Computational Analysis of Andrographolide and Berbamine for Targeting Glioblastoma Associated with Viral Infections

Computational Analysis of Andrographolide and Berbamine for Targeting Glioblastoma Associated with Viral Infections | 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 Computational Analysis of Andrographolide and Berbamine for Targeting Glioblastoma Associated with Viral Infections Vijeta Prakash, reema gabrani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7447288/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 Glioblastoma (GB) patients are prone to developing viral infections pertaining to a weakened immune system. Coronavirus disease 2019 (COVID-19), Cytomegalovirus (CMV) infection and Human Papillomavirus (HPV) infection have shown evidence to co-occur with GB individually. The combination of andrographolide and berbamine has previously been shown to synergistically inhibit the growth of GB. In this study, the common protein targets were identified for viral infections and GB and the identified phytocompounds interacting with the same targets was studied by bioinformatics and network pharmacology. To validate the targets of andrographolide and berbamine against GB-viral co-occurrence, a variety of open-source datasets and a Venn Diagram tool were used. Several molecular mechanisms were identified against GB and viral comorbidities (SARS-CoV-2, CMV, and HPV) by using several bioinformatic tools. Six common critical targets and 41 transcription factors have been identified using Venny 2.0. The critical targets were found to be enriched in pathways like EGFR tyrosine kinase inhibitor resistance, ErbB signaling pathway, which are necessary for GB targeting. Berbamine and andrographolide indicated potential as therapeutic agents for glioblastoma, particularly in the context of co-infection with the studied viral diseases. The regulation of important targets (SRC, ERBB2, PRKCA, LYN, KDR, ABL1) and the ERK1 and ERK2 cascade seems to be connected to the underlying mechanisms. The study paves the way for developing novel treatments targeting glioblastoma and its associated viral comorbidities. Bioinformatic analysis Co-occurrence Molecular docking Phytocompounds Protein-protein interaction (PPI) network Synergy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Glioblastoma treatment is a significant global challenge, exacerbated by immune suppression and oncomodulation, where viral proteins and non-coding RNAs promote oncogenic processes [1]. These processes disrupt signaling pathways, promoting cell proliferation, invasion, immune evasion, and epigenetic changes, while inhibiting apoptosis and DNA repair [2]. They also affect virus latency, reactivation, and angiogenesis. SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has severely impacted global health. Researchers have found that bone marrow mesenchymal stem cells (BMSCs), neural precursor cells (NPCs), and endothelial cells (ECs) express higher levels of ACE2, making them more susceptible to infection [3]. Glioblastoma cells have shown elevated ACE2 expression, increasing their vulnerability to severe infections [4]. Studies suggest that ACE2 may facilitate CNS invasion by SARS-CoV-2, aided by CTSB/L, highlighting the virus's neurotropic characteristics and potential therapeutic targets for neurological symptoms in COVID-19 patients [5]. Several tumours, including glioblastomas, medulloblastomas, and neuroblastomas, as well as cancers of the prostate, breast, colon, and ovary, are associated with Human cytomegalovirus (HCMV) [6]. Recent data reveal that over 90% of malignant gliomas are linked to HCMV, promoting tumour growth and aggressiveness [7]. Research by Priel et al. found that the HCMV genome and proteins are present in more than 64% of glioblastoma tissues [8]. Cobbs et al. first observed increased tumour growth when glioblastoma cells were exposed to HCMV [9]. This suggests that HCMV infection may play a crucial role in development and progression of glioblastoma. Additionally, a study detected Human papillomavirus (HPV) DNA in 23% of glioblastoma samples, with HPV16 and HPV6 being the most prevalent strains. Ongoing viral protein production was confirmed in GBM cells through chromogenic in situ hybridization (CISH) and immunohistochemistry (IHC) [10]. HPV infection was linked to poorer patient outcomes, suggesting it may be an independent prognostic factor, highlighting the need for preventive strategies [10]. Growing evidence indicates that natural products offer promising alternatives for combating viral infections. Andrographolide exhibits strong antiviral properties, blocking viral entry, inhibiting RNA and DNA synthesis, and modulating immune responses [11]. It has demonstrated efficacy against influenza, hepatitis B and C, dengue, HIV, and SARS-CoV-2, reducing viral replication and improving immune responses [12]. Preclinical studies and clinical trials support its safety and therapeutic potential, particularly in combination therapies to enhance efficacy and reduce drug resistance [13]. Similarly, berbamine has shown effectiveness against multiple viruses, including hepatitis B, influenza and SARS-CoV-2 mainly by reducing replication, and Herpes simplex virus (HSV) by lowering viral load [14]. Preclinical studies have confirmed berbamine’s antiviral activity, and ongoing clinical trials are evaluating its safety and effectiveness, particularly against COVID-19 [15]. Berbamine has also exhibited synergistic effects when used alongside other antiviral agents, enhancing therapeutic outcomes [16]. The combination of andrographolide and berbamine have been found to act synergistically against GB cells (paper from lab under communication). The integrative study in this paper, combines bioinformatics analysis and molecular docking, to reveal key interactions between phytocompounds and viral disease-related targets in glioblastoma, potentially uncovering novel therapeutic avenues for co-infections like SARS-CoV-2, HPV, and CMV. MATERIALS AND METHODS Identification of targets for phytocompounds The targets of andrographolide and berbamine were searched from Comparative Toxicoomics Database (CTD, http://ctdbase.org/ ) [17], Swiss Target Prediction ( http://swisstargetprediction.ch/ ) [18], and TargetNet ( http://targetnet.scbdd.com/ ) [19]. Collecting Targets related to the viral diseases and GB Targets related to GB, SARS-CoV-2, HPV, and CMV were obtained from DisGeNET ( https://www.disgenet.org/home/ ), CTD [20], GeneCards ( https://www.genecards.org/ ) [21], Therapeutic Target Database (TTD, http://db.idrblab.net/ttd/ ), PubChem ( https://pubchem.ncbi.nlm.nih.gov/ ) [22] and DrugBank database ( https://www.drugbank.com ) [23]. Subsequently, the targets were mapped using Uniprot database to obtain a standard nomenclature ( https://www.uniprot.org ). Studying the overlapping target genes between phytocompounds and viral disease Venny 2.0, the Venn Diagram tool ( https://bioinfogp.cnb.csic.es/tools/venny/ ) was used to plot Venn diagrams of common targets between the phytocompounds (andrographolide and berbamine), GB and the viral diseases (SARS-CoV-2, HPV and CMV) [24]. Construction of PPI network and analysis of critical targets The common targets were subjected to the STRING 11.5 database ( https://string-db.org/ ) for construction of PPI network [25]. “ Homo sapiens ” was selected under the tab of “organisms” and the minimum required interaction score was set to 0.4. Cytoscape 3.7.2 software ( https://cytoscape.org/ ) was used to visualize the PPI network [26]. The CytoNCA plug-in was used in Cytoscape to analyze topological parameters (degree, betweenness, closeness, network and eigenvector). MCODE plugin was utilized to score the clusters of network [26]. Of note, the top five targets with the highest degree values in the CytoNCA output was further subjected to docking. Key Transcription Factors Analysis of Critical Targets The association of transcription factors with the targets was done through the tool, Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST, https://www.grnpedia.org/trrust/ ) [27]. A huge amount of information on the 8,444 transcription factors (TFs)-target network is available on the database. The targets were fed as input to TRRUST database with the species selection of “Human.” Using the Cytoscape 3.7.2 program, the top 10 TFs ranking according to p value, from small to large, were chosen to build the TFs-target network. Tissue-Specific Enrichment Analysis of Critical Targets Genotype-Tissue Expression (GETx) is an online resource for researching human tissue expression and genetic diversity ( https://www.gtexportal.org/ ). Tissue-specific enrichment analysis was performed on the top targets, ranked from high to low in terms of the degree values of the modules. The heat map displayed the relationship between various samples and targets; the targets' more significant tissues were represented by darker colors. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology Enhancement Examinations of Important Targets was done using GProfiler ( https://biit.cs.ut.ee/gprofiler/gost ), GO enrichment analysis was carried out for biological process (BP), molecular function (MF), and cellular component (CC), in addition to KEGG pathway enrichment analysis [28]. Molecular Docking Analysis of the Top Targets Molecular docking is frequently utilized in treatment compound detection to study the interaction between ligands and targets. Using the AutoDock software (Vina 1.5.6, http://autodock.scripps.edu/ ) (Shen et al., 2021; Trott and Olson, 2010), which is frequently used to determine the molecular contact force between protein and ligand, molecular docking was performed between andrographolide and berbamine with the top five targets individually. Both the compounds’ two-dimensional structural data was saved in the SDF format and downloaded from the PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ). Open Babel software was used to convert the andrographolide and berbamine molecular structure file from SDF format to PDB format. The three-dimensional structures of important target proteins were extracted from the RCSB PDB database ( https://www.rcsb.org/ ). Molecular docking was carried out by converting the molecular structure file into PDBQT format using the Auto Dock Tools 1.5.6 program. The visualization of the findings was computed using Discovery Studio software [29]. MD Simulation Molecular dynamics simulations for the selected protein-ligand complex were performed using Gromacs-2019.4. The system was configured with vacuum minimization for 1500 steps using the steepest descent technique. The complex structures were then solvated with the simple point charge (SPC) water model in a cubic periodic box of 0.5 nm dimensions. Next, the appropriate amount of Na + and Cl − counterions were added to maintain a salt concentration of 0.15 M in the complex systems. Every structure that emerged from the NPT equilibration phase underwent a final production run in the NPT ensemble for a simulated duration of 100 ns. The GROMACS simulation suite of Protein RMSD, RMSF, RG, SASA, and H-Bond was used to conduct trajectory analysis [30]. RESULTS Human targets for andrographolide and berbamine were obtained from CTD and overlapped with targets of GB and viral infections. Andrographolide and berbamine, which was identified as the synergistic combination to target glioblastoma, were found to interact with 128 and 134 proteins, respectively. Venn diagrams depicted in Fig. 6.1 illustrate the shared targets between GB and viral infections, andrographolide and berbamine for SARS-CoV-2 (Fig. 1 A), HPV (Fig. 1 B), and CMV (Fig. 1 C). Upon preparing Venn Diagram, the number of common targets which can be targeted by combination of andrographolide and berbamine for GBM-SARS-CoV-2, GBM-HPV and GBM-CMV were 97, 76 and 70 (Fig. 1 ). The large number of common factors between the viral co-occurrences considered with GB and the phytocompound(s) -targets depict possible successful targeting of GB-viral complexities with the combination of andrographolide and berbamine [31]. Construction of Protein-Protein Interaction (PPI) Network Construction and acquisition of critical targets acquisition The network of SARS-CoV-2, comprised 97 nodes and 431 edges with a network density of 0.105 (Fig. 2 A). The PPI network for HPV had 76 nodes and 339 edges, with a density of 0.133, while the CMV-associated network contained 70 nodes and 262 edges with a density of 0.130 (Fig. 2 C). The networks were further analyzed using the MCODE algorithm, which identified distinct clusters in each case. Upon examining the MCODE clusters for all three viral associations (SARS-CoV-2, HPV, and CMV) with andrographolide and berbamine targets, various network properties were identified (Fig. 3 ). In the GB-SARS-CoV-2 PPI network, three distinct clusters were formed, with the top cluster (cluster 1) having a score of 6.526, consisted of 20 nodes and 62 edges (Fig. 3 A). The network of GB-HPV targets formed three clusters, wherein cluster 1 had 8 nodes and 19 edges, with a score of 5.429, while the other two clusters scored 5.286 and 3.857, respectively (Fig. 3 B). For GB-CMV, four clusters were identified, with the highest-scoring cluster (cluster 3) achieving an MCODE score of 4.5, comprising 16 nodes and 46 edges ((Fig. 3 C)). These findings highlight the complex interactions within the PPI networks of shared targets, offering insights into potential therapeutic interventions for these co-occurring diseases. The interaction of factors associated with GB with SARS-CoV-2, HPV and CMV point towards the fact that there might be a strong interplay in the co-occurrences, which could be directly targeted by andrographolide and berbamine [32]. The target nodes from the network were proceeded with cytoNCA plugin to get the properties of each node, and then the nodes with a degree of more than 10 were selected for further analysis (Table 1 ). Similar method was used by Jiang et al., where kaempferol was modelled as a potential pharmacological agent for COVID-19/PF co-occurrence [33]. The analysis resulted in the identification of three key targets: SRC, ERBB2 and PRKCA. SRC is a non-receptor tyrosine kinase involved in numerous cellular processes such as proliferation, differentiation, and survival [34]. In the context of COVID-19, SRC activation is associated with the regulation of the cell cytoskeleton, facilitating viral entry and replication [35]. The SARS-CoV-2 spike protein has been found to enhance platelet activity by interacting with integrin αIIbβ3, leading to increased platelet aggregation and potential blood clots in COVID-19 patients [36]. Targeting SRC has been shown to reduce SARS-CoV-2 viral load as well as HPV oncoproteins E6 and E7, while its inhibition also downregulates proteins involved in cell cycle regulation and survival. HPV16 E7 oncoprotein could interact with AKT and Src signaling, promoting cervical cancer progression [37]. Additionally, CMV envelope protein US28 might regulate SRC activity during CMV latency, balancing viral replication and latency establishment [38]. In glioblastoma, SRC activation drives tumour invasiveness and is linked to poor prognosis, making it a potential target for treatment [39]. For example, PPFIBP1, which is associated with increased invasion in glioblastoma, activated key pathways like FAK and SRC [40]. Similarly, GBP5 contributed to tumour growth and invasion via the SRC/ERK1/2/MMP3 pathway, suggesting that targeting SRC may offer therapeutic benefits in glioblastoma [41]. Additionally, along the progression factors for GB and viruses, SRC is also a common target for andrographolide and berbamine, making it an effective therapeutic target through these phytocompounds. ERBB2 plays a significant role in various cancers, such as ACE2 expression has been linked to EGFR pathway activation in non-small cell lung cancer (NSCLC) cells, potentially affecting COVID-19 outcomes [42]. Inhibitors of ErbB family proteins have been shown to suppress SARS-CoV-2 and other viruses by targeting host factors, reducing viral entry and replication, inflammation, and organ damage [43]. ErbB2 is involved in HPV infection, enhancing the activity of the long control region, a key regulatory region. Inhibition of ErbB2 has been shown to suppress the transformation process driven by HPV16 E6 in cervical cancer cells, making it a potential target for cancer treatment [44]. The higher expression of ERBB2 is linked to more aggressive forms of the disease and poorer patient outcomes, correlating with immune checkpoint proteins and influencing immune response in tumours [45]. Therefore, targeting ERBB2 can be instrumental in targeting the co-occurrence of viral infections with GB, since it has emerged as a common target of andrographolide and berbamine. PRKCA is a protein kinase involved in multiple cellular functions, including cancer progression, migration, invasion, angiogenesis, and metastasis [4746 Although its specific connection to SARS-CoV-2 and CMV is not directly mentioned, the role of protein kinases in regulating cellular processes may have implications for the disease. HPV has been reported in silico studies to have a dysregulated signature of factors, including PRKCA, which emerged as an oncogene [47]. PRKCA receptors