Construction of an MLR-QSAR Model Based on Dietary Flavonoids and Screening of Natural α-Glucosidase Inhibitors

Exploring synergistic inhibitory mechanisms of flavonoid mixtures on α-glucosidase by experimental analysis and molecular dynamics simulation

The study was the first to evaluate the synergistic interaction of luteolin + quercetin, luteolin + 3-O-methylquercetin, and quercetin + 3-O-methylquercetin mixtures on α-glucosidase and the binding mechanisms were explored using both experimental and theoretical approaches. The results showed that three flavonoid mixtures exhibited a mixed type of inhibition and demonstrated the most potent synergistic effects on α-glucosidase inhibition at 6:4 ratio, with interaction index (γ) of 0.85, 0.78 and 0.73, respectively. The three mixtures had a great influence on α-glucosidase secondary structures. Molecular simulation further demonstrated that three flavonoid mixtures formed hydrophobic interactions and hydrogen bonds with amino acid residues at different sites of α-glucosidase. Collectively, luteolin + quercetin, luteolin + 3-O-methylquercetin and quercetin + 3-O-methylquercetin were found to inhibit α-glucosidase in a synergistic manner and can be potentially used for the development of hypoglycemic food products.

Computational modeling of air pollutants for aquatic risk: Prediction of ecological toxicity and exploring structural characteristics

Assessing the aquatic toxicity originating from air pollutants is essential in sustaining water resources and maintaining the ecosystem's safety. Quantitative structure-activity relationship (QSAR) models provide a computational tool for predicting pollutant toxicity, facilitating the identification/evaluation of the contaminants and identifying responsible structural fragments. One-vs-all (OvA) QSAR is a tailored approach to address multi-class QSAR problems. The study aims to determine five distinct levels of aquatic hazard categories for airborne pollutants using OvA-QSAR modeling containing 254 air contaminants. This QSAR analysis reveals the critical descriptors of air pollutants to target for molecular modification. Various factors, including the selection of relevant mechanistic descriptors, data quality, and outliers, determine the reliability of QSAR models. By employing feature selection and outlier identification approaches, the robustness and accuracy of our QSAR models were significantly increased, leading to more reliable predictions in chemical hazard assessment. The results revealed that models using the Random Forest algorithm performed the best based on the selected descriptors, with internal and external validation accuracy ranging from 71.90% to 97.53% and 76.47%-98.03%, respectively. This study indicated that the aquatic risk of air contaminants might be attributed predominantly to their sp3/sp2 carbon ratio, hydrogen-bond acceptor capability, hydrophilicity/lipophilicity, and van der Waals volumes. These structures can be critical in developing innovative strategies to mitigate or avoid the chemicals' harmful effects. Supporting air quality improvement, this study contributes to the rapid implementation of measures to protect aquatic ecosystems affected by air pollution.

Identification of Antioxidant Methyl Derivatives of Ortho-Carbonyl Hydroquinones That Reduce Caco-2 Cell Energetic Metabolism and Alpha-Glucosidase Activity

α-glucosidase, a pharmacological target for type 2 diabetes mellitus (T2DM), is present in the intestinal brush border membrane and catalyzes the hydrolysis of sugar linkages during carbohydrate digestion. Since α-glucosidase inhibitors (AGIs) modulate intestinal metabolism, they may influence oxidative stress and glycolysis inhibition, potentially addressing intestinal dysfunction associated with T2DM. Herein, we report on a study of an ortho-carbonyl substituted hydroquinone series, whose members differ only in the number and position of methyl groups on a common scaffold, on radical-scavenging activities (ORAC assay) and correlate them with some parameters obtained by density functional theory (DFT) analysis. These compounds' effect on enzymatic activity, their molecular modeling on α-glucosidase, and their impact on the mitochondrial respiration and glycolysis of the intestinal Caco-2 cell line were evaluated. Three groups of compounds, according their effects on the Caco-2 cells metabolism, were characterized: group A (compounds 2, 3, 5, 8, 9, and 10) reduces the glycolysis, group B (compounds 1 and 6) reduces the basal mitochondrial oxygen consumption rate (OCR) and increases the extracellular acidification rate (ECAR), suggesting that it induces a metabolic remodeling toward glycolysis, and group C (compounds 4 and 7) increases the glycolysis lacking effect on OCR. Compounds 5 and 10 were more potent as α-glucosidase inhibitors (AGIs) than acarbose, a well-known AGI with clinical use. Moreover, compound 5 was an OCR/ECAR inhibitor, and compound 10 was a dual agent, increasing the proton leak-driven OCR and inhibiting the maximal electron transport flux. Additionally, menadione-induced ROS production was prevented by compound 5 in Caco-2 cells. These results reveal that slight structural variations in a hydroquinone scaffold led to diverse antioxidant capability, α-glucosidase inhibition, and the regulation of mitochondrial bioenergetics in Caco-2 cells, which may be useful in the design of new drugs for T2DM and metabolic syndrome.

Deciphering the binding behavior and interaction mechanism of apigenin and α-glucosidase based on multi-spectroscopic and molecular simulation studies

This study investigated the molecular mechanism underlying the binding interaction between apigenin (API) and α-glucosidase (α-glu) by a combination of experimental techniques and computational simulation strategies. The spontaneously formation of stable API-α-glu complex was mainly driven by hydrogen bonds and hydrophobic forces, leading to a static fluorescence quenching of α-glu. The binding of API induced secondary structure and conformation changes of α-glu, decreasing the surface hydrophobicity of protein. Computational simulation results demonstrated that API could bind into the active cavity of α-glu via its interaction with active residues at the binding site. The important roles of key residues responsible for the binding stability and affinity between API and α-glu were further revealed by MM/PBSA results. In addition, it can be found that the entrance of active site tended to close after API binding as a result of its interaction with gate keeping residues. Furthermore, the structural basis for the binding interaction behavior of API was revealed and visualized by weak interaction analysis. The findings of our study revealed atomic-level mechanism of the interaction between API, which might shed light on the development of better inhibitors.

Probing the binding mechanism of tea polyphenols from different processing methods to anti-obesity and TMAO production-related enzymes through in silico molecular docking

Tea polyphenols possess anti-obesity properties and reduce TMAO levels. However, the variability of tea polyphenols under different processing methods and their preventive efficacy requires further exploration. This study systematically evaluated the antioxidant, hypoglycemic, and hypolipotropic enzyme capacities of GT, YT and DT through UPLC-ESI-MS/MS analysis of catechin profiles. OPLS, correlation analysis, and molecular docking were employed to investigate the compounds and inhibitory mechanisms targeting hypoglycemic, hypolipidemic, and TMAO-producing enzymes. GT exhibited significantly lower IC50 values for biological activity and higher catechins contents compared to YT and DT (p < 0.05). Strong positive correlations were observed between EGCG, CG, and ECG and biological activities (r ≥ 7.4, p < 0.001). Molecular docking results highlighted the establishment of stable hydrogen bonds and hydrophobic interactions between EGCG, CG, ECG, and the receptor. These findings contribute novel insights into the mechanisms by which tea polyphenols prevent obesity and inhibit TMAO production.