{"paper_id":"448d939c-2bcb-4b6e-b11e-870c956cf94d","body_text":"Mechanisms of Yinchen combined with Huangbai against Non-alcoholic fatty liver disease based on Network pharmacology, Molecular docking and Molecular Dynamics Simulations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Mechanisms of Yinchen combined with Huangbai against Non-alcoholic fatty liver disease based on Network pharmacology, Molecular docking and Molecular Dynamics Simulations Li Zhu, Yucheng Chen, Qianhan Wang, Long Yang, Wenhua Zhang, Yan Jiang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7535069/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Non-alcoholic fatty liver disease (NAFLD) is a growing health concern with increasing prevalence. Traditional Chinese Medicine (TCM), particularly Yinchen and Huangbai, shows significant promise in NAFLD prevention and treatment. This study aims to explore their mechanisms against NAFLD by identifying active compounds and potential targets. We employed network pharmacology, integrating multiple databases including TCMSP, GeneCards, Therapeutic Target Database, and OMIM. Venn diagrams from VENNY2.1 identified overlapping targets, which were analyzed via PPI networks in STRING and visualized in Cytoscape 3.9.1. Key targets were further analyzed using Metascape for GO and KEGG enrichment. Molecular docking assessed the affinity between key targets and active compounds, followed by MD simulations to evaluate complex stability. Results showed TNF, AKT1, PPARG, STAT3, and HSP90AA1 as top targets, with Genkwanin and Rutaecarpine as key compounds. These compounds demonstrated effective binding to TNF, PPARG, and HSP90AA1 through docking and MD simulations. In conclusion, Genkwanin and Rutaecarpine may alleviate NAFLD by modulating related pathways, with potential therapeutic targets including TNF, HSP90AA1, and PPARG. This study provides valuable insights into the mechanisms of Yinchen and Huangbai in NAFLD treatment, offering directions for future research in managing age-related diseases. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Biological sciences/Drug discovery Non-alcoholic Fatty Liver Disease Yinchen Huangbai Network Pharmacology Molecular Docking Molecular Dynamics Simulations Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Non-alcoholic fatty liver disease (NAFLD), considered a metabolic disorder, is marked by an undue buildup of fat in the liver. The occurrence and development of NAFLD encompass various of complex pathological stages, including simple steatosis (NAFL), nonalcoholic steatohepatitis (NASH), liver fibrosis, cirrhosis, hepatocellular carcinoma, and liver failure [1] . Key factors contributing to the progression of NAFLD [2] include type 2 diabetes, insulin resistance, obesity, hypertension, hyperlipidemia, pituitary hypofunction, environmental factors, genetic factors, etc [3] . In recent years, the incidence of NAFLD is increasing [4] , a systematic review and meta-analysis has revealed that between 1990 and 2021, the global incident cases of NAFLD increased by 71%, from 17.05 million (95% UI: 14.70.58–19.77) to 29.08 million (95% UI: 24.99–33.49) [5] . Despite this, the medical community faces a shortage of drugs backed by substantial clinical evidence for effectively managing NAFLD [6] . As such, identifying and developing drugs that can significantly halt or reverse the progression of NAFLD is of critical importance. Throughout its thousands of years of history in China, Traditional Chinese medicine (TCM) has played an an indispensable role in disease prevention and treatment, contributing significantly to the advancement of human health and medical practices. Yinchen has proven effective in treating various hepatobiliary conditions, including cholestasis [7] , hepatic lipid accumulation [8] , Liver fibrosis [9] , liver injury and hepatocyte apoptosis [10] . On the other hand, Huangbai has been known to inhibit the expression of inflammatory factors, curtail oxidative damage, and reduce hepatocyte apoptosis [11] . Previous researches indicates that Huangbai can ameliorate hepatic steatosis and reduce lipid synthesis [12] . However, the current knowledge regarding the combined effects of Yinchen and Huangbai on the progression of Non-alcoholic fatty liver disease (NAFLD) is limited. Therefore, this study is dedicated to uncovering the underlying mechanisms through which Yinchen combined and Huangbai collectively counteract NAFLD. Network pharmacology effectively facilitates the exploration and visualization of drug-disease interaction networks by leveraging a variety of databases and analytical techniques [13] . It actualizes the fusion of computer science and medicine, predicting drug action mechanisms through the construction of intricate “multi-gene, multi-target and multi-pathway” interaction networks [14] . Molecular docking serves as a potent computational method capable of predicting the interaction mechanisms between drugs and disease targets, by estimating the binding free energy between molecules (active ingredients of drugs) and targets (core proteins/therapeutic targets) at the molecular level [15] . Molecular dynamics simulations, based on molecular mechanics principles, model atomic movements over time within a system, providing insights into molecular behavior and dynamics parameters. These simulations help estimate system properties over specific timeframes and are widely used in early-stage drug development [15] . As experimental costs in drug research rise, interdisciplinary approaches combining molecular docking, molecular dynamics simulations, and network pharmacology are gaining attention. This method integrates traditional pharmacology with modern bioinformatics, enhancing the scientific rigor of network pharmacology results. It aids in deciphering complex biomolecular networks, uncovering new drug targets, and predicting mechanisms of action, thus playing a pivotal role in advancing modern drug discovery and precision medicine [16] . Therefore, in our research, we harnessed the combined strengths of network pharmacology、molecular docking and molecular dynamics simulations methods to investigate the potential mechanisms by which Yinchen and Huangbai synergistically prevent and treat NAFLD. The comprehensive methodology and conceptual framework of our research are depicted in Figure 1. We hypothesize that the active compounds in Yinchen and Huangbai, particularly Genkwanin and Rutaecarpine, ameliorate NAFLD by modulating key targets and pathways involved in lipid metabolism, inflammation, and insulin resistance. 2. Materials and Methods This study solely involved the use of network pharmacology and bioinformatics methods to explore the mechanisms of Yinchen combined with Huangbai in combating NAFLD. This research did not include any animal experiments or clinical trials, therefore ethical approval was not required for this study. 2.1 Screening the active components of Yinchen and Huangbai and further predict their targets. The active ingredients of Capillary Wormwood Herb and golden cypress were identified through Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, http://tcmspw.com) [17] .The primary active ingredients were selected based a drug-likeness (DL) value of greater than or equal to 0.18and oral bioavailability (OB) of greater than or equal to 30% [18] . PubChem Database (https://pubchem.ncbi.nlm.nih.gov/) [19] serves as a repository for chemical modules, offering access to a wide array of small chemical molecules' 2D and 3D structures. For our study, we utilized the PubChem database to acquire the 3D chemical structures of the principal bioactive constituents found in Capillary Wormwood Herb and golden cypress. These structures were subsequently uploaded to the Swiss Target Prediction database (http://www.swisstargetprediction.ch/) to forecast the potential targets of these active components [20] . Targets with a Probability score greater than 0 were considered significant and retained as the active targets for further analysis. 2.2 Screening disease targets related to NAFLD. By accessing the GeneCards (https://www.genecards.org) [21] , Therapeutic Target Database (https://db.idrblab.net/ttd/), and OMIM (https://www.omim.org/) databases [22] and using \"NAFLD\" as the search term, we conducted a comprehensive search for genes and proteins linked to NAFLD. The relevant biological targets associated with NAFLD were systematically identified and compiled as disease targets for further investigation. 2.3 Constructing protein-protein interaction network diagram of intersecting genes. The active components of two medicinal herbs, Capillary Wormwood Herb and golden cypress, along with the disease targets of NAFLD, were input into Venny2.1 (https://bioinfogp.cnb.csic.es/tools/venny/) [23] to create a Venn diagram. The overlapping gene identified as the key target of the medicinal herbs in improving NAFLD. To obtain the protein-protein interaction network (PPI) of the intersecting genes, the key targets associated with the medicinal herbs, as determined by VENNY, were entered into the STRING database (http://string-db.org) [24] specifying the species as humans. Ultimately, the results were uploaded to the Cytoscape3.9.1 software for visualization and network analysis. 2.4 GO (Gene Ontology) enrichment analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis. The principal targets of two drugs identified in section 2.3 for the amelioration of NAFLD were uploaded to the Metascape database (https://metascape.org) [25] . Subsequently, GO enrichment analysis and KEGG pathway enrichment analysis were conducted with the criteria of minimum Overlap > 5 and P-Value < 0.01 [26] . 2.5 Drug- compound-disease-target (D-C-D-T) network. The active ingredients and crucial targets of the two drugs, identified in section \"2.3\" for the amelioration of NAFLD, were uploaded into Cytoscape3.9.1 software. The network of drugs-compounds-diseases-targets was constructed and visualized by using Cytoscape3.9.1 software [27] . 2.6 Molecular docking. The target protein structures (macromolecule, receptor) were retrieved from the RCSB PDB database [28] . The 3D structures of the drugs' active compounds (small molecules, ligands) were acquired from PubChem in SDF file format. OpenBabel2.4.1 software [29] was utilized to convert the SDF format of 3D structure to mol2 format. Pymol software [30] was used to remove water and solvent molecules from the target protein structure. The target proteins were then imported into AutodockTools software for the addition of all hydrogen atoms and conversion to pdbqt format. Similarly, the active compounds (small molecules, ligands) of the drugs were imported into AutodockTools, hydrogenated, set as ligands, and converted to pdbqt format. AutodockTools was employed to define the docking boxes for the target proteins and active compounds, to carry out the molecular docking and to calculate the binding affinity. Finally, Pymol software was employed to simulate and visually display the results of molecular docking. 