Systematic In-silico Analysis of Fisetin-Proteins Interactions Revealing the PTGS2 as a Potential Therapeutic Target

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Ramjan Sheikh, Mahima Hoque Utsha, Jarin Tasnim, Md. Riyad Alam, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7955881/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 Fisetin is a nutraceutical that provides many health benefits, including anticancer, anti-aging, anti-inflammatory, and antidiabetic activities. The present study revealed the molecular mechanism of fisetin through the PCI and PPI interactions network analysis. The optimized geometry of fisetin, free energy, and polar response were estimated using Gaussian 9.0. AutoDock Vina was used to perform the molecular docking between fisetin and the STITCH-identified proteins. MD simulations were also performed by GROMACS for 100 ns to validate the Docking results and analyze the stability and dynamic behavior of the fisetin-PTGS2 complex under the physiological condition. This study identified 110 proteins by PCI and PPI, and also obtained 15 crucial proteins that regulate autophagy, cell growth, protein-serine kinase activity, cytokine activity, and different pathways. Docking studies revealed that fisetin strongly interacted with PTGS2 and ADAM9 with the binding affinities of -9.4 and -8.9 kcal/mol, respectively. DFT calculations and MD studies reveal that fisetin has a strong electronic reactivity and can efficiently interact with PTGS2, leading to the potential use of this compound as an antineoplastic/oxidative stress therapeutic agent. Overall, these findings describe the molecular basis for fisetin's multiple beneficial effects and suggest its further development into a health-promoting therapeutic agent. Keywords Fisetin, Network pharmacology, DFT calculation, MD simulation Biological sciences/Biochemistry Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Biological sciences/Drug discovery Fisetin Network pharmacology DFT calculation MD simulation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Phytochemicals, which are bioactive compounds produced by plants, play a significant role in the prevention and treatment of human diseases. These compounds are derived from plants and exhibit a range of pharmacological properties, including anti-inflammatory, antimicrobial, antioxidant, and antitumor activities 1 . A variety of families of phytochemical compounds, including alkaloids, tannins, saponins, flavonoids, phenols, steroids, and carotenoids, have demonstrated the capacity to influence cellular functions in the context of immune or oxidative stress-related diseases 2 . Fisetin is a natural flavonoid found in different fruits and vegetables, including strawberries, apples, and peaches, as well as in certain plants. Its efficacy has been evaluated across multiple chronic diseases 3 . Fisetin exhibits significant antioxidant and anti-inflammatory properties, suppresses the growth and spread of cancer cells, and serves as a neuroprotective agent for various neurological conditions, including Alzheimer’s disease, Parkinson’s disease, and vascular dementia 4 . The ability of this compound to interact with multiple signaling pathways underscores its significance as a multitargeting agent for conditions associated with cancer and inflammation 5 . Proteins are crucial molecules that mediate biological activity by interacting with these natural products; understanding their interactions is essential for gaining new insights into disease pathogenesis and developing selective treatments 6 . Despite the increasing evidence from in vivo and in vitro findings, the detailed molecular pathways by which fisetin modulates protein activity have not been well characterized. Proteomics and network pharmacology strategies have been practical tools for investigating the health benefits of bioactive molecules by mapping the intricate mechanisms of protein signaling networks 7 . The expression of the PTGS2, CDK6, AKT1, TP53, MTOR, AR, IL4, ADAM9, and TNF proteins was linked to proliferation, signal transduction, immune response, and tumor progression processes 8 9 10 . These expressions are involved in diseases including cancer, diabetes, neurodegenerative diseases, and cardiovascular diseases. Prostaglandin-endoperoxide synthase 2 (PTGS2) or COX-2 is an inflammatory enzyme that mediates the synthesis of pro-inflammatory, pain and fever-producing prostaglandins in inflamed tissues 11 . The overexpression of PTGS2 has been initially associated with various cancer types, including breast tumors, colorectal, and lung carcinomas, where it promotes proliferation, angiogenesis, and tumorigenesis, while simultaneously inhibiting apoptosis 12 . Fisetin has also been reported to downregulate the type 2 carcinogenesis proteins, including AKT1, MTOR, and PTGS2; as well as upregulate a tumor suppressor (TP53 expression) 13 . Fisetin has inhibitory capabilities for the expression of PTGS2 and further represses inflammation, proliferation, and angiogenesis through NF-κB/MAPK pathways 14 . Computational techniques can offer insights into the primary determinants of these interactions, including the specific atomic-level contacts that are not otherwise available from experimental studies. Recent in silico approaches provide us opportunity for a great elucidation of the molecular aspects related to the protein-ligand binding interactions. Computer-assisted drug design (CADD) enables researchers to rapidly identify therapeutic leads and is a means to reduce time and cost in drug discovery 15 . Molecular dynamics (MD) simulations provide atomistic detail to the molecular stability and conformational transitions, while density functional theory (DFT) computationally describes quantum understanding of reactivity and charge distribution on the molecule 16 17 . This study aimed to investigate the interaction mechanism between fisetin and the key human cancer and inflammation-related proteins, adopting network pharmacology tools, combined with molecular docking, MD simulations, and DFT analysis. We hypothesize that fisetin may act as an anticancer and anti-inflammatory agent through the possibility of its interaction with multiple protein targets, because it modulates well-known cancer signaling pathways like proliferation, apoptosis, and inflammation. Understanding these molecular interactions might be of value for the rational drug design of fisetin-related drugs in cancer and inflammation-based diseases. Methods Identification of the proteins that interact with fisetin Potential fisetin-interacted protein targets were identified through the bioinformatics database STITCH 5.0 (http://stitch.embl.de/), which searches for experimental and computationally analyzed protein-chemical interactions from multiple sources, including manually curated information, text-mining of literature, and bioinformatics-based predictions 18 . We used a confidence score threshold of ≥0.700 to get high-quality interactions. The derived protein set was subjected to subsequent network analysis. Construction of the protein-protein interaction (PPI) network The candidate proteins were then submitted to the STRING 12.0 database (https://string-db.org/) for the protein‐protein interaction (PPI) network analysis. STRING incorporates both experimental and predicted interactions, offering a comprehensive view of protein connectivity in organisms 19 . The interaction information was exported as TSV and imported into Cytoscape 3.10.2, which is a popular bioinformatics software for visualizing molecular complexes. Identification of crucial hub proteins In Cytoscape, the CytoHubba plugin was used to find the hub proteins according to degree centrality, the key topological parameter indicating how many proteins directly interact with each protein node 18 . The top 15 hub proteins were selected as possible major targets of fisetin due to their high centrality, which implies significant biological importance in the interaction network. Pathway and functional enrichment analysis Moreover, enrichment analysis was conducted via the Enrichr web server (https://maayanlab.cloud/Enrichr/) to clarify the biological pathways and molecular functions of these high-scoring proteins. The KEGG (Kyoto Encyclopedia of Genes and Genomes) 20 and Reactome portals of the Enrich r database were used to ascertain enriched pathways associated with inflammation, oxidative stress, or cancer-related processes. The findings gave the functional implications of the protein targets obtained from the network-based strategy. Protein selection strategy for analysis of the hypothetical docking interactions Subsequently, molecular docking analysis was performed to explore the structural and functional basis of the interaction between fisetin and its target proteins, as they are associated with various human disorders (Table 01). It further validates the predicted protein-chemical interactions (PCI) as obtained from databases such as STITCH and STRING by cross-validating them using energy-based calculations. Through the integration of network pharmacology and CADD, this approach shows strong and meaningful connections between the drug and its targets. Overall, this computational method improves the accuracy and understanding of these predicted interactions. Table 1. Ten proteins related to diseases and their mechanisms of action and expression level. Proteins Disease Mechanism Expression CDK6 Cancer, Diabetes, Lymphoma. It acts as a kinase-independent transcription regulator, promoting the expression of pro-angiogenic factors and cell cycle progression in tumor angiogenesis 21 . Overexpression in Lymphoma 21 . AKT1 Parkinson, Cancer, Metabolic, Cardiovascular, Neurodegeneration. It elevated cell