To explore the potential mechanism of Resveratrol against Helicobacter pylori based on network pharmacology, molecular docking, and molecular dynamics simulations Abstract

To explore the potential mechanism of Resveratrol against Helicobacter pylori based on network pharmacology, molecular docking, and molecular dynamics simulations Abstract | 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 Research Article To explore the potential mechanism of Resveratrol against Helicobacter pylori based on network pharmacology, molecular docking, and molecular dynamics simulations Abstract Yingzi Li, Chou Hou, Ailing Zhao, Yipin Yipin Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2995283/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 The global public health sector recognizes Helicobacter pylori (H. pylori) infection as a significant challenge, and its treatment largely relies on triple or quadruple therapy involving antibiotics. However, the emergence of antibiotic resistance compromises the effectiveness of these treatments. Resveratrol targets from well-known databases such as PubChem, TCMSP, TCMIP, and Swiss Target Prediction were integrated with H. pylori infection-related targets retrieved from GeneCards and OMIM databases to address this issue. By leveraging the STRING database, it is possible to identify the underlying target relationships and, thus, the core targets. The DAVID database was also used for Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of potential targets. In addition, AutoDock Vina is used for molecular docking, which facilitates the identification of interactions between core targets and active ingredients. GO analysis revealed involvement in reactive oxygen species metabolism, phosphatase binding, and protein serine/threonine kinase activity. KEGG pathway analysis suggests that Resveratrol may disrupt the invasion and persistence of Helicobacter pylori through vascular endothelial growth factor (VEGF) and tumor necrosis factor (TNF) pathways. Protein-protein interaction analysis identifies five core targets (AKT1, TP53, IL1B, TNF, and PTGS2), further validated through molecular docking and molecular dynamics (MD) simulation. This study explores the potential core targets and mechanisms of action of Resveratrol against Helicobacter pylori infection, offering novel insights for treating this infection. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Helicobacter pylori infection is strongly associated with gastrointestinal diseases, including chronic gastritis, peptic gastric ulcer, and gastric cancer[ 1 ]. With a global infection rate of approximately 44 percent and even higher rates in developing countries, effective treatment for H. pylori infection is crucial to prevent and manage these diseases[ 2 ]. The current standard treatment involves triple or quadruple antibiotic therapy[ 3 ]; however, it has limitations such as single targeting, significant adverse reactions, and variations in individual response, leading to poor compliance[ 4 ]. Additionally, the emergence of antimicrobial resistance further complicates eradication efforts. Therefore, developing new anti-H. Pylori drugs are of utmost importance. Resveratrol, a polyphenolic phytoalexin found in grapes, peanuts, and berries, has many beneficial properties, including anti-inflammatory, antioxidant, anti-cancer, anticoagulant, and vascular protective effects[ 5 , 6 ]. Recent studies[ 7 ] have highlighted the powerful antibacterial ability of Resveratrol. At the same time, some scholars have made contributions to the research of Resveratrol against Helicobacter pylori, such as (2003) Mahady et al. [ 8 ], (2011) Martini et al. [ 9 ], (2013) Brown et al.[ 10 ], (2020) Lodovico et al.[ 11 ], and (2022) Biswas et al.[ 12 ]. Network pharmacology, an approach that integrates high-throughput histology, systems biology, and bioinformatics, has become a powerful tool for studying the relationship between active ingredients, target proteins, pathways, and disease phenotypes[ 13 – 15 ]. Resveratrol has synergistic effects on multiple targets and pathways, showing potent antibacterial effects with few adverse reactions and side effects[ 16 ]. However, the specific molecular mechanisms by which Resveratrol acts against H. pylori remain unclear. Using multiple databases, this study adopted a systematic network pharmacology approach to identify the targets associated with resveratrol and H. pylori. Bioinformatics analysis, molecular docking, and molecular dynamics (MD) simulation[ 17 , 18 ] verification of the critical proteins in the enriched pathways will reveal the molecular mechanism of the anti-H. pylori effect of Resveratrol will have important clinical significance for H. pylori treatment. A workflow chart is shown in Fig. 1 . Methods and materials Target prediction for Resveratrol The canonical SMILES structures of Resveratrol were obtained from the PubChem database. These structures were then imported into the Swiss Target Prediction database to identify targets associated with Resveratrol. Additionally, the targets of Resveratrol from the TCMSP and TCMIP databases were included. Duplicate values were removed, resulting in the final list of drug targets for Resveratrol. Target prediction for Helicobacter pylori infection A search for "Helicobacter pylori infection" was conducted in GeneCards and OMIM databases to identify targets related to the disease. The targets from both databases were integrated, and duplicate values were removed. Construction of the Drug-Target-Disease Network Venn diagram was used to map the targets of Resveratrol and Helicobacter pylori infection-related targets, and the intersection genes were determined. Cytoscape3.8.2 software[ 19 ] was used to construct the "drug target" network, and the relationship between drugs and their targets and diseases was visualized. Protein-Protein Interaction (PPI) Network data The intersection genes were uploaded to the STRING database for protein interaction analysis. Then the obtained data were imported into Cytoscape3.8.2 to construct the PPI network of resveratrol target proteins against H. pylori infection. Cytoscape was used to analyze the topological parameters of the PPI network, with node size and color representing degree values and edge thickness representing combination scores. Core targets were selected to create protein interaction network maps. GO and KEGG enrichment The symbols of the intersection genes were converted into Entrez IDs using RStudio's org.Hs.eg.db. GO enrichment analysis and KEGG pathway analysis of the target genes were performed using the clusterProfiler package. The analysis focused on the human species, with a significance threshold of p < 0.05 for enrichment screening. The top 20 rankings were selected for plotting, and the Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) aspects of target proteins of Resveratrol in the treatment of Helicobacter pylori infection were analyzed. Molecular docking In order to verify the interaction activity between Resveratrol and critical targets, molecular docking was performed using AutoDock Vina (version 1.1.2). The following specific methods were employed: The 3D structural compounds in SDF format were downloaded from PubChem and subjected to energy minimization using Chembio3D. The compounds were then imported into AutodockTools-1.5.6 for hydrogenation, charge calculation, charge allocation, and rotation bond setting. Finally, the compounds were saved in "pdbqt" format. Essential target proteins were obtained from the PDB database, prioritizing human proteins with high structural similarity to the original ligand. The selection was also based on high resolution. The protein structures were processed using PyMoL (version 2.3.0) to remove the original ligand and water molecules. Subsequently, the proteins were imported into AutoDocktools (version 1.5.6) for hydrogenation, charge calculation, assignment of charge, specification of atom type, and saving in "pdbqt" format. The original protein ligand served as the center of the docking box. If no original ligand was present, the docking region was selected near reported vital amino acid residues. The lattice box size was 50×50×50, with a lattice point spacing of 0.375 Å. Other parameters were set to default settings. Interaction mode analysis was conducted using PyMOL and Ligplot to gain insights into the binding patterns between Resveratrol and the key targets. Molecular dynamics (MD) simulation MD simulation[ 20 ] of the ligand-receptor docked complex was conducted using GROMACS (version 2021.2)[ 21 ]. The protein topology file was generated using the AMBER99SB-ILDN force field, while the