitro biological activitiesAntioxidant and anti-Alzheimer activities of the tested samples are summarized in Table 1. Generally, the studied legume methanolic extracts showed good antioxidant and anti-Alzheimer activities with chickpea seeds exerting almost the best results as revealed by their lower IC50 values which were close to standard (Table 1). In the antioxidant assays (ABTS, FRAP, and DPPH), all the samples showed better results than the standard (Trolox). Whereas, in AChE assay, CP showed the highest activity among other tested samples as compared to the reference drug (Donepezil), followed by KB sample (Table 1). In BuChE assay, the studied legume methanolic extracts showed strong activity43 as compared to the reference drug, with CP and KB exhibiting the best results.Table 1 In-vitro antioxidant and anti-Alzheimer activities of five seeds of Fabaceae family.Legumes are a source of the biologically active compounds classified as phenolic acids, flavanols, flavan-3-ols, tocopherols, anthocyanins/anthocyanidins, vitamin C, and condensed tannins/proanthocyanidins, which are responsible for their antioxidant activity44,45. P. Siddhuraju, and K. Becker (2007) proved the antioxidant activity of BP using tests such as DPPH, ABTS, and FRAP, and the results were significantly lower than the reference drugs (quercetin, butylated hydroxyanisole, and Trolox)46. It is worth highlighting that the studied legume methanolic extracts were investigated for the first time as ChE inhibitors.UHPLC/MS metabolite profilingLegumes are a habitual part of the diet in several countries worldwide, especially as a source of phytochemicals and nutrients. The methanolic extracts of five commonly used seeds in Fabaceae family were analyzed using UHPLC-Q-TOF-MS in both negative and positive ionization modes to obtain their comprehensive profiles and correlate their metabolites with anticholinergic potential. The representative base peak chromatograms of the samples are demonstrated in Supplementary Materials Fig. S1. The identification was based on several databases the MassBank of North America (https://mona.fiehnlab.ucdavis.edu/https://mona.fiehnlab.ucdavis.edu/), LOTUS: Natural Products Online Database (https://lotus.naturalproducts.net/), Human Metabolome Database (http://www.hmdb.ca/), KEGG (https://www.genome.jp/kegg/kegg1.html), LipidMaps (https://www.lipidmaps.org/), and MassBank of North America (https://mona.fiehnlab.ucdavis.edu/). In addition, a literature review of the reported chemical constituents in Fabaceae family22,47,48,49,50,51. In total, 139 compounds were tentatively identified using MS2 level of confidence52,53 and characterized by their retention time (tr), accurate molecular monoisotopic mass, and MS/MS spectra in the five extracts. The details on their accurate masses, molecular formulas, retention time, tentative identification, and chemical class are shown in Table S1, Fig. S1. These compounds can be classified into several chemical classes, including, amino acids and amine derivatives, carbohydrates, phenolic acids, alkaloids, anthocyanins, saponins, fatty acids and fatty acyls, flavonoids, isoflavonoids, lignans, iridoids, stilbenes, sterols and terpenes, benzophenones, and phospholipids. To the best of our knowledge, this is the first comparative metabolite profiling of the five seeds coupled with chemometrics and computational analysis, which is traced to provide chemical-based evidence for their differential biological effect on Alzheimer’s key enzymes.Amino acids and sugarsEight amino acids were identified including choline, tyramine, isovaline, pipecolic acid, Glu-Tyr, isoleucine, Glu-Leu, and Glu-Phe, additionally to one amine derivative, thermospermine. All were detected in positive ionization mode. Amino acids have a neuroprotective role by improving cognitive function and memory performance54, ameliorating the injury-induced cognitive impairment to prevent the progression of