survival, proliferation, and metabolism. Prevent apoptosis by inhibiting Bcl2 and MDM2 pathways 22 . Overexpressed in prostate Cancer 22 . TP53 Most Cancers. It inhibits cell cycle progression, triggers apoptosis 23 . Suppressed 23 . mTOR Cancer, Diabetes, and Obesity, Cardiovascular, and Neurological disorders. It promotes tumor growth by regulating the differentiation and function of immune cells 24 . Overexpressed in cancer 25 . AR Prostate cancer, Male infertility, Breast cancer, and Rheumatoid arthritis. It increases cell cycle progression in prostate cancer 26 . Overexpressed in prostate cancer. IL4 Anemia, Glomerulonephritis, Autoimmune disease, and Cancer. It either expands the tumor via upregulating antiapoptotic gene expression in tumor cells or prevents tumor growth by inhibiting growth and inducing apoptosis 27 . Overexpressed in cancer. ADAM9 Rheumatoid arthritis, Alzheimer's disease, Cardiac hypertrophy, Asthma, and Cancer. It promotes tumorigenesis through vascular remodeling, particularly by increasing the function of VEGFA, ANGPT2, and PLAT 28 . Overexpressed in cancer. TNF Autoimmune diseases, Cancer. It progresses the tumor via activating NF-κB or a PKCα- and AP-1-dependent pathway 29 . Overexpressed in cancer. PTGS2 Alzheimer's, Cardiovascular, Inflammatory diseases, Osteoarthritis, and Cancer. It develops tumor initiation, progression, angiogenesis, and metastasis 30 . Overexpressed in cancer. MMP1 Cancer, Rheumatoid arthritis, Pulmonary emphysema, and Fibrotic disorders. It degrades the extracellular matrix (ECM), assisting tumor cells in migration and invasion of surrounding tissues and blood vessels 31 . Overexpressed in cancer. Density function theory (DFT) and molecular electrostatic potential (MEP) calculations In the present study, DFT is employed to systematically investigate those factors that are essential for electromagnetic response characteristic (EMRC), dynamical thermodynamics, and absorption properties, with special focus on binding energies. Numerous quantum chemical characteristics were computed, encompassing border orbital energies (HOMO and LUMO), energy interval, hardness (η), softness (σ), electronegativity (χ), chemical resistance (μ), electrophilicity index (ω), and molecule electrostatic potentials 32 . The Fisetin was optimized for its molecular geometries using Gaussian 09 software. This TICA is optimized using density functional theory (DFT) with the three-parameter hybrid B3LYP function and the 6-311G (d, p) basis set to calculate accurate electronic structures 33 . This diagram provides a graphical illustration of the electrostatic potential (ESP) overlaid on the electron density (ED) surface, showcasing a color gradient that transitions from the darkest red to the brightest blue. This graphic illustration clearly illustrates the electrostatic properties of the salt atom, providing significant insight through the transportation of electricity across its surface 34 . Collections and preparations of protein structures for docking interactions The 3D structures of the hub proteins were obtained from the Protein Data Bank (PDB) in the PDB format. Only human ( Homo sapiens ) proteins with resolution by crystallography ≤2.5 Å were considered. The pre-processed structure (with water molecules, co-crystallized ligands, and heteroatoms removed) of each target was prepared using UCSF Chimera 1.16. Hydrogen atoms were added, and the Gasteiger charges were determined. Ligand preparation The 3-D structure of fisetin was downloaded from the PubChem (CID: 5281614). The ligand was docked to UCSF Chimera, and the most stable conformation was energy-minimized using the AMBER ff14SB force field. The ligand was prepared and converted to PDBQT format with AutoDock Tools 1.5.7 for virtual screening. Molecular docking procedure Docking studies were performed using AutoDock Vina 1.2.3, a popular and well-established open-source absorbable tool for predicting the position of ligand-receptor binding in terms of free energy of binding (kcal/mol). The active site of each protein was defined by co-crystallized ligands or literature information. To cover all possible binding residues, the grid box size was adjusted to fit the binding pocket. The docking exhaustiveness was set at 8 for a balance between precision and computational cost. The docking poses with the least energy conformations were chosen to visualize and interpret. Visualization and interaction analysis Hydrogen bonds, hydrophobic contacts, van der Waals interactions, and π-π stacking between fisetin and the target proteins in the docked complexes were evaluated using Discovery Studio Visualizer 2021. The interaction patterns were then plotted to reveal the position of fisetin inside the binding cavities for each protein, which would give a clue to structural information for possible mechanisms. Assessing the physicochemical, pharmacokinetics, and toxicity profiles of fisetin The SMILES of fisetin was obtained from the PubChem database, then submitted to the SwissADME (http://www.swissadme.ch/) and Pro Tox 3.0 (https://tox.charite.de/protox3/) servers to evaluate its pharmacokinetic properties and toxicity profile. SwissADME is a free web tool that predicts the physicochemical properties, pharmacokinetics, drug-likeness, and medicinal chemistry friendliness of small molecules 35 , whereas Pro Tox predicts toxicity endpoints based on molecular similarity and machine learning models 36 . Molecular dynamics (MD) simulations MD simulations were carried out with the fisetin-PTGS2 complex with the best binding affinity in docking to investigate its stability, flexibility, and binding nature. Simulations were done as described using GROMACS 2023 with the CHARMM36m force field and TIP3P water model, and complex box was filled with cubic box with 0.15M NaCl 37 . The system was equilibrated after energy minimization in NVT (300K) and NPT (1bar). Using production runs of 100ns and a timestep of 2 fs, with PME electrostatics and LINCS constraints, it was possible to analyze in detail the interactions of fisetin and its possible inhibitory mechanism. Trajectory analysis Stability and dynamics were calculated using the standard GROMACS utilities, including RMSD, RMSF, Rg, SASA, hydrogen bonds (H-bonds), potential energy, pressure, density, and temperature. Averages are taken over equilibrium trajectories, and sample plots are selected 38 . Results Identification of proteins that interact with fisetin by PCI network analysis A total of 10 proteins (CDK6, ADAM9, MTOR, AR, TP53, AKT1, TNF, MMP1, IL4, and PTGS20 have been identified through PCI analysis from the STITCH 5.0 database (Figure 1). Node size indicates interaction frequency or biological relevance, while edge thickness/color represents the confidence of the interaction. Larger nodes indicate more, and darker edges present higher-confidence predictions. Construction and analysis of the PPI network Fig. 1 presents a network comprising 100 protein-protein interactions associated with the 10 proteins identified by STITCH. The proteins located in the innermost circle represent hub proteins, which exhibit the highest number of interactions. The middle circle contains proteins with fewer interactions, while the outer circle consists of proteins with the lowest number of interactions. Identification of crucial proteins and their respective pathways Fig. 2 presents the top 15 core protein-protein interactions based on degree centrality. The degree is positively correlated with the relationship between proteins. The color of the node changing from yellow to red is equivalent to the degree from small to large. The centrality value measures the importance of the target. Fig. 3 shows the enriched significant Reactome and KEGG pathway’s including signal transduction, modulation of the immune system, regulation of apoptosis, and response mechanism to external stimuli. These pathways are consistent with the pharmacologically confirmed therapeutic activities of fisetin, such as cancer and inflammation. The results revealed the effect of fisetin on human health by affecting proteins that may be involved in certain important signaling pathways associated with disease. Density function theory (DFT) and molecular electrostatic potential (MEP) calculations The optimized molecular structure of the fisetin was derived by DFT calculation, which finds the lowest energy atomic configuration. Fisetin is a natural flavonol and a member of the flavonoid group, with four hydroxyl groups located at 3, 3′, 4′, and 7. These active moieties are largely responsible for its antioxidant actions. The electron-donating and free radical scavenging properties of hydroxyl groups are also reinforced by the aromatic consecutive rings, and planarity of the extra ring, all of which show that this molecule should be reactive to PM. Altogether, these structural characteristics rationalize fisetin’s medicinal properties and reveal its dual role as an antioxidant and anticancer agent. From FMO analysis, HOMO and LUMO energy levels were found to be -0.20606 eV and -0.05415 eV, respectively, with a lower energy gap of 0.15191 (Fig. 4). The HOMO was localized mainly on aromatic rings and hydroxyl groups, while the LUMO was distributed over the conjugated π-system. The observed low HOMO-LUMO gap reflects the high electronic reactivity and favorable charge transfer that is associated with the antioxidant and anticancer potential of the compound. The MEP calculation demonstrates that the hydroxyl oxygen atoms of fisetin have highly negative electrostatic potential, suggesting that these sites are susceptible to electrophilic attack (Fig. 5). Charge distribution in the solvent phase is more diffuse due to medium polarization, and hence reactivity may be lower as