2.7 Molecular Dynamics Simulations 2.7.1 Setup of Simulation Environment and Parameters The optimal conformation obtained from molecular docking analysis was selected as the initial structure. Molecular dynamics simulations were performed using GROMACS 2020.6 software. The AMBERGS force field was chosen for the simulations, and the SPC water model was used to solvate the protein-ligand complex, creating a water box. Sodium or chloride ions were added to neutralize the total charge of the solution [31] . Prior to the molecular dynamics simulation, the system was preprocessed through energy minimization using the steepest descent method, with a maximum of 50,000 steps and a convergence criterion of 1000 kJ/mol/nm. Subsequently, the system entered the equilibration phase, during which the positions of the protein and ligand were strictly restrained. First, the system was equilibrated for 1 ns under the canonical ensemble (NVT) conditions, with the temperature gradually increased from 0 K to 310 K. Next, the system was equilibrated for 2 ns under the isothermal-isobaric ensemble (NPT) conditions, maintaining a temperature of 310 K and a pressure of 1.0 bar. After completing the equilibration phase, a 100 ns molecular dynamics simulation was performed with a time step of 2 fs, and simulation data were recorded every 1 ps to ensure the accuracy and reliability of the results. 2.7.2 Analysis of Simulation Results The trajectory files generated from the molecular dynamics simulations were analyzed in depth to evaluate the stability and dynamic behavior of the protein-ligand complex during the simulation. First, the root mean square deviation (RMSD) was calculated to assess the overall structural stability of the complex. The changes in RMSD values reflect the conformational fluctuations of the complex during the simulation and serve as an important indicator of system equilibrium. Subsequently, the root mean square fluctuation (RMSF) was calculated to analyze the flexibility of individual residues. RMSF values reveal the dynamic changes of different parts of the protein during the simulation, which is crucial for understanding functional regions and ligand-binding sites. Additionally, the study focused on the hydrogen bond interactions between the protein and ligand during the dynamics simulation, as well as the calculation of the binding free energy between the protein and ligand. 3. Results 3.1 Screening of active ingredients and target prediction of Yinchen and Huangbai. In the TCMSP database, the screening criteria were set as follows: Drug-Likeness (DL)≥0.18 and Oral Bioavailability (OB)≥30%. Subsequently,12 active components of Yinchen (Table 1) and 26 active components of Huangbai (Table 2) were selected. The Swiss Target Prediction database was employed to predict the targest of active ingredients, resulting in a total of 662 targets with Probability values greater than 0 (Supplementary Table 1). 3.2 Screening of NAFLD disease targets. Using \"NAFLD\" as the keyword, targets related to NAFLD were searched in the human GeneCards, Therapeutic Target Database, and OMIM databases (Supplementary Table 2). 3.3 Constructing protein-protein interaction (PPI) network of intersecting genes. The active components of two drugs were matched with the drug action targets, and a Venn diagram was created (Figure 2A) with the disease targets for NAFLD retrieved from the three databases. The intersecting genes are the key targets for the treatment of NAFLD with Yinchen and Huangbai. Key targets were imported into the STRING database for protein-protein interaction network (PPI) diagram (Figure 2C and Figure 2D). The software Cytoscape3.9.1 was employed to calculate the degree value and related parameters (Supplementary Table 3), and finally, visual analysis was carried out (Figure 2B). The results show that the PPI network diagram contains 148 nodes and 1625 edges. The top 10 core targets with the highest connectivity are located at the core of PPI network, which are TNF (degree 97), AKT1 (degree 94), STAT3 (degree 70), PPARG (degree 70), HSP90AA1 (degree 69), EGFR (degree 67), CASP3 (degree 67) and CTNNB1(degree 64)、HIF1A(degree 64)、ESR1(degree 62). We have listed the specific information of the top 10 key targets (table 3). 3.4 GO enrichment analysis. The key target (intersection gene) obtained from section \"2.3\" was imported into the Metascape database for GO enrichment analysis and KEGG pathway enrichment analysis. The results were then imported into bioinformatics tools for visual analysis. GO enrichment analysis encompasses three categories: biological process (BP), cell composition (CC) and molecular function (MF), with the top 10 items from each category selected for visualization (Figure 2E). GO BP results indicated that the intersection genes were primarily involved in cellular responses to organic nitrogen compounds, peptides, hormones and insulin. In the CC analysis, these genes were significantly enriched in membrane raft, membrane microdomain, caveola, receptor complex, ficolin-1-rich granules. MF analysis revealed that the molecular functions of the intersection genes were mainly related to protein serine/threonine/tyrosine kinase activity, protein kinase activity, phosphotransferase activity with an alcohol group as acceptor, and kinase activity. 3.5 KEGG pathway enrichment analysis. KEGG enrichment analysis highlighted the top 10 signaling pathways for visualization. As depicted in Figure 2F, the signaling pathways that were significantly enriched in intersection genes mainly include: Lipid and atherosclerosis, Alcoholic liver disease, Fluid shear stress and atherosclerosis, Shigellosis, AGE-RAGE signaling pathway in Diabetic Complications, Kaposi Sarcoma-Associated Herpesvirus Infection, Insulin resistance, Salmonella infection, Non-alcoholic fatty liver disease, Human cytomegalovirus infection. Moreover, Figure 2G clearly illustrates the relationships between these enrichment items, with nodes sharing the same cluster ID typically positioned in close proximity to one another. 3.6 Drug- compound-disease-target （ D-C-D-T ） network We utilized Cytoscape3.9.1 software to construct the network of D-C-D-T. The top 10 active ingredients are listed according to their Degree value, as follows: Genkwanin , Rutaecarpine, Beta-sitosterol, Demethoxycapillarisin, Artepillin A, Palmatine, Capillarisin, Skimmianin, Magnograndiolide, Palmidin A. Additionally, we present the details of the top 10 active compounds in Table 4. It can be observed that among the 12 active components of Yinchen , Genkwanin exhibits the highest degree of connectivity with other targets, while among the 26 active components of Huangbai , Rutaecarpine shows the highest degree of connectivity. Furthermore, quercetin (MOL000098) is the active compound shared by both Yinchen and Huangbai . (Figure 3 and Supplementary Table 4) 3.7 Molecular docking Based on the results obtained from network pharmacology analysis, we identified the top five key targets in the PPI network, namely: TNF, AKT1, PPARG, STAT3, and HSP90AA1. From the D-C-D-T network, Genkwanin and Rutaecarpine were selected as active components of the drugs. We used these five key targets and the two active components to conduct molecular docking, calculating their binding affinities. Previous studies suggest that the lower the affinity, the more effective the molecular docking [32] . Moreover, an affinity less than -5 kcal/mol indicates a higher likelihood of significant interaction between the molecules [33] . Molecular docking results clearly demonstrated that the affinities between the five key targets and the two active compounds are all less than 0, indicating that the key targets and active compounds can bind effectively. Among all docking simulations, the interaction between Rutaecarpine and PPARG resulted in the lowest affinity (-9.44 Kcal/mol), while the interaction between Genkwanin and STAT3 exhibited the highest affinity (-5.03 Kcal/mol) (Table 5). Furthermore, we selected the molecular docking simulation between Genkwanin and TNF, AKT1, PPARG, STAT3, HSP90AA1for visualization, and selected the molecular docking simulation between Rutaecarpine and TNF, AKT1, PPARG, STAT3, HSP90AA1for visualization (Table 6). Genkwanin forms a hydrogen bond with LYS-90, GLN-47, ALA-134, LEU-26 and GLY-24 in TNF, two hydrogen bonds with TRP-28 and three hydrogen bonds with ASN-46 (Figure 4A). Genkwanin and LEU-52, GLN-47 and GLN-43 formed one hydrogen bond respectively and two hydrogen bonds with ALA-50 in AKT1 (Figure 4B). Genkwanin and GLU-291 and LYS-265 in PPARG respectively formed a hydrogen bond (Figure 4C). Genkwanin formed two hydrogen bonds with LYS-370, ARG-379 and ASN-491 respectively, and two hydrogen bonds with ASP-369 and LEU-438 in STAT3(Figure 4D). Genkwanin and GLN-23 in HSP90AA1 form a hydrogen bond (Figure 4E). There is no hydrogen bond connection between Rutaecarpine and TNF (Figure 4F). Rutaecarpine and CYS-60 and GLN-104 in AKT1 form a hydrogen bond (Figure 4G). Rutaecarpine forms one hydrogen bond with GLU-343 in PPARG and two hydrogen bonds with GLU-343 (Figure 4H). Rutaecarpine formed one hydrogen bond with GLN-361 and two hydrogen bonds with GLU-444 in STAT3(Figure 4I). Rutaecarpine forms a hydrogen bond with GLY-135 in HSP90AA1(Figure 4J). The results above indicate that Genkwanin can effectively bind with TNF, AKT1 and PPARG, while Rutaecarpine can effectively bind with AKT1 and PPARG. Consequently, we have reason to speculate that the two active compounds, Genkwanin and Rutaecarpine, play a key role in the amelioration of NAFLD (Figure 5). 3.8 Molecular dynamics simulation analysis Due to the inability of semi-flexible docking used in molecular docking to account for the flexibility of protein structures, temperature, pressure, solvent effects, etc., 100-ns molecular dynamics simulations of Genkwanin, Rutaecarpine, and the PPARG, HSP90AA, and TNF proteins were performed in this paper. The co-crystallized ligands of the respective proteins were used as positive controls to further investigate the stability of the complexes in a comparative manner. The root mean square deviation (RMSD) is a critical indicator for evaluating the kinetic stability of the ligand-protein system in MD simulations. RMSD fluctuation analysis revealed that the RMSD values of the Genkwanin-PPARG, Rutaecarpine-PPARG, Genkwanin-HSP90AA, and Rutaecarpine-TNF complexes reached equilibrium after a period of MD simulation, with only minor fluctuations (Figure 6A). This indicates that the constructed simulation systems exhibited good overall stability. Notably, the RMSD values of the Genkwanin-PPARG and Rutaecarpine-PPARG complexes remained within 0.2 nm and were lower than those of the PPARG apo-protein. The Genkwanin-HSP90AA complex stabilized around 80 nm, with RMSD values lower than those of the HSP90AA apo-protein. These results suggest that Genkwanin and Rutaecarpine binding to PPARG, as well as Genkwanin binding to HSP90AA, resulted in higher stability and fewer conformational changes. Recent studies have indicated that fluctuations in RMSF values between 0.1 and 0.3 nm suggest relative stability in the docked complex system. In the four complex systems, the Genkwanin and Rutaecarpine complexes with PPARG reduced residue flexibility, while the Genkwanin-HSP90AA complex system altered the flexibility of residues in different regions (Figure 6B). The radius of gyration (Rg), which measures the compactness of biomolecular structures, is a parameter for evaluating the behavior and stability of biological systems during MD simulations. A smaller Rg value indicates a more compact molecular structure with atoms closer to the