have been implicated in glioma progression, with recent research confirming their involvement in astrocytic gliomas and identifying gene expression changes that may have diagnostic and therapeutic relevance [48]. Table 1 Depiction of targets with highest degree according to cytoNCA plugin of Cytoscape Name of target genes Degree SARS-CoV-2 HPV CMV SRC 46 39 46 ERBB2 23 20 23 PRKCA 23 18 23 LYN 20 -NA- 20 KDR 20 18 -NA- ABL1 -NA- 18 19 Gene Ontology Enrichment Analysis Gene Ontology (GO) enrichment analysis for critical viral targets and GB, revealed significant associations with various molecular functions, biological processes, and cellular components, highlighting their involvement in key signaling pathways (Fig. 4 ). In terms of Gene Ontology - Biological Processes (GO-BP) (Fig. 4 A), the most significantly enriched terms included regulation of the ERK1 and ERK2 cascade, positive regulation of cell adhesion, regulation of the MAPK cascade, and other key cellular processes like cell communication, population proliferation, and signaling (Fig. 4 B). This is in sync with the previously reported mechanism of andrographolide, which enhanced the phosphorylation of the c-Raf/MEK/ERK pathway in GB8401 cells [49]. Additionally, berbamine has been shown to significantly suppress phosphorylation of NF-κB and MAPK in a macrophage cell line. It highlights the potential impact of berbamine on the co-infection of studied viruses and GB [50]. Gene Ontology - Cellular Component (GO-CC) was studied to analyze the impact of treatment on cellular component-related factors. The enrichment highlighted areas like postsynaptic specialization, ruffles, membrane rafts, membrane microdomains, extrinsic components of the cytoplasmic side of the plasma membrane, and the cell leading edge (Fig. 4 C). These enrichments emphasized the diverse roles of the critical targets in various cellular structures and functions upon the co-occurrence of GB and viral infections, which can be targeted by andrographolide and berbamine. Further analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment revealed that the critical targets were mainly associated with pathways such as resistance to EGFR tyrosine kinase inhibitors, ErbB signaling, focal adhesion, VEGF signaling, and adherens junctions, among others (Fig. 4 D). RTK alterations are key drivers in GB progression and treatment resistance [51]. Changes in VEGFR2, EGFR, PDGFR and ERBB2 can contribute to tumour growth, angiogenesis, and poor prognosis [52]. For example, EGFR and MET amplifications are linked to aggressive subtypes [54], while PDGFR and ERBB2 influence survival and therapy response [51]. Studying these alterations helps in identifying therapeutic targets and developing personalized treatments for GB. The enriched pathways provided insight into the molecular mechanisms by which viral infections may influence glioblastoma progression and offer potential therapeutic targets for intervention. Key Transcription Factors Acquisition Transcription factors could be obtained linked to four (ERBB2, KDR, SRC and PRKCA) of all the six identified critical targets (Fig. 5 ). The TFs-target network contained 40 nodes. Red nodes represented the critical targets, while turquoise squares represented TFs. Among these, ERBB2 was the critical target connected to the most transcription factors. The critical factors obtained after cytoNCA analysis were subjected to a transcription factor study, which provides insight into genetic regulation and pathway mapping. Concerning viruses, ERBB2 is related to host cell proliferation, entry of virus [54], immune cell evasion [55]. While, for GB, it is related to proliferation [56]. Efficacy of binding of common Critical Targets with phytocompounds The top five targets with the highest degree scores were proto-oncogene tyrosine-protein kinase SRC (SRC), v-erb-b2 avian erythroblastic leukemia viral oncogene homolog 2 (ERBB2), protein kinase C alpha (PRKCA), LYN Proto-Oncogene (LYN), Kinase insert domain receptor (KDR) and ABL proto-oncogene 1, non-receptor tyrosine kinase (ABL1) were proceeded for docking (Table 2 ) A binding energy less than 0 indicates spontaneous binding of the ligand and receptor. The lower binding energy indicates a better binding effect. The literature indicates that binding energy < − 7 kcal mol − 1 indicates a promising binding activity, therefore, − 11.11 kcal mol − 1 binding energy represents stable and favorable interaction between SRC and berbamine [575]. The better docking result was selected for molecular docking visualization by using Discovery Studio software (Fig. 6 ). The image illustrates the molecular docking interactions of andrographolide and berbamine with various protein targets, highlighting different types of bonds (Fig. 6 ). The Alkyl and Pi-Alkyl interactions stabilize the ligand in the hydrophobic pockets of the protein, and Pi-Sigma and Pi-Pi stacked interactions help position the ligand and add stability to the complex. They include conventional hydrogen bonds that are critical for strong and specific binding between the ligand and protein, and van der Waals interactions that provide additional weak stabilization. Table 2 Depiction of binding scores targets with phytocompounds Phytocompounds Name of Targets Andrographolide Berbamine SRC -7.8 -11.11 PRKCA -7.5 -8 LYN -7 -7.8 ERBB2 -7.2 -8.4 KDR -7.5 -8.2 ABL1 -7.3 -9.3 6HOU -7.5 -8 Molecular dynamics A 100-nanosecond (ns) all-atom molecular dynamics (MD) simulation was conducted to assess the stability of the protein-ligand complexes. Specifically, the analysis focused on the SRC bound to the andrographolide and berbamine, which included the average values for root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), and solvent accessible surface area (SASA). Hydrogen bond (H-bond) interactions seem to play a crucial role in the complex stability of SRC with andrographolide and berbamine. During the simulation, the hydrogen bonds observed in molecular docking analysis were confirmed, reinforcing the stability of the complexes. Figure 7 depicts the formation and consistency of hydrogen bonds throughout the simulation, further confirming that the protein-ligand interactions remained stable. These findings are in line with previous studies emphasizing the role of stable hydrogen bonding and conformational rigidity in enhancing the efficacy of kinase-targeting ligands [58]. The radius of gyration (Rg) is a measure of the protein's compactness, representing the mass-weighted root mean square distance of atoms from their center of mass. A consistent Rg value suggests that the protein maintains its compact, folded state throughout the simulation. The Rg plot (shown in Fig. 7 ) indicates that the complexes exhibited stable folding and size during the simulation, with little variation in compactness over time. Consistent Rg values implied that the overall compactness and solvent exposure of SRC did not undergo significant alterations, which is critical for maintaining functional structural states in a biological environment [59]. SASA measures the solvent-exposed surface area of the SRC, providing insights into how the protein interacts with its environment, particularly water molecules. The average SASA values over the 100 ns period showed no significant changes in solvent exposure during the simulation. This implies that the overall surface area of the protein accessible to solvents remained stable, suggesting minimal structural changes (Fig. 7 ). The minimal fluctuation in SASA and compact Rg values supported a retained functional state of SRC in complex [60]. RMSF values measure the flexibility of individual amino acids in the protein. Higher RMSF values indicate greater fluctuations and more flexibility, which could lead to structural instability. RMSF values were calculated over the entire 100 ns simulation. The results suggest that no significant fluctuations occurred during the simulation, implying that the protein folding was stable throughout the simulation (Fig. 7 ). RMSF analysis further confirmed limited residue-level flexibility, reinforcing the idea of a stable protein fold during ligand interaction [61]. The RMSD values provide insights into the structural stability of the protein-ligand complex of SRC with andrographolide and berbamine over the simulation time, wherein the lower RMSD indicates more stable the protein’s conformation [62]. In this study, the average RMSD over the 100 ns simulation was consistent, indicating that the SRC interaction with andrographolide and berbamine maintained structural stability throughout the simulation. The average RMSD values for the proteins were relatively low, suggesting