Alzheimer55. Five sugars were identified in the negative mode i.e., stachyose, galactosyl ciceritol, raffinose, sucrose, and dehydro-ciceritol (Table S1). Sugars showed a neuroprotective effect through the elevation of glutathione level56 in previous works.Flavonoids, benzophenone, and phenolic acidPhenolics have been demonstrated to have neuroprotective roles by boosting neuronal survival, tissue perfusion, cerebral blood flow, and reducing ischemic-related apoptosis57,58. Flavonoid subclasses, mainly flavonols and isoflavones, are widely distributed in Fabaceae and exhibit antioxidant and anti-inflammatory activities. Four kaempferol and two quercetin derivatives were identified by fragment ions at m/z 285 for kaempferol, and 301 for quercetin59. Further fragment ions were at m/z 178 (C8H3O5) (1,2A-) and 151 (C7H3O4) (1,3A-), due to the release after the retro Diels-Alder fission and retrocyclization, respectively60. Two flavan-3-ols; dihydro-(epi)catechin-diglucoside and catechin-3-O-glucopyranoside were also detected as previously reported in the Fabaceae family61. Five isoflavonoids; sissotrin, 6”-O-malonylgenistin, genistein 7-O-apiofuranosyl-(1 → 6)-glucoside, glabridin, and genistin were detected with characteristic ions related to the fission at 0,3B-, such as fragment ions with m/z values above 133.0294 (even ion) (C8H5O2) and 132.0217 (odd ion) (C8H4O2)60. Caffeic acid derivatives were detected in the investigated samples at early retention time and characteristic fragmentation pattern of caffeic acid at m/z 341, 191, 179, and 16162. Trihydroxy methoxybenzophenone was detected with main fragments at m/z 245, 229, 213, and 211 due to the successive losses of hydroxyl groups63 (Table S1).Lignans and irridoidsTo date, all the available data suggest that the iridoids are a family of natural lipophile chemicals with the features of endogenous neurotrophic factors, which could be considered promising leads for the treatment of neurological disorders64, mainly through their anti-inflammatory effects65. Furthermore, lignans are among the compounds revealing nitric oxide (NO)-inhibiting activity, where NO is one of the most studied promoters of neuroinflammation66. Also, lignans showed inhibitory effects on beta-amyloid (Aβ1-42) aggregation67.Oleuropein and its aglycon were identified as secoiridoids and showed main fragments at m/z 377 (M − 162), 345 (M − 194), 307 (M − 232), and m/z 275 (M − 264)68 (Fig. S2).Three lignans syringaresinol, secoisolariciresinol diglucoside, and its isomer were detected and showed main fragments at m/z 179 and 219 which are fragment ions formed through benzylic cleavage and containing one aromatic ring and one hydroxy group (m/z 179) or one hydroxy and one methoxy group (m/z 219)69.Saponins, sterols, and terpenesSaponins have been demonstrated to have neuroprotective activity by improving neuronal synthesis, and synaptic activity70. Fourten soyasponins; phaseoside IV, soyasapogenol C, phaseoside I, soyasaponin Bd, soyasaponin Be, soyasaponin Bb, soyasaponin III, kaikasaponin III, soyasaponin alphag II, dehydro-soyasaponin I, soyasaponin alphag I, soyasapogenol E, dihydro-soyasaponin Ba, and soyasaponin II were detected.A set of peaks belonging to group B of soyasaponins, namely soyasaponins Ba, Bb, Bd, and Be, showed mass fragments at m/z 797, 617, 599, and 581 due to successive losses of a hexose moiety along with the neutral loss of one and two molecules of water, respectively. The product ion at m/z 459 is generated from the cleavage of the glycosidic bond and formation of soyasapogenol B aglycone; the ensuing product ions at m/z 441, 423, and 405 are most likely due to one, two, and three water loss, respectively71. Four sterols dihydro-dihydroxy megastigmadien-one-[apiosyl-glucoside], coumestrol acetate, β-sitosterol-3-O-glucoside, and stigmastenone were also detected as non-polar metabolites in addition to gibberellin A8 (diterpenes), characteristic to family