compared to the gas phase. These findings reveal the importance of solvation on fisetin's electronic and reactive features. Hypothetical docking interactions between fisetin and STITCH identified proteins Molecular docking analysis using AutoDock Vina exhibited Fisetin's binding potential with ten target proteins (AKT1, IL4, MMP1, AR, CDK6, MTOR, TNF, TP53, ADAM9, and PTGS2). The results revealed that robust interactions with ADAM9 (-8.9 kcal/mol) and PTGS2 (-9.4 kcal/mol), exhibiting the highest binding affinities among all examined complexes. Visualization of the 3D and 2D structures (Fig. 6) illustrates the specific interaction patterns, with color-coded bonds representing various molecular interactions. The complete set of binding free energies for all protein-ligand complexes is provided in Table 2. Table 2. List of proteins and ligands with their binding affinity scores. Ligand Proteins Binding Affinity (kcal/mol) Fisetin ADAM metallopeptidase domain 9 (ADAM9) -8.9 V-akt murine thymoma viral oncogene homolog 1(AKT1) -8 Interleukin 4 (IL4) -6.1 Matrix metallopeptidase 1 (MMP1) -7.1 Mechanistic target of rapamycin (MTOR) -8 Prostaglandin-endoperoxide synthase 2 (PTGS2) -9.4 Tumor necrosis factor (TNF) -6.5 Tumor protein p53 (TP53) -7.7 Androgen receptor (AR) -7.3 Cyclin-dependent kinase 6 (CDK6) -8.8 Physicochemical and pharmacokinetic properties of fisetin This study aimed to identify the impact of fisetin on human health. Initially, we obtained fisetin’s physicochemical and Pharmacokinetics properties from PubChem and SwissADME, which are listed in Table 3. The 2D structure of the fisetin was presented in Fig. 1. A. The pharmacokinetic properties, such as high GI absorption and lead-likeness, made it an appropriate drug candidate. While we presented the toxicity of fisetin in the Supplementary materials, derived through ProTox 3.0 - Prediction of Toxicity of Chemicals. The data provide that fisetin has probabilities for active Hepatotoxicity (0.69), Neurotoxicity (0.87), Respiratory toxicity (0.98), Immunotoxicity (0.96), and Aromatase/ER-LBD activation (1.0), while most nuclear receptors, stress responses, and metabolic pathways (e.g., CYP1A2, CYP2D6) are predicted inactive with high confidence (0.70-1.0). Notable risks include potential estrogenic activity (ERα/ER-LBD), AChE inhibition (0.60), and CYP2C9/CYP3A4 interactions, suggesting multi-organ and endocrine disruption concerns. So, appropriate dose prediction is compulsory for using fisetin as a drug. Table 3. Physicochemical and pharmacokinetic properties of fisetin. Property types Name Value Physicochemical Ligand Name Fisetin PubChem CID 5281614 Molecular formula C15H10O6 Molecular weight (g/mol) 286.24 Smiles C1=CC(=C(C=C1C2=C(C(=O)C3=C(O2)C=C(C=C3)O)O)O)O TPSA 111.13 Ų Number of rotatable bonds 1 Lipophilicity Consensus Log Po/w 1.55 Pharmacokinetic GI absorption High Bioavailability Score 0.55 Log S (ESOL) (Water Solubility) -3.35 (Moderate solubility) Lead likeness Yes Molecular dynamics (MD) simulations of the fisetin-PTGS2 complex MD simulation of the fisetin-PTGS2 complex was carried out for 100 ns in order to determine the stability and dynamicity of the system. Root-mean-square deviation (RMSD) and root-mean-square fluctuation (RMSF) were used to analyze the structural stability and flexibility, respectively. Thermodynamic quantities such as total energy and system density were utilized to gain access to the compactness and stability of the complex. Hydrogen bond analysis further identified several intermolecular contacts that are responsible for the binding strength and structural stability of the fisetin-PTGS2 complex. Taken together, these simulations provide an overall picture of the dynamical and energetic properties driving the fisetin-target protein interaction. The RMSD plot illustrates how the PTGS2 and the fisetin-PTGS2 complex shift shapes over 100 ns (Fig. 7a). The protein alone has larger RMSDs, around 3 Å, showing that it bends more. The PTGS2-fisetin complex remains rather stable, with the RMSD approximately 2 Å, suggesting that ligand binding makes the protein structure more stable by decreasing its fluctuations. The initial increase of RMSD during the first 10 ns indicates the system adaptation, and then the complex is less fluctuating and more stable. The RMSF plot describes the flexibility of the residue throughout the simulation (Fig. 7b). The protein (Fisetin) has more flexibility, especially at the N-terminal, where there are flexible segments. The RMSF values of the PTGS2-fisetin complex are less than those of the apo structure, indicating that ligand binding stabilizes the protein via decreased mobility of residues. This emphasizes the increase in ligand (Fisetin) binding on protein stability. The density fluctuation plot (Fig. 7c) shows that, over the 100 ns molecular dynamics simulation period, both the apo PTGS2 protein and fisetin-PTGS2 complex follow the stable density pattern. Small changes were observed during the production run, again reflecting both systems equilibrated systems. Remarkably, the average density of the fisetin-PTGS2 complex was a little lower than that of the unbound protein, which could be indicative of the packing change or solvent effect influenced by ligand binding. These findings suggest that the systems are reasonably stable and the simulation protocol is sound. As shown in Fig. 7d, the number of hydrogen bonds fluctuated mainly between four and two between PTGS2 and fisetin during the 100 ns simulation, which shows the dynamic interaction between the ligand and the protein. These transient hydrogen bonds probably indicate transitory yet significant interactions that contribute to the thermodynamic stability of the complex. Furthermore, the radius of gyration (Rg) profiles of the unbound protein and the fisetin-PTGS2 complex followed the same trend with little deviation, suggesting stable and compact conformations that have been maintained throughout the simulation. Interestingly, the complex displayed a modestly lower Rg than the apo protein, consistent with a more compact and structurally stable protein conformation upon ligand binding. As can be seen from the temperature fluctuation plot, the fisetin and fisetin-PTGS2 complex reached an equilibrium state at around 300 K during MD (Fig. 7f). This uniform temperature distribution indicates a reasonable thermal equilibrium process and quality of the simulation data. Discussion Fisetin is a naturally occurring phytochemical found in apples, guavas, strawberries, and onions, has drawn much attention for its extensive pharmacological properties. This illustrates the antioxidant properties that reduce oxidative stress, production of ROS, brain damage, neuro-inflammation, and mental disorders 39 . Fisetin has been shown to induce apoptosis in colon cancer cells and inhibit their growth by targeting the COX2 and Wnt/EGFR/NF-κB signaling pathways 40 . It also induces cell cycle arrest, caspase-dependent cell death, and enhances the cytotoxic effects of chemotherapeutic agents in the triple-negative breast cancer cells 41 . Protein-chemical interaction (PCI) and protein-protein interaction (PPI) studies of fisetin, along with molecular docking interactions, showed that ten human proteins, such as PTGS2, ADAM9, AKT1, MTOR, TP53, CDK6, MMP1, TNF, AR, and IL4, are the major targets for fisetin. Molecular docking showed strong binding affinities with PTGS2 and ADAM9, which could be major targets responsible for the bioactivity of fisetin. PTGS2 (COX-2) is a rate-limiting enzyme in the synthesis of prostaglandin and plays an important role in inflammation. Fisetin binds with high affinity to PTGS2 and may inhibit its catalytic activity, leading to reduced PGE2 production, decreased NF-κB signaling, and lower levels of pro-inflammatory cytokines such as TNF-α and IL-6 40 . Docking results also indicated the formation of hydrogen bonds and hydrophobic contacts in the PTGS2 active site region, which could stabilize a complex. The association of ADAM9 and MMP1 suggests it's likely suppressive function on extracellular matrix degradation and cancer invasion. Potent inhibition of ADAM9-MT1‐MMP proteolytic activity by high-affinity binding can be possible, leading to the blocking of cancer cell migration and metastasis, as reported before 42 . The binding aspects of fisetin to AKT1 and MTOR point toward the regulation of PI3K/AKT/mTOR signaling, which leads to autophagy upregulation and control of flexible cell growth 43 . Binding with TP53 and CDK6 suggests apoptosis activation, G1/S cell cycle arrest 40 , respectively, whereas TNF, IL4, and AR binding imply further regulation of inflammatory and hormonal signaling. Overall, these docking and network analyses show that fisetin is a multitargeting natural compound. Its concomitant modulation of PTGS2, ADAM9, AKT1/MTOR, and TP53, as well as other signaling nodes, highlights its potential as an anti-inflammatory, pro-apoptotic agent that suppresses autophagy and invasion. These findings provide a mechanism for the in vitro and in vivo validation of fisetin as a natural anti-inflammatory, anticancer compound. Functional enrichment analysis suggested that fisetin could be a promising agent for the treatment of cancer, age-related diseases involving cell cycle regulation, DNA damage response, and signal transduction 44 . Additionally, the KEGG and Reactome pathway analyses also indicated its potential participation in prostate cancer, Th17 differentiation, and the AGE-RAGE signaling pathway in diabetic complications, which played an important role in the tumorigenesis process, autoimmune diseases, cancers, and diabetic complications 45 . Nevertheless, toxicity profiling indicated potential harmful effects of fisetin, such as