minimal deviation from the original structure (Fig. 7 ). Overall, the MD simulation results indicate that andrographolide and berbamine with SRC complexes were structurally stable over the 100 ns period, with minimal deviations in RMSD, RMSF, Rg, and SASA values, as well as consistent hydrogen bonding, suggesting robust and stable interactions between the proteins and ligands. These results highlight the potential of andrographolide and berbamine as stable SRC inhibitors, functioning as therapeutic agents for targeting SRC-related pathways in the treatment of glioblastoma co-occurrence with viruses SARS-CoV-2, HPV and CMV. CONCLUSION This study is the first to elucidate the effect of andrographolide and berbamine against GBM co-occurrence with SARS-CoV-2, HPV and CMV individually by bioinformatics and systems pharmacology tools. The underlying mechanisms of andrographolide and berbamine against the viral co-occurrence may be related to bind to SRC, PRKCA, LYN, ERBB2, KDR, ABL1 and 6HOU. The phytocompounds might regulate protein tyrosine kinase activity and ERK cascade. Molecular and metabolic analyses revealed significant disruption of structural proteins and key metabolic pathways, leading to impaired cellular function. Additionally, their interaction with shared targets like SRC suggests potential for dual action against glioblastoma and associated viral infections. Overall, andrographolide-berbamine combination offers a promising and multifaceted approach to glioblastoma treatment. The findings depict the potential of these phytocompounds as multitargeted agents for treating GB with co-occurring viral infections. Declarations Acknowledgement The authors would like to acknowledge the Jaypee Institute of Information Technology for the necessary resources required for the work. Data availability statement The data supporting the findings of this study are available from the corresponding author upon reasonable request. Funding statement None Author contribution statement V.P. performed the experiments, analyzed the data, and wrote the manuscript. R.G. designed the experiments, reviewed and approved the final manuscript. Conflict of interest disclosure The authors declare no conflict of interest. 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M., Biradar, M. S., Esther, V. A., Murav, A. K., and Maurya, S. N. (2025). Molecular docking, drug-likeness properties, and toxicity prediction of alkaloidal phytoconstituents of Piper longum against monoamine oxidase enzyme-A as an anti-depressive agent. Discover Chemistry 2 , 105. Patil, R., Yadav, A., Mishra, A., Gupta, M., Srivastava, S., and Kumar, V. (2010). Optimized hydrophobic interactions and hydrogen bonding at the target–ligand interface lead the pathways of drug-designing. PLoS One 5 , e12029. Hon, K. W., Kamaruddin, M. F., Tan, W. S., Chan, K. Y., Lim, W. F., and Abidin, I. Z. (2025). Identification of SRC, AKT1 and MAPK3 as therapeutic targets of apigenin and luteolin in colorectal and colon carcinoma through network pharmacology. Food Biosci. 67 , 106313. Yang, S., Banavali, N. K., and Roux, B. (2009). Mapping the conformational transition in Src activation by cumulating the information from multiple molecular dynamics trajectories. Proc. Natl. Acad. Sci. USA 106 , 3776–3781. Zhang, L., Wang, P., Yang, Z., Du, F., Li, Z., Wu, C., and Zhou, G. (2020). Molecular dynamics simulation exploration of the interaction between curcumin and myosin combined with the results of spectroscopy techniques. Food Hydrocoll. 101 , 105455. Aier, I., Varadwaj, P. K., and Raj, U. (2016). Structural insights into conformational stability of both wild-type and mutant EZH2 receptor. Sci. Rep. 6 , 34984. 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-7447288","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":510591446,"identity":"36f44d69-2a20-488c-912f-a3cc344e85d1","order_by":0,"name":"Vijeta Prakash","email":"","orcid":"","institution":"Jaypee Institute of Information Technology","correspondingAuthor":false,"prefix":"","firstName":"Vijeta","middleName":"","lastName":"Prakash","suffix":""},{"id":510591447,"identity":"04d82c95-192d-482c-a14e-0b01f461fe1d","order_by":1,"name":"reema gabrani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYDADAxCRUMHGwwbhSxCr5QxQCxtJWhjbgAQbAZXm7c1HN/xgqJU3lz787MHDeXwyfPINjB9+MFjk4dIic+ZY2s0ehuOGO/vSzA0St4EdxizZwyBRjEuLhESO2Q0ehmOMG84wmElAtTBIAyUSG3BpkX//7eYfhmP2G86wf5NInAOx5TdeLRI8bLd5GGoSN5zhAdrSANbCht8WnjSz2zIGB5J39vCUSSQcA2lJbLPsMcCjhf3ws5tvKupst/Owb5P8UXPMXr758OEbPyrqcGqBAIPDMNYxIGZsgEYTXlAHY9QQVDoKRsEoGAUjDwAAMWtKpJLsX54AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-9306-2600","institution":"Jaypee Institute of Information Technology","correspondingAuthor":true,"prefix":"","firstName":"reema","middleName":"","lastName":"gabrani","suffix":""}],"badges":[],"createdAt":"2025-08-24 16:05:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7447288/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7447288/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90893569,"identity":"1a9888ff-278f-4294-8f1d-a282de2280aa","added_by":"auto","created_at":"2025-09-09 11:24:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":66081,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagrams depicting the common targets of andrographolide and berbamine related to GB with co-occurrence of (A) SARS-CoV-2 (B) HPV (C) CMV\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7447288/v1/c64bbe957696cfb63c9e39b7.png"},{"id":90897295,"identity":"dc7b2efb-f44e-4f57-bcfe-c077e2b2ca3b","added_by":"auto","created_at":"2025-09-09 11:40:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1085094,"visible":true,"origin":"","legend":"\u003cp\u003eThe protein-protein interaction network of targets of (A) SARS-CoV-2 (B) HPV (C) CMV\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7447288/v1/e66c0187cb92378be36f81b7.png"},{"id":90893574,"identity":"45d4a439-8669-4a86-8d1b-0346b34cbb5f","added_by":"auto","created_at":"2025-09-09 11:24:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":431222,"visible":true,"origin":"","legend":"\u003cp\u003eMCODE clusters of andrographolide and berbamine targets in combination with GB and (A) SARS-CoV-2 (B) HPV (C) CMV\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7447288/v1/0994344ef434e27dca78ac83.png"},{"id":90895200,"identity":"6b697327-4ee4-49ae-a7af-fbba1f45ecf9","added_by":"auto","created_at":"2025-09-09 11:32:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":117334,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentation of gene ontology of critical targets (A) molecular function enrichment (B) biological process enrichment (C) cellular component enrichment (D) KEGG pathway enrichment\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7447288/v1/7444be03543230b945924035.png"},{"id":90897302,"identity":"bc2c8d8b-61eb-4330-880c-57a315fd55e9","added_by":"auto","created_at":"2025-09-09 11:40:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":70791,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork of transcription factors of critical genes as obtained from TRRUST\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7447288/v1/65e84b4521629f44d297eae4.png"},{"id":90893578,"identity":"52d783b0-2912-45d1-b6c9-fc0b955c7b0f","added_by":"auto","created_at":"2025-09-09 11:24:34","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":321936,"visible":true,"origin":"","legend":"\u003cp\u003e2D depiction of phytocompound interaction with critical targets with andrographolide (A) SRC(C) PRKCA(E) LYN(G) ERBB2(I) KDR(K) ABL1(M) 6HOU. Interaction with critical targets with berbamine is shown in (B) SRC (D) PRKCA(F) LYN(H) ERBB2(J) KDR(L) ABL1(N) 6HOU\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7447288/v1/1aad63f33ce1fa92d8c30139.png"},{"id":91148868,"identity":"a17eddae-d899-4abf-a692-769036b6843c","added_by":"auto","created_at":"2025-09-12 06:46:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2776153,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7447288/v1/aa0e6760-8b3a-41c4-9794-683fef686a4b.pdf"}],"financialInterests":"","formattedTitle":"Computational Analysis of Andrographolide and Berbamine for Targeting Glioblastoma Associated with Viral Infections","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eGlioblastoma treatment is a significant global challenge, exacerbated by immune suppression and oncomodulation, where viral proteins and non-coding RNAs promote oncogenic processes [1]. These processes disrupt signaling pathways, promoting cell proliferation, invasion, immune evasion, and epigenetic changes, while inhibiting apoptosis and DNA repair [2]. They also affect virus latency, reactivation, and angiogenesis. SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has severely impacted global health. Researchers have found that bone marrow mesenchymal stem cells (BMSCs), neural precursor cells (NPCs), and endothelial cells (ECs) express higher levels of ACE2, making them more susceptible to infection [3]. Glioblastoma cells have shown elevated ACE2 expression, increasing their vulnerability to severe infections [4]. Studies suggest that ACE2 may facilitate CNS invasion by SARS-CoV-2, aided by CTSB/L, highlighting the virus's neurotropic characteristics and potential therapeutic targets for neurological symptoms in COVID-19 patients [5].