Fabaceae.Alkaloids, anthocyanins, and stilbeneAlkaloids are a broad class of naturally occurring compounds that have the potential to impact the central nervous system, regulating several body processes and behavior. They are also regarded as cholinesterase inhibitors that enhance memory functions70. Two glycoalkaloids were identified in positive ionization mode (Table S1). Fragmentation of β-chaconine (solanidine-glucose-rhamnose) showed fragment ions at m/z 559 and 397 due to the loss of rhamnose and glucose moieties, respectively72. Fragments at m/z 204 and 150 are also diagnostic of fragment formulas C14H22N and C10H16N, respectively73. N, N′-Diferuloyl-putrescine has antioxidative and skin-whitening activities74. It was detected in our profile with main fragments at m/z 177, 145, and 11775.Anthocyanins are natural pigments commonly detected in some fruits and vegetables. The most common anthocyanidins in foods are cyanidin, peonidin, pelargonidin, petunidin, malvidin, and delphinidin. Anthocyanins have been documented in several clinical studies as being effective in preventing different disorders such as cardiovascular and neurological disorders76. Three anthocyanins were detected in positive ionization mode namely, pelargonidin-3-O-glucoside, prodelphinidin B3, and procyanidin dimer B7. Prodelphinidin dimer (prodelphinidin B3) consisting of (epi) gallocatechin—(epi)catechin (m/z 593.1305) was tentatively identified. The MS2 spectrum of this sequence produced ions at m/z 467, 425, 407, and 289, for heterolytic ring fission of the C ring with a characteristic loss of 126 Da, retro-Diels-Alder fragmentation with a neutral loss of 152 Da, followed by the loss of a water molecule unit (−18 Da), and quinone methide-fission of the inter-flavan bond producing a distinctive loss of 288 Da, respectively77. Procyanidin dimer (procyanidins B1, B2, or C1) (m/z 577.1342) was detected and confirmed by MS2 at m/z 451, 425, 407 and 28977. The m/z 451 was attributed to heterolytic ring fission of the C ring with a characteristic loss of 126 Da. The ion at m/z 425 was due to retro-Diels-Alder fragmentation with a neutral loss of 152 Da, then the loss of a water molecule unit (−18 Da) at m/z 407 [M-H − 152 − 18]−. The ion at m/z 289 was due to quinone methide-fission of the interflavan bond producing a distinctive loss of 288 Da. Additionally, pelargonidin-3-O-glucoside was identified with the main fragment at m/z 271, which corresponded to the pelargonidin aglycon, due to the loss of glucose moiety78.Piceid (resveratrol glucoside) was also detected as stilbenoids with main fragments at m/z 269, 241, and 29977.Fatty acids and lipidsPolyunsaturated fatty acids can alleviate the cognitive deficits of Alzheimer’s by limiting amyloid polymerization in neuronal cells79. The identified fatty acids (Table S1) were malyngic acid, 10-oxo-nonadecanoic acid, hydroxy-tetracosanoic acid, 16-hydroxypalmitic acid, linolenic acid, linoleic acid, octadecadien-1-ol, and β-acetoxyolean-en-oic acid. In addition to, two fatty acyl compounds (tuberonic acid glucoside I, and tuberonic acid glucoside II) and one fatty acid derivative (coumaroyl-caffeoyl-palmitic acid derivative).Furthermore, the LC-MS analysis revealed the presence of 64 lipids from various classes (Table S1). Each is distinguished by a distinct polar moiety (“head group”) and differs on the inside by the numerous contributing fatty acids with varying degrees of unsaturation and chain length, resulting in distinct fragmentation patterns for each lipid type. In the MS/MS spectra, the presence of carboxylate ions [RCOO]− identifies the particular fatty acids esterified on the glycerol skeleton, e.g., m/z 281 for octadecenoic acid (18:1), 279 for octadecadienoic (18:2), 277 for octadecatrienoic (18:3), and 255 for hexadecanoic (16:0) acids, where the esterification position can be determined through the relative intensities of both fatty acid ions (the higher abundant