center of mass, while a larger Rg value suggests increased structural irregularity. By calculating the Rg values, we observed changes in protein structural compactness during the simulation. The Rg values of the four complexes fluctuated between 1.90 and 1.96 nm, 1.64 and 1.74 nm, and 1.60 and 1.70 nm during the 100 ns simulation and reached equilibrium after a certain period (Figure 6C). This indicates that these two compounds can interact with amino acids in the active pockets of the respective proteins. The solvent-accessible surface area (SASA) is a parameter describing the molecular surface area involved in protein-solvent interactions. A higher SASA value indicates a larger protein-solvent contact area, while a lower SASA value suggests a smaller contact area. The SASA values of the three systems remained relatively stable during the simulation (Figure 6D). Compared to the TNF apo-protein, the SASA value of the Rutaecarpine-TNF system was significantly reduced, indicating that Rutaecarpine binding decreased the protein surface area exposed to the solvent, potentially affecting protein hydrophobicity. To investigate the nature of hydrogen bonds at the complex binding site, the number of hydrogen bonds was calculated in this study. Hydrogen bonds are the primary interaction bonds that stabilize ligand-protein complexes. Precise counting during MD simulations revealed that the Genkwanin-PPARG complex formed 18,631 hydrogen bond interactions throughout the simulation, while the Rutaecarpine-PPARG, Genkwanin-HSP90AA, and Rutaecarpine-TNF complexes formed 11,089, 12,447, and 2,852 hydrogen bond interactions, respectively (Figure 7A). Throughout the MD simulation, the Genkwanin-PPARG complex consistently formed more than two hydrogen bonds, demonstrating extremely strong stability between Genkwanin and PPARG. Free energy landscapes (FELs) were calculated using the Gromacs built-in script g_sham and the xpm2txt.py script to obtain the relative Gibbs free energy based on RMSD and Rg values. Three-dimensional plots were generated by assigning RMSD, Rg, and Gibbs relative free energy values to the X, Y, and Z axes, respectively. FELs describe the conformations with the lowest energy throughout the simulation of complex structural dynamics. Weak or unstable protein-ligand interactions result in multiple, rough-surfaced minimum energy clusters in the FEL, whereas strong and stable interactions form single, smooth energy clusters. In the figures, blue spots indicate stable structures with minimum energy, while red/yellow spots represent unstable structures. The minima and representative structures of the three systems were also determined (Figure 7B, C, D, E). The FELs of the Genkwanin-HSP90AA and Rutaecarpine-TNF protein complexes exhibited multiple minimum energy clusters, indicating slightly lower complex stability. In contrast, the FELs of the Genkwanin-PPARG and Rutaecarpine-PPARG complexes showed single minimum energy clusters, indicating higher complex stability. 4. Discussion In our research, we have found that there are 12 active compounds in Yinchen from TCMSP database, including Genkwanin, beta sitosterol, Demethoxycapillarisin, Artecillin A and capillarisin. Various studies have shown that Genkwanin has significant anti-inflammatory, antioxidant, antibacterial, anti-tumor and immunomodulatory effects [34] .β-sitosterol can not only reduce liver injury, improve obesity-related chronic inflammation, and fight against low-density lipoprotein(LDH) [35] , but it can also reduce serum cholesterol and inhibit intestinal cholesterol absorption [36] . In addition, we also found that there are 26 active compounds in Huangbai , such as rutaecarpine, palmatine, Skimmianin, Magnograndiolide, Palmidin A. Rutaecarpine can alleviate drug-induced liver injury [37] , and can also inhibit MAPK and NF- κ B signaling pathway to reduce inflammation [38] . Furthermore, Rutaecarpine can decrease the production of reactive oxygen species (ROS), reduce cytotoxicity [39] . Additionally, a review has reported that palmatine can improve metabolic syndrome and its related complications [40] , palmatine can also significantly reduce apoptosis and ROS level HT-22 cell [41] . Considering the above conclusions, it is evident that these active ingredients may play a crucial role in the effectiveness of Yinchen and Huangbai against NAFLD. Consequently, we have further employed the method of network pharmacology combined with molecular docking to identify the key targets and potential molecular mechanisms of the aforementioned active compounds in the treatment of NAFLD. Based on our KEGG enrichment analysis results, it is noticeable that the signaling pathways involved in the combined use of Yinchen and Huangbai against NAFLD primarily include: Lipid and aromatherapy, Non-alcoholic fatty liver disease, TNF signaling pathway. We hypothesize that the Non-alcoholic fatty liver disease pathway (Figure 5) may be the crucial pathway in the action of Yinchen combined with Huangbai against NAFLD. In the PPI network, we obtained 148 nodes and 1625 edges. We speculate that the core targets of Yinchen combined with Huangbai against NAFLD primarily comprise: TNF, AKT1, PPARG, STAT3, HSP90AA1, and CASP3, with particular emphasis on TNF, AKT1, PPARG, CASP3. As a key T-helper type 1 (Th1) cytokine, tumor necrosis factor (TNF) is produced by monocytes/macrophages and T cells [42] . TNF can induce a wide range of intracellular signaling pathways, including apoptosis, cell survival, inflammatory response and immune response [43] . In the non-alcoholic fatty liver disease signaling pathway, obesity can promote the generation of the classic inflammatory factor TNF- α [44] . TNF- α can also bind to the receptor TNFR1 on the cell membrane, causing the activation of TNF signal pathway and PI3K-AKT signal pathway, which in turn induces insulin resistance in liver cells and eventually leads to fatty acid biosynthesis. It is obvious that the key targets (TNF and AKT1) identified through PPI play an irreplaceable role in the first stage (Simple steatosis: without inflammation and fibrosis) of the non-alcoholic fatty liver disease signaling pathway. The peroxisome proliferator-activated receptor gamma (PPARG), also known as the \"energy balance receptor\", can regulate the expression of numerous anti-fibrosis miRNAs [45] . This means that PPARG can be utilized as a therapeutic target for anti-liver fibrosis [46] . Some studies have also shown that PPARG is a key regulator of insulin resistance (IR) [47] and NAFLD [48] . In our study, it is clear that during the progression of NAFLD, free fatty acids (FFAs) can induce the activation of PPAR signaling pathway. This causes lipid accumulation and a reduction in lipolysis in the second stage of the NAFLD signal pathway, non-alcoholic steatohepatitis (NASH), which is characterized by hepatic inflammation and fibrosis, and ultimately exacerbates the progression of NAFLD. Obviously, PPARG (PPAR-γ), a key enzyme that promotes lipid synthesis in the development of NAFLD, aligns with our PPI screening results. In addition, during the second stage of the NAFLD signaling pathway (NASH), FFA can facilitate the entry of the classic inflammatory factor TNF- αinto cells and induce the release of apoptosis factors CASP8, CASP3 and CASP7 [49] , aggravating liver injury. Based on the analysis results of network pharmacology mentioned above, we further employed the molecular docking analysis methodfor verification. Genkwanin, the most effective active compound of Yinchen , and Rutaecarpine, the most effective active compound of Huangbai , were selected for molecular docking with five targets TNF, AKT1, PPARG, STAT3, and HSP90AA1, respectively. The affinity values between the five key targets and the two active compounds are all less than -5 kcal/mol, which indicates that the key targets and active ingredients can effectively bind. Among them, Rutaecarpine has the lowest affinity with PPARG (-9.44 Kcal/mol), indicating that Rutaecarpine and PPARG are most closely bound. We further selected the complexes of Genkwanin-PPARG, Rutaecarpine-PPARG, Genkwanin-HSP90AA, and Rutaecarpine-TNF for molecular dynamics simulations to investigate the stability of the complex systems. Based on the above analysis and exploration, we can easily conclude that the active ingredients Genkwanin and Rutaecarpine may improve NAFLD by targeting TNF, PPARG and HSP90AA1. 5. Conclusion By combining network pharmacology、molecular docking technology and molecular dynamics simulations, we have preliminarily investigated the principal active components and potential molecular mechanisms of Yinchen and Huangbai against NAFLD. Our research suggests that Genkwanin and Rutaecarpine may be the key active components of Yinchen and Huangbai , respectively. These active compounds are likely to exert their beneficial effects on NAFLD primarily through the Lipid and atherosclerosis, Non-alcoholic fatty liver disease and TNF signaling pathway, with a particular focus on the Non-alcoholic fatty liver disease. The potential therapeutic targets of the above active compounds may include the target proteins TNF, PPARG and HSP90AA1. However, our research has certain limitations. Since our approach relies on Since our approach and database screening methods, including network pharmacology and molecular docking, there might be undiscovered information about active compounds and disease targets not yet present in the current database. Furthermore, the active components Genkwanin and Rutaecarpine, which we employed for molecular docking, do not fully represent all the constituents of the two drugs, ( Yinchen and Huangbai . Most importantly, our research remains at the theoretical data level and requires validation through practical experiments such as animal experiments, cell experiments and molecular biology experiments. Declarations Data Availability Statement All data generated or analysed during this study are included in this published article and its supplementary information files. Funding declaration statement All authors have read and approved the final manuscript. This work was supported by the following grants: Yunnan Province Clinical Research Center for Metabolic diseases（202102AA100056）. Yunnan Clinical Medical Center for Endocrine and Metabolic Diseases (YWLCYXZXXYS20221005). Science and Technology Innovation Team of Diagnosis and Treatment for Glucolipid Metabolic Diseases in Kunming Medical University（CXTD202106）. Kunming Medical University Joint Special Project - Key Project（202201AY070001-041）. Project Funding Support under Yunnan Province's Xingdian Talent Support Plan（RLMY20220001）.The National Science Foundation of China (No. 82160125 to Z.C.H). Yunnan Province Metabolism-related Cardiovascular Disease Innovation Team，grant number 202405AS350014. Yunnan Clinical Medical Center for Endocrine and Metabolic Diseases Research Project（2024YNLCYXZX0069）. Credit of author statement Li Zhu wrote the manuscript. Yucheng Chen, and Qianhan Wang checked the date and revised the manuscript. Li Zhu, Yucheng Chen, and Qianhan Wang contributed equally to this work. Long Yang, Yan Jiang and Wenhua Zhang Completed the data collection from the database. Yingcheng Guo and Juwei zhang revised the data. Yushan Xu, Chenhong Zheng and Tingdong Yu designed and coordinated the study. Yushan Xu, Chenhong Zheng and Tingdong Yu accept full responsibility for the work. The final version of the manuscript was reviewed and approved by all authors. Declaration of Competing Interest The authors indicated no conflicts of interest with regard to the content of this article. References ENGIN A. Nonalcoholic Fatty Liver Disease and Staging of Hepatic Fibrosis [J]. Adv Exp Med Biol, 2024, 1460: 539-74. JUNG C, PARK S, KIM H. Association between vitamin A, E, and folate levels and risk of non-alcoholic fatty liver disease in adults with diabetes mellitus [J]. Sci Rep, 2025, 15(1): 11844. MOMENI S, HAJIZADEH-SHARAFABAD F, PASHAEI M R. 