\u003c/p\u003e\u003cp\u003eSeveral tumours, including glioblastomas, medulloblastomas, and neuroblastomas, as well as cancers of the prostate, breast, colon, and ovary, are associated with Human cytomegalovirus (HCMV) [6]. Recent data reveal that over 90% of malignant gliomas are linked to HCMV, promoting tumour growth and aggressiveness [7]. Research by Priel et al. found that the HCMV genome and proteins are present in more than 64% of glioblastoma tissues [8]. Cobbs et al. first observed increased tumour growth when glioblastoma cells were exposed to HCMV [9]. This suggests that HCMV infection may play a crucial role in development and progression of glioblastoma.\u003c/p\u003e\u003cp\u003eAdditionally, a study detected Human papillomavirus (HPV) DNA in 23% of glioblastoma samples, with HPV16 and HPV6 being the most prevalent strains. Ongoing viral protein production was confirmed in GBM cells through chromogenic in situ hybridization (CISH) and immunohistochemistry (IHC) [10]. HPV infection was linked to poorer patient outcomes, suggesting it may be an independent prognostic factor, highlighting the need for preventive strategies [10].\u003c/p\u003e\u003cp\u003eGrowing evidence indicates that natural products offer promising alternatives for combating viral infections. Andrographolide exhibits strong antiviral properties, blocking viral entry, inhibiting RNA and DNA synthesis, and modulating immune responses [11]. It has demonstrated efficacy against influenza, hepatitis B and C, dengue, HIV, and SARS-CoV-2, reducing viral replication and improving immune responses [12]. Preclinical studies and clinical trials support its safety and therapeutic potential, particularly in combination therapies to enhance efficacy and reduce drug resistance [13].\u003c/p\u003e\u003cp\u003eSimilarly, berbamine has shown effectiveness against multiple viruses, including hepatitis B, influenza and SARS-CoV-2 mainly by reducing replication, and Herpes simplex virus (HSV) by lowering viral load [14]. Preclinical studies have confirmed berbamine\u0026rsquo;s antiviral activity, and ongoing clinical trials are evaluating its safety and effectiveness, particularly against COVID-19 [15]. Berbamine has also exhibited synergistic effects when used alongside other antiviral agents, enhancing therapeutic outcomes [16].\u003c/p\u003e\u003cp\u003eThe combination of andrographolide and berbamine have been found to act synergistically against GB cells (paper from lab under communication). The integrative study in this paper, combines bioinformatics analysis and molecular docking, to reveal key interactions between phytocompounds and viral disease-related targets in glioblastoma, potentially uncovering novel therapeutic avenues for co-infections like SARS-CoV-2, HPV, and CMV.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eIdentification of targets for phytocompounds\u003c/h2\u003e\u003cp\u003eThe targets of andrographolide and berbamine were searched from Comparative Toxicoomics Database (CTD, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ctdbase.org/\u003c/span\u003e\u003cspan address=\"http://ctdbase.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [17], Swiss Target Prediction (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://swisstargetprediction.ch/\u003c/span\u003e\u003cspan address=\"http://swisstargetprediction.ch/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [18], and TargetNet (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://targetnet.scbdd.com/\u003c/span\u003e\u003cspan address=\"http://targetnet.scbdd.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [19].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCollecting Targets related to the viral diseases and GB\u003c/h3\u003e\n\u003cp\u003eTargets related to GB, SARS-CoV-2, HPV, and CMV were obtained from DisGeNET (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.disgenet.org/home/\u003c/span\u003e\u003cspan address=\"https://www.disgenet.org/home/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), CTD [20], GeneCards (\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) [21], Therapeutic Target Database (TTD, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://db.idrblab.net/ttd/\u003c/span\u003e\u003cspan address=\"http://db.idrblab.net/ttd/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), PubChem (\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) [22] and DrugBank database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.drugbank.com\u003c/span\u003e\u003cspan address=\"https://www.drugbank.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [23]. Subsequently, the targets were mapped using Uniprot database to obtain a standard nomenclature (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eStudying the overlapping target genes between phytocompounds and viral disease\u003c/h3\u003e\n\u003cp\u003eVenny 2.0, the Venn Diagram tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinfogp.cnb.csic.es/tools/venny/\u003c/span\u003e\u003cspan address=\"https://bioinfogp.cnb.csic.es/tools/venny/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to plot Venn diagrams of common targets between the phytocompounds (andrographolide and berbamine), GB and the viral diseases (SARS-CoV-2, HPV and CMV) [24].\u003c/p\u003e\n\u003ch3\u003eConstruction of PPI network and analysis of critical targets\u003c/h3\u003e\n\u003cp\u003eThe common targets were subjected to the STRING 11.5 database (\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) for construction of PPI network [25]. \u0026ldquo;\u003cem\u003eHomo sapiens\u003c/em\u003e\u0026rdquo; was selected under the tab of \u0026ldquo;organisms\u0026rdquo; and the minimum required interaction score was set to 0.4. Cytoscape 3.7.2 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cytoscape.org/\u003c/span\u003e\u003cspan address=\"https://cytoscape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to visualize the PPI network [26]. The CytoNCA plug-in was used in Cytoscape to analyze topological parameters (degree, betweenness, closeness, network and eigenvector). MCODE plugin was utilized to score the clusters of network [26]. Of note, the top five targets with the highest degree values in the CytoNCA output was further subjected to docking.\u003c/p\u003e\n\u003ch3\u003eKey Transcription Factors Analysis of Critical Targets\u003c/h3\u003e\n\u003cp\u003eThe association of transcription factors with the targets was done through the tool, Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.grnpedia.org/trrust/\u003c/span\u003e\u003cspan address=\"https://www.grnpedia.org/trrust/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [27]. A huge amount of information on the 8,444 transcription factors (TFs)-target network is available on the database. The targets were fed as input to TRRUST database with the species selection of \u0026ldquo;Human.\u0026rdquo; Using the Cytoscape 3.7.2 program, the top 10 TFs ranking according to p value, from small to large, were chosen to build the TFs-target network.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eTissue-Specific Enrichment Analysis of Critical Targets\u003c/h2\u003e\u003cp\u003eGenotype-Tissue Expression (GETx) is an online resource for researching human tissue expression and genetic diversity (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gtexportal.org/\u003c/span\u003e\u003cspan address=\"https://www.gtexportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Tissue-specific enrichment analysis was performed on the top targets, ranked from high to low in terms of the degree values of the modules. The heat map displayed the relationship between various samples and targets; the targets' more significant tissues were represented by darker colors. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology Enhancement Examinations of Important Targets was done using GProfiler (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biit.cs.ut.ee/gprofiler/gost\u003c/span\u003e\u003cspan address=\"https://biit.cs.ut.ee/gprofiler/gost\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), GO enrichment analysis was carried out for biological process (BP), molecular function (MF), and cellular component (CC), in addition to KEGG pathway enrichment analysis [28].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMolecular Docking Analysis of the Top Targets\u003c/h3\u003e\n\u003cp\u003eMolecular docking is frequently utilized in treatment compound detection to study the interaction between ligands and targets. Using the AutoDock software (Vina 1.5.6, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://autodock.scripps.edu/\u003c/span\u003e\u003cspan address=\"http://autodock.scripps.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Shen et al., 2021; Trott and Olson, 2010), which is frequently used to determine the molecular contact force between protein and ligand, molecular docking was performed between andrographolide and berbamine with the top five targets individually. Both the compounds\u0026rsquo; two-dimensional structural data was saved in the SDF format and downloaded from the PubChem database (\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). Open Babel software was used to convert the andrographolide and berbamine molecular structure file from SDF format to PDB format. The three-dimensional structures of important target proteins were extracted from the RCSB PDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Molecular docking was carried out by converting the molecular structure file into PDBQT format using the Auto Dock Tools 1.5.6 program. The visualization of the findings was computed using Discovery Studio software [29].\u003c/p\u003e\n\u003ch3\u003eMD Simulation\u003c/h3\u003e\n\u003cp\u003eMolecular dynamics simulations for the selected protein-ligand complex were performed using Gromacs-2019.4. The system was configured with vacuum minimization for 1500 steps using the steepest descent technique. The complex structures were then solvated with the simple point charge (SPC) water model in a cubic periodic box of 0.5 nm dimensions. Next, the appropriate amount of Na\u003csup\u003e+\u003c/sup\u003e and Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e counterions were added to maintain a salt concentration of 0.15 M in the complex systems. Every structure that emerged from the NPT equilibration phase underwent a final production run in the NPT ensemble for a simulated duration of 100 ns. The GROMACS simulation suite of Protein RMSD, RMSF, RG, SASA, and H-Bond was used to conduct trajectory analysis [30].\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eHuman targets for andrographolide and berbamine were obtained from CTD and overlapped with targets of GB and viral infections. Andrographolide and berbamine, which was identified as the synergistic combination to target glioblastoma, were found to interact with 128 and 134 proteins, respectively. Venn diagrams depicted in Fig. 6.1 illustrate the shared targets between GB and viral infections, andrographolide and berbamine for SARS-CoV-2 (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA), HPV (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB), and CMV (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e\n\u003cp\u003eUpon preparing Venn Diagram, the number of common targets which can be targeted by combination of andrographolide and berbamine for GBM-SARS-CoV-2, GBM-HPV and GBM-CMV were 97, 76 and 70 (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The large number of common factors between the viral co-occurrences considered with GB and the phytocompound(s) -targets depict possible successful targeting of GB-viral complexities with the combination of andrographolide and berbamine [31].\u003c/p\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eConstruction of Protein-Protein Interaction (PPI) Network Construction and acquisition of critical targets acquisition\u003c/h2\u003e\n \u003cp\u003eThe network of SARS-CoV-2, comprised 97 nodes and 431 edges with a network density of 0.105 (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). The PPI network for HPV had 76 nodes and 339 edges, with a density of 0.133, while the CMV-associated network contained 70 nodes and 262 edges with a density of 0.130 (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). The networks were further analyzed using the MCODE algorithm, which identified distinct clusters in each case.\u003c/p\u003e\n \u003cp\u003eUpon examining the MCODE clusters for all three viral associations (SARS-CoV-2, HPV, and CMV) with andrographolide and berbamine targets, various network properties were identified (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). In the GB-SARS-CoV-2 PPI network, three distinct clusters were formed, with the top cluster (cluster 1) having a score of 6.526, consisted of 20 nodes and 62 edges (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). The network of GB-HPV targets formed three clusters, wherein cluster 1 had 8 nodes and 19 edges, with a score of 5.429, while the other two clusters scored 5.286 and 3.857, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). For GB-CMV, four clusters were identified, with the highest-scoring cluster (cluster 3) achieving an MCODE score of 4.5, comprising 16 nodes and 46 edges ((Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC)). These findings highlight the complex interactions within the PPI networks of shared targets, offering insights into potential therapeutic interventions for these co-occurring diseases. The interaction of factors associated with GB with SARS-CoV-2, HPV and CMV point towards the fact that there might be a strong interplay in the co-occurrences, which could be directly targeted by andrographolide and berbamine [32].\u003c/p\u003e\n \u003cp\u003eThe target nodes from the network were proceeded with cytoNCA plugin to get the properties of each node, and then the nodes with a degree of more than 10 were selected for further analysis (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Similar method was used by Jiang et al., where kaempferol was modelled as a potential pharmacological agent for COVID-19/PF co-occurrence [33].\u003c/p\u003e\n \u003cp\u003eThe analysis resulted in the identification of three key targets: SRC, ERBB2 and PRKCA. SRC is a non-receptor tyrosine kinase involved in numerous cellular processes such as proliferation, differentiation, and survival [34]. In the context of COVID-19, SRC activation is associated with the regulation of the cell cytoskeleton, facilitating viral entry and replication [35]. The SARS-CoV-2 spike protein has been found to enhance platelet activity by interacting with integrin \u0026alpha;IIb\u0026beta;3, leading to increased platelet aggregation and potential blood clots in COVID-19 patients [36]. Targeting SRC has been shown to reduce SARS-CoV-2 viral load as well as HPV oncoproteins E6 and E7, while its inhibition also downregulates proteins involved in cell cycle regulation and survival. HPV16 E7 oncoprotein could interact with AKT and Src signaling, promoting cervical cancer progression [37]. Additionally, CMV envelope protein US28 might regulate SRC activity during CMV latency, balancing viral replication and latency establishment [38]. In glioblastoma, SRC activation drives tumour invasiveness and is linked to poor prognosis, making it a potential target for treatment [39]. For example, PPFIBP1, which is associated with increased invasion in glioblastoma, activated key pathways like FAK and SRC [40]. Similarly, GBP5 contributed to tumour growth and invasion via the SRC/ERK1/2/MMP3 pathway, suggesting that targeting SRC may offer therapeutic benefits in glioblastoma [41]. Additionally, along the progression factors for GB and viruses, SRC is also a common target for andrographolide and berbamine, making it an effective therapeutic target through these phytocompounds.\u003c/p\u003e\n \u003cp\u003eERBB2 plays a significant role in various cancers, such as ACE2 expression has been linked to EGFR pathway activation in non-small cell lung cancer (NSCLC) cells, potentially affecting COVID-19 outcomes [42]. Inhibitors of ErbB family proteins have been shown to suppress SARS-CoV-2 and other viruses by targeting host factors, reducing viral entry and replication, inflammation, and organ damage [43]. ErbB2 is involved in HPV infection, enhancing the activity of the long control region, a key regulatory region. Inhibition of ErbB2 has been shown to suppress the transformation process driven by HPV16 E6 in cervical cancer cells, making it a potential target for cancer treatment [44]. The higher expression of ERBB2 is linked to more aggressive forms of the disease and poorer patient outcomes, correlating with immune checkpoint proteins and influencing immune response in tumours [45]. Therefore, targeting ERBB2 can be instrumental in targeting the co-occurrence of viral infections with GB, since it has emerged as a common target of andrographolide and berbamine.