peak assigned for the ion at sn-2)80. Phospholipids could improve cognitive performance, and exhibit a neuroprotective effect against Alzheimer’s by inhibiting amyloid beta deposition in neural cells81.Our analysis revealed the presence of various chemical classes in the analyzed extracts, confirming the enrichment of these seeds in multifunctional nutraceuticals against Alzheimer’s disease. In consequence, the multivariate data analyses were applied based on the tentatively identified metabolites and their corresponding peak areas to get current insights into the chemical heterogeneity between the Fabaceae seeds and to correlate the key bioactive compounds to the anti-cholinesterase activity of the extracts.Metabolite profiles comparison and differentiating metabolites analysisMultivariate statistics were used to further assess the difference in the metabolic profiles among the five legume samples. The unsupervised HCA analysis was performed using the mass data of the identified compounds (Table S1) to classify the five legumes based on the relative differences in the accumulation of secondary metabolites and to identify the holistic discrepancy and similarity in their metabolic profiles in an untargeted and throughput manner. The HCA results shown in Fig. 1 demonstrated that the five legumes were clearly divided into two clusters: KB and CP samples shared comparable metabolic profiles, forming cluster I, whereas FB, RL, and BP samples had distinct chemical composition and/or component levels, constituting cluster II. It is worth noting that within cluster II, FB and RL samples showed higher chemical similarity when compared to BP sample.
Figure 1Hierarchical cluster analysis (HCA) dendrogram of the five analyzed legumes based on cluster analysis of mass spectrometric biochemical profiles.After that, a volcano plot (Fig. 2) was used to obtain further insight into the metabolic differences between these two clusters and to determine the differently accumulated compounds and their expression levels. A fold-change (FC) score ≥ 2 or ≤ 0.5 among the identified metabolites with p-value < 0.05 was used as an identification criterion. There were 46 differential accumulated metabolites (31 upregulated and 15 downregulated) between cluster I (KB and CP) and cluster II (FB, RL, and BP) samples. Consequentially, the discriminating metabolites colored red were substantially higher in cluster I while being lower in cluster II (FC ≥ 2.0), whereas those colored blue were significantly lower in cluster I but higher in cluster II (FC ≤ 0.5) (Fig. 2). These metabolites mainly comprised saponins, flavonoids, isoflavonoids, alkaloids, phenolic acid derivatives, phospholipids, and procyanidins, as well as sugars, fatty acyls and acids.Figure 2Volcano plot of differential metabolites of cluster I (KB, CP) vs. cluster II (FB, RL, BP). The fold-change value (FC) for each differential metabolite was transformed as Log2, and the corresponding p-value was transformed as − Log10.Further, PCA modeling was performed to provide a general visual separation of all of the samples. At 95% confidence limit, two principal components described the positions of the distinct metabolome clusters with PC1/PC2 accounting for 79.3% of the variance in metabolic profiles of the analyzed extracts. In the PCA scores plot (Fig. 3A), the 3 biological replicates of each sample were coincidently grouped together affirming the extraction method consistency, as well as the data analysis stability and repeatability. It was noticeable that the metabolic profiles of CP and KB extracts are grouped on the far-right side of the plot (positive PC1 values) and are fairly distinctive and separated from the other three samples (FB, RL, and PB), located at the left side of the plot (negative PC1 values). The results were similar to the HCA model, with the five samples grouped into two distinct areas in the plot. Examination of the loading plot (Fig. 3B) revealed