hepatotoxicity, neurotoxicity, and estrogenic activity, thus suggesting the dose optimization and safety assessment to be carried out before clinical use 46 . The optimized geometrical parameters of fisetin obtained from DFT calculations are important structural details for understanding the antioxidant and anticancer activities. Four -OH groups favouring electron donation and radical scavenging at 3, 7 (position), 3′,4′ positions, which are joined by a large conjugated aromatic system. The small HOMO-LUMO gap (0.15191 eV) supports high electronic reactivity and charge transfer, which is in agreement with its strong free radical scavenging potential and related biological interactions to cancer and oxidative stress pathways 47 48 . Molecular dynamics simulation of the fisetin-PTGS2 complex for 100 ns shows that ligand binding stabilizes the protein structure. The complex shows less RMSD (2 Å) and RMSF values than the free protein (3 Å), indicating decreased flexibility, particularly in functional areas, which is in favor of inhibition. Analysis of hydrogen bonds reveals that there are unstable, but very significant intermolecular interactions which make a contribution to the binding strength of -9.4 kcal/mol at the Hartree‐Fock level of theory. Further validation of the simulation methodology is provided by radius of gyration and solvent accessible surface area, which suggest that a more compact, eventually partially exposed protein conformation exists upon binding; stable profiles in both temperature and density lend further support to our simulations 49 50 51 . Although this study was primarily intended to investigate fisetin’s multi-target potential, due to the high binding affinity and disease correlation with the fisetin-PTGS2 complex, MD simulation was conducted by using this complex. Hence, PTGS2 can be regarded as a typical case to describe the anti-cancer and anti-inflammatory mechanism of fisetin; other proteins from the PPI network could also display the broad regulatory role. Combining PCI, PPI, DFT calculation, molecular docking, and MD simulation approaches, this study offers a well-defined computational model for the therapeutic effect of fisetin. However, these in silico observations need to be experimentally verified in cellular and in vivo models for both effectiveness and therapeutic applicability. In the future, we could also explore coordination with other important proteins based on MD simulations and include multi-omics analyses for a comprehensive investigation of fisetin's therapeutic potential. Conclusion Fisetin, a bioactive flavonoid commonly found in fruits and vegetables, has therapeutic effects on many diseases such as cancer, neurodegenerative diseases, cardiovascular disease, and metabolic syndrome. In the present integrative proteomics work, its multi-target mode of action is unveiled, revealing high-affinity binding interactors among key regulatory proteins (ADAM9, PTGS2, AKT1, and TP53) and potential molecular networks. This drug might act upon to exert influence on essential pathways in cancer, inflammation, and cellular homeostasis. The high binding affinities (-6.1 to -9.0 kcal/mol), as well as the electronic reactivity calculated by DFT and stability derived from MD simulation for fisetin-PTGS2 complex, further confirm that it can be considered a potential drug candidate for cancer and inflammation. However, predicted toxicity risks involving hepatotoxicity, neurotoxicity, and estrogenic properties stress a careful preclinical optimization of dosing and formulation. Taken together, these results established a molecular basis for future structure-activity relationship studies and therapeutic potentiality analyses. Declarations Data availability The authors confirm that the data supporting the findings of this study are available within the article. Funding The Authors received no external and internal funding for this work. References Riaz, M. et al. Phytobioactive compounds as therapeutic agents for human diseases: A review. Food Sci Nutr . 11 , 2500-2529 (2023). Muscolo, A. Mariateresa, O. Giulio, T. & Mariateresa, R. Oxidative Stress: The role of antioxidant phytochemicals in the prevention and treatment of diseases. Int J Mol Sci . 25 , 3264 (2024). Pal, H. C. Pearlman, R. L. & Afaq, F. Fisetin and its role in chronic diseases. Springer International Publishing 213-244 (2016). Maher, P. Preventing and treating neurological disorders with the flavonol fisetin. Brain Plast . 6 , 155-166 (2020). Fatima, R. et al. Fisetin as a chemoprotective and chemotherapeutic agent: mechanistic insights and future directions in cancer therapy. Med Oncol . 42 , 104 (2025). Chautard, E. 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07:09:22","extension":"xml","order_by":36,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":125962,"visible":true,"origin":"","legend":"","description":"","filename":"159c087ac3ce465dad6de0afd184317e1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7955881/v1/e44b1f33b9a5c885fe446645.xml"},{"id":96794154,"identity":"235fd9af-080d-4df4-9220-1d73994baeb0","added_by":"auto","created_at":"2025-11-26 07:09:22","extension":"html","order_by":37,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":143067,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7955881/v1/2d7ea223a985056d30a93e48.html"},{"id":96916986,"identity":"f7a103ac-bdb6-4657-8329-f19665a1ebd5","added_by":"auto","created_at":"2025-11-27 14:09:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":485875,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization of protein-protein interaction network in degree centrality manner through Cytoscape 3.10.3. Hub proteins (center) have the most interactions, followed by the middle and outer circles with progressively fewer interactions.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7955881/v1/c28f81eeb89837c85c092705.png"},{"id":96794115,"identity":"ae3cc401-44f6-43b6-9fbe-b4c104b1a30b","added_by":"auto","created_at":"2025-11-26 07:09:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":247938,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization of the top 15 primary proteins that interact with fisetin, as identified by Cytoscape\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7955881/v1/62a49f54d2a5cec715c1e059.png"},{"id":96794113,"identity":"77fd60ef-fa7f-43a4-a2b3-aa6d11407bb0","added_by":"auto","created_at":"2025-11-26 07:09:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":132641,"visible":true,"origin":"","legend":"\u003cp\u003ePathways that interact with the top 15 proteins, (A) Reactome, (B) KEGG. The KEGG pathway clustergarm was retrieved from the KEGG portal of the Enrichr database \u003csup\u003e20\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7955881/v1/990942754cde0db20b032e3b.png"},{"id":96794121,"identity":"b5ebae5b-bf9a-4c6a-9df7-626ef3be52ea","added_by":"auto","created_at":"2025-11-26 07:09:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":174726,"visible":true,"origin":"","legend":"\u003cp\u003eFMO surfaces of the fisetin\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7955881/v1/792d600f65e3994fd6be1891.png"},{"id":96916085,"identity":"01bfa2f8-1579-4855-a4d5-cdfbf5ff605c","added_by":"auto","created_at":"2025-11-27 14:08:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":299045,"visible":true,"origin":"","legend":"\u003cp\u003eMEP maps of fisetin computed at B3LYP/6-31G (d, p) level: (A) gas phase and (B)solvent phase (PCM, water). The red color indicates electron-rich points (potential electrophilic attack sites), while the blue corresponds to electron-poor areas.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7955881/v1/8f37c31177a7ebd015151f92.png"},{"id":96916334,"identity":"82aa4492-0a74-41b7-b717-c450199e69c6","added_by":"auto","created_at":"2025-11-27 14:08:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3407063,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking of fisetin with target proteins that demonstrating the potential binding interactions. The color code indicated the bond type in the interaction.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7955881/v1/9a87e20b7234b87b8daba0ed.png"},{"id":96794126,"identity":"74a1df90-3879-43bc-b704-0d3d2dadd55a","added_by":"auto","created_at":"2025-11-26 07:09:21","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":601277,"visible":true,"origin":"","legend":"\u003cp\u003eFindings from the 100 ns MD simulations of the fisetin-PTGS2 complex. \u003cstrong\u003e(A)\u003c/strong\u003e shows the RMSD plot. \u003cstrong\u003e(B)\u003c/strong\u003e represents the RMSF plot. \u003cstrong\u003e(C) \u003c/strong\u003edensity fluctuation of the PTGS2 and fisetin-PTGS2 complex. \u003cstrong\u003e(D)\u003c/strong\u003e shows the hydrogen bond plotting. \u003cstrong\u003e(E)\u003c/strong\u003e shows the radius of gyration plotting. \u003cstrong\u003e(F)\u003c/strong\u003etemperature fluctuations of the free protein and PTGS2-fisetin complex.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7955881/v1/319141b6385a24f65664faf1.png"},{"id":99313348,"identity":"55e61261-444a-4e22-a06f-262869750cf2","added_by":"auto","created_at":"2025-12-31 16:20:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6100316,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7955881/v1/ff581475-7b07-46ce-96d7-99748530ee62.pdf"},{"id":96916410,"identity":"1e71f3d2-897d-4b28-bf5e-77ba0606f977","added_by":"auto","created_at":"2025-11-27 14:08:33","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":17468,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile.docx","url":"https://assets-eu.researchsquare.com/files/rs-7955881/v1/72c102649957ab58ac57c697.