ligand topology file was generated using the ACPYPE script with the AMBER force field. The MD simulation was carried out in a triclinic box filled with TIP3 water molecules, and periodic boundary conditions were applied. To ensure system neutrality, NaCl counter ions were added. Prior to the MD simulation, the complex underwent a minimization step consisting of 1000 steps, followed by equilibration in the NVT and NPT ensembles for 100 ps. Subsequently, the MD simulation was performed for 100 ns for each system under periodic boundary conditions at a temperature of 310 K. To calculate the binding free energy of the active ingredient and protein, the MMPBA.py module was employed[ 22 ]. The database is shown in Table 1 . Table 1 Data source Database Uniform Resource Locator Reference PubChem https://pubchem.ncbi.nlm.nih.gov/ [ 75 ] Swiss Target Prediction http://www.swisstargetprediction.ch/ [ 76 ] TCMSP https://old.tcmsp-e.com/tcmsp.php/ [ 77 ] TCMIP http://www.tcmip.cn/TCMIP/index.php/Home/ [ 78 ] GeneCards https://www.genecards.org/ [ 79 ] OMIM https://omim.org/ [ 80 ] STRING https://string-db.org/ [ 81 ] PDB http://www.rcsb.org/ [ 82 ] Results Potential targets of the agonists/diseases and their related targets Canonical SMILES of Resveratrol were utilized to conduct target prediction using SwissTargetPrediction. Applying a filter condition of "probability > 0," 27 targets were retrieved. The TCMIP database contributed 25 targets, while the TCMSP database yielded 124 drug targets. Combining the target sets from all three databases obtained a comprehensive list of 99 targets associated with Resveratrol. For H. pylori infection, a search in the GeneCards database retrieved 2040 targets, while the OMIM database provided 121 targets. Integrating the target sets from both databases led to 2144 targets related to H. pylori infection. The details of these findings are depicted in Fig. 2A. Construction of the drugs-diseases-targets network The disease targets related to H. pylori infection and the drug targets of Resveratrol were compared, resulting in a total of 45 common targets. These targets were used to construct the "network.xlsx" file and the "type.xlsx" file, which contained information about the drug-disease-target relationships. The files were then imported into Cytoscape 3.8.2 for visualization and mapping. The resulting network comprised 46 nodes representing the targets and 45 edges representing the connections between them. This network is displayed in Fig. 2B. Analysis of the PPI network of the related targets The 45 common targets related to drug targets and Helicobacter pylori infection, identified through the Venn diagram analysis, were used to predict protein-protein interactions using the STRING database. The species was set as Homo sapiens, and the resulting network file was saved in TSV format. This TSV file was then imported into Cytoscape 3.8.2 software to visualize and analyze the protein interaction network. The network topology was examined, with the degree value determining the size and color of the nodes and the combined score value indicating the thickness of the edges. The resulting protein-protein interaction network is shown in Fig. 3, consisting of 42 nodes and 264 edges. AKT1, TP53, TNF, PTGS2, and IL1B were identified as the core targets, as detailed in Table 2 . Table 2 Top five core targets (ranked by degree) and molecular docking results (affinity). Rank Target gene Full name of gene UniProt KB Degree PDB ID affinity(kcal/mol) 1 AKT1 AKT serine/threonine kinase 1 P31749 32 1UNQ -6.1 2 TP53 tumor protein p53 P04637 29 1UOL -5.5 3 TNF tumor necrosis factor P01375 27 2E7A -7.6 4 PTGS2 prostaglandin-endoperoxide synthase2 P35354 25 5F19 -7.2 5 IL1B interleukin 1 beta P01584 25 6I8Y -5.1 GO and KEGG enrichment analysis The intersection genes of Resveratrol against H. pylori infection were subjected to gene functional enrichment analysis using the DAVID database. A total of 1892 GO entries were identified, with 1693 related to biological processes (BP), including reactive oxygen metabolism, response to drugs, oxidative stress response, cell response to chemical stress, regulation of reactive oxygen metabolism, and more. Cellular components (CC) included 67 entries, including membrane raft, membrane region, nuclear membrane cavity, organelle envelope cavity, microvilli, and others. The molecular functions (MF) encompassed 132 entries, including heme binding, drug binding, phosphatase binding, protein serine/threonine kinase activity, integrin binding, and more. The top 10 entries for BP, CC, and MF were selected and visualized in Fig. 4. To examine the relationship between drugs and disease pathways, the typical targets of H. pylori infection and Resveratrol were used to construct a drug-target-KEGG network diagram (Fig. 5). KEGG pathway analysis identified 154 pathways associated with Resveratrol in the treatment of H. pylori infection. These pathways included the VEGF signaling pathway, C-type lectin receptor signaling pathway, sphingolipid signaling pathway, PI3K-Akt signaling pathway, HIF-1 signaling pathway, and TNF signaling pathway. The top 10 pathways were selected and visualized in Fig. 5B, illustrating how Resveratrol may modulate the H. pylori infection process through target genes within these pathways. A bubble plot was also generated to display the top 20 KEGG metabolic pathways based on their p-values, visually representing the enriched genes and their significance (Fig. 5A). Detailed information on the top 10 KEGG pathways, including enriched and overlapping genes, is presented in Table 3 . Furthermore, essential genes within the VEGF and TNF pathways are depicted in Fig. 6, highlighting their locations and involvement in the process. Table 3 Top 10 overlapping KEGG pathways and its core targets ranked by p value. Term Description p value Gene hsa04370 VEGF signaling pathway 1.95726E-08 VEGFR2, PIK3, PKC, COX2, PKE/Akt, p38 hsa04664 Fc epsilon RI signaling pathway 5.36996E-08 PI3K, Akt, PKC, TNFα, p38, Syk, 5-LO hsa04625 C-type lectin receptor signaling pathway 5.54925E-08 TNF, AP1, COX2, PI3K, PKB, IL1B, pro-IL1B, p38, Sky hsa04071 Sphingolipid signaling pathway 1.59432E-07 TNFα, PI3K, Akt, PKC, Bcl-2, p38, p53, eNOS hsa04151 PI3K-Akt signaling pathway 1.77899E-07 RTK, Akt, PI3K, PKCs, Bcl-2, p53, eNOS, Syk, GSK3, ITGA/B hsa04919 Thyroid hormone signaling pathway 1.81503E-07 ITGB, PKC, ERα, GSK3β, Akt/PKB, PI3K, GLUT1, p53 hsa05207 Chemical carcinogenesis - receptor activation 1.24721E-06 PI3K, Akt, ER, VDR/AP1, JUN, PKC, Bcl-2, β-AR hsa04066 HIF-1 signaling pathway 1.40621E-06 PI3K, Akt, PKC, Glut, Glut1, eNOS, iNOS, Bcl-2 hsa04668 TNF signaling pathway 1.68978E-06 TNF, LTA, Akt, PI3K, AP1, JUN, c-Jun, p38, Ptgs2, IL1B hsa04722 Neurotrophin signaling pathway 2.54225E-06 PI3K, Akt, GSK3β, Bcl-2, p38, p53, c-Jun Analysis of molecular docking Molecular docking of Resveratrol with the core target proteins was conducted, and the results are presented in Table 2 . The "affinity" score indicates the binding strength between the small molecule and its target protein. A binding energy score less than 0 suggests good binding, with lower values indicating a higher likelihood of binding[ 23 – 25 ]. Generally, a binding energy score above 5.0 indicates good binding activity, while a score above 7.0 indicates solid binding activity[ 26 ]. This study selected the top five core targets (TNF, PTGS2, AKT1, TP53, and IL1B) for semi-flexible docking with Resveratrol. The docking results demonstrated that all small molecules could successfully enter the active center of their target proteins. The affinity scores ranked as follows: TNF > PTGS2 > AKT1 > TP53 > IL1B (Table 2 ). Resveratrol formed hydrogen bonds with specific amino acid residues of the target proteins, such as His89 and Glu95 of AKT1, Ser125, and Glu25 of IL1B, Ser99, Arg98, Asn112, and Gln102 of TNF, and Asp268, Ser269, and His115 of TP53. The lengths of these hydrogen bonds were also provided. Moreover, the small molecules exhibited solid hydrophobic interactions with surrounding amino acid residues, contributing to enhanced binding ability and stability. The interaction modes between Resveratrol and the five core target proteins are depicted in Fig. 7, providing visual representations of their binding configurations. Molecular dynamics (MD) simulation result To analyze the dynamic behavior of the TNF-resveratrol complex, a 100 ns molecular dynamics (MD) simulation was conducted. The root means square deviation (RMSD) curve was employed to assess the equilibrium and stability of the protein conformation during the simulation[ 27 ]. From Fig. 8a, it can be observed that the RMSD