that MS variables contributed mainly to the separation of CP in the score plot are genistein 7-O-apiofuranosyl-(1 → 6)-glucoside, tuberonic acid glucoside II, trihydroxy methoxybenzophenone, sissotrin, PC (18:1/18:3), and kaikasaponin III. While, those of KB samples are soyasapogenol E, PC (18:2/18:3), dihydro-(epi)catechin-diglucoside, malyngic acid, kaempferol 3-[galactosyl-(1 → 6)-glucoside] 7-[rhamnosyl-(1 → 3)-rhamnoside], dehydro-soyasaponin I, ferutinin, LysoPG(18:2), and LPC(18:0). The signals of secoisolariciresinol diglucoside II, soyasaponin I, solanidine, and PE (18:1/18:2) were the major variables contributing to BP sample’ discrimination. Regarding FB and RL samples, nine metabolites, hydroxy ferutinin, soyasaponin Be, oleuropein aglycone, procyanidin C1, LysoPG(16:0/0:0), PC(20:2/14:1), isoleucine, kaempferol 3-O-[6”-malonyl-apiofuranosyl-(1 → 2)-glucoside] glucoside, and PE(18:2/18:3), accounted for their segregation in the PCA scores plot.Figure 3Principal component analysis (PCA) scores plot (A) and loadings plot visualization (B) using the identified metabolites by LC/MS analysis of the five legume samples (n = 3).Unlike PCA, PLS-DA is a supervised multivariate analysis method that can maximize the differences between different groups by using partial least squares regression to model the relationship between metabolite expression and sample class to achieve modeling prediction of the studied samples. Therefore, five-class PLS-DA model (Fig. 4) was used to identify the metabolites that were responsible for the observed separation in PCA. High predictability (Q2) and strong goodness of fit (R2X, R2Y) of the PLS-DA model were observed (Q2 = 0.91, R2X = 0.73, R2Y = 0.93). The fivefold CV-ANOVA and permutation of the cross-validation test (20 iterations) revealed great predictability and goodness of fit of the constructed PLS-DA model (Fig. S3). As can be observed in the PLS-DA scores plot (Fig. 4A), the five legumes were clearly separated from each other and the variable importance in the projection (VIP) value of the first principal component of the PLS-DA model was used at p < 0.05 to find the unique chemical markers for each sample (Fig. 4B) metabolites. Accordingly, the useful markers of BP sample are phaseoside IV, β-chaconine, soyasaponin I, thermospermine, and solanidine. Likewise, CP extract was enriched in genistein 7-O-apiofuranosyl-(1 → 6)-glucoside, sissotrin, and genistin. In contrast, FB sample was characterized by a high abundance of phaseoside I, hydroxy ferutinin, and isoleucine. On the other hand, dehydro-soyasaponin I, kaempferol 3-O-[6”-malonyl-apiofuranosyl-(1 → 2)-glucoside], kaikasaponin III, soyasapogenol E, malyngic acid, and kaempferol 3-O-(2”-O-glucosyl)-rutinoside are enriched in KB extract. Finally, RL sample demonstrated higher levels of kaempferol 3-O-[6”-malonyl-apiofuranosyl-(1 → 2)-glucoside] glucoside, PE(18:2/18:3), PC(18:3/18:1), oleuropein, soyasaponin Be, LysoPG(16:0/0:0), and oleuropein aglycone.Figure 4Partial least squares discriminant analysis (PLS-DA) scores plot (A) and the variable importance in projection (VIP) score showing the top 25 differential metabolites (VIP scores > 1) (B) in the methanolic extracts of the five analyzed legumes.Metabolic pathway enrichment analysis based on KEGG databaseThe Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to link the identified metabolites (Table S1) to their metabolic pathways. Metabolite set enrichment analysis (MSEA) was utilized to classify the chemical groups of all identified compounds and highlight the most enriched metabolic pathway in the analyzed legume samples (Fig. 5). The enrichment bubble diagram (Fig. 5A) represents the chemical classifications of the enriched metabolite sets (top 25). As can be observed, among the top 25 chemical classes, the chemical groups with a higher enrichment ratio were cholines, oligosaccharides, fatty acid conjugates, flavonoids, glycerophosphoglycerols, prenol