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Rutaecarpine attenuates high glucose-induced damage in AC16 cardiomyocytes by suppressing the MAPK pathway [J]. J Appl Toxicol, 2023, 43(9): 1306-18. TARABASZ D, KUKULA-KOCH W. Palmatine: A review of pharmacological properties and pharmacokinetics [J]. Phytother Res, 2020, 34(1): 33-50. PEI H, ZENG J, HE Z, et al. Palmatine ameliorates LPS-induced HT-22 cells and mouse models of depression by regulating apoptosis and oxidative stress [J]. J Biochem Mol Toxicol, 2023, 37(1): e23225. HORIUCHI T, MITOMA H, HARASHIMA S, et al. Transmembrane TNF-alpha: structure, function and interaction with anti-TNF agents [J]. Rheumatology (Oxford), 2010, 49(7): 1215-28. LI J, ZHANG H, HUANG W, et al. TNF-α inhibitors with anti-oxidative stress activity from natural products [J]. Curr Top Med Chem, 2012, 12(13): 1408-21. POTOUPNI V, GEORGIADOU M, CHATZIGRIVA E, et al. Circulating tumor necrosis factor-α levels in non-alcoholic fatty liver disease: A systematic review and a meta-analysis [J]. J Gastroenterol Hepatol, 2021, 36(11): 3002-14. SHYPULIN V P, MARTYNCHUK O A, RUDENKO N N, et al. ASSOCIATION ANALYSIS OF PIOGLITAZONE EFFECTIVENESS IN TREATMENT OF NAFLD PATIENTS WITH OBESITY AND PPARG RS1801282 (PRO12ALA) GENOTYPE [J]. Wiad Lek, 2021, 74(7): 1617-21. GUO C, LAI L, MA B, et al. Notoginsenoside R1 targets PPAR-γ to inhibit hepatic stellate cell activation and ameliorates liver fibrosis [J]. Exp Cell Res, 2024, 437(1): 113992. YANG X, LI X, HU M, et al. EPA and DHA differentially improve insulin resistance by reducing adipose tissue inflammation-targeting GPR120/PPARγ pathway [J]. J Nutr Biochem, 2024, 130: 109648. WANG Z, GAO P, GAO J, et al. Daphnetin ameliorates hepatic steatosis by suppressing peroxisome proliferator-activated receptor gamma (PPARG) in ob/ob mice [J]. Biochem Pharmacol, 2024, 230(Pt 3): 116610. NASIRI-ANSARI N, NIKOLOPOULOU C, PAPOUTSI K, et al. Empagliflozin Attenuates Non-Alcoholic Fatty Liver Disease (NAFLD) in High Fat Diet Fed ApoE((-/-)) Mice by Activating Autophagy and Reducing ER Stress and Apoptosis [J]. Int J Mol Sci, 2021, 22(2). Tables Tables 1 to 6 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx Table2.docx Table3.docx Table4.docx Table5.docx Table6.docx Supplementarytable1.xlsx Supplementary Table 1: Targets of Yinchen and Huangbai Supplementarytable2.xlsx Supplementary Table 2: Targets of NAFLD Supplementarytable3.xlsx Supplementary Table 3: Detailed information on the protein-protein interaction (PPI) network Supplementarytable4.xlsx Supplementary Table 4: Detailed information on the Drug- compound-disease-target（D-C-D-T）network Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-7535069\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":530839601,\"identity\":\"2d0b98ed-7d4d-499a-91b0-65a14468eb47\",\"order_by\":0,\"name\":\"Li Zhu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The First Affiliated Hospital of Kunming Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Li\",\"middleName\":\"\",\"lastName\":\"Zhu\",\"suffix\":\"\"},{\"id\":530839602,\"identity\":\"bd606414-9494-43f7-8ae7-3fe61acbac1f\",\"order_by\":1,\"name\":\"Yucheng 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Guo\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Institute of Ethnic Medicine of Xishuangbanna Dai Autonomous Prefecture (Dai Medical Hospital)\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yingcheng\",\"middleName\":\"\",\"lastName\":\"Guo\",\"suffix\":\"\"},{\"id\":530839608,\"identity\":\"5fe44d71-ce5c-4e4d-b0ab-c3c3e8e90cec\",\"order_by\":7,\"name\":\"Juwei zhang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Kunming Medical University Haiyuan College\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Juwei\",\"middleName\":\"\",\"lastName\":\"zhang\",\"suffix\":\"\"},{\"id\":530839609,\"identity\":\"421314ec-51ca-4003-817b-06cbd632da21\",\"order_by\":8,\"name\":\"Tingdong Yu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The Third Affiliated Hospital of Kunming Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Tingdong\",\"middleName\":\"\",\"lastName\":\"Yu\",\"suffix\":\"\"},{\"id\":530839610,\"identity\":\"f55bdb8d-6cd6-4128-8649-c3b2bf3f5dcd\",\"order_by\":9,\"name\":\"Chenhong Zheng\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Chinese People’s Liberation Army General Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Chenhong\",\"middleName\":\"\",\"lastName\":\"Zheng\",\"suffix\":\"\"},{\"id\":530839611,\"identity\":\"122714fa-b3de-45e5-b2f0-24d0b23d42b0\",\"order_by\":10,\"name\":\"Yushan Xu\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYLACxgYGBn4eBJtILZI9JGsxOEOsFoMbOWaSP3fY5RmfOZ26mYfBRnbDAeZnD/BpkZyRYybNeya52Oxs77bbPAxpxhsOsJkb4NPCLwHUwtjGnLjtPC9Iy+HEDQd42CTwaWGTADmsrT5xcz9Yy3/CWkC2SPC2AQ3nBTvsAGEtkj3Piq15244nzjhzdtvNOQbJxjMPs5nh1WJwPHnjzZ9t1Yn9PbnbbrypsJPtO978DK8WBgYO5OABsZnxqwcC9gcElYyCUTAKRsEIBwDNIEo59059HwAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"The First Affiliated Hospital of Kunming Medical University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Yushan\",\"middleName\":\"\",\"lastName\":\"Xu\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-09-04 10:23:33\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-7535069/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-7535069/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":93790037,\"identity\":\"0a54210c-7782-4772-a7d6-bcad145d3a73\",\"added_by\":\"auto\",\"created_at\":\"2025-10-17 14:50:20\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1945717,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFlow chart of our research about the mechanism of \\u003cem\\u003eYinchen\\u003c/em\\u003e combined with \\u003cem\\u003eHuangbai\\u003c/em\\u003e against NAFLD. The active components of \\u003cem\\u003eYinchen\\u003c/em\\u003e and \\u003cem\\u003eHuangbai\\u003c/em\\u003e were screened from TCMSP, and the corresponding targets of the active components were collected. Filtering NAFLD related targets from GeneCards database. The targets corresponding to the active compounds and the targets shared by the disease targets are regarded as the key targets. Selecting the key targets for GO and KEGG enrichment analysis. Selecting the key targets for PPI network analysis. Selecting the key targets to construct Drug-Component-Disease-Target(D-C-D-T) network. Selecting the most effective active compounds and the most core targets for molecular docking.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7535069/v1/16f4433884ba63bc949e61d8.png\"},{\"id\":93790044,\"identity\":\"b79fa1cd-84c0-423f-a8e2-0c014b2ebaa5\",\"added_by\":\"auto\",\"created_at\":\"2025-10-17 14:50:20\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":6164070,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eUsing network pharmacology and bioinformatics to identify targets for the combined use of Yinchen and Huangbai against NAFLD. Venn diagram of the targets of Drugs and the targets of NAFLD(A). The PPI network of 148 key target (intersection gene) from the Cytoscape software. Nodes represent key target (intersection gene), and the colors from orange to red represent the degree of binding between the key target (intersection gene). Edge represents protein-protein association(B). The PPI network of 148 key targets from the String database. Nodes represent key targets. Edge represents protein-protein association (C, D). Top 10 GO enrichment analysis terms of intersection genes (E). Enrichment analysis results of KEGG signal pathway of intersection genes. Top 20 KEGG signaling pathway of intersection genes(F). Presentation of network of enriched terms in: colored by the cluster ID, nodes represent enriched items(G).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7535069/v1/d6bfcfdd20edcfe009b3a876.png\"},{\"id\":93790038,\"identity\":\"1a154f54-634c-47ee-9bc4-ebcf63dfb49b\",\"added_by\":\"auto\",\"created_at\":\"2025-10-17 14:50:20\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1378790,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDrug-compound-disease-target(D-C-D-T) network. Round rectangle represents 148 intersection genes of drugs and diseases. Hexagon represents drugs. Circle represents active ingredients of Yinchen. Diamond represents active ingredients of Huangbai. Triangle represents active ingredients shared by \\u003cem\\u003eYinchen \\u003c/em\\u003eand \\u003cem\\u003eHuangbai\\u003c/em\\u003e.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7535069/v1/894b7347939ce99c81a5a4fd.png\"},{\"id\":93791364,\"identity\":\"3f4f5203-6f35-468b-b326-409eb48cf7e8\",\"added_by\":\"auto\",\"created_at\":\"2025-10-17 15:06:20\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":9012941,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMolecular docking results of living components (Genkwanin and Rutaecarpine) and target. (A)Genkwanin-TNF (B)Genkwanin-AKT1 (C)Genkwanin-PPARG (D)Genkwanin-STAT3 (E)Genkwanin- HSP90AA1 (F) Rutaecarpine-TNF (G)Rutaecarpine-AKT1 (H)Rutaecarpine-PPAGR (I) Rutaecarpine-STAT3 (J) Rutaecarpine-HSP90AA1. Cyan represents the structure of the target proteins (macromolecule, receptor), yellow represents the structure of the active compounds (small molecule, ligand), pink represents the amino acid residues that docks with the hydrogen bonds, and green represents the rod structures.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7535069/v1/bde5e735b33b9d0a96dfb1cc.png\"},{\"id\":93790042,\"identity\":\"57eefedd-b0b6-4035-9345-2d31506b749d\",\"added_by\":\"auto\",\"created_at\":\"2025-10-17 14:50:20\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":541206,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eEffects of the active components of \\u003cem\\u003eYinchen\\u003c/em\\u003e and \\u003cem\\u003eHuangbai\\u003c/em\\u003e on the targets of non-alcoholic fatty liver signaling pathway. The red rectangle represents the key targets in this signaling pathway (\\u003ca href=\\\"http://www.kegg.jp/feedback/copyright.html\\\"\\u003ewww.kegg.jp/feedback/copyright.html\\u003c/a\\u003e).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7535069/v1/3f43f5cc27e4813a0a1fe531.png\"},{\"id\":93790972,\"identity\":\"e2993b42-063b-4f77-9a8e-119c680c934a\",\"added_by\":\"auto\",\"created_at\":\"2025-10-17 14:58:20\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":2438384,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMolecular dynamics simulation of PPARG, HSP90AA, TNF empty proteins, and Genkwanin-PPARG, Rutaecarpine-PPARG, Genkwanin-HSP90AA, Rutaecarpine-TNF complexes. (A) RMSD plot during 100 ns MD simulation; (B) RMSF plot; (C) Rg change plot; (D) SASA plot.