curve initially exhibits fluctuations ranging from 0.1 nm to 0.15 nm. However, TNF-resveratrol reaches a stable state at approximately 0.15 nm during the 100 ns MD simulation. This suggests that the protein conformation remains relatively unchanged after the binding of the small molecule ligand, indicating a stable and favorable complex formation. The root means square fluctuation (RMSF) of each residue in the TNF-resveratrol complex was calculated to evaluate the flexibility of individual residues contributing to structural fluctuations. Figure 8b shows that the protein's intermediate region exhibits greater flexibility than other regions. This region is based on a cyclic peptide chain, inherently granting it higher flexibility. The compactness of the protein structure was assessed by calculating the radius of gyration (Rg). Figure 8c demonstrates that TNF-resveratrol maintains an average Rg value of 2.07 nm, indicating a stable radius of gyration throughout the simulation. This observation aligns with the RMSD results and suggests that the protein structure remains tightly folded and stable even in the presence of the bound ligand. Hydrogen bonds play a crucial role in stabilizing protein-ligand complexes. To investigate the binding capacity of the ligand to the protein, hydrogen bond interactions were analyzed based on the MD trajectories. The total number of hydrogen bonds formed between the TNF-resveratrol complex ranged from 0 to 7. The results indicate that the number of hydrogen bonds remains stable throughout the simulation, and the amino acid residues in the active site persistently contribute to the stability of the complex structure (Fig. 8d). The TNF-resveratrol's RMSD stationary phase (10–50 ns) trajectory was also selected to calculate the binding free energy. The binding free energy between the two was determined to be -20.621 kcal/mol. The Van der Waals potential (-106.279 kcal/mol) and electrostatic interactions (-43.591 kcal/mol) favor the binding of TNF and Resveratrol, contributing to the overall stability of the complex[ 28 ]. Discussion Recently, there has been increasing research on the use of traditional Chinese medicine[ 29 ], plant compounds[ 12 ], probiotics[ 30 ], and nanotechnology[ 31 ] for the treatment of Helicobacter pylori infection, particularly in light of the growing problem of drug resistance[ 32 ]. Resveratrol, a polyphenolic phytoalexin studied in this paper, has been found to exhibit multitarget and multi-pathway synergistic regulatory effects in disease treatment[ 33 ]. However, it is essential to note that further studies are needed to validate these findings before Resveratrol can be recommended as an effective supplement for preventing or treating diseases[ 34 ]. This study predicted 45 common targets between Resveratrol and H. pylori infection. The protein-protein interaction (PPI) network of Resveratrol against Helicobacter pylori infection was constructed, and core target proteins were identified. Based on the top 5 Degree values, the core targets were AKT1, TP53, TNF, PTGS2, and IL1B. The pathogenesis of diseases related to Helicobacter pylori infection is complex, and the core targets identified in this study primarily involve processes such as cell proliferation, apoptosis, and inflammation. The tumor suppressor p53 encoded by the TP53 gene plays a vital role in DNA damage and various stress responses[ 35 ]. Resveratrol may be partially involved in maintaining the anti-aging effect of chromosomal DNA through transient induction of TP53 gene expression[ 36 ]. It has previously been shown that activation of the PI3K/Akt pathway in H. pylori-infected tissues induces oxidative stress, leading to DNA damage and interfering with TP53 expression[ 37 ]. Abnormal expression of TP53 can destroy the balance of the cell cycle, promote the imbalance of cell proliferation, and promote the occurrence and development of inflammatory tumors [ 38 ]. AKT1 is an essential target of Resveratrol. It has been reported that AKT1 may inhibit NLRP3 inflammasome activation in cardiomyocytes and cardiac inflammation after acute sympathetic stress and that Resveratrol inhibits AKT1 activation after activation of β-adrenoceptors[ 39 ]. AKT1 encodes a serine/threonine protein kinase that mediates the regulation of various processes, including metabolism, proliferation, and cell survival. According to our results, Resveratrol may target AKT1 to achieve anti-H. pylori. PTGS2 is the critical enzyme in the conversion of arachidonic acid to prostaglandins, also known as cyclooxygenase 2 (COX2)[ 40 ]. Its expression correlates with the induction of growth factors and inflammatory cytokines, particularly reactive oxygen species and gastrin[ 41 ]. Helicobacter pylori-associated gastritis is associated with the expression of PTGS2 in gastric mucosal epithelial cells, especially during chronic expression.[ 42 – 44 ]. IL1B is a member of the proinflammatory cytokine group that plays a central role in infection-related inflammation[ 45 ]. It is a potent inhibitor of gastric acid secretion[ 46 ]. Single nucleotide polymorphisms (SNPs) in the promoter region of IL1B are closely related to the occurrence and development of Helicobacter pylori-related diseases, especially gastroduodenal diseases. Additionally, IL1B SNPs may increase the risk of H. pylori infection[ 47 ]. Resveratrol, primarily found in red wine, grape skins, cranberries, and blueberries, has been associated with inhibiting Helicobacter pylori and its toxins[ 48 ]. Red wine has been shown to inhibit VacA toxin[ 49 ], while cranberry may induce the formation of an inactive globular form of H. pylori, inhibiting its reproduction[ 50 ]. Resveratrol has also been linked to the TNF family of core targets in various diseases, such as cardiovascular disease prevention[ 51 ], inflammatory bowel disease[ 52 ], allergic rhinitis[ 53 ], and rheumatoid arthritis[ 54 ], playing a crucial role in oxidative stress. To further understand the mechanism of Resveratrol against H. pylori, KEGG enrichment analysis was performed, and the critical pathway identified was the VEGF signaling pathway. The VEGF family plays a crucial role in angiogenesis as regulators of vascular endothelial growth[ 55 ]. It enhances vascular permeability, promotes cell migration, and affects the immune system and tumor cells[ 56 ]. Studies[ 57 ] have shown that VEGF expression is increased in the gastric mucosa during H. pylori-associated gastritis. H. pylori infection can up-regulate serum VEGF levels, and the virulence of H. pylori's CagA gene is positively correlated with serum VEGF levels[ 58 ]. In the context of tumor relevance, VEGF has been found to mediate tumor cell signaling pathways and is associated with poor prognosis and clinical features in gastric cancer, including tumor invasion and lymph node metastasis[ 59 , 60 ]. Understanding the critical virulence factor CagA protein encoded and secreted by the CAG phaticity island (CAGPAI) of Helicobacter pylori is of great significance for understanding the pathogenesis of Helicobacter pylori infection. CagA is transferred into target cells via H. pylori-specific type IV secretion system (T4SS) and interacts with Src family proteins and Src homologous two phosphatases[ 61 ]. This interaction triggers multiple signaling pathways, including Ras-ERK MAP kinase, Wnt-β signaling, and PI3/Akt signaling[ 62 ]. These pathways involve phosphatases, transferases, and molecular functions such as protein kinase activity, protein kinase binding, and protein phosphatase binding. These reactions occur in different cell parts, including the membrane and cytoplasm. GO analysis is consistent with these findings and suggests that bioactive compounds in medicinal plants may interfere with these processes. In addition, H. pylori induce the production of reactive oxygen species (ROS) and reactive nitrogen species (RNS) in gastric epithelial cells and inflammatory cells, leading to oxidative stress and DNA damage, including tumor suppressor genes such as p53, which are implicated in the development and progression of H. pylorus-associated gastric cancer[ 63 ]. Resveratrol exhibits significant antibacterial activity against a range of microorganisms, including Gram-negative and Gram-positive bacteria[ 64 – 66 ], viruses[ 67 , 68 ], fungi[ 69 ], and chlamydia[ 70 ]. It destroys cell structure by blocking the glycopeptide group formation and transpeptidase reactions required for peptidoglycan polymerization of the cell wall, thereby increasing cell permeability and the rate of antimicrobial drug passage. Resveratrol can also directly target mitochondria, leading to oxidative stress, DNA damage, and