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Systematic In-silico Analysis of Fisetin-Proteins Interactions Revealing the PTGS2 as a Potential Therapeutic Target","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePhytochemicals, which are bioactive compounds produced by plants, play a significant role in the prevention and treatment of human diseases. These compounds are derived from plants and exhibit a range of pharmacological properties, including anti-inflammatory, antimicrobial, antioxidant, and antitumor activities\u0026nbsp;\u003csup\u003e1\u003c/sup\u003e. A variety of families of phytochemical compounds, including alkaloids, tannins, saponins, flavonoids, phenols, steroids, and carotenoids, have demonstrated the capacity to influence cellular functions in the context of immune or oxidative stress-related diseases\u0026nbsp;\u003csup\u003e2\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFisetin is a natural flavonoid found in different fruits and vegetables, including strawberries, apples, and peaches, as well as in certain plants. Its efficacy has been evaluated across multiple chronic diseases\u0026nbsp;\u003csup\u003e3\u003c/sup\u003e. Fisetin exhibits significant antioxidant and anti-inflammatory properties, suppresses the growth and spread of cancer cells, and serves as a neuroprotective agent for various neurological conditions, including Alzheimer’s disease, Parkinson’s disease, and vascular dementia\u0026nbsp;\u003csup\u003e4\u003c/sup\u003e. The ability of this compound to interact with multiple signaling pathways underscores its significance as a multitargeting agent for conditions associated with cancer and inflammation\u0026nbsp;\u003csup\u003e5\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eProteins are crucial molecules that mediate biological activity by interacting with these natural products; understanding their interactions is essential for gaining new insights into disease pathogenesis and developing selective treatments\u0026nbsp;\u003csup\u003e6\u003c/sup\u003e. Despite the increasing evidence from in vivo and in vitro findings, the detailed molecular pathways by which fisetin modulates protein activity have not been well characterized. Proteomics and network pharmacology strategies have been practical tools for investigating the health benefits of bioactive molecules by mapping the intricate mechanisms of protein signaling networks\u0026nbsp;\u003csup\u003e7\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe expression of the PTGS2, CDK6, AKT1, TP53, MTOR, AR, IL4, ADAM9, and TNF proteins was linked to proliferation, signal transduction, immune response, and tumor progression processes\u0026nbsp;\u003csup\u003e8\u003c/sup\u003e \u003csup\u003e9\u003c/sup\u003e \u003csup\u003e10\u003c/sup\u003e. These expressions are involved in diseases including cancer, diabetes, neurodegenerative diseases, and cardiovascular diseases. Prostaglandin-endoperoxide synthase 2 (PTGS2) or COX-2 is an inflammatory enzyme that mediates the synthesis of pro-inflammatory, pain and fever-producing prostaglandins in inflamed tissues\u0026nbsp;\u003csup\u003e11\u003c/sup\u003e. The overexpression of PTGS2 has been initially associated with various cancer types, including breast tumors, colorectal, and lung carcinomas, where it promotes proliferation, angiogenesis, and tumorigenesis, while simultaneously inhibiting apoptosis\u0026nbsp;\u003csup\u003e12\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFisetin has also been reported to downregulate the type 2 carcinogenesis proteins, including AKT1, MTOR, and PTGS2; as well as upregulate a tumor suppressor (TP53 expression)\u0026nbsp;\u003csup\u003e13\u003c/sup\u003e. Fisetin has inhibitory capabilities for the expression of PTGS2 and further represses inflammation, proliferation, and angiogenesis through NF-κB/MAPK pathways\u0026nbsp;\u003csup\u003e14\u003c/sup\u003e. Computational techniques can offer insights into the primary determinants of these interactions, including the specific atomic-level contacts that are not otherwise available from experimental studies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRecent in silico approaches provide us opportunity for a great elucidation of the molecular aspects related to the protein-ligand binding interactions. Computer-assisted drug design (CADD) enables researchers to rapidly identify therapeutic leads and is a means to reduce time and cost in drug discovery\u0026nbsp;\u003csup\u003e15\u003c/sup\u003e. Molecular dynamics (MD) simulations provide atomistic detail to the molecular stability and conformational transitions, while density functional theory (DFT) computationally describes quantum understanding of reactivity and charge distribution on the molecule\u0026nbsp;\u003csup\u003e16\u003c/sup\u003e \u003csup\u003e17\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study aimed to investigate the interaction mechanism between fisetin and the key human cancer and inflammation-related proteins, adopting network pharmacology tools, combined with molecular docking, MD simulations, and DFT analysis. We hypothesize that fisetin may act as an anticancer and anti-inflammatory agent through the possibility of its interaction with multiple protein targets, because it modulates well-known cancer signaling pathways like proliferation, apoptosis, and inflammation. Understanding these molecular interactions might be of value for the rational drug design of fisetin-related drugs in cancer and inflammation-based diseases.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eIdentification of the proteins that interact with fisetin\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePotential fisetin-interacted protein targets were identified through the bioinformatics database STITCH 5.0 (http://stitch.embl.de/), which searches for experimental and computationally analyzed protein-chemical interactions from multiple sources, including manually curated information, text-mining of literature, and bioinformatics-based predictions\u0026nbsp;\u003csup\u003e18\u003c/sup\u003e. We used a confidence score threshold of \u0026ge;0.700 to get high-quality interactions. The derived protein set was subjected to subsequent network analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of the protein-protein interaction (PPI) network\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe candidate proteins were then submitted to the STRING 12.0 database (https://string-db.org/) for the protein‐protein interaction (PPI) network analysis. STRING incorporates both experimental and predicted interactions, offering a comprehensive view of protein connectivity in organisms\u0026nbsp;\u003csup\u003e19\u003c/sup\u003e. The interaction information was exported as TSV and imported into Cytoscape 3.10.2, which is a popular bioinformatics software for visualizing molecular complexes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of crucial hub proteins\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn Cytoscape, the CytoHubba plugin was used to find the hub proteins according to degree centrality, the key topological parameter indicating how many proteins directly interact with each protein node\u0026nbsp;\u003csup\u003e18\u003c/sup\u003e. The top 15 hub proteins were selected as possible major targets of fisetin due to their high centrality, which implies significant biological importance in the interaction network.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePathway and functional enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMoreover, enrichment analysis was conducted via the Enrichr web server (https://maayanlab.cloud/Enrichr/) to clarify the biological pathways and molecular functions of these high-scoring proteins. The KEGG (Kyoto Encyclopedia of Genes and Genomes) \u003csup\u003e20\u003c/sup\u003e and Reactome portals of the Enrich r database were used to ascertain enriched pathways associated with inflammation, oxidative stress, or cancer-related processes. The findings gave the functional implications of the protein targets obtained from the network-based strategy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein selection strategy for analysis of the hypothetical docking interactions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubsequently, molecular docking analysis was performed to explore the structural and functional basis of the interaction between fisetin and its target proteins, as they are associated with various human disorders (Table 01). It further validates the predicted protein-chemical interactions (PCI) as obtained from databases such as STITCH and STRING by cross-validating them using energy-based calculations. Through the integration of network pharmacology and CADD, this approach shows strong and meaningful connections between the drug and its targets. Overall, this computational method improves the accuracy and understanding of these predicted interactions. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Ten proteins related to diseases and their mechanisms of action and expression level.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"671\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eProteins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDisease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMechanism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExpression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCDK6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCancer, Diabetes, Lymphoma.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIt acts as a kinase-independent transcription regulator, promoting the expression of pro-angiogenic factors and cell cycle progression in tumor angiogenesis \u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOverexpression in Lymphoma \u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAKT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eParkinson, Cancer, Metabolic, Cardiovascular, Neurodegeneration.