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7535069/v1/019fd061bb49729c8f400363.png\"},{\"id\":93790971,\"identity\":\"0e3a78ed-c93c-4ce8-958c-29c6280ca779\",\"added_by\":\"auto\",\"created_at\":\"2025-10-17 14:58:20\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":527039,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e(A) Fluctuation curve of hydrogen bond numbers. (B), (C), (D), (E) Free energy distribution maps, where the vertical axis represents energy values, color scale intensity represents energy levels, and the zero point indicates the system's lowest energy state, showing typical conformations of low-energy regions.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7535069/v1/d0faef6e0adbc9d2dd40ec87.png\"},{\"id\":103440449,\"identity\":\"33f4ef2b-662e-443c-9240-8c7e0153f5f6\",\"added_by\":\"auto\",\"created_at\":\"2026-02-25 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14:58:20\",\"extension\":\"docx\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":17121,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Table4.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7535069/v1/a9147fa2460072ae40469331.docx\"},{\"id\":93790046,\"identity\":\"f2ae3f75-cc74-4365-9cc7-1af706ce8335\",\"added_by\":\"auto\",\"created_at\":\"2025-10-17 14:50:20\",\"extension\":\"docx\",\"order_by\":5,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":16366,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Table5.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7535069/v1/3e66d6f0b89df2757f488573.docx\"},{\"id\":93790052,\"identity\":\"44495ffc-7341-4c56-a7f5-ee2ee00985e9\",\"added_by\":\"auto\",\"created_at\":\"2025-10-17 14:50:20\",\"extension\":\"docx\",\"order_by\":6,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":1318651,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Table6.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7535069/v1/e93c72506753e379a584c7a1.docx\"},{\"id\":93790049,\"identity\":\"e24c9aef-e450-49aa-9f0b-2581dd046e96\",\"added_by\":\"auto\",\"created_at\":\"2025-10-17 14:50:20\",\"extension\":\"xlsx\",\"order_by\":7,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":25425,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSupplementary Table 1: Targets of Yinchen and Huangbai\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Supplementarytable1.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7535069/v1/574de475b95d87538e5aa7a5.xlsx\"},{\"id\":93790054,\"identity\":\"0a047048-dfe0-455b-bf59-dbb64c5740e6\",\"added_by\":\"auto\",\"created_at\":\"2025-10-17 14:50:20\",\"extension\":\"xlsx\",\"order_by\":8,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":87938,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSupplementary Table 2: Targets of NAFLD\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Supplementarytable2.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7535069/v1/ac7ef0d0b51b935e1f3aa03a.xlsx\"},{\"id\":93790053,\"identity\":\"cf312831-bd76-42ed-97b2-7afc97575cc1\",\"added_by\":\"auto\",\"created_at\":\"2025-10-17 14:50:20\",\"extension\":\"xlsx\",\"order_by\":9,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":31402,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSupplementary Table 3: Detailed information on the protein-protein interaction (PPI) network\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Supplementarytable3.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7535069/v1/df768ebc59d87968306c6e25.xlsx\"},{\"id\":93790975,\"identity\":\"4eca1858-38c8-461b-a835-694309c19b73\",\"added_by\":\"auto\",\"created_at\":\"2025-10-17 14:58:20\",\"extension\":\"xlsx\",\"order_by\":10,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":28744,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSupplementary Table 4: Detailed information on the Drug- compound-disease-target（D-C-D-T）network\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Supplementarytable4.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7535069/v1/8faf430d854c33252318533c.xlsx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Mechanisms of Yinchen combined with Huangbai against Non-alcoholic fatty liver disease based on Network pharmacology, Molecular docking and Molecular Dynamics Simulations\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eNon-alcoholic fatty liver disease (NAFLD), considered a metabolic disorder, is marked by an undue buildup of fat in the liver. The occurrence and development of NAFLD encompass various of complex pathological stages, including simple steatosis (NAFL), nonalcoholic steatohepatitis (NASH), liver fibrosis, cirrhosis, hepatocellular carcinoma, and liver failure\\u003csup\\u003e[1]\\u003c/sup\\u003e. Key factors contributing to the progression of NAFLD\\u003csup\\u003e[2]\\u003c/sup\\u003e include type 2 diabetes, insulin resistance, obesity, hypertension, hyperlipidemia, pituitary hypofunction, environmental factors, genetic factors, etc\\u003csup\\u003e[3]\\u003c/sup\\u003e . In recent years, the incidence of NAFLD is increasing\\u003csup\\u003e[4]\\u003c/sup\\u003e, a systematic review and meta-analysis has revealed that\\u0026nbsp;between 1990 and 2021, the global incident cases of NAFLD increased by 71%, from 17.05 million (95% UI: 14.70.58\\u0026ndash;19.77) to 29.08 million (95% UI: 24.99\\u0026ndash;33.49)\\u003csup\\u003e[5]\\u003c/sup\\u003e. \\u0026nbsp;Despite this, the medical community faces a shortage of drugs backed by substantial clinical evidence for effectively managing NAFLD\\u003csup\\u003e[6]\\u003c/sup\\u003e. As such, identifying and developing drugs that can significantly halt or reverse the progression of NAFLD is of critical importance.\\u003c/p\\u003e\\n\\u003cp\\u003eThroughout its thousands of years of history in China, Traditional Chinese medicine (TCM) has played an an indispensable role in disease prevention and treatment, contributing significantly to the advancement of human health and medical practices. Yinchen has proven effective in treating various hepatobiliary conditions, including cholestasis\\u003csup\\u003e[7]\\u003c/sup\\u003e, hepatic lipid accumulation\\u003csup\\u003e[8]\\u003c/sup\\u003e, Liver fibrosis\\u003csup\\u003e[9]\\u003c/sup\\u003e, liver injury and hepatocyte apoptosis\\u003csup\\u003e[10]\\u003c/sup\\u003e. On the other hand, Huangbai has been known to inhibit the expression of inflammatory factors, curtail oxidative damage, and reduce hepatocyte apoptosis\\u003csup\\u003e[11]\\u003c/sup\\u003e. Previous researches indicates that\\u003cem\\u003e\\u0026nbsp;Huangbai\\u003c/em\\u003e can ameliorate hepatic steatosis and reduce lipid synthesis\\u003csup\\u003e[12]\\u003c/sup\\u003e. However, the current knowledge regarding the combined effects of Yinchen and Huangbai on the progression of Non-alcoholic fatty liver disease (NAFLD) is limited. Therefore, this study is dedicated to uncovering the underlying mechanisms through which Yinchen combined and Huangbai collectively counteract NAFLD.\\u003c/p\\u003e\\n\\u003cp\\u003eNetwork pharmacology effectively facilitates the exploration and visualization of drug-disease interaction networks by leveraging a variety of databases and analytical techniques\\u003csup\\u003e[13]\\u003c/sup\\u003e. It actualizes the fusion of computer science and medicine, predicting drug action mechanisms through the construction of intricate \\u0026ldquo;multi-gene, multi-target and multi-pathway\\u0026rdquo; interaction networks\\u003csup\\u003e[14]\\u003c/sup\\u003e.\\u0026nbsp;Molecular docking serves as a potent computational method capable of predicting the interaction mechanisms between drugs and disease targets, by estimating the binding free energy between molecules (active ingredients of drugs) and targets (core proteins/therapeutic targets) at the molecular level\\u003csup\\u003e[15]\\u003c/sup\\u003e.\\u0026nbsp;Molecular dynamics simulations, based on molecular mechanics principles, model atomic movements over time within a system, providing insights into molecular behavior and dynamics parameters. These simulations help estimate system properties over specific timeframes and are widely used in early-stage drug development\\u003csup\\u003e[15]\\u003c/sup\\u003e. As experimental costs in drug research rise, interdisciplinary approaches combining molecular docking, molecular dynamics simulations, and network pharmacology are gaining attention. This method integrates traditional pharmacology with modern bioinformatics, enhancing the scientific rigor of network pharmacology results. It aids in deciphering complex biomolecular networks, uncovering new drug targets, and predicting mechanisms of action, thus playing a pivotal role in advancing modern drug discovery and precision medicine\\u003csup\\u003e[16]\\u003c/sup\\u003e.\\u0026nbsp;Therefore, in our research, we harnessed the combined strengths of network pharmacology、molecular docking and \\u0026nbsp;molecular dynamics simulations methods to investigate the potential mechanisms by which \\u003cem\\u003eYinchen\\u003c/em\\u003e and \\u003cem\\u003eHuangbai\\u0026nbsp;\\u003c/em\\u003esynergistically prevent and treat NAFLD. The comprehensive methodology and conceptual framework of our research are depicted in Figure 1. We hypothesize that the active compounds in Yinchen and Huangbai, particularly Genkwanin and Rutaecarpine, ameliorate NAFLD by modulating key targets and pathways involved in lipid metabolism, inflammation, and insulin resistance.\\u003c/p\\u003e\"},{\"header\":\"2. Materials and Methods\",\"content\":\"\\u003cp\\u003eThis study solely involved the use of network pharmacology and bioinformatics methods to explore the mechanisms of Yinchen combined with Huangbai in combating NAFLD. This research did not include any animal experiments or clinical trials, therefore ethical approval was not required for this study.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003e2.1 Screening the active components of Yinchen and Huangbai and further predict their targets.\\u0026nbsp;\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe active ingredients of Capillary Wormwood Herb and golden cypress were identified through Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, http://tcmspw.com)\\u003csup\\u003e[17]\\u003c/sup\\u003e .The primary active ingredients were selected based a drug-likeness (DL) value of greater than or equal to 0.18and oral bioavailability (OB) of greater than or equal to 30% \\u003csup\\u003e[18]\\u003c/sup\\u003e. PubChem Database (https://pubchem.ncbi.nlm.nih.gov/)\\u003csup\\u003e[19]\\u003c/sup\\u003e serves as a repository for chemical modules, offering access to a wide array of small chemical molecules\\u0026apos; 2D and 3D structures. For our study, we utilized the PubChem database to acquire the 3D chemical structures of the principal bioactive constituents found in Capillary Wormwood Herb and golden cypress. These structures were subsequently uploaded to the Swiss Target Prediction database (http://www.swisstargetprediction.ch/) to forecast the potential targets of these active components\\u003csup\\u003e[20]\\u003c/sup\\u003e. Targets with a Probability score greater than 0 were considered significant and retained as the active targets for further analysis.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003e2.2 Screening disease targets related to NAFLD.