cellular apoptosis in bacteria like Salmonella typhimurium[ 71 ]. Furthermore, the antibacterial efficacy of Resveratrol against methicillin-resistant Staphylococcus aureus is enhanced when combined with other antibiotics[ 72 ]. These findings suggest that Resveratrol may modulate H. pylori resistance through oxidative stress, cellular structural modifications, and drug binding mechanisms. The stable molecular docking models have demonstrated efficient binding between Resveratrol and the central targets, confirming the relationship between resveratrol and H. pylori. Molecular dynamics (MD) simulations have analyzed the stability of the receptor-ligand complex, showing that equilibrium is reached after 10 ns of simulation. As assessed by RMSF, residual flexibility primarily occurs in the protein terminal residues and loop regions. The average Rg values for TNF-resveratrol indicate a compact receptor structure, suggesting a minimal impact of the core compound on the protein structure. The presence of hydrogen bond interactions explains the high stability and strong binding affinity between the core compound and the target. Furthermore, the free energy per residue decomposition using the MM-PBSA method provides insights into the energetic contributions of individual residues[ 73 , 74 ]. While the present results provide valuable theoretical support for Resveratrol in assisting the treatment of H. pylori, it is essential to note the limitations of our study, including the lack of validation analysis. The predicted core compounds, central targets, and associated pathways derived from computational tools require further validation through in vitro and in vivo studies to confirm the molecular mechanisms involved. Conclusion Based on the bioinformatics analysis conducted in this research, it has been observed that Resveratrol exhibits multitarget and multi-pathway characteristics in the treatment of Helicobacter pylori infection. Considering the current research landscape, the combination of plant extracts with antibiotic therapy holds promise as a potential strategy for the prevention, management, and treatment of Helicobacter pylori infection and the gastrointestinal diseases associated with it in the future. The findings of this study provide valuable insights into the pharmacological effects and molecular mechanisms of Resveratrol and serve as a guide for future experimental investigations. By further exploring and validating these results through basic experiments, we can gain a deeper understanding of the therapeutic potential of Resveratrol in combating Helicobacter pylori-related conditions. Declarations Data availability statement All the data can be obtained from the open-source platform provided in the article. Author contributions Yingzi Li: conception and design; acquisition, analysis, and interpretation of data; writing, review, and/or revision of the manuscript. Yingzi Li and Yipin Liu: administrative, technical, or material support. Yipin Liu: assist in revising the manuscript. Chou Hou and Ailing Zhao: study supervision. All authors participated in the writing of the final manuscript and approved the final submission. Conflict of interest The authors declare that they have no known competing financial interests in this paper. References Fischbach W, Malfertheiner P. Helicobacter Pylori Infection. 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15:18:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2891022,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2995283/v1/9a6405b9-1a33-48d4-8e8e-29bb500deb47.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"To explore the potential mechanism of Resveratrol against Helicobacter pylori based on network pharmacology, molecular docking, and molecular dynamics simulations Abstract","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHelicobacter pylori infection is strongly associated with gastrointestinal diseases, including chronic gastritis, peptic gastric ulcer, and gastric cancer[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. With a global infection rate of approximately 44 percent and even higher rates in developing countries, effective treatment for H. pylori infection is crucial to prevent and manage these diseases[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The current standard treatment involves triple or quadruple antibiotic therapy[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]; however, it has limitations such as single targeting, significant adverse reactions, and variations in individual response, leading to poor compliance[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Additionally, the emergence of antimicrobial resistance further complicates eradication efforts. Therefore, developing new anti-H. Pylori drugs are of utmost importance.\u003c/p\u003e \u003cp\u003eResveratrol, a polyphenolic phytoalexin found in grapes, peanuts, and berries, has many beneficial properties, including anti-inflammatory, antioxidant, anti-cancer, anticoagulant, and vascular protective effects[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Recent studies[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] have highlighted the powerful antibacterial ability of Resveratrol. At the same time, some scholars have made contributions to the research of Resveratrol against Helicobacter pylori, such as (2003) Mahady et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], (2011) Martini et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], (2013) Brown et al.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], (2020) Lodovico et al.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and (2022) Biswas et al.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Network pharmacology, an approach that integrates high-throughput histology, systems biology, and bioinformatics, has become a powerful tool for studying the relationship between active ingredients, target proteins, pathways, and disease phenotypes[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Resveratrol has synergistic effects on multiple targets and pathways, showing potent antibacterial effects with few adverse reactions and side effects[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, the specific molecular mechanisms by which Resveratrol acts against H. pylori remain unclear. Using multiple databases, this study adopted a systematic network pharmacology approach to identify the targets associated with resveratrol and H. pylori. Bioinformatics analysis, molecular docking, and molecular dynamics (MD) simulation[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] verification of the critical proteins in the enriched pathways will reveal the molecular mechanism of the anti-H. pylori effect of Resveratrol will have important clinical significance for H. pylori treatment. A workflow chart is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methods and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTarget prediction for Resveratrol\u003c/h2\u003e \u003cp\u003eThe canonical SMILES structures of Resveratrol were obtained from the PubChem database. These structures were then imported into the Swiss Target Prediction database to identify targets associated with Resveratrol. Additionally, the targets of Resveratrol from the TCMSP and TCMIP databases were included. Duplicate values were removed, resulting in the final list of drug targets for Resveratrol.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eTarget prediction for Helicobacter pylori infection\u003c/h2\u003e \u003cp\u003eA search for \"Helicobacter pylori infection\" was conducted in GeneCards and OMIM databases to identify targets related to the disease. The targets from both databases were integrated, and duplicate values were removed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of the Drug-Target-Disease Network\u003c/h2\u003e \u003cp\u003eVenn diagram was used to map the targets of Resveratrol and Helicobacter pylori infection-related targets, and the intersection genes were determined. Cytoscape3.8.2 software[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] was used to construct the \"drug target\" network, and the relationship between drugs and their targets and diseases was visualized.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eProtein-Protein Interaction (PPI) Network data\u003c/h2\u003e \u003cp\u003eThe intersection genes were uploaded to the STRING database for protein interaction analysis. Then the obtained data were imported into Cytoscape3.8.2 to construct the PPI network of resveratrol target proteins against H. pylori infection. Cytoscape was used to analyze the topological parameters of the PPI network, with node size and color representing degree values and edge thickness representing combination scores. Core targets were selected to create protein interaction network maps.