lipids, benzamides, octadecanoids, isoprenoids, and fatty acyl glycosides. The metabolic pathways of the metabolites were also analyzed according to the KEGG database, which reflected the most significant biochemical metabolic pathways involved by the identified metabolites in the five legumes. The identified metabolites covered a total of 29 pathways or metabolisms (Fig. S4, Table S2) and the top 15 enriched pathway terms are shown in the KEGG enrichment bar chart by calculating the -log(P-value) of each pathway, including glycerophospholipid metabolism, glycine, serine and threonine metabolism, unsaturated fatty acids biosynthesis, linoleic acid metabolism, ether lipid metabolism, valine, leucine and isoleucine biosynthesis, flavonoid biosynthesis, anthocyanin biosynthesis, and galactose metabolism (Fig. 5B). However, most metabolic reactions involved multiple metabolites, and the variation of these metabolites amounts among the five legume samples was inconsistent. Therefore, it cannot be simply said that the expression of some metabolic pathways was increased or decreased in a certain legume species. Previous studies reported that the major metabolic pathways in legumes included flavone and flavonol biosynthesis, aminoacyl-tRNA biosynthesis, isoquinoline alkaloid biosynthesis, the biosynthesis of amino acids, and isoflavonoid biosynthesis82,83 which is in accordance with our results (Table S2). Interestingly, many of the enriched metabolic pathways in the five legumes were associated with the biosynthesis of plant secondary metabolites, such as sterols, saponins, alkaloids, isoprenoids, and flavonoids. Secondary metabolites in plants are non-essential small molecular organic molecules generated by secondary metabolism and often have bioactivity. As a result, metabolic pathway analysis is beneficial for investigating complicated biological processes that occur throughout the metabolite accumulation process in plants84. Indeed, the characterized chemical classes in the analyzed legumes such as flavonoids, cinnamic acids, benzoic acids, alkaloids, and sterols have been proven to exhibit strong antioxidant and anti-cholinesterase activities85,86,87. In this context, potential metabolites identified in metabolic pathways might serve as therapeutic targets and contribute to the development of broad-spectrum drugs. It can aid in the development of testable predictions, the understanding of drug action mechanisms, and the increase of research productivity towards novel drug discovery88.Figure 5(A) Classification of the identified metabolites in the five analyzed legumes. The color of the dots represents the transformed P-value of the hypergeometric test, and the size represents the number of differential metabolites, and the larger the size, the greater the number of differential metabolites within the chemical class. (B) Top 15 different enriched KEGG pathways, the horizontal coordinate indicates the ratio of the differential metabolite numbers in the corresponding pathway to the total identified metabolite numbers in this pathway, and the larger the ratio value, the greater the enrichment of this pathway, and the vertical coordinate indicates the name of the pathway. The color intensity reflects the statistical significance of the identified pathways, the darker the color, the more affected the pathway.Correlation between metabolites and anti-cholinesterase activityThe association between the identified metabolites and anti-cholinesterase activity of the tested legume extracts was established using Pearson’s correlation analysis. As can be observed in Fig. 6, certain relationships between the abundance of the identified metabolites in the five extracts and the bioactivity with Pearson’s correlation coefficient (r) r > ± 0.5 at p < 0.05 and false discovery rate (FDR) < 0.01. Indeed, 18 metabolites were positively correlated to the anti-cholinesterase activity of the extracts, including, 