\\u003c/em\\u003e\\u003c/strong\\u003e\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBy accessing the GeneCards (https://www.genecards.org)\\u003csup\\u003e[21]\\u003c/sup\\u003e, Therapeutic Target Database (https://db.idrblab.net/ttd/), and OMIM (https://www.omim.org/) databases\\u003csup\\u003e[22]\\u003c/sup\\u003e and using \\u0026quot;NAFLD\\u0026quot; as the search term, we conducted a comprehensive search for genes and proteins linked to NAFLD. The relevant biological targets associated with NAFLD were systematically identified and compiled as disease targets for further investigation.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003e2.3 Constructing protein-protein interaction network diagram of intersecting genes.\\u003c/em\\u003e\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe active components of two medicinal herbs, Capillary Wormwood Herb and golden cypress, along with the disease targets of NAFLD, were input into Venny2.1 (https://bioinfogp.cnb.csic.es/tools/venny/)\\u003csup\\u003e[23]\\u003c/sup\\u003eto create a Venn diagram. The overlapping gene identified as the key target of the medicinal herbs in improving NAFLD.\\u0026nbsp;To obtain the protein-protein interaction network (PPI) of the intersecting genes, the key targets associated with the medicinal herbs, as determined by VENNY, were entered into the STRING database (http://string-db.org)\\u003csup\\u003e[24]\\u003c/sup\\u003e specifying the species as humans. Ultimately, the results were uploaded to the Cytoscape3.9.1 software for visualization and network analysis.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003e2.4 GO (Gene Ontology) enrichment analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis.\\u0026nbsp;\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe principal targets of two drugs identified in section 2.3 for the amelioration of NAFLD were uploaded to the Metascape database (https://metascape.org)\\u003csup\\u003e[25]\\u003c/sup\\u003e. Subsequently, GO enrichment analysis and KEGG pathway enrichment analysis were conducted with the criteria of minimum Overlap \\u0026gt; 5 and P-Value \\u0026lt; 0.01\\u003csup\\u003e[26]\\u003c/sup\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003e2.5 Drug- compound-disease-target (D-C-D-T) network.\\u003c/em\\u003e\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe active ingredients and crucial targets of the two drugs, identified in section \\u0026quot;2.3\\u0026quot; for the amelioration of NAFLD, were uploaded into Cytoscape3.9.1 software.\\u0026nbsp;The network of drugs-compounds-diseases-targets was constructed and visualized by using Cytoscape3.9.1 software\\u003csup\\u003e[27]\\u003c/sup\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003e2.6 Molecular docking.\\u003c/em\\u003e\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe target protein structures (macromolecule, receptor) were retrieved from the RCSB PDB database\\u003csup\\u003e[28]\\u003c/sup\\u003e. The 3D structures of the drugs\\u0026apos; active compounds (small molecules, ligands) were acquired from PubChem in SDF file format. OpenBabel2.4.1 software \\u003csup\\u003e[29]\\u003c/sup\\u003e was utilized to convert the SDF format of 3D structure to mol2 format. Pymol software\\u003csup\\u003e[30]\\u003c/sup\\u003e was used to remove water and solvent molecules from the target protein structure. The target proteins were then imported into AutodockTools software for the addition of all hydrogen atoms and conversion to pdbqt format. Similarly, the active compounds (small molecules, ligands) of the drugs were imported into AutodockTools, hydrogenated, set as ligands, and converted to pdbqt format. AutodockTools was employed to define the docking boxes for the target proteins and active compounds, to carry out the molecular docking and to calculate the binding affinity. Finally, Pymol software was employed to simulate and visually display the results of molecular docking.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.7 \\u003cem\\u003eMolecular Dynamics Simulations\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003e2.7.1 Setup of Simulation Environment and Parameters\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe optimal conformation obtained from molecular docking analysis was selected as the initial structure. Molecular dynamics simulations were performed using GROMACS 2020.6 software. The AMBERGS force field was chosen for the simulations, and the SPC water model was used to solvate the protein-ligand complex, creating a water box. Sodium or chloride ions were added to neutralize the total charge of the solution\\u003csup\\u003e[31]\\u003c/sup\\u003e. Prior to the molecular dynamics simulation, the system was preprocessed through energy minimization using the steepest descent method, with a maximum of 50,000 steps and a convergence criterion of 1000 kJ/mol/nm. Subsequently, the system entered the equilibration phase, during which the positions of the protein and ligand were strictly restrained. First, the system was equilibrated for 1 ns under the canonical ensemble (NVT) conditions, with the temperature gradually increased from 0 K to 310 K. Next, the system was equilibrated for 2 ns under the isothermal-isobaric ensemble (NPT) conditions, maintaining a temperature of 310 K and a pressure of 1.0 bar. After completing the equilibration phase, a 100 ns molecular dynamics simulation was performed with a time step of 2 fs, and simulation data were recorded every 1 ps to ensure the accuracy and reliability of the results.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003e2.7.2 Analysis of Simulation Results\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe trajectory files generated from the molecular dynamics simulations were analyzed in depth to evaluate the stability and dynamic behavior of the protein-ligand complex during the simulation. First, the root mean square deviation (RMSD) was calculated to assess the overall structural stability of the complex. The changes in RMSD values reflect the conformational fluctuations of the complex during the simulation and serve as an important indicator of system equilibrium. Subsequently, the root mean square fluctuation (RMSF) was calculated to analyze the flexibility of individual residues. RMSF values reveal the dynamic changes of different parts of the protein during the simulation, which is crucial for understanding functional regions and ligand-binding sites. Additionally, the study focused on the hydrogen bond interactions between the protein and ligand during the dynamics simulation, as well as the calculation of the binding free energy between the protein and ligand.\\u003c/p\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003e3.1 Screening of active ingredients and target prediction of Yinchen and Huangbai.\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e\\u003c/strong\\u003eIn the TCMSP database, the screening criteria were set as follows: Drug-Likeness (DL)\\u0026ge;0.18 and Oral Bioavailability (OB)\\u0026ge;30%. Subsequently,12 active components of \\u003cem\\u003eYinchen\\u003c/em\\u003e (Table 1) and 26 active components of \\u003cem\\u003eHuangbai\\u0026nbsp;\\u003c/em\\u003e(Table 2) were selected. The Swiss Target Prediction database was employed to predict the targest of active ingredients, resulting in a total of 662 targets with Probability values greater than 0 (Supplementary Table 1).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003e3.2 Screening of NAFLD disease targets.\\u0026nbsp;\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eUsing \\u0026quot;NAFLD\\u0026quot; as the keyword, targets related to NAFLD were searched in the human GeneCards, Therapeutic Target Database, and OMIM databases (Supplementary Table 2).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003e3.3 Constructing protein-protein interaction (PPI) network of intersecting genes.\\u0026nbsp;\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe active components of two drugs were matched with the drug action targets, and a Venn diagram was created (Figure 2A) with the disease targets for NAFLD retrieved from the three databases. The intersecting genes are the key targets for the treatment of NAFLD with Yinchen and Huangbai. Key targets were imported into the STRING database for protein-protein interaction network (PPI) diagram (Figure 2C and Figure 2D). The software Cytoscape3.9.1 was employed to calculate the degree value and related parameters (Supplementary Table 3), and finally, visual analysis was carried out (Figure 2B). The results show that the PPI network diagram contains 148 nodes and 1625 edges. The top 10 core targets with the highest connectivity are located at the core of PPI network, which are TNF (degree 97), AKT1 (degree 94), STAT3 (degree 70), PPARG (degree 70), HSP90AA1 (degree 69), EGFR (degree 67), CASP3 (degree 67) and CTNNB1(degree 64)、HIF1A(degree 64)、ESR1(degree 62). We have listed the specific information of the top 10 key targets (table 3).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003e3.4 GO enrichment analysis.\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe key target (intersection gene) obtained from section \\u0026quot;2.3\\u0026quot; was imported into the Metascape database for GO enrichment analysis and KEGG pathway enrichment analysis. The results were then imported into bioinformatics tools for visual analysis. GO enrichment analysis encompasses three categories: biological process (BP), cell composition (CC) and molecular function (MF), with the top 10 items from each category selected for visualization (Figure 2E). GO BP results indicated that the intersection genes were primarily involved in cellular responses to organic nitrogen compounds, peptides, hormones and insulin. In the CC analysis, these genes were significantly enriched in membrane raft, membrane microdomain, caveola, receptor complex, ficolin-1-rich granules. MF analysis revealed that the molecular functions of the intersection genes were mainly related to protein serine/threonine/tyrosine kinase activity, protein kinase activity, phosphotransferase activity with an alcohol group as acceptor, and kinase activity.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003e3.5 KEGG pathway enrichment analysis.\\u003c/em\\u003e\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eKEGG enrichment analysis highlighted the top 10 signaling pathways for visualization. As depicted in Figure 2F, the signaling pathways that were significantly enriched in intersection genes mainly include: Lipid and atherosclerosis, Alcoholic liver disease, Fluid shear stress and atherosclerosis, Shigellosis, AGE-RAGE signaling pathway in Diabetic Complications, Kaposi Sarcoma-Associated Herpesvirus Infection, Insulin resistance, Salmonella infection, Non-alcoholic fatty liver disease, Human cytomegalovirus infection. Moreover, Figure 2G clearly illustrates the relationships between these enrichment items, with nodes sharing the same cluster ID typically positioned in close proximity to one another.