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIt elevated cell survival, proliferation, and metabolism. Prevent apoptosis by inhibiting Bcl2 and MDM2 pathways \u003csup\u003e22\u003c/sup\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOverexpressed in prostate Cancer \u003csup\u003e22\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTP53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMost Cancers.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIt inhibits cell cycle progression, triggers apoptosis \u003csup\u003e23\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSuppressed \u003csup\u003e23\u003c/sup\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003emTOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCancer, Diabetes, and Obesity, Cardiovascular, and Neurological disorders.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIt promotes tumor growth by regulating the differentiation and function of immune cells \u003csup\u003e24\u003c/sup\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOverexpressed in cancer \u003csup\u003e25\u003c/sup\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eProstate cancer, Male infertility, Breast cancer, and Rheumatoid arthritis.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIt increases cell cycle progression in prostate cancer \u003csup\u003e26\u003c/sup\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOverexpressed in prostate cancer.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIL4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAnemia, Glomerulonephritis, Autoimmune disease, and Cancer.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIt either expands the tumor via upregulating antiapoptotic gene expression in tumor cells or prevents tumor growth by inhibiting growth and inducing apoptosis\u003csup\u003e27\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOverexpressed in cancer.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eADAM9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRheumatoid arthritis, Alzheimer\u0026apos;s disease, Cardiac hypertrophy, Asthma, and Cancer.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIt promotes tumorigenesis through vascular remodeling, particularly by increasing the function of VEGFA, ANGPT2, and PLAT \u003csup\u003e28\u003c/sup\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOverexpressed in cancer.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTNF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAutoimmune diseases, Cancer.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIt progresses the tumor via activating NF-\u0026kappa;B or a PKC\u0026alpha;- and AP-1-dependent pathway \u003csup\u003e29\u003c/sup\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOverexpressed in cancer.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePTGS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAlzheimer\u0026apos;s, Cardiovascular, Inflammatory diseases, Osteoarthritis, and Cancer.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIt develops tumor initiation, progression, angiogenesis, and metastasis \u003csup\u003e30\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOverexpressed in cancer.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMMP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCancer, Rheumatoid arthritis, Pulmonary emphysema, and Fibrotic disorders.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIt degrades the extracellular matrix (ECM), assisting tumor cells in migration and invasion of surrounding tissues and blood vessels \u003csup\u003e31\u003c/sup\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOverexpressed in cancer.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDensity function theory (DFT) and molecular electrostatic potential (MEP) calculations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the present study, DFT is employed to systematically investigate those factors that are essential for electromagnetic response characteristic (EMRC), dynamical thermodynamics, and absorption properties, with special focus on binding energies. \u0026nbsp;Numerous quantum chemical characteristics were computed, encompassing border orbital energies (HOMO and LUMO), energy interval, hardness (\u0026eta;), softness (\u0026sigma;), electronegativity (\u0026chi;), chemical resistance (\u0026mu;), electrophilicity index (\u0026omega;), and molecule electrostatic potentials\u0026nbsp;\u003csup\u003e32\u003c/sup\u003e. The Fisetin was optimized for its molecular geometries using Gaussian 09 software. This TICA is optimized using density functional theory (DFT) with the three-parameter hybrid B3LYP function and the 6-311G (d, p) basis set to calculate accurate electronic structures\u0026nbsp;\u003csup\u003e33\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThis diagram provides a graphical illustration of the electrostatic potential (ESP) overlaid on the electron density (ED) surface, showcasing a color gradient that transitions from the darkest red to the brightest blue. This graphic illustration clearly illustrates the electrostatic properties of the salt atom, providing significant insight through the transportation of electricity across its surface\u0026nbsp;\u003csup\u003e34\u003c/sup\u003e. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCollections and preparations of protein structures for docking interactions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 3D structures of the hub proteins were obtained from the Protein Data Bank (PDB) in the PDB format. Only human (\u003cem\u003eHomo sapiens\u003c/em\u003e) proteins with resolution by crystallography \u0026le;2.5 \u0026Aring; were considered. The pre-processed structure (with water molecules, co-crystallized ligands, and heteroatoms removed) of each target was prepared using UCSF Chimera 1.16. Hydrogen atoms were added, and the Gasteiger charges were determined.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLigand preparation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 3-D structure of fisetin was downloaded from the PubChem (CID: 5281614). The ligand was docked to UCSF Chimera, and the most stable conformation was energy-minimized using the AMBER ff14SB force field. The ligand was prepared and converted to PDBQT format with AutoDock Tools 1.5.7 for virtual screening.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular docking procedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDocking studies were performed using AutoDock Vina 1.2.3, a popular and well-established open-source absorbable tool for predicting the position of ligand-receptor binding in terms of free energy of binding (kcal/mol). The active site of each protein was defined by co-crystallized ligands or literature information. To cover all possible binding residues, the grid box size was adjusted to fit the binding pocket. The docking exhaustiveness was set at 8 for a balance between precision and computational cost. The docking poses with the least energy conformations were chosen to visualize and interpret.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVisualization and interaction analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHydrogen bonds, hydrophobic contacts, van der Waals interactions, and \u0026pi;-\u0026pi; stacking between fisetin and the target proteins in the docked complexes were evaluated using Discovery Studio Visualizer 2021. The interaction patterns were then plotted to reveal the position of fisetin inside the binding cavities for each protein, which would give a clue to structural information for possible mechanisms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessing the physicochemical, pharmacokinetics, and toxicity profiles of fisetin\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe SMILES of fisetin was obtained from the PubChem database, then submitted to the SwissADME (http://www.swissadme.ch/) and Pro Tox 3.0 (https://tox.charite.de/protox3/) servers to evaluate its pharmacokinetic properties and toxicity profile. SwissADME is a free web tool that predicts the physicochemical properties, pharmacokinetics, drug-likeness, and medicinal chemistry friendliness of small molecules\u0026nbsp;\u003csup\u003e35\u003c/sup\u003e, whereas Pro Tox predicts toxicity endpoints based on molecular similarity and machine learning models\u0026nbsp;\u003csup\u003e36\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular dynamics (MD) simulations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMD simulations were carried out with the fisetin-PTGS2 complex with the best binding affinity in docking to investigate its stability, flexibility, and binding nature. Simulations were done as described using GROMACS 2023 with the CHARMM36m force field and TIP3P water model, and complex box was filled with cubic box with 0.15M NaCl\u0026nbsp;\u003csup\u003e37\u003c/sup\u003e. The system was equilibrated after energy minimization in NVT (300K) and NPT (1bar). Using production runs of 100ns and a timestep of 2 fs, with PME electrostatics and LINCS constraints, it was possible to analyze in detail the interactions of fisetin and its possible inhibitory mechanism.