3 isoflavonoids (sissotrin, genistin, and genistein 7-O-apiofuranosyl-(1–6)-glucoside), 4 flavonoids (quercetin 3,4′-diglucoside, catechin 3-glucoside, kaempferol 3-O-(2”-O-glucosyl)-rutinoside, kaempferol 3-O-[6”-malonyl-apiofuranosyl-(1–2)-glucoside]), 5 saponins (phaseoside I, dehydro-soyasaponin I, soyasapogenol E, kaikasaponin III, soyasaponin alphag I), 2 alkaloids (β-chaconine and solanidine), and 2 phenolic acid derivative (N, N′-diferuloyl putrescine and ferutinin), as well as, the seco-iridoid oleuropein and the procyanidin, prodelphinidin B3. In contrast, only 6 metabolites exhibited a negative correlation with the activity and were mainly fatty acids and lipids. To further validate these findings a docking study was performed to explore the potential binding modes and the intermolecular interactions of the detected biomarkers with AChE and BuChE active sites.Figure 6Top 25 metabolites correlated by Pearson’s correlation coefficients with the anti-cholinesterase activity of the five legumes (p < 0.05, n = 3).Docking studyTwo molecular docking studiesThe 18 positively correlated metabolites from the five seeds obtained by Pearson’s correlation analysis were docked against both AChE and BuChE targets to clarify their anti-Alzheimer effects at the molecular levels (Table S3). The docked co-crystals of AChE and BuChE achieved binding scores of −9.51 and −8.89 kcal/mol, respectively. Based on the binding scores and binding modes; beta-chaconine, dehydro-soyasaponin I, kaempferol-3-O-(2”-O-glucosyl)-rutinoside, kaikasaponin III, N, N’-diferuloyl putrescine, and oleuropein members were found to be the most promising, Table 2. β-Chaconine described binding scores of −7.86 and −10.32 kcal/mol against the AChE and BuChE targets, respectively. It bound Trp286 (H-bond and H-pi bond), Ser293 (two H-bonds), and Phe295 (one H-bond) of AChE. Also, it bound Tyr332 (H-pi), Glu197 (two H-bonds), Gly116 (H-bond), and His438 (H-bond) of BuChE. Dehydro-soyasaponin I binding scores were − 6.37 and − 11.41 kcal/mol towards the AChE and BuChE receptors, respectively. It formed three H-bonds with Ser293 and one H-bond with Asp74 of the AChE; besides, it formed one H-bond with Asn289 and one H-bond with Thr332 of the BuChE. Moreover, kaempferol-3-O-(2”-O-glucosyl)-rutinoside achieved binding scores of -8.33 and -10.30 kcal/mol within the binding pockets of AChE and BuChE, respectively. It showed the formation of two H-pi interactions with Trp286, two H-bonds with Ser293, and one H-bond with Phe295 of AChE. However, it formed two H-bonds with Asp70 and one H-bond with Asn289 of BuChE. Furthermore, kaikasaponin III showed binding scores of−5.77 and − 11.36 kcal/mol against the binding sites of AChE and BuChE, respectively. Kaikasaponin III represented two H-bonds with Ser293 and one H-bond with Glu292 of the AChE receptor: besides, one H-bond with Asn289 and one H-bond with Tyr332 of the BuChE. N, N’-diferuloyl putrescine on the other side was found to interact with the binding sites of AChE and BuChE achieving scores of −9.57 and −8.41 kcal/mol, respectively. It bound both Trp286 and Glu202 amino acids of AChE with pi-H and H-bond, respectively. Also, it bound both Asp70 and Gly116 amino acids of BuChE with H-bond and pi-H interaction, respectively. Finally, oleuropein formed one H-bond with Asp74 and one pi–pi interaction with Tyr341 of the AChE receptor and showed a binding score of −9.28 kcal/mol. Besides, it bound Ser287 (H-bond), Gly 116 (H-bond), and Tyr332 (H-pi bond) of BuChE and described a binding score of −9.25 kcal/mol. Briefly, the aforementioned findings suggest the very promising inhibitory activities of the identified metabolites towards the AChE and BuChE targets.Table 2 3D pocket interactions and positioning for beta-chaconine, dehydro-soyasaponin I, kaempferol-3-O-(2”-O-glucosyl)-rutinoside, kaikasaponin III, N, N’-diferuloyl putrescine, and oleuropein towards the AChE and BuChE targets.