Dissecting molecular mechanisms underlying the inhibition of β-glucuronidase by alkaloids from Hibiscus trionum: Integrating in vitro and in silico perspectives

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 180, P. 108969 - 108969

Published: July 31, 2024

Language: Английский

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Unveiling Anti-Diabetic Potential of Baicalin and Baicalein from Baikal Skullcap: LC–MS, In Silico, and In Vitro Studies

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(7), P. 3654 - 3654

Published: March 25, 2024

Type 2 diabetes mellitus (T2DM) is marked by persistent hyperglycemia, insulin resistance, and pancreatic β-cell dysfunction, imposing substantial health burdens elevating the risk of systemic complications cardiovascular diseases. While pathogenesis remains elusive, a cyclical relationship between resistance inflammation acknowledged, wherein exacerbates perpetuating deleterious cycle. Consequently, anti-inflammatory interventions offer therapeutic avenue for T2DM management. In this study, herb called Baikal skullcap, renowned its repertoire bioactive compounds with potential, posited as promising source novel strategies. Our study probed anti-diabetic properties from skullcap via network pharmacology, molecular docking, cellular assays, concentrating on their dual modulatory effects through Protein Tyrosine Phosphatase 1B (PTP1B) enzyme inhibition actions. We identified major in using liquid chromatography–mass spectrometry (LC–MS), highlighting six flavonoids, including well-studied baicalein, potent inhibitors PTP1B. Furthermore, experiments revealed that baicalin baicalein exhibited enhanced responses compared to active constituents licorice, known agent TCM. findings confirmed mitigate two distinct pathways: PTP1B effects. Additionally, we have flavonoid molecules potential drug development, thereby augmenting pharmacotherapeutic arsenal promoting integration herb-derived treatments into modern pharmacology.

Language: Английский

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Mechanistic Study of Protein Interaction with Natto Inhibitory Peptides Targeting Xanthine Oxidase: Insights from Machine Learning and Molecular Dynamics Simulations

Journal of Chemical Information and Modeling, Journal Year: 2025, Volume and Issue: unknown

Published: March 24, 2025

Bioactive peptides from food sources offer a safe and biocompatible approach to enzyme inhibition, with potential applications in managing metabolic disorders such as hyperuricemia gout, conditions linked excessive xanthine oxidase activity. Using machine learning-based screening inspired by the bioactivity of natto, two peptides, ECFK FECK, were identified Bacillus subtilis proteome validated inhibitors IC50 values 37.36 71.57 mM, respectively. Further experiments confirmed their safety through cytotoxicity assays, electronic tongue analysis demonstrated mild sensory properties, supporting edibility. Molecular dynamics simulations revealed that these stabilize critical regions, showing higher dissociation energy barrier (52.08 kcal/mol) than FECK (46.39 kcal/mol), indicating strong, stable interactions. This study highlights food-derived natural oxidase, offering promising therapeutic for disorder management.

Language: Английский

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Identification and Exploration of Immunity‐Related Genes and Natural Products for Alzheimer's Disease Based on Bioinformatics, Molecular Docking, and Molecular Dynamics

Immunity Inflammation and Disease, Journal Year: 2025, Volume and Issue: 13(4)

Published: April 1, 2025

ABSTRACT Background Recent research highlights the immune system's role in AD pathogenesis and promising prospects of natural compounds treatment. This study explores immunity‐related biomarkers potential products using bioinformatics, machine learning, molecular docking, kinetic simulation. Methods Differentially expressed genes (DEGs) were analyzed GSE5281 GSE132903 datasets. Important module identified a weighted co‐expression algorithm (WGCNA), immune‐related (IRGs) obtained from ImmPortPortal database. Intersecting these yielded important IRGs. Then, least absolute shrinkage selection operator (LASSO) other methods screened common markers. Biological pathways explored through Gene Ontology (GO), Kyoto Encyclopedia Genes Genomes (KEGG), Set Enrichment Analysis (GSEA). The accuracy markers was assessed by subject signature (ROC) curves validated GSE122063 dataset. datasets then subjected to immunoinfiltration analysis. Multiple compound databases used analyze core Chinese medicines components. Molecular docking simulation verification for further verification. Results A total 1360 differential 5 (PGF, GFAP, GPI, SST, NFKBIA) identified, showing excellent diagnostic efficiency. GSEA revealed associated with Oxidative phosphorylation, Nicotine addiction, Hippo signaling pathway. Immune infiltration analysis showed dysregulation multiple cell types brains, significant interactions between types. 27 possible herbs 7 eventually identified. binding environment GPI‐luteolin GPI‐stigasterol relatively stable good affinity. Conclusions PGF, NFKBIA early diagnosis, cells brains. compounds, including luteolin stigmasterol, targeting biomarkers.