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGO and KEGG enrichment\u003c/h2\u003e \u003cp\u003eThe symbols of the intersection genes were converted into Entrez IDs using RStudio's org.Hs.eg.db. GO enrichment analysis and KEGG pathway analysis of the target genes were performed using the clusterProfiler package. The analysis focused on the human species, with a significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for enrichment screening. The top 20 rankings were selected for plotting, and the Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) aspects of target proteins of Resveratrol in the treatment of Helicobacter pylori infection were analyzed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMolecular docking\u003c/h2\u003e \u003cp\u003eIn order to verify the interaction activity between Resveratrol and critical targets, molecular docking was performed using AutoDock Vina (version 1.1.2). The following specific methods were employed:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe 3D structural compounds in SDF format were downloaded from PubChem and subjected to energy minimization using Chembio3D. The compounds were then imported into AutodockTools-1.5.6 for hydrogenation, charge calculation, charge allocation, and rotation bond setting. Finally, the compounds were saved in \"pdbqt\" format.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEssential target proteins were obtained from the PDB database, prioritizing human proteins with high structural similarity to the original ligand. The selection was also based on high resolution.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe protein structures were processed using PyMoL (version 2.3.0) to remove the original ligand and water molecules. Subsequently, the proteins were imported into AutoDocktools (version 1.5.6) for hydrogenation, charge calculation, assignment of charge, specification of atom type, and saving in \"pdbqt\" format.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe original protein ligand served as the center of the docking box. If no original ligand was present, the docking region was selected near reported vital amino acid residues. The lattice box size was 50\u0026times;50\u0026times;50, with a lattice point spacing of 0.375 \u0026Aring;. Other parameters were set to default settings.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eInteraction mode analysis was conducted using PyMOL and Ligplot to gain insights into the binding patterns between Resveratrol and the key targets.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMolecular dynamics (MD) simulation\u003c/h2\u003e \u003cp\u003eMD simulation[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] of the ligand-receptor docked complex was conducted using GROMACS (version 2021.2)[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The protein topology file was generated using the AMBER99SB-ILDN force field, while the ligand topology file was generated using the ACPYPE script with the AMBER force field. The MD simulation was carried out in a triclinic box filled with TIP3 water molecules, and periodic boundary conditions were applied. To ensure system neutrality, NaCl counter ions were added. Prior to the MD simulation, the complex underwent a minimization step consisting of 1000 steps, followed by equilibration in the NVT and NPT ensembles for 100 ps. Subsequently, the MD simulation was performed for 100 ns for each system under periodic boundary conditions at a temperature of 310 K. To calculate the binding free energy of the active ingredient and protein, the MMPBA.py module was employed[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe database is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData source\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDatabase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniform Resource Locator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePubChem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwiss Target Prediction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swisstargetprediction.ch/\u003c/span\u003e\u003cspan address=\"http://www.swisstargetprediction.ch/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCMSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://old.tcmsp-e.com/tcmsp.php/\u003c/span\u003e\u003cspan address=\"https://old.tcmsp-e.com/tcmsp.php/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCMIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.tcmip.cn/TCMIP/index.php/Home/\u003c/span\u003e\u003cspan address=\"http://www.tcmip.cn/TCMIP/index.php/Home/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneCards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOMIM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://omim.org/\u003c/span\u003e\u003cspan address=\"https://omim.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTRING\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePDB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"http://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePotential targets of the agonists/diseases and their related targets\u003c/h2\u003e \u003cp\u003eCanonical SMILES of Resveratrol were utilized to conduct target prediction using SwissTargetPrediction. Applying a filter condition of \"probability\u0026thinsp;\u0026gt;\u0026thinsp;0,\" 27 targets were retrieved. The TCMIP database contributed 25 targets, while the TCMSP database yielded 124 drug targets. Combining the target sets from all three databases obtained a comprehensive list of 99 targets associated with Resveratrol. For H. pylori infection, a search in the GeneCards database retrieved 2040 targets, while the OMIM database provided 121 targets. Integrating the target sets from both databases led to 2144 targets related to H. pylori infection. The details of these findings are depicted in Fig.\u0026nbsp;2A.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of the drugs-diseases-targets network\u003c/h2\u003e \u003cp\u003eThe disease targets related to H. pylori infection and the drug targets of Resveratrol were compared, resulting in a total of 45 common targets. These targets were used to construct the \"network.xlsx\" file and the \"type.xlsx\" file, which contained information about the drug-disease-target relationships. The files were then imported into Cytoscape 3.8.2 for visualization and mapping. The resulting network comprised 46 nodes representing the targets and 45 edges representing the connections between them. This network is displayed in Fig.\u0026nbsp;2B.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of the PPI network of the related targets\u003c/h2\u003e \u003cp\u003eThe 45 common targets related to drug targets and Helicobacter pylori infection, identified through the Venn diagram analysis, were used to predict protein-protein interactions using the STRING database. The species was set as Homo sapiens, and the resulting network file was saved in TSV format. This TSV file was then imported into Cytoscape 3.8.2 software to visualize and analyze the protein interaction network. The network topology was examined, with the degree value determining the size and color of the nodes and the combined score value indicating the thickness of the edges. The resulting protein-protein interaction network is shown in Fig.\u0026nbsp;3, consisting of 42 nodes and 264 edges. AKT1, TP53, TNF, PTGS2, and IL1B were identified as the core targets, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop five core targets (ranked by degree) and molecular docking results (affinity).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTarget gene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFull name of gene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUniProt KB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePDB ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eaffinity(kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAKT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAKT serine/threonine kinase 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP31749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1UNQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-6.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTP53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etumor protein p53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP04637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1UOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-5.