\u003c/p\u003e\n \u003cp\u003ePRKCA is a protein kinase involved in multiple cellular functions, including cancer progression, migration, invasion, angiogenesis, and metastasis [4746 Although its specific connection to SARS-CoV-2 and CMV is not directly mentioned, the role of protein kinases in regulating cellular processes may have implications for the disease. HPV has been reported in silico studies to have a dysregulated signature of factors, including PRKCA, which emerged as an oncogene [47]. PRKCA receptors have been implicated in glioma progression, with recent research confirming their involvement in astrocytic gliomas and identifying gene expression changes that may have diagnostic and therapeutic relevance [48].\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDepiction of targets with highest degree according to cytoNCA plugin of Cytoscape\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eName of target genes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eDegree\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSARS-CoV-2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHPV\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCMV\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSRC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eERBB2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePRKCA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLYN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-NA-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eKDR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-NA-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eABL1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-NA-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eGene Ontology Enrichment Analysis\u003c/h2\u003e\n \u003cp\u003eGene Ontology (GO) enrichment analysis for critical viral targets and GB, revealed significant associations with various molecular functions, biological processes, and cellular components, highlighting their involvement in key signaling pathways (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). In terms of Gene Ontology - Biological Processes (GO-BP) (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA), the most significantly enriched terms included regulation of the ERK1 and ERK2 cascade, positive regulation of cell adhesion, regulation of the MAPK cascade, and other key cellular processes like cell communication, population proliferation, and signaling (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). This is in sync with the previously reported mechanism of andrographolide, which enhanced the phosphorylation of the c-Raf/MEK/ERK pathway in GB8401 cells [49]. Additionally, berbamine has been shown to significantly suppress phosphorylation of NF-\u0026kappa;B and MAPK in a macrophage cell line. It highlights the potential impact of berbamine on the co-infection of studied viruses and GB [50].\u003c/p\u003e\n \u003cp\u003eGene Ontology - Cellular Component (GO-CC) was studied to analyze the impact of treatment on cellular component-related factors. The enrichment highlighted areas like postsynaptic specialization, ruffles, membrane rafts, membrane microdomains, extrinsic components of the cytoplasmic side of the plasma membrane, and the cell leading edge (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC). These enrichments emphasized the diverse roles of the critical targets in various cellular structures and functions upon the co-occurrence of GB and viral infections, which can be targeted by andrographolide and berbamine.\u003c/p\u003e\n \u003cp\u003eFurther analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment revealed that the critical targets were mainly associated with pathways such as resistance to EGFR tyrosine kinase inhibitors, ErbB signaling, focal adhesion, VEGF signaling, and adherens junctions, among others (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD). RTK alterations are key drivers in GB progression and treatment resistance [51]. Changes in VEGFR2, EGFR, PDGFR and ERBB2 can contribute to tumour growth, angiogenesis, and poor prognosis [52]. For example, EGFR and MET amplifications are linked to aggressive subtypes [54], while PDGFR and ERBB2 influence survival and therapy response [51]. Studying these alterations helps in identifying therapeutic targets and developing personalized treatments for GB. The enriched pathways provided insight into the molecular mechanisms by which viral infections may influence glioblastoma progression and offer potential therapeutic targets for intervention.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eKey Transcription Factors Acquisition\u003c/h2\u003e\n \u003cp\u003eTranscription factors could be obtained linked to four (ERBB2, KDR, SRC and PRKCA) of all the six identified critical targets (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The TFs-target network contained 40 nodes. Red nodes represented the critical targets, while turquoise squares represented TFs. Among these, ERBB2 was the critical target connected to the most transcription factors. The critical factors obtained after cytoNCA analysis were subjected to a transcription factor study, which provides insight into genetic regulation and pathway mapping. Concerning viruses, ERBB2 is related to host cell proliferation, entry of virus [54], immune cell evasion [55]. While, for GB, it is related to proliferation [56].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eEfficacy of binding of common Critical Targets with phytocompounds\u003c/h2\u003e\n \u003cp\u003eThe top five targets with the highest degree scores were proto-oncogene tyrosine-protein kinase SRC (SRC), v-erb-b2 avian erythroblastic leukemia viral oncogene homolog 2 (ERBB2), protein kinase C alpha (PRKCA), LYN Proto-Oncogene (LYN), Kinase insert domain receptor (KDR) and ABL proto-oncogene 1, non-receptor tyrosine kinase (ABL1) were proceeded for docking (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eA binding energy less than 0 indicates spontaneous binding of the ligand and receptor. The lower binding energy indicates a better binding effect. The literature indicates that binding energy\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;7 kcal mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e indicates a promising binding activity, therefore, \u0026minus;\u0026thinsp;11.11 kcal mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e binding energy represents stable and favorable interaction between SRC and berbamine [575]. The better docking result was selected for molecular docking visualization by using Discovery Studio software (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The image illustrates the molecular docking interactions of andrographolide and berbamine with various protein targets, highlighting different types of bonds (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The Alkyl and Pi-Alkyl interactions stabilize the ligand in the hydrophobic pockets of the protein, and Pi-Sigma and Pi-Pi stacked interactions help position the ligand and add stability to the complex. They include conventional hydrogen bonds that are critical for strong and specific binding between the ligand and protein, and van der Waals interactions that provide additional weak stabilization.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDepiction of binding scores targets with phytocompounds\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePhytocompounds\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eName of Targets\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAndrographolide\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBerbamine\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSRC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRKCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLYN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eERBB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eABL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6HOU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eMolecular dynamics\u003c/h2\u003e\n \u003cp\u003eA 100-nanosecond (ns) all-atom molecular dynamics (MD) simulation was conducted to assess the stability of the protein-ligand complexes. Specifically, the analysis focused on the SRC bound to the andrographolide and berbamine, which included the average values for root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), and solvent accessible surface area (SASA).