Soy isoflavones have been reported as endocrine disruptors due to their ability to modulate the activity of estrogen receptors (ERs) in mammals; however, its ability to modulate other metabolic ...pathways is not entirely clear, which makes it necessary to identify new pharmacological targets that interact with these compounds present in soybean. In this work, a virtual screening was executed to identify potential targets of nine soy isoflavones, employing human proteins target from PharmMapper. The best 25 fit scores were selected and prepared for AutoDock Vina docking protocols. The results suggest that equol, daidzein and biochanin A, have the potential to interact with targets such as phenylethanolamine N-methyltransferase, sex hormone-binding globulin and vitamin D3 receptor, respectively. The validations of docking protocols showed good pose reproducibility (root-mean-square deviation (RMSD) ranged 0.001-3.854 Å) and a modest correlation between binding affinities and agonist concentration, AC50 (correlation coefficient (R) = 0.643, p < 0.001). Protein interaction network revealed that predicted targets for soy isoflavones are involved in different pathways, including neurotransmission, metabolism, and cancer remarking the need of a better understanding of the effects of these compounds on human health.
The current study addresses the growing demand for sustainable plant-based cheese alternatives by employing molecular docking and deep learning algorithms to optimize protein-ligand interactions. ...Focusing on key proteins (zein, soy, and almond protein) along with tocopherol and retinol, the goal was to improve texture, nutritional value, and flavor characteristics via dynamic simulations. The findings demonstrated that the docking analysis presented high accuracy in predicting conformational changes. Flexible docking algorithms provided insights into dynamic interactions, while analysis of energetics revealed variations in binding strengths. Tocopherol exhibited stronger affinity (−5.8Kcal/mol) to zein compared to retinol (−4.1Kcal/mol). Molecular dynamics simulations offered comprehensive insights into stability and behavior over time. The integration of machine learning algorithms improved the classification and the prediction accuracy, achieving a rate of 71.59%. This study underscores the significance of molecular understanding in driving innovation in the plant-based cheese industry, facilitating the development of sustainable alternatives to traditional dairy products.
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•Investigation into enhancing texture of plant-based cheese through molecular docking.•MD Analysis of tocopherol and retinol complexes with zein protein.•Integration of SVM for enhanced understanding of protein-ligand interactions.•Utilization of computational techniques for cheese innovation and development.
Multi-Target Inhibitors are the upcoming frontrunners of the antibiotic world as they provide significant advantage over drug resistance development. Antibacterial drug discovery research, requires ...more robust and innovative approaches such as multi-target inhibiting drugs, which over comes the innate hurdles in the field of antibiotics. In this current study, a curated set of 5,112 phytochemical molecules were virtually screened for its multi-target inhibition potential against 7 antibacterial protein drug-targets. Behenic Acid was identified to be the most significant phytochemical molecule with potential to inhibit Catalase Peroxidase (KatG), Adenylosuccinate Synthetase (ADSS) and Pyridoxine 5'-Phosphate Synthase (PdxJ), based on SeeSAR and AutoDock Vina results. Further, the inhibition potential of Behenic Acid was validated using 500 ns Molecular Dynamics (MD) Simulation based on Desmond analysis. Behenic Acid was further investigated in-vitro using agar-well-diffusion and Minimal Inhibitory Concentration (MIC) assay, where it demonstrated 20 ± 1mm zone-of-inhibition and 50 µg/ml MIC value against both
and
. Zebrafish based investigations was carried to confirm the in-vivo antibacterial efficacy of Behenic Acid. It was observed that, there is a progressive dose-dependent recovery from the bacterial infection, with highest recovery and survival observed in fishes fed with 100 µg/day of Behenic Acid. Results of the in-vitro and in-vivo assays strongly support the in-silico prediction of the antibacterial activity of Behenic Acid. Based on the results presented in this study, it is concluded that, Behenic Acid is a strong multi-target antibacterial phytochemical, that exerts antagonism against aquaculture bacterial pathogens such as
and
Communicated by Ramaswamy H. Sarma.
Molecular docking is the most popular and widely used method for identifying novel molecules against a target of interest. However, docking procedures and their validation are still under intense ...development. In the present investigation, we evaluate a quantum free-orbital AlteQ method for evaluating docking complexes generated by taking EGFR complexes as an example. The AlteQ method calculates the electron density using Slater's type atomic contributions in the interspace between the receptor and the ligand. Since the interactions are determined by the overlap of electron clouds, they follow the complementarity principle, and an equation can be obtained that describes these interactions. The AlteQ method evaluates the quality of the interaction between the receptor and the ligand, how complementary the interactions are, and due to this, it is used to reject less realistic structures obtained by docking methods. Here, three different equations were used to determine the quality of the interactions in experimental complexes and docked complexes obtained using AutoDock Vina and AutoDock 4.2.6.