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTNF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etumor necrosis factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP01375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2E7A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-7.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTGS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eprostaglandin-endoperoxide synthase2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP35354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5F19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-7.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIL1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003einterleukin 1 beta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP01584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6I8Y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-5.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGO and KEGG enrichment analysis\u003c/h2\u003e \u003cp\u003eThe intersection genes of Resveratrol against H. pylori infection were subjected to gene functional enrichment analysis using the DAVID database. A total of 1892 GO entries were identified, with 1693 related to biological processes (BP), including reactive oxygen metabolism, response to drugs, oxidative stress response, cell response to chemical stress, regulation of reactive oxygen metabolism, and more. Cellular components (CC) included 67 entries, including membrane raft, membrane region, nuclear membrane cavity, organelle envelope cavity, microvilli, and others. The molecular functions (MF) encompassed 132 entries, including heme binding, drug binding, phosphatase binding, protein serine/threonine kinase activity, integrin binding, and more. The top 10 entries for BP, CC, and MF were selected and visualized in Fig.\u0026nbsp;4.\u003c/p\u003e \u003cp\u003eTo examine the relationship between drugs and disease pathways, the typical targets of H. pylori infection and Resveratrol were used to construct a drug-target-KEGG network diagram (Fig.\u0026nbsp;5). KEGG pathway analysis identified 154 pathways associated with Resveratrol in the treatment of H. pylori infection. These pathways included the VEGF signaling pathway, C-type lectin receptor signaling pathway, sphingolipid signaling pathway, PI3K-Akt signaling pathway, HIF-1 signaling pathway, and TNF signaling pathway. The top 10 pathways were selected and visualized in Fig.\u0026nbsp;5B, illustrating how Resveratrol may modulate the H. pylori infection process through target genes within these pathways. A bubble plot was also generated to display the top 20 KEGG metabolic pathways based on their p-values, visually representing the enriched genes and their significance (Fig.\u0026nbsp;5A). Detailed information on the top 10 KEGG pathways, including enriched and overlapping genes, is presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Furthermore, essential genes within the VEGF and TNF pathways are depicted in Fig.\u0026nbsp;6, highlighting their locations and involvement in the process.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 overlapping KEGG pathways and its core targets ranked by \u003cem\u003ep\u003c/em\u003e value.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa04370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVEGF signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.95726E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVEGFR2, PIK3, PKC, COX2, PKE/Akt, p38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa04664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFc epsilon RI signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.36996E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePI3K, Akt, PKC, TNFα, p38, Syk, 5-LO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa04625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC-type lectin receptor signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.54925E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTNF, AP1, COX2, PI3K, PKB, IL1B, pro-IL1B, p38, Sky\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa04071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSphingolipid signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.59432E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTNFα, PI3K, Akt, PKC, Bcl-2, p38, p53, eNOS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa04151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePI3K-Akt signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.77899E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRTK, Akt, PI3K, PKCs, Bcl-2, p53, eNOS, Syk, GSK3, ITGA/B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa04919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThyroid hormone signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.81503E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eITGB, PKC, ERα, GSK3β, Akt/PKB, PI3K, GLUT1, p53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa05207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChemical carcinogenesis - receptor activation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24721E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePI3K, Akt, ER, VDR/AP1, JUN, PKC, Bcl-2, β-AR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa04066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHIF-1 signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40621E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePI3K, Akt, PKC, Glut, Glut1, eNOS, iNOS, Bcl-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa04668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTNF signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.68978E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTNF, LTA, Akt, PI3K, AP1, JUN, c-Jun, p38, Ptgs2, IL1B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa04722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeurotrophin signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.54225E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePI3K, Akt, GSK3β, Bcl-2, p38, p53, c-Jun\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of molecular docking\u003c/h2\u003e \u003cp\u003eMolecular docking of Resveratrol with the core target proteins was conducted, and the results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The \"affinity\" score indicates the binding strength between the small molecule and its target protein. A binding energy score less than 0 suggests good binding, with lower values indicating a higher likelihood of binding[\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Generally, a binding energy score above 5.0 indicates good binding activity, while a score above 7.0 indicates solid binding activity[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study selected the top five core targets (TNF, PTGS2, AKT1, TP53, and IL1B) for semi-flexible docking with Resveratrol. The docking results demonstrated that all small molecules could successfully enter the active center of their target proteins. The affinity scores ranked as follows: TNF\u0026thinsp;\u0026gt;\u0026thinsp;PTGS2\u0026thinsp;\u0026gt;\u0026thinsp;AKT1\u0026thinsp;\u0026gt;\u0026thinsp;TP53\u0026thinsp;\u0026gt;\u0026thinsp;IL1B (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Resveratrol formed hydrogen bonds with specific amino acid residues of the target proteins, such as His89 and Glu95 of AKT1, Ser125, and Glu25 of IL1B, Ser99, Arg98, Asn112, and Gln102 of TNF, and Asp268, Ser269, and His115 of TP53. The lengths of these hydrogen bonds were also provided.\u003c/p\u003e \u003cp\u003eMoreover, the small molecules exhibited solid hydrophobic interactions with surrounding amino acid residues, contributing to enhanced binding ability and stability. The interaction modes between Resveratrol and the five core target proteins are depicted in Fig.\u0026nbsp;7, providing visual representations of their binding configurations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMolecular dynamics (MD) simulation result\u003c/h2\u003e \u003cp\u003eTo analyze the dynamic behavior of the TNF-resveratrol complex, a 100 ns molecular dynamics (MD) simulation was conducted. The root means square deviation (RMSD) curve was employed to assess the equilibrium and stability of the protein conformation during the simulation[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. From Fig.\u0026nbsp;8a, it can be observed that the RMSD curve initially exhibits fluctuations ranging from 0.1 nm to 0.15 nm. However, TNF-resveratrol reaches a stable state at approximately 0.15 nm during the 100 ns MD simulation. This suggests that the protein conformation remains relatively unchanged after the binding of the small molecule ligand, indicating a stable and favorable complex formation.