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrajectory analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStability and dynamics were calculated using the standard GROMACS utilities, including RMSD, RMSF, Rg, SASA, hydrogen bonds (H-bonds), potential energy, pressure, density, and temperature. Averages are taken over equilibrium trajectories, and sample plots are selected\u0026nbsp;\u003csup\u003e38\u003c/sup\u003e. \u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eIdentification of proteins that interact with fisetin by PCI network analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 10 proteins (CDK6, ADAM9, MTOR, AR, TP53, AKT1, TNF, MMP1, IL4, and PTGS20 have been identified through PCI analysis from the STITCH 5.0 database (Figure 1). Node size indicates interaction frequency or biological relevance, while edge thickness/color represents the confidence of the interaction. Larger nodes indicate more, and darker edges present higher-confidence predictions. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction and analysis of the PPI network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFig. 1 presents a network comprising 100 protein-protein interactions associated with the 10 proteins identified by STITCH. The proteins located in the innermost circle represent hub proteins, which exhibit the highest number of interactions. The middle circle contains proteins with fewer interactions, while the outer circle consists of proteins with the lowest number of interactions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of crucial proteins and their respective pathways\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFig. 2 presents the top 15 core protein-protein interactions based on degree centrality. The degree is positively correlated with the relationship between proteins. The color of the node changing from yellow to red is equivalent to the degree from small to large. The centrality value measures the importance of the target. Fig. 3 shows the enriched significant Reactome and KEGG pathway\u0026rsquo;s including signal transduction, modulation of the immune system, regulation of apoptosis, and response mechanism to external stimuli. These pathways are consistent with the pharmacologically confirmed therapeutic activities of fisetin, such as cancer and inflammation. The results revealed the effect of fisetin on human health by affecting proteins that may be involved in certain important signaling pathways associated with disease.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDensity function theory (DFT) and molecular electrostatic potential (MEP) calculations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe optimized molecular structure of the fisetin was derived by DFT calculation, which finds the lowest energy atomic configuration. Fisetin is a natural flavonol and a member of the flavonoid group, with four hydroxyl groups located at 3, 3\u0026prime;, 4\u0026prime;, and 7. These active moieties are largely responsible for its antioxidant actions. The electron-donating and free radical scavenging properties of hydroxyl groups are also reinforced by the aromatic consecutive rings, and planarity of the extra ring, all of which show that this molecule should be reactive to PM. Altogether, these structural characteristics rationalize fisetin\u0026rsquo;s medicinal properties and reveal its dual role as an antioxidant and anticancer agent.\u003c/p\u003e\n\u003cp\u003eFrom FMO analysis, HOMO and LUMO energy levels were found to be -0.20606 eV and -0.05415 eV, respectively, with a lower energy gap of 0.15191 (Fig. 4). The HOMO was localized mainly on aromatic rings and hydroxyl groups, while the LUMO was distributed over the conjugated \u0026pi;-system. The observed low HOMO-LUMO gap reflects the high electronic reactivity and favorable charge transfer that is associated with the antioxidant and anticancer potential of the compound.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe MEP calculation demonstrates that the hydroxyl oxygen atoms of fisetin have highly negative electrostatic potential, suggesting that these sites are susceptible to electrophilic attack (Fig. 5). Charge distribution in the solvent phase is more diffuse due to medium polarization, and hence reactivity may be lower as compared to the gas phase. These findings reveal the importance of solvation on fisetin\u0026apos;s electronic and reactive features.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothetical docking interactions between fisetin and STITCH identified proteins\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMolecular docking analysis using AutoDock Vina exhibited Fisetin\u0026apos;s binding potential with ten target proteins (AKT1, IL4, MMP1, AR, CDK6, MTOR, TNF, TP53, ADAM9, and PTGS2). The results revealed that robust interactions with ADAM9 (-8.9 kcal/mol) and PTGS2 (-9.4 kcal/mol), exhibiting the highest binding affinities among all examined complexes. Visualization of the 3D and 2D structures (Fig. 6) illustrates the specific interaction patterns, with color-coded bonds representing various molecular interactions. The complete set of binding free energies for all protein-ligand complexes is provided in Table 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e List of proteins and ligands with their binding affinity scores.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"621\" style=\"margin-right: calc(39%); width: 61%;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLigand\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60.7086%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProteins\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1765%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBinding Affinity (kcal/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"27\" style=\"width: 0.9631%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"20\" valign=\"top\"\u003e\n \u003cp\u003eFisetin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 60.7086%;\"\u003e\n \u003cp\u003eADAM metallopeptidase domain 9 (ADAM9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1765%;\"\u003e\n \u003cp\u003e-8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"27\" style=\"width: 0.9631%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.1765%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"45\" style=\"width: 0.9631%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 60.7086%;\"\u003e\n \u003cp\u003eV-akt murine thymoma viral oncogene homolog 1(AKT1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1765%;\"\u003e\n \u003cp\u003e-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"27\" style=\"width: 0.9631%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.1765%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"45\" style=\"width: 0.9631%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 60.7086%;\"\u003e\n \u003cp\u003eInterleukin 4 (IL4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1765%;\"\u003e\n \u003cp\u003e-6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"27\" style=\"width: 0.9631%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.1765%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"45\" style=\"width: 0.9631%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 60.7086%;\"\u003e\n \u003cp\u003eMatrix metallopeptidase 1 (MMP1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1765%;\"\u003e\n \u003cp\u003e-7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"27\" style=\"width: 0.9631%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.1765%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"45\" style=\"width: 0.9631%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 60.7086%;\"\u003e\n \u003cp\u003eMechanistic target of rapamycin (MTOR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1765%;\"\u003e\n \u003cp\u003e-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"27\" style=\"width: 0.9631%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.1765%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"45\" style=\"width: 0.9631%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 60.7086%;\"\u003e\n \u003cp\u003eProstaglandin-endoperoxide synthase 2 (PTGS2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1765%;\"\u003e\n \u003cp\u003e-9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"27\" style=\"width: 0.9631%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.1765%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"45\" style=\"width: 0.9631%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 60.7086%;\"\u003e\n \u003cp\u003eTumor necrosis factor (TNF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1765%;\"\u003e\n \u003cp\u003e-6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"27\" style=\"width: 0.9631%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.1765%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"45\" style=\"width: 0.9631%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 60.7086%;\"\u003e\n \u003cp\u003eTumor protein p53 (TP53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1765%;\"\u003e\n \u003cp\u003e-7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"27\" style=\"width: 0.9631%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.1765%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"45\" style=\"width: 0.9631%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 60.7086%;\"\u003e\n \u003cp\u003eAndrogen receptor (AR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1765%;\"\u003e\n \u003cp\u003e-7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"27\" style=\"width: 0.9631%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.1765%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"45\" style=\"width: 0.9631%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 60.7086%;\"\u003e\n \u003cp\u003eCyclin-dependent kinase 6 (CDK6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1765%;\"\u003e\n \u003cp\u003e-8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"27\" style=\"width: 0.9631%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.1765%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"45\" style=\"width: 0.9631%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhysicochemical and pharmacokinetic properties of fisetin\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aimed to identify the impact of fisetin on human health. Initially, we obtained fisetin\u0026rsquo;s physicochemical and Pharmacokinetics properties from PubChem and SwissADME, which are listed in Table 3. The 2D structure of the fisetin was presented in Fig. 1. A. The pharmacokinetic properties, such as high GI absorption and lead-likeness, made it an appropriate drug candidate. While we presented the toxicity of fisetin in the Supplementary materials, derived through ProTox 3.0 - Prediction of Toxicity of Chemicals. The data provide that fisetin has probabilities for active Hepatotoxicity (0.69), Neurotoxicity (0.87), Respiratory toxicity (0.98), Immunotoxicity (0.96), and Aromatase/ER-LBD activation (1.0), while most nuclear receptors, stress responses, and metabolic pathways (e.g., CYP1A2, CYP2D6) are predicted inactive with high confidence (0.70-1.0). Notable risks include potential estrogenic activity (ER\u0026alpha;/ER-LBD), AChE inhibition (0.60), and CYP2C9/CYP3A4 interactions, suggesting multi-organ and endocrine disruption concerns. So, appropriate dose prediction is compulsory for using fisetin as a drug.