Network pharmacology and molecular docking analysis reveal insights into the molecular mechanism of shiliao decoction in the treatment of cancer-associated malnutrition

Transplanting network pharmacology technology into food science research: A comprehensive review on uncovering food-sourced functional factors and their health benefits

Network pharmacology is an emerging interdisciplinary research method. The application of network pharmacology to reveal the nutritional effects and mechanisms of active ingredients in food is of great significance in promoting the development of functional food, facilitating personalized nutrition, and exploring the mechanisms of food health effects. This article systematically reviews the application of network pharmacology in the field of food science using a literature review method. The application progress of network pharmacology in food science is discussed, and the mechanisms of functional factors in food on the basis of network pharmacology are explored. Additionally, the limitations and challenges of network pharmacology are discussed, and future directions and application prospects are proposed. Network pharmacology serves as an important tool to reveal the mechanisms of action and health benefits of functional factors in food. It helps to conduct in-depth research on the biological activities of individual ingredients, composite foods, and compounds in food, and assessment of the potential health effects of food components. Moreover, it can help to control and enhance their functionality through relevant information during the production and processing of samples to guarantee food safety. The application of network pharmacology in exploring the mechanisms of functional factors in food is further analyzed and summarized. Combining machine learning, artificial intelligence, clinical experiments, and in vitro validation, the achievement transformation of functional factor in food driven by network pharmacology is of great significance for the future development of network pharmacology research.

In silico screening of Fyn kinase inhibitors using classification-based QSAR model, molecular docking, molecular dynamics and ADME study

This study aimed to use a computational approach that combined the classification-based QSAR model, molecular docking, ADME studies, and molecular dynamics (MD) to identify potential inhibitors of Fyn kinase. First, a robust classification model was developed from a dataset of 1,078 compounds with known Fyn kinase inhibitory activity, using the XGBoost algorithm. After that, molecular docking was performed between potential compounds identified from the QSAR model and Fyn kinase to assess their binding strengths and key interactions, followed by MD simulations. ADME studies were additionally conducted to preliminarily evaluate the pharmacokinetics and drug-like characteristics of these compounds. The results showed that our obtained model exhibited good predictive performance with an accuracy of 0.95 on the test set, affirming its reliability in identifying potent Fyn kinase inhibitors. Through the application of this model in conjunction with molecular docking and ADME studies, nine compounds were identified as potential Fyn kinase inhibitors, including 208 (ZINC70708110), 728 (ZINC8792432), 734 (ZINC8792187), 736 (ZINC8792350), 738 (ZINC8792286), 739 (ZINC8792309), 817 (ZINC33901069), 852 (ZINC20759145), and 1227 (ZINC100006936). MD simulations further demonstrated that the four most promising compounds, 728, 734, 736, and 852 exhibited stable binding with Fyn kinase during the simulation process. Additionally, a web-based platform ( https://fynkinase.streamlit.app/ ) has been developed to streamline the screening process. This platform enables users to predict the activity of their substances of interest on Fyn kinase from their SMILES, using our classification-based QSAR model and molecular docking.

Identification of potential FAK inhibitors using mol2vec molecular descriptor-based QSAR, molecular docking, ADMET study, and molecular dynamics simulation

This study aims to identify potential focal adhesion kinase (FAK) inhibitors through an integrated computational approach, combining mol2vec descriptor-based QSAR, molecular docking, ADMET study, and molecular dynamics simulation. A dataset of 437 compounds with known FAK inhibitory activities was used to develop QSAR models using machine learning algorithms combined with mol2vec descriptors. Subsequently, the most promising compounds were subjected to molecular docking against FAK to evaluate their binding affinities and key interactions. ADMET study and molecular dynamics simulation were also employed to investigate the pharmacokinetic, drug-like properties, and the stability of the protein-ligand complexes. The results showed that the mol2vec descriptor-based QSAR model established by support vector regression demonstrated good predictive performance (R2 = 0.813, RMSE = 0.453, MAE = 0.263 in case of training set, and R2 = 0.729, RMSE = 0.635, MAE = 0.477 in case of test set), indicating their reliability in identifying potent FAK inhibitors. Using this QSAR model and molecular docking, compound 21 (ZINC000004523722) was identified as the most potential compound, with predicted logIC50 value and binding energy of 2.59 and - 9.3 kcal/mol, respectively. The results of molecular dynamics simulation and ADMET study also further suggested its potential as a promising drug candidate. However, because our research was merely theoretical, additional in vitro and in vivo studies are required for the verification of these results.

Investigation of the mechanism of action of chemical constituents of celery seed against gout disease using network pharmacology, molecular docking, and molecular dynamics simulations

Celery (Apium graveolens L.) has long been considered as a potential herbal medicine for the prevention and treatment of gout. However, the relationship between the chemical constituents and pharmacological activities of this medicinal plant has not been fully investigated yet. Therefore, this study aims to apply network pharmacology, molecular docking and molecular dynamics to explore the relationship between the chemical constituents of celery seed and its biological effects in the treatment of gout. Network pharmacology was built and analyzed based on the data collected from GeneCards, OMIM databases and SwissTargetPrediction web server using Cytoscape 3.9.0 software. The GO and KEGG pathway analysis of the potential targets of celery seed related to gout disease was performed using the ShinyGO v0.75 app. Molecular docking and molecular dynamics were carried out using Autodock vina and NAMD 2.14 software, respectively. The network analysis identified 16 active compounds and thirteen key targets of celery seed in the treatment of gout. The GO analysis and the KEGG pathway enrichment analysis suggested that the mechanism of action of the chemical constituents of celery seed might be involved in several pathways, notably the PI3K-Akt signaling pathway, Ras signaling pathway, and HIF-1 signaling pathway, respectively. Molecular docking and molecular dynamics revealed that apiumetin might be an important chemical that plays a key role in the pharmacological effect of celery seed. These results might be useful to select the Q-markers to control the quality of the products from celery seeds.Communicated by Ramaswamy H. Sarma.