Language: Английский

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Integrating Computational and Experimental Methods to Identify Novel Sweet Peptides from Egg and Soy Proteins

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(10), P. 5430 - 5430

Published: May 16, 2024

Sweetness in food delivers a delightful sensory experience, underscoring the crucial role of sweeteners industry. However, widespread use has sparked health concerns. This underscores importance developing and screening natural, health-conscious sweeteners. Our study represents groundbreaking venture into discovery such derived from egg soy proteins. Employing virtual hydrolysis as novel technique, our research entailed comprehensive process that evaluated biological activity, solubility, toxicity compounds. We harnessed cutting-edge machine learning methodologies, specifically latest graph neural network models, for predicting sweetness molecules. Subsequent refinements were made through molecular docking screenings dynamics simulations. meticulous approach culminated identification three promising sweet peptides: DCY(Asp-Cys-Tyr), GGR(Gly-Gly-Arg), IGR(Ile-Gly-Arg). Their binding affinity with T1R2/T1R3 was lower than -15 kcal/mol. Using an electronic tongue, we verified taste profiles these peptides, IGR emerging most favorable terms value 19.29 bitterness 1.71. not only reveals potential natural peptides healthier alternatives to traditional applications but also demonstrates successful synergy computational predictions experimental validations realm flavor science.

Language: Английский

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Computational Insights into Reproductive Toxicity: Clustering, Mechanism Analysis, and Predictive Models

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(14), P. 7978 - 7978

Published: July 22, 2024

Reproductive toxicity poses significant risks to fertility and progeny health, making its identification in pharmaceutical compounds crucial. In this study, we conducted a comprehensive silico investigation of reproductive toxic molecules, identifying three distinct categories represented by Dimethylhydantoin, Phenol, Dicyclohexyl phthalate. Our analysis included physicochemical properties, target prediction, KEGG GO pathway analyses, revealing diverse complex mechanisms toxicity. Given the complexity these mechanisms, traditional molecule-target research approaches proved insufficient. Support Vector Machines (SVMs) combined with molecular descriptors achieved an accuracy 0.85 test dataset, while our custom deep learning model, integrating SMILES graphs, 0.88 dataset. These models effectively predicted toxicity, highlighting potential computational methods safety evaluation. study provides robust framework for utilizing enhance evaluation compounds.

Language: Английский

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Unveiling the Anti-Obesity Potential of Thunder God Vine: Network Pharmacology and Computational Insights into Celastrol-like Molecules

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(23), P. 12501 - 12501

Published: Nov. 21, 2024

Obesity, characterized by abnormal or excessive fat accumulation, has become a chronic degenerative health condition that poses significant threats to overall well-being. Pharmacological intervention stands at the forefront of strategies combat this issue. Recent studies, notably Umut Ozcan's team, have uncovered remarkable potential Celastrol, small-molecule compound derived from traditional Chinese herb thunder god vine (Tripterygium wilfordii) as an anti-obesity agent. In research, computational chemical analysis was employed, incorporating "TriDimensional Hierarchical Fingerprint Clustering with Tanimoto Representative Selection (3DHFC-TRS)" algorithm systematically explore 139 active small molecules vine. These compounds were classified into six categories, particular focus on Category 1 for their exceptional binding affinity obesity-related targets, offering new avenues therapeutic development. Using advanced molecular docking techniques and Cytoscape prediction models, representative Celastrol-like identified, namely 3-Epikatonic Acid, Hederagenin, Triptonide, Triptotriterpenic Acid B, C, Ursolic Acid. demonstrated superior specificity toward two key obesity PPARG PTGS2, suggesting regulate metabolism mitigate inflammatory responses. To further substantiate these findings, dynamics simulations MM-PBSA free-energy calculations applied analyze dynamic interactions between enzymatic sites targets. The results provide robust theoretical evidence support feasibility promising candidates therapies. This study underscores power 3DHFC-TRS in uncovering bioactive natural sources, such vine, highlights promise PTGS2 novel Furthermore, it emphasizes essential role science expediting drug discovery, paving way personalized precision-based treatments heralding future more effective healthcare solutions.

Language: Английский

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Investigating the anti-obesity potential of Nelumbo nucifera leaf bioactive compounds through machine learning and computational biology methods

Frontiers in Pharmacology, Journal Year: 2024, Volume and Issue: 15

Published: Dec. 18, 2024

Obesity, a growing global health concern, is linked to severe ailments such as cardiovascular diseases, type 2 diabetes, cancer, and neuropsychiatric disorders. Conventional pharmacological treatments often have significant side effects, highlighting the need for safer alternatives. Traditional Chinese Medicine (TCM) offers potential solutions, with plant extracts like those from

Language: Английский

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