\u003c/p\u003e \u003cp\u003eThe root means square fluctuation (RMSF) of each residue in the TNF-resveratrol complex was calculated to evaluate the flexibility of individual residues contributing to structural fluctuations. Figure\u0026nbsp;8b shows that the protein's intermediate region exhibits greater flexibility than other regions. This region is based on a cyclic peptide chain, inherently granting it higher flexibility.\u003c/p\u003e \u003cp\u003eThe compactness of the protein structure was assessed by calculating the radius of gyration (Rg). Figure\u0026nbsp;8c demonstrates that TNF-resveratrol maintains an average Rg value of 2.07 nm, indicating a stable radius of gyration throughout the simulation. This observation aligns with the RMSD results and suggests that the protein structure remains tightly folded and stable even in the presence of the bound ligand.\u003c/p\u003e \u003cp\u003eHydrogen bonds play a crucial role in stabilizing protein-ligand complexes. To investigate the binding capacity of the ligand to the protein, hydrogen bond interactions were analyzed based on the MD trajectories. The total number of hydrogen bonds formed between the TNF-resveratrol complex ranged from 0 to 7. The results indicate that the number of hydrogen bonds remains stable throughout the simulation, and the amino acid residues in the active site persistently contribute to the stability of the complex structure (Fig.\u0026nbsp;8d).\u003c/p\u003e \u003cp\u003eThe TNF-resveratrol's RMSD stationary phase (10\u0026ndash;50 ns) trajectory was also selected to calculate the binding free energy. The binding free energy between the two was determined to be -20.621 kcal/mol. The Van der Waals potential (-106.279 kcal/mol) and electrostatic interactions (-43.591 kcal/mol) favor the binding of TNF and Resveratrol, contributing to the overall stability of the complex[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eRecently, there has been increasing research on the use of traditional Chinese medicine[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], plant compounds[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], probiotics[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and nanotechnology[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] for the treatment of Helicobacter pylori infection, particularly in light of the growing problem of drug resistance[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Resveratrol, a polyphenolic phytoalexin studied in this paper, has been found to exhibit multitarget and multi-pathway synergistic regulatory effects in disease treatment[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. However, it is essential to note that further studies are needed to validate these findings before Resveratrol can be recommended as an effective supplement for preventing or treating diseases[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study predicted 45 common targets between Resveratrol and H. pylori infection. The protein-protein interaction (PPI) network of Resveratrol against Helicobacter pylori infection was constructed, and core target proteins were identified. Based on the top 5 Degree values, the core targets were AKT1, TP53, TNF, PTGS2, and IL1B. The pathogenesis of diseases related to Helicobacter pylori infection is complex, and the core targets identified in this study primarily involve processes such as cell proliferation, apoptosis, and inflammation.\u003c/p\u003e \u003cp\u003eThe tumor suppressor p53 encoded by the TP53 gene plays a vital role in DNA damage and various stress responses[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Resveratrol may be partially involved in maintaining the anti-aging effect of chromosomal DNA through transient induction of TP53 gene expression[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. It has previously been shown that activation of the PI3K/Akt pathway in H. pylori-infected tissues induces oxidative stress, leading to DNA damage and interfering with TP53 expression[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Abnormal expression of TP53 can destroy the balance of the cell cycle, promote the imbalance of cell proliferation, and promote the occurrence and development of inflammatory tumors [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAKT1 is an essential target of Resveratrol. It has been reported that AKT1 may inhibit NLRP3 inflammasome activation in cardiomyocytes and cardiac inflammation after acute sympathetic stress and that Resveratrol inhibits AKT1 activation after activation of β-adrenoceptors[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. AKT1 encodes a serine/threonine protein kinase that mediates the regulation of various processes, including metabolism, proliferation, and cell survival. According to our results, Resveratrol may target AKT1 to achieve anti-H. pylori.\u003c/p\u003e \u003cp\u003ePTGS2 is the critical enzyme in the conversion of arachidonic acid to prostaglandins, also known as cyclooxygenase 2 (COX2)[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Its expression correlates with the induction of growth factors and inflammatory cytokines, particularly reactive oxygen species and gastrin[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Helicobacter pylori-associated gastritis is associated with the expression of PTGS2 in gastric mucosal epithelial cells, especially during chronic expression.[\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIL1B is a member of the proinflammatory cytokine group that plays a central role in infection-related inflammation[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. It is a potent inhibitor of gastric acid secretion[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Single nucleotide polymorphisms (SNPs) in the promoter region of IL1B are closely related to the occurrence and development of Helicobacter pylori-related diseases, especially gastroduodenal diseases. Additionally, IL1B SNPs may increase the risk of H. pylori infection[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResveratrol, primarily found in red wine, grape skins, cranberries, and blueberries, has been associated with inhibiting Helicobacter pylori and its toxins[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Red wine has been shown to inhibit VacA toxin[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], while cranberry may induce the formation of an inactive globular form of H. pylori, inhibiting its reproduction[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Resveratrol has also been linked to the TNF family of core targets in various diseases, such as cardiovascular disease prevention[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], inflammatory bowel disease[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], allergic rhinitis[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], and rheumatoid arthritis[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], playing a crucial role in oxidative stress.\u003c/p\u003e \u003cp\u003eTo further understand the mechanism of Resveratrol against H. pylori, KEGG enrichment analysis was performed, and the critical pathway identified was the VEGF signaling pathway. The VEGF family plays a crucial role in angiogenesis as regulators of vascular endothelial growth[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. It enhances vascular permeability, promotes cell migration, and affects the immune system and tumor cells[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Studies[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] have shown that VEGF expression is increased in the gastric mucosa during H. pylori-associated gastritis. H. pylori infection can up-regulate serum VEGF levels, and the virulence of H. pylori's CagA gene is positively correlated with serum VEGF levels[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. In the context of tumor relevance, VEGF has been found to mediate tumor cell signaling pathways and is associated with poor prognosis and clinical features in gastric cancer, including tumor invasion and lymph node metastasis[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUnderstanding the critical virulence factor CagA protein encoded and secreted by the CAG phaticity island (CAGPAI) of Helicobacter pylori is of great significance for understanding the pathogenesis of Helicobacter pylori infection. CagA is transferred into target cells via H. pylori-specific type IV secretion system (T4SS) and interacts with Src family proteins and