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003e3.6 Drug- compound-disease-target\\u003c/em\\u003e\\u003c/strong\\u003e（\\u003cstrong\\u003e\\u003cem\\u003eD-C-D-T\\u003c/em\\u003e\\u003c/strong\\u003e）\\u003cstrong\\u003e\\u003cem\\u003enetwork\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe utilized Cytoscape3.9.1 software to construct the network of D-C-D-T. The top 10 active ingredients are listed according to their Degree value, as follows:\\u003cu\\u003eGenkwanin\\u003c/u\\u003e, Rutaecarpine, Beta-sitosterol, Demethoxycapillarisin, Artepillin A, Palmatine, Capillarisin, Skimmianin, Magnograndiolide, Palmidin A. Additionally, we present the details of the top 10 active compounds in Table 4. It can be observed that among the 12 active components of \\u003cem\\u003eYinchen\\u003c/em\\u003e, Genkwanin exhibits the highest degree of connectivity with other targets, while among the 26 active components of \\u003cem\\u003eHuangbai\\u003c/em\\u003e, Rutaecarpine shows the highest degree of connectivity. Furthermore, quercetin (MOL000098) is the active compound shared by both \\u003cem\\u003eYinchen\\u003c/em\\u003e and \\u003cem\\u003eHuangbai\\u003c/em\\u003e. (Figure 3 and Supplementary Table 4)\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003e3.7 Molecular docking\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBased on the results obtained from network pharmacology analysis, we identified the top five key targets in the PPI network, namely: TNF, AKT1, PPARG, STAT3, and HSP90AA1. From the D-C-D-T network, Genkwanin and Rutaecarpine were selected as active components of the drugs. We used these five key targets and the two active components to conduct molecular docking, calculating their binding affinities. Previous studies suggest that the lower the affinity, the more effective the molecular docking \\u003csup\\u003e[32]\\u003c/sup\\u003e. Moreover, an affinity less than -5 kcal/mol indicates a higher likelihood of significant interaction between the molecules\\u003csup\\u003e[33]\\u003c/sup\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003eMolecular docking results clearly demonstrated that the affinities between the five key targets and the two active compounds are all less than 0, indicating that the key targets and active compounds can bind effectively. Among all docking simulations, the interaction between Rutaecarpine and PPARG resulted in the lowest affinity (-9.44 Kcal/mol), while the interaction between Genkwanin and STAT3 exhibited the highest affinity (-5.03 Kcal/mol) (Table 5).\\u003c/p\\u003e\\n\\u003cp\\u003eFurthermore, we selected the molecular docking simulation between Genkwanin and TNF, AKT1, PPARG, STAT3, HSP90AA1for visualization, and selected the molecular docking simulation between Rutaecarpine and TNF, AKT1, PPARG, STAT3, HSP90AA1for visualization (Table 6). Genkwanin forms a hydrogen bond with LYS-90, GLN-47, ALA-134, LEU-26 and GLY-24 in TNF, two hydrogen bonds with TRP-28 and three hydrogen bonds with ASN-46 (Figure 4A). Genkwanin and LEU-52, GLN-47 and GLN-43 formed one hydrogen bond respectively and two hydrogen bonds with ALA-50 in AKT1 (Figure 4B). Genkwanin and GLU-291 and LYS-265 in PPARG respectively formed a hydrogen bond (Figure 4C). Genkwanin formed two hydrogen bonds with LYS-370, ARG-379 and ASN-491 respectively, and two hydrogen bonds with ASP-369 and LEU-438 in STAT3(Figure 4D). Genkwanin and GLN-23 in HSP90AA1 form a hydrogen bond (Figure 4E). There is no hydrogen bond connection between Rutaecarpine and TNF (Figure 4F). Rutaecarpine and CYS-60 and GLN-104 in AKT1 form a hydrogen bond (Figure 4G). Rutaecarpine forms one hydrogen bond with GLU-343 in PPARG and two hydrogen bonds with GLU-343 (Figure 4H). Rutaecarpine formed one hydrogen bond with GLN-361 and two hydrogen bonds with GLU-444 in STAT3(Figure 4I). Rutaecarpine forms a hydrogen bond with GLY-135 in HSP90AA1(Figure 4J).\\u003c/p\\u003e\\n\\u003cp\\u003eThe results above indicate that Genkwanin can effectively bind with TNF, AKT1 and PPARG, while Rutaecarpine can effectively bind with AKT1 and PPARG. Consequently, we have reason to speculate that the two active compounds, Genkwanin and Rutaecarpine, play a key role in the amelioration of NAFLD (Figure 5).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003e3.8 Molecular dynamics simulation analysis\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eDue to the inability of semi-flexible docking used in molecular docking to account for the flexibility of protein structures, temperature, pressure, solvent effects, etc., 100-ns molecular dynamics simulations of Genkwanin, Rutaecarpine, and the PPARG, HSP90AA, and TNF proteins were performed in this paper. The co-crystallized ligands of the respective proteins were used as positive controls to further investigate the stability of the complexes in a comparative manner.\\u003c/p\\u003e\\n\\u003cp\\u003eThe root mean square deviation (RMSD) is a critical indicator for evaluating the kinetic stability of the ligand-protein system in MD simulations. RMSD fluctuation analysis revealed that the RMSD values of the Genkwanin-PPARG, Rutaecarpine-PPARG, Genkwanin-HSP90AA, and Rutaecarpine-TNF complexes reached equilibrium after a period of MD simulation, with only minor fluctuations (Figure\\u0026nbsp;6A). This indicates that the constructed simulation systems exhibited good overall stability. Notably, the RMSD values of the Genkwanin-PPARG and Rutaecarpine-PPARG complexes remained within 0.2 nm and were lower than those of the PPARG apo-protein. The Genkwanin-HSP90AA complex stabilized around 80 nm, with RMSD values lower than those of the HSP90AA apo-protein. These results suggest that Genkwanin and Rutaecarpine binding to PPARG, as well as Genkwanin binding to HSP90AA, resulted in higher stability and fewer conformational changes.\\u003c/p\\u003e\\n\\u003cp\\u003eRecent studies have indicated that fluctuations in RMSF values between 0.1 and 0.3 nm suggest relative stability in the docked complex system. In the four complex systems, the Genkwanin and Rutaecarpine complexes with PPARG reduced residue flexibility, while the Genkwanin-HSP90AA complex system altered the flexibility of residues in different regions (Figure 6B).\\u003c/p\\u003e\\n\\u003cp\\u003eThe radius of gyration (Rg), which measures the compactness of biomolecular structures, is a parameter for evaluating the behavior and stability of biological systems during MD simulations. A smaller Rg value indicates a more compact molecular structure with atoms closer to the center of mass, while a larger Rg value suggests increased structural irregularity. By calculating the Rg values, we observed changes in protein structural compactness during the simulation. The Rg values of the four complexes fluctuated between 1.90 and 1.96 nm, 1.64 and 1.74 nm, and 1.60 and 1.70 nm during the 100 ns simulation and reached equilibrium after a certain period (Figure 6C). This indicates that these two compounds can interact with amino acids in the active pockets of the respective proteins.\\u003c/p\\u003e\\n\\u003cp\\u003eThe solvent-accessible surface area (SASA) is a parameter describing the molecular surface area involved in protein-solvent interactions. A higher SASA value indicates a larger protein-solvent contact area, while a lower SASA value suggests a smaller contact area. The SASA values of the three systems remained relatively stable during the simulation (Figure 6D). Compared to the TNF apo-protein, the SASA value of the Rutaecarpine-TNF system was significantly reduced, indicating that Rutaecarpine binding decreased the protein surface area exposed to the solvent, potentially affecting protein hydrophobicity.\\u003c/p\\u003e\\n\\u003cp\\u003eTo investigate the nature of hydrogen bonds at the complex binding site, the number of hydrogen bonds was calculated in this study. Hydrogen bonds are the primary interaction bonds that stabilize ligand-protein complexes. Precise counting during MD simulations revealed that the Genkwanin-PPARG complex formed 18,631 hydrogen bond interactions throughout the simulation, while the Rutaecarpine-PPARG, Genkwanin-HSP90AA, and Rutaecarpine-TNF complexes formed 11,089, 12,447, and 2,852 hydrogen bond interactions, respectively (Figure 7A). Throughout the MD simulation, the Genkwanin-PPARG complex consistently formed more than two hydrogen bonds, demonstrating extremely strong stability between Genkwanin and PPARG.\\u003c/p\\u003e\\n\\u003cp\\u003eFree energy landscapes (FELs) were calculated using the Gromacs built-in script g_sham and the xpm2txt.py script to obtain the relative Gibbs free energy based on RMSD and Rg values. Three-dimensional plots were generated by assigning RMSD, Rg, and Gibbs relative free energy values to the X, Y, and Z axes, respectively. FELs describe the conformations with the lowest energy throughout the simulation of complex structural dynamics. Weak or unstable protein-ligand interactions result in multiple, rough-surfaced minimum energy clusters in the FEL, whereas strong and stable interactions form single, smooth energy clusters.\\u003c/p\\u003e\\n\\u003cp\\u003eIn the figures, blue spots indicate stable structures with minimum energy, while red/yellow spots represent unstable structures. The minima and representative structures of the three systems were also determined (Figure 7B, C, D, E). The FELs of the Genkwanin-HSP90AA and Rutaecarpine-TNF protein complexes exhibited multiple minimum energy clusters, indicating slightly lower complex stability. In contrast, the FELs of the Genkwanin-PPARG and Rutaecarpine-PPARG complexes showed single minimum energy clusters, indicating higher complex stability.\\u003c/p\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003eIn our research, we have found that there are 12 active compounds in \\u003cem\\u003eYinchen\\u003c/em\\u003e from TCMSP database, including Genkwanin, beta sitosterol, Demethoxycapillarisin, Artecillin A and capillarisin. Various studies have shown that Genkwanin has significant anti-inflammatory, antioxidant, antibacterial, anti-tumor and immunomodulatory effects\\u003csup\\u003e[34]\\u003c/sup\\u003e.\\u0026beta;-sitosterol can not only reduce liver injury, improve obesity-related chronic inflammation, and fight against low-density lipoprotein(LDH)\\u003csup\\u003e[35]\\u003c/sup\\u003e, but it can also reduce serum cholesterol and inhibit intestinal cholesterol absorption\\u003csup\\u003e[36]\\u003c/sup\\u003e. In addition, we also found that there are 26 active compounds in \\u003cem\\u003eHuangbai\\u003c/em\\u003e, such as rutaecarpine, palmatine, Skimmianin, Magnograndiolide, Palmidin A. Rutaecarpine can alleviate drug-induced liver injury\\u003csup\\u003e[37]\\u003c/sup\\u003e, and can also inhibit MAPK and NF- \\u0026kappa; B signaling pathway to reduce inflammation\\u003csup\\u003e[38]\\u003c/sup\\u003e. Furthermore, Rutaecarpine can decrease the production of reactive oxygen species (ROS), reduce cytotoxicity\\u003csup\\u003e[39]\\u003c/sup\\u003e. Additionally, a review has reported that palmatine can improve metabolic syndrome and its related complications\\u003csup\\u003e[40]\\u003c/sup\\u003e, palmatine can also significantly reduce apoptosis and ROS level HT-22 cell\\u003csup\\u003e[41]\\u003c/sup\\u003e. Considering the above conclusions, it is evident that these active ingredients may play a crucial role in the effectiveness of Yinchen and Huangbai against NAFLD. Consequently, we have further employed the method of network pharmacology combined with molecular docking to identify the key targets and potential molecular mechanisms of the aforementioned active compounds in the treatment of NAFLD.