The effect and mechanism of Huangqin-Baishao herb pair in the treatment of dextran sulfate sodium-induced ulcerative colitis

Background

The haungqing (Scutellariae Radix) and baishao (Paeoniae Radix Alba) herb pair (HBHP) is a common prescribed herbal formula or is added to other traditional Chinese medicine (TCM) prescriptions to treat ulcerative colitis (UC). However, the underlying mechanism is unclear.

Purpose

Elucidate the efficacy and potential mechanism of HBHP against UC.

Methods

First, The UC model of mice induced by dextran sulfate sodium (DSS) was established. The mice were randomly divided into Control group, DSS group, SASP group (390 mg/kg), and HPHP group (1.95 g/kg), with 8 mice per group. Drugs were administrated via oral gavage for 7 days. Then, Disease activity index (DAI), length of the colon, histopathology, and changes in inflammatory cytokines in colonic tissues were analyzed to assess the effect of HBHP on UC. Besides, Network pharmacology was applied to identify the active compounds, core targets of HBHP in the treatment of UC, and the corresponding signaling pathways to explore the underlying mechanisms. Finally, Western blot (WB), immunohistochemistry (IHC) and molecular docking were performed to validate the results.

Results

HBHP significantly reduced DAI score and decreased colon length shortening in DSS-induced UC mice. The administration of HBHP was able to effectively alleviated mucosal ulceration and epithelial destruction. In addition, HBHP treatment obviously - reduced the expressions of TNF-α, IL-6, and IL-1β in colon tissues (p < 0.05 or p < 0.01). 35 bioactive compounds and 290 HBHP targets related to UC were obtained. Among them 3 key active compounds (baicalein, panicolin, and norwogonin) with higher degree values in the drug-compound-target network and 21 hub genes (STAT3, JAK2, SRC, AKT1, PIK3CA, and VEGFA, etc.) were identified. KEGG enrichment analysis suggested that HBHP's mechanisms mainly involve the JAK-STAT pathway. Abnormal activation of JAK/STAT signaling is believed to be involved in the pathogeneses of UC. Notably, WB and IHC showed that HBHP significantly down-regulated the protein expression levels of p-JAK2 (p < 0.05) and p-STAT3 (p < 0.05 or p < 0.01). JAK2 and STAT3 might be core targets for the action of HBHP; this possibility was also supported by molecular docking.

Conclusions

HBHP could alleviate DSS-induced UC, reduce tissue inflammation, and its mechanism might primarily be achieved by inhibiting JAK2/STAT3 signaling pathway. Meanwhile, our work revealed that network pharmacology combined with experimental verification is a cogent means of studying the mechanism of TCM.

The activities and mechanisms of intestinal microbiota metabolites of TCM herbal ingredients could be illustrated by a strategy integrating spectrum-effects, network pharmacology, metabolomics and molecular docking analysis: Platycodin D as an example

BACKGROUND

The intestinal microbiota plays a key role in understanding the mechanism of traditional Chinese medicine (TCM), as it could transform the herbal ingredients to metabolites with higher bioavailability and activity comparing to their prototypes. Nevertheless, the study of the activity and mechanism of microbiota metabolites reported by the published literature still lacks viable ways. Hence a new strategy is proposed to solve this issue.

PURPOSE

A new strategy to study the activity and mechanism of intestinal microbiota metabolites of TCM herbal ingredients by integrating spectrum-effect relationship, network pharmacology, metabolomics analysis and molecular docking together was developed and proposed.

METHOD

Platycodin D (PD) and its microbiota metabolites with antitussive and expectorant effect were selected as an example for demonstration. First, the PD and its microbiota metabolites with important contribution to antitussive and/or expectorant effects were screened through spectrum-effect relationship analysis. Second, network pharmacology and metabolomics analysis were integrated to identify the upstream key targets of PD and its microbiota metabolites as well as the downstream endogenous metabolites. Finally, the active forms of PD were further confirmed by molecular docking.

RESULTS

Results showed that PD was an active ingredient with antitussive and/or expectorant effects, and the active forms of PD were its microbiota metabolites: 3-O-β-d-glucopyranosyl platycodigenin, 3-O-β-d-glucopyranosyl isoplatycodigenin, 7‑hydroxyl-3-O-β-d-glucopyranosyl platycodigenin, platycodigenin and isoplatycodigenin. In addition, those microbiota metabolites could bind the key targets of PAH, PLA2G2A, ALOX5, CYP2C9 and CYP2D6 to exert antitussive effects by regulating four metabolic pathways of phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, glycerophospholipid metabolism and linoleic acid metabolism. Similarly, they could also bind the key targets of PLA2G1B, ALOX5, CYP2C9 and CYP2D6 to exert expectorant effect by regulating two pathways of glycerophospholipid metabolism and linoleic acid metabolism.

CONCLUSION

The proposed strategy paves a new way for the illustration of the activities and mechanisms of TCM herbal ingredients, which is very important to reconcile the conundrums of TCM herbal ingredients with low oral bioavailability but high activity.