\u003c/p\u003e\n \u003cp\u003eHydrogen bond (H-bond) interactions seem to play a crucial role in the complex stability of SRC with andrographolide and berbamine. During the simulation, the hydrogen bonds observed in molecular docking analysis were confirmed, reinforcing the stability of the complexes. Figure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e depicts the formation and consistency of hydrogen bonds throughout the simulation, further confirming that the protein-ligand interactions remained stable. These findings are in line with previous studies emphasizing the role of stable hydrogen bonding and conformational rigidity in enhancing the efficacy of kinase-targeting ligands [58].\u003c/p\u003e\n \u003cp\u003eThe radius of gyration (Rg) is a measure of the protein\u0026apos;s compactness, representing the mass-weighted root mean square distance of atoms from their center of mass. A consistent Rg value suggests that the protein maintains its compact, folded state throughout the simulation. The Rg plot (shown in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e) indicates that the complexes exhibited stable folding and size during the simulation, with little variation in compactness over time. Consistent Rg values implied that the overall compactness and solvent exposure of SRC did not undergo significant alterations, which is critical for maintaining functional structural states in a biological environment [59].\u003c/p\u003e\n \u003cp\u003eSASA measures the solvent-exposed surface area of the SRC, providing insights into how the protein interacts with its environment, particularly water molecules. The average SASA values over the 100 ns period showed no significant changes in solvent exposure during the simulation. This implies that the overall surface area of the protein accessible to solvents remained stable, suggesting minimal structural changes (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). The minimal fluctuation in SASA and compact Rg values supported a retained functional state of SRC in complex [60].\u003c/p\u003e\n \u003cp\u003eRMSF values measure the flexibility of individual amino acids in the protein. Higher RMSF values indicate greater fluctuations and more flexibility, which could lead to structural instability. RMSF values were calculated over the entire 100 ns simulation. The results suggest that no significant fluctuations occurred during the simulation, implying that the protein folding was stable throughout the simulation (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). RMSF analysis further confirmed limited residue-level flexibility, reinforcing the idea of a stable protein fold during ligand interaction [61].\u003c/p\u003e\n \u003cp\u003eThe RMSD values provide insights into the structural stability of the protein-ligand complex of SRC with andrographolide and berbamine over the simulation time, wherein the lower RMSD indicates more stable the protein\u0026rsquo;s conformation [62]. In this study, the average RMSD over the 100 ns simulation was consistent, indicating that the SRC interaction with andrographolide and berbamine maintained structural stability throughout the simulation. The average RMSD values for the proteins were relatively low, suggesting minimal deviation from the original structure (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eOverall, the MD simulation results indicate that andrographolide and berbamine with SRC complexes were structurally stable over the 100 ns period, with minimal deviations in RMSD, RMSF, Rg, and SASA values, as well as consistent hydrogen bonding, suggesting robust and stable interactions between the proteins and ligands.\u003c/p\u003e\n \u003cp\u003eThese results highlight the potential of andrographolide and berbamine as stable SRC inhibitors, functioning as therapeutic agents for targeting SRC-related pathways in the treatment of glioblastoma co-occurrence with viruses SARS-CoV-2, HPV and CMV.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study is the first to elucidate the effect of andrographolide and berbamine against GBM co-occurrence with SARS-CoV-2, HPV and CMV individually by bioinformatics and systems pharmacology tools. The underlying mechanisms of andrographolide and berbamine against the viral co-occurrence may be related to bind to SRC, PRKCA, LYN, ERBB2, KDR, ABL1 and 6HOU. The phytocompounds might regulate protein tyrosine kinase activity and ERK cascade. Molecular and metabolic analyses revealed significant disruption of structural proteins and key metabolic pathways, leading to impaired cellular function. Additionally, their interaction with shared targets like SRC suggests potential for dual action against glioblastoma and associated viral infections. Overall, andrographolide-berbamine combination offers a promising and multifaceted approach to glioblastoma treatment. The findings depict the potential of these phytocompounds as multitargeted agents for treating GB with co-occurring viral infections.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge the Jaypee Institute of Information Technology for the necessary resources required for the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eV.P. performed the experiments, analyzed the data, and wrote the manuscript. R.G. designed the experiments, reviewed and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest disclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eOizel, K., Yang, C., Renoult, O., Gautier, F., Do, Q. N., Joalland, N., \u0026hellip; and Pecqueur, C. (2020). Glutamine uptake and utilization of human mesenchymal glioblastoma in orthotopic mouse model. \u003cem\u003eCancer Metab.\u003c/em\u003e\u003cstrong\u003e8\u003c/strong\u003e, 1\u0026ndash;11.\u003c/li\u003e\n \u003cli\u003eGunasegaran, B., Ashley, C. 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Rep.\u003c/em\u003e\u003cstrong\u003e6\u003c/strong\u003e, 34984.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Bioinformatic analysis, Co-occurrence, Molecular docking, Phytocompounds, Protein-protein interaction (PPI) network, Synergy","lastPublishedDoi":"10.21203/rs.3.rs-7447288/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7447288/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGlioblastoma (GB) patients are prone to developing viral infections pertaining to a weakened immune system. Coronavirus disease 2019 (COVID-19), Cytomegalovirus (CMV) infection and Human Papillomavirus (HPV) infection have shown evidence to co-occur with GB individually. The combination of andrographolide and berbamine has previously been shown to synergistically inhibit the growth of GB. In this study, the common protein targets were identified for viral infections and GB and the identified phytocompounds interacting with the same targets was studied by bioinformatics and network pharmacology. To validate the targets of andrographolide and berbamine against GB-viral co-occurrence, a variety of open-source datasets and a Venn Diagram tool were used. Several molecular mechanisms were identified against GB and viral comorbidities (SARS-CoV-2, CMV, and HPV) by using several bioinformatic tools. Six common critical targets and 41 transcription factors have been identified using Venny 2.0. The critical targets were found to be enriched in pathways like EGFR tyrosine kinase inhibitor resistance, ErbB signaling pathway, which are \u0026nbsp;necessary for GB targeting. Berbamine and andrographolide indicated potential as therapeutic agents for glioblastoma, particularly in the context of co-infection with the studied viral diseases. The regulation of important targets (SRC, ERBB2, PRKCA, LYN, KDR, ABL1) and the ERK1 and ERK2 cascade seems to be connected to the underlying mechanisms. The study paves the way for developing novel treatments targeting glioblastoma and its associated viral comorbidities.\u003c/p\u003e","manuscriptTitle":"Computational Analysis of Andrographolide and Berbamine for Targeting Glioblastoma Associated with Viral Infections","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 11:24:29","doi":"10.21203/rs.3.rs-7447288/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":"1f218e91-8e4d-4c6d-84ae-c60d0c9e3519","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-09T11:24:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-09 11:24:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7447288","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7447288","identity":"rs-7447288","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}