Communicated by Ramaswamy H. Sarma
The recent explosion of interest in the bioactivity of the flavonoids of microalgae is due to the potential health benefits of the polyphenolic components that are major dietary constituents. The ...present study focuses on the phytochemical screening and in silico studies of flavonoids. Total flavonoids content in Chlorella pyrenoidosa was estimated in two modes of cultivation (Autotrophic and Heterotrophic) and its implication in anti-proliferation and anti-inflammatory activity was assessed through in silico approach. H-Ras p21(PDB-4L9S) and Lipoxygenase (PDB-3V99) involved in proliferation pathway and inflammatory pathway were selected as the target proteins for in silico studies. Seven compounds were selected for molecular docking. Pharmacokinetic properties of these compounds were calculated using online tools and docking was performed using Auto Dock Vina. By comparing and analyzing their binding energies in Maestro Schrodinger, suite, it was observed that Epigallocatechin gallate exhibited least binding energy of −9.1 kcal/mol and hence has anti-inflammatory activity. Catechin has best binding affinity with H-Ras p21 and hence has anti proliferative activity.
•In both the modes, aqueous extract yielded more phytochemicals (1.21 mg PE/mg of total phenols and 0.87 mg RE/mg dry cell weight of flavonoids) when compared to hexane and ethyl acetate extracts.•Chlorella pyrenoidosa, showed the presence of flavonoids caffeine, catechin, epicatechin, epigallocatechingallate,dihyroquerecetin-7,4′-dimethyl-ether,caffeoyl-d-Glucose and protocatechuic acid.•It was observed that Epigallocatechin gallate had the best binding energy (BE) indicating the best possible pose with a BE of −9.1 kcal/mol against the target molecule 3V99.
Ginsenosides are the main bioactive ingredients in plants of the genus Panax. Vina-ginsenoside R7 (VG-R7) is one of the rare high-value ginsenosides with health benefits. The only reported method for ...preparing VG-R7 involves inefficient and low-yield isolation from highly valuable natural resources. Notoginsenoside Fc (NG-Fc) isolated in the leaves and stems of Panax notoginseng is a suitable substrate for the preparation of VG-R7 via specific hydrolysis of the outside xylose at the C-20 position. Here, we first screened putative enzymes belonging to the glycoside hydrolase (GH) families 1, 3, and 43 and found that KfGH01 can specifically hydrolyze the β-d-xylopyranosyl-(1 → 6)-β-d-glucopyranoside linkage of NG-Fc to form VG-R7. The I248F/Y410R variant of KfGH01 obtained by protein engineering displayed a k cat/K M value (305.3 min–1 mM–1) for the reaction enhanced by approximately 270-fold compared with wild-type KfGH01. A change in the shape of the substrate binding pockets in the mutant allows the substrate to sit closer to the catalytic residues which may explain the enhanced catalytic efficiency of the engineered enzyme. This study identifies the first glycosidase for bioconversion of a ginsenoside with more than four sugar units, and it will inspire efforts to investigate other promising enzymes to obtain valuable natural products.
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•A two-step protocol enables more effcient computational receptor-ligand docking.•Machine learning-based pipeline provides an effective way to evaluate binding regions for ...protein–ligand complexes.•The algorithm works for both peptide and non-peptide ligands, and also in cases where the binding site is buried.•When binding region data are not available, the algorithm provides a fast and effective alternative to classical predictors.•The approach can be generalized by extending the training dataset to include other classes of compounds.
We present a combined computational approach to protein–ligand binding, which consists of two steps: (1) a deep neural network is used to locate a binding region on a target protein, and (2) molecular docking of a ligand is performed within the specified region to obtain the best pose using Autodock Vina. Our in-house designed neural network was trained using the PepBDB dataset. Although the training dataset consisted of protein-peptide complexes, we show that the approach is not limited to peptides, but also works remarkably well for a large class of non-peptide ligands. The results are compared with those in which the binding region (first step) was provided by Accluster. In cases where no prior experimental data on the binding region are available, our deep neural network provides a fast and effective alternative to classical software for its localization. Our code is available at https://github.com/mksmd/NNforDocking.
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