\\u003c/p\\u003e\\n\\u003cp\\u003eBased on our KEGG enrichment analysis results, it is noticeable that the signaling pathways involved in the combined use of\\u003cem\\u003eYinchen\\u003c/em\\u003e and \\u003cem\\u003eHuangbai\\u003c/em\\u003e against NAFLD primarily include: Lipid and aromatherapy, Non-alcoholic fatty liver disease, TNF signaling pathway. We hypothesize that the Non-alcoholic fatty liver disease pathway (Figure 5) may be the crucial pathway in the action of Yinchen combined with Huangbai against NAFLD. In the PPI network, we obtained 148 nodes and 1625 edges. We speculate that the core targets of \\u003cem\\u003eYinchen\\u003c/em\\u003e combined with \\u003cem\\u003eHuangbai\\u003c/em\\u003e against NAFLD primarily comprise: TNF, AKT1, PPARG, STAT3, HSP90AA1, and CASP3, with particular emphasis on TNF, AKT1, PPARG, CASP3.\\u003c/p\\u003e\\n\\u003cp\\u003eAs a key T-helper type 1 (Th1) cytokine, tumor necrosis factor (TNF) is produced by monocytes/macrophages and T cells\\u003csup\\u003e[42]\\u003c/sup\\u003e. TNF can induce a wide range of intracellular signaling pathways, including apoptosis, cell survival, inflammatory response and immune response\\u003csup\\u003e[43]\\u003c/sup\\u003e. In the non-alcoholic fatty liver disease signaling pathway, obesity can promote the generation of the classic inflammatory factor TNF- \\u0026alpha;\\u003csup\\u003e[44]\\u003c/sup\\u003e. TNF- \\u0026alpha; can also bind to the receptor TNFR1 on the cell membrane, causing the activation of TNF signal pathway and PI3K-AKT signal pathway, which in turn induces insulin resistance in liver cells and eventually leads to fatty acid biosynthesis. It is obvious that the key targets (TNF and AKT1) identified through PPI play an irreplaceable role in the first stage (Simple steatosis: without inflammation and fibrosis) of the non-alcoholic fatty liver disease signaling pathway.\\u003c/p\\u003e\\n\\u003cp\\u003eThe peroxisome proliferator-activated receptor gamma (PPARG), also known as the \\u0026quot;energy balance receptor\\u0026quot;, can regulate the expression of numerous anti-fibrosis miRNAs\\u003csup\\u003e[45]\\u003c/sup\\u003e. This means that PPARG can be utilized as a therapeutic target for anti-liver fibrosis\\u003csup\\u003e[46]\\u003c/sup\\u003e. Some studies have also shown that PPARG is a key regulator of insulin resistance (IR)\\u003csup\\u003e[47]\\u003c/sup\\u003e and NAFLD\\u003csup\\u003e[48]\\u003c/sup\\u003e . In our study, it is clear that during the progression of NAFLD, free fatty acids (FFAs) can induce the activation of PPAR signaling pathway. This causes lipid accumulation and a reduction in lipolysis in the second stage of the NAFLD signal pathway, non-alcoholic steatohepatitis (NASH), which is characterized by hepatic inflammation and fibrosis, and ultimately exacerbates the progression of NAFLD. Obviously, PPARG (PPAR-\\u0026gamma;), a key enzyme that promotes lipid synthesis in the development of NAFLD, aligns with our PPI screening results. In addition, during the second stage of the NAFLD signaling pathway (NASH), FFA can facilitate the entry of the classic inflammatory factor TNF- \\u0026alpha;into cells and induce the release of apoptosis factors CASP8, CASP3 and CASP7\\u003csup\\u003e[49]\\u003c/sup\\u003e, aggravating liver injury.\\u003c/p\\u003e\\n\\u003cp\\u003eBased on the analysis results of network pharmacology mentioned above, we further employed the molecular docking analysis methodfor verification. Genkwanin, the most effective active compound of \\u003cem\\u003eYinchen\\u003c/em\\u003e, and Rutaecarpine, the most effective active compound of \\u003cem\\u003eHuangbai\\u003c/em\\u003e, were selected for molecular docking with five targets TNF, AKT1, PPARG, STAT3, and HSP90AA1, respectively. The affinity values between the five key targets and the two active compounds are all less than -5 kcal/mol, which indicates that the key targets and active ingredients can effectively bind. Among them, Rutaecarpine has the lowest affinity with PPARG (-9.44 Kcal/mol), indicating that Rutaecarpine and PPARG are most closely bound. We further selected the complexes of Genkwanin-PPARG, Rutaecarpine-PPARG, Genkwanin-HSP90AA, and Rutaecarpine-TNF for molecular dynamics simulations to investigate the stability of the complex systems. Based on the above analysis and exploration, we can easily conclude that the active ingredients Genkwanin and Rutaecarpine may improve NAFLD by targeting TNF, PPARG and HSP90AA1.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"5. Conclusion\",\"content\":\"\\u003cp\\u003eBy combining network pharmacology、molecular docking technology and molecular dynamics simulations, we have preliminarily investigated the principal active components and potential molecular mechanisms of \\u003cem\\u003eYinchen\\u003c/em\\u003e and \\u003cem\\u003eHuangbai\\u003c/em\\u003e against NAFLD. Our research suggests that Genkwanin and Rutaecarpine may be the key active components of \\u003cem\\u003eYinchen\\u003c/em\\u003e and \\u003cem\\u003eHuangbai\\u003c/em\\u003e, respectively. These active compounds are likely to exert their beneficial effects on NAFLD primarily through the Lipid and atherosclerosis, Non-alcoholic fatty liver disease and TNF signaling pathway, with a particular focus on the Non-alcoholic fatty liver disease. The potential therapeutic targets of the above active compounds may include the target proteins TNF, PPARG and HSP90AA1. However, our research has certain limitations. Since our approach relies on Since our approach and database screening methods, including network pharmacology and molecular docking, there might be undiscovered information about active compounds and disease targets not yet present in the current database. Furthermore, the active components Genkwanin and Rutaecarpine, which we employed for molecular docking, do not fully represent all the constituents of the two drugs, (\\u003cem\\u003eYinchen\\u003c/em\\u003e and \\u003cem\\u003eHuangbai\\u003c/em\\u003e. Most importantly, our research remains at the theoretical data level and requires validation through practical experiments such as animal experiments, cell experiments and molecular biology experiments.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eData Availability Statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll data generated or analysed during this study are included in this published article and its supplementary information files.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding declaration statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll authors have read and approved the final manuscript. This work was supported by the following grants: Yunnan Province Clinical Research Center for Metabolic diseases（202102AA100056）. Yunnan Clinical Medical Center for Endocrine and Metabolic Diseases (YWLCYXZXXYS20221005). Science and Technology Innovation Team of Diagnosis and Treatment for Glucolipid Metabolic Diseases in Kunming Medical University（CXTD202106）. Kunming Medical University Joint Special Project - Key Project（202201AY070001-041）. Project Funding Support under Yunnan Province\\u0026apos;s Xingdian Talent Support Plan（RLMY20220001）.The National Science Foundation of China (No. 82160125 to Z.C.H). Yunnan Province Metabolism-related Cardiovascular Disease Innovation Team，grant number 202405AS350014. Yunnan Clinical Medical Center for Endocrine and Metabolic Diseases Research Project（2024YNLCYXZX0069）.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCredit of author statement\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eLi Zhu wrote the manuscript. Yucheng Chen, and Qianhan Wang checked the date and revised the manuscript. Li Zhu, Yucheng Chen, and Qianhan Wang contributed equally to this work. Long Yang, Yan Jiang and Wenhua Zhang Completed the data collection from the database. Yingcheng Guo and Juwei zhang revised the data. Yushan Xu, Chenhong Zheng and Tingdong Yu designed and coordinated the study. Yushan Xu, Chenhong Zheng and Tingdong Yu accept full responsibility for the work. The final version of the manuscript was reviewed and approved by all authors.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDeclaration of Competing Interest\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors indicated no conflicts of interest with regard to the content of this article.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eENGIN A. Nonalcoholic Fatty Liver Disease and Staging of Hepatic Fibrosis [J]. 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Int J Mol Sci, 2021, 22(2).\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"},{\"header\":\"Tables\",\"content\":\"\\u003cp\\u003eTables 1 to 6 are available in the Supplementary Files section.\\u003c/p\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Non-alcoholic Fatty Liver Disease Yinchen, Huangbai, Network Pharmacology, Molecular Docking, Molecular Dynamics Simulations\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7535069/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7535069/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"Non-alcoholic fatty liver disease (NAFLD) is a growing health concern with increasing prevalence. Traditional Chinese Medicine (TCM), particularly Yinchen and Huangbai, shows significant promise in NAFLD prevention and treatment. This study aims to explore their mechanisms against NAFLD by identifying active compounds and potential targets. We employed network pharmacology, integrating multiple databases including TCMSP, GeneCards, Therapeutic Target Database, and OMIM. Venn diagrams from VENNY2.1 identified overlapping targets, which were analyzed via PPI networks in STRING and visualized in Cytoscape 3.9.1. Key targets were further analyzed using Metascape for GO and KEGG enrichment. Molecular docking assessed the affinity between key targets and active compounds, followed by MD simulations to evaluate complex stability. Results showed TNF, AKT1, PPARG, STAT3, and HSP90AA1 as top targets, with Genkwanin and Rutaecarpine as key compounds. These compounds demonstrated effective binding to TNF, PPARG, and HSP90AA1 through docking and MD simulations. In conclusion, Genkwanin and Rutaecarpine may alleviate NAFLD by modulating related pathways, with potential therapeutic targets including TNF, HSP90AA1, and PPARG. This study provides valuable insights into the mechanisms of Yinchen and Huangbai in NAFLD treatment, offering directions for future research in managing age-related diseases.\",\"manuscriptTitle\":\"Mechanisms of Yinchen combined with Huangbai against Non-alcoholic fatty liver disease based on Network pharmacology, Molecular docking and Molecular Dynamics Simulations\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-10-17 14:50:16\",\"doi\":\"10.21203/rs.3.rs-7535069/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"d51ddbfb-a402-4730-9260-24a423e94bba\",\"owner\":[],\"postedDate\":\"October 17th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":56434361,\"name\":\"Biological sciences/Computational biology and bioinformatics\"},{\"id\":56434362,\"name\":\"Health sciences/Diseases\"},{\"id\":56434363,\"name\":\"Biological sciences/Drug discovery\"}],\"tags\":[],\"updatedAt\":\"2026-02-25T17:09:35+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-10-17 14:50:16\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7535069\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7535069\",\"identity\":\"rs-7535069\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}