Src homologous two phosphatases[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. This interaction triggers multiple signaling pathways, including Ras-ERK MAP kinase, Wnt-β signaling, and PI3/Akt signaling[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. These pathways involve phosphatases, transferases, and molecular functions such as protein kinase activity, protein kinase binding, and protein phosphatase binding. These reactions occur in different cell parts, including the membrane and cytoplasm. GO analysis is consistent with these findings and suggests that bioactive compounds in medicinal plants may interfere with these processes. In addition, H. pylori induce the production of reactive oxygen species (ROS) and reactive nitrogen species (RNS) in gastric epithelial cells and inflammatory cells, leading to oxidative stress and DNA damage, including tumor suppressor genes such as p53, which are implicated in the development and progression of H. pylorus-associated gastric cancer[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResveratrol exhibits significant antibacterial activity against a range of microorganisms, including Gram-negative and Gram-positive bacteria[\u003cspan additionalcitationids=\"CR65\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], viruses[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e], fungi[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], and chlamydia[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. It destroys cell structure by blocking the glycopeptide group formation and transpeptidase reactions required for peptidoglycan polymerization of the cell wall, thereby increasing cell permeability and the rate of antimicrobial drug passage. Resveratrol can also directly target mitochondria, leading to oxidative stress, DNA damage, and cellular apoptosis in bacteria like Salmonella typhimurium[\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Furthermore, the antibacterial efficacy of Resveratrol against methicillin-resistant Staphylococcus aureus is enhanced when combined with other antibiotics[\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. These findings suggest that Resveratrol may modulate H. pylori resistance through oxidative stress, cellular structural modifications, and drug binding mechanisms.\u003c/p\u003e \u003cp\u003eThe stable molecular docking models have demonstrated efficient binding between Resveratrol and the central targets, confirming the relationship between resveratrol and H. pylori. Molecular dynamics (MD) simulations have analyzed the stability of the receptor-ligand complex, showing that equilibrium is reached after 10 ns of simulation. As assessed by RMSF, residual flexibility primarily occurs in the protein terminal residues and loop regions. The average Rg values for TNF-resveratrol indicate a compact receptor structure, suggesting a minimal impact of the core compound on the protein structure. The presence of hydrogen bond interactions explains the high stability and strong binding affinity between the core compound and the target. Furthermore, the free energy per residue decomposition using the MM-PBSA method provides insights into the energetic contributions of individual residues[\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile the present results provide valuable theoretical support for Resveratrol in assisting the treatment of H. pylori, it is essential to note the limitations of our study, including the lack of validation analysis. The predicted core compounds, central targets, and associated pathways derived from computational tools require further validation through in vitro and in vivo studies to confirm the molecular mechanisms involved.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBased on the bioinformatics analysis conducted in this research, it has been observed that Resveratrol exhibits multitarget and multi-pathway characteristics in the treatment of Helicobacter pylori infection. Considering the current research landscape, the combination of plant extracts with antibiotic therapy holds promise as a potential strategy for the prevention, management, and treatment of Helicobacter pylori infection and the gastrointestinal diseases associated with it in the future. The findings of this study provide valuable insights into the pharmacological effects and molecular mechanisms of Resveratrol and serve as a guide for future experimental investigations. By further exploring and validating these results through basic experiments, we can gain a deeper understanding of the therapeutic potential of Resveratrol in combating Helicobacter pylori-related conditions.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data can be obtained from the open-source platform provided in the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYingzi Li: conception and design;\u0026nbsp;acquisition, analysis, and interpretation of data;\u0026nbsp;writing, review, and/or revision of the manuscript. Yingzi Li and Yipin Liu: administrative, technical, or material support. Yipin Liu: assist in revising the manuscript. Chou Hou and Ailing Zhao: study supervision. All authors participated in the writing of the final manuscript and approved the final submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFischbach W, Malfertheiner P. Helicobacter Pylori Infection. Dtsch Arztebl Int. 2018;115(25):429-36. doi: 10.3238/arztebl.2018.0429.\u003c/li\u003e\n\u003cli\u003eSong Z, Chen Y, Lu H, Zeng Z, Wang W, Liu X, et al. Diagnosis and treatment of Helicobacter pylori infection by physicians in China: A nationwide cross-sectional study. Helicobacter. 2022;27(3):e12889. doi: 10.1111/hel.12889.\u003c/li\u003e\n\u003cli\u003eMalfertheiner P, Megraud F, Rokkas T, Gisbert JP, Liou JM, Schulz C, et al. Management of Helicobacter pylori infection: the Maastricht VI/Florence consensus report. 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Methods Mol Biol. 2017;1607:627-41. doi: 10.1007/978-1-4939-7000-1_26.\u003c/li\u003e\n\u003c/ol\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":"[email protected]","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":"","lastPublishedDoi":"10.21203/rs.3.rs-2995283/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2995283/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The global public health sector recognizes Helicobacter pylori (H. pylori) infection as a significant challenge, and its treatment largely relies on triple or quadruple therapy involving antibiotics. However, the emergence of antibiotic resistance compromises the effectiveness of these treatments. Resveratrol targets from well-known databases such as PubChem, TCMSP, TCMIP, and Swiss Target Prediction were integrated with H. pylori infection-related targets retrieved from GeneCards and OMIM databases to address this issue. By leveraging the STRING database, it is possible to identify the underlying target relationships and, thus, the core targets. The DAVID database was also used for Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of potential targets. In addition, AutoDock Vina is used for molecular docking, which facilitates the identification of interactions between core targets and active ingredients. GO analysis revealed involvement in reactive oxygen species metabolism, phosphatase binding, and protein serine/threonine kinase activity. KEGG pathway analysis suggests that Resveratrol may disrupt the invasion and persistence of Helicobacter pylori through vascular endothelial growth factor (VEGF) and tumor necrosis factor (TNF) pathways. Protein-protein interaction analysis identifies five core targets (AKT1, TP53, IL1B, TNF, and PTGS2), further validated through molecular docking and molecular dynamics (MD) simulation. This study explores the potential core targets and mechanisms of action of Resveratrol against Helicobacter pylori infection, offering novel insights for treating this infection.","manuscriptTitle":"To explore the potential mechanism of Resveratrol against Helicobacter pylori based on network pharmacology, molecular docking, and molecular dynamics simulations Abstract","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-05-31 15:02:01","doi":"10.21203/rs.3.rs-2995283/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"060521d4-882f-4a1b-9b35-02f28ab378bb","owner":[],"postedDate":"May 31st, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-05-31T15:02:04+00:00","versionOfRecord":[],"versionCreatedAt":"2023-05-31 15:02:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2995283","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2995283","identity":"rs-2995283","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}