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Physicochemical and pharmacokinetic properties of fisetin.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"657\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProperty types\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003ePhysicochemical\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLigand Name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFisetin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePubChem CID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5281614\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMolecular formula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC15H10O6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMolecular weight (g/mol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e286.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSmiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC1=CC(=C(C=C1C2=C(C(=O)C3=C(O2)C=C(C=C3)O)O)O)O\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTPSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111.13 \u0026Aring;\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber of rotatable bonds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLipophilicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eConsensus Log Po/w\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003ePharmacokinetic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGI absorption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBioavailability Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLog \u003cem\u003eS\u003c/em\u003e (ESOL) (Water Solubility)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.35 (Moderate solubility)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLead likeness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular dynamics (MD) simulations of the fisetin-PTGS2 complex\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMD simulation of the fisetin-PTGS2 complex was carried out for 100 ns in order to determine the stability and dynamicity of the system. Root-mean-square deviation (RMSD) and root-mean-square fluctuation (RMSF) were used to analyze the structural stability and flexibility, respectively. Thermodynamic quantities such as total energy and system density were utilized to gain access to the compactness and stability of the complex. Hydrogen bond analysis further identified several intermolecular contacts that are responsible for the binding strength and structural stability of the fisetin-PTGS2 complex. Taken together, these simulations provide an overall picture of the dynamical and energetic properties driving the fisetin-target protein interaction.\u003c/p\u003e\n\u003cp\u003eThe RMSD plot illustrates how the PTGS2 and the fisetin-PTGS2 complex shift shapes over 100 ns (Fig. 7a). The protein alone has larger RMSDs, around 3 \u0026Aring;, showing that it bends more. The PTGS2-fisetin complex remains rather stable, with the RMSD approximately 2 \u0026Aring;, suggesting that ligand binding makes the protein structure more stable by decreasing its fluctuations. The initial increase of RMSD during the first 10 ns indicates the system adaptation, and then the complex is less fluctuating and more stable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe RMSF plot describes the flexibility of the residue throughout the simulation (Fig. 7b). The protein (Fisetin) has more flexibility, especially at the N-terminal, where there are flexible segments. The RMSF values of the PTGS2-fisetin complex are less than those of the apo structure, indicating that ligand binding stabilizes the protein via decreased mobility of residues. This emphasizes the increase in ligand (Fisetin) binding on protein stability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe density fluctuation plot (Fig. 7c) shows that, over the 100 ns molecular dynamics simulation period, both the apo PTGS2 protein and fisetin-PTGS2 complex follow the stable density pattern. Small changes were observed during the production run, again reflecting both systems equilibrated systems. Remarkably, the average density of the fisetin-PTGS2 complex was a little lower than that of the unbound protein, which could be indicative of the packing change or solvent effect influenced by ligand binding. These findings suggest that the systems are reasonably stable and the simulation protocol is sound. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs shown in Fig. 7d, the number of hydrogen bonds fluctuated mainly between four and two between PTGS2 and fisetin during the 100 ns simulation, which shows the dynamic interaction between the ligand and the protein. These transient hydrogen bonds probably indicate transitory yet significant interactions that contribute to the thermodynamic stability of the complex. Furthermore, the radius of gyration (Rg) profiles of the unbound protein and the fisetin-PTGS2 complex followed the same trend with little deviation, suggesting stable and compact conformations that have been maintained throughout the simulation. Interestingly, the complex displayed a modestly lower Rg than the apo protein, consistent with a more compact and structurally stable protein conformation upon ligand binding.\u003c/p\u003e\n\u003cp\u003eAs can be seen from the temperature fluctuation plot, the fisetin and fisetin-PTGS2 complex reached an equilibrium state at around 300 K during MD (Fig. 7f). This uniform temperature distribution indicates a reasonable thermal equilibrium process and quality of the simulation data.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eFisetin is a naturally occurring phytochemical found in apples, guavas, strawberries, and onions, has drawn much attention for its extensive pharmacological properties. This illustrates the antioxidant properties that reduce oxidative stress, production of ROS, brain damage, neuro-inflammation, and mental disorders \u003csup\u003e39\u003c/sup\u003e. Fisetin has been shown to induce apoptosis in colon cancer cells and inhibit their growth by targeting the COX2 and Wnt/EGFR/NF-\u0026kappa;B signaling pathways \u003csup\u003e40\u003c/sup\u003e. It also induces cell cycle arrest, caspase-dependent cell death, and enhances the cytotoxic effects of chemotherapeutic agents in the triple-negative breast cancer cells \u003csup\u003e41\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eProtein-chemical interaction (PCI) and protein-protein interaction (PPI) studies of fisetin, along with molecular docking interactions, showed that ten human proteins, such as PTGS2, ADAM9, AKT1, MTOR, TP53, CDK6, MMP1, TNF, AR, and IL4, are the major targets for fisetin. Molecular docking showed strong binding affinities with PTGS2 and ADAM9, which could be major targets responsible for the bioactivity of fisetin. PTGS2 (COX-2) is a rate-limiting enzyme in the synthesis of prostaglandin and plays an important role in inflammation. Fisetin binds with high affinity to PTGS2 and may inhibit its catalytic activity, leading to reduced PGE2 production, decreased NF-\u0026kappa;B signaling, and lower levels of pro-inflammatory cytokines such as TNF-\u0026alpha; and IL-6 \u003csup\u003e40\u003c/sup\u003e. Docking results also indicated the formation of hydrogen bonds and hydrophobic contacts in the PTGS2 active site region, which could stabilize a complex.\u003c/p\u003e\n\u003cp\u003eThe association of ADAM9 and MMP1 suggests it\u0026apos;s likely suppressive function on extracellular matrix degradation and cancer invasion. Potent inhibition of ADAM9-MT1‐MMP proteolytic activity by high-affinity binding can be possible, leading to the blocking of cancer cell migration and metastasis, as reported before \u003csup\u003e42\u003c/sup\u003e. The binding aspects of fisetin to AKT1 and MTOR point toward the regulation of PI3K/AKT/mTOR signaling, which leads to autophagy upregulation and control of flexible cell growth \u003csup\u003e43\u003c/sup\u003e. Binding with TP53 and CDK6 suggests apoptosis activation, G1/S cell cycle arrest \u003csup\u003e40\u003c/sup\u003e, respectively, whereas TNF, IL4, and AR binding imply further regulation of inflammatory and hormonal signaling.\u003c/p\u003e\n\u003cp\u003eOverall, these docking and network analyses show that fisetin is a multitargeting natural compound. Its concomitant modulation of PTGS2, ADAM9, AKT1/MTOR, and TP53, as well as other signaling nodes, highlights its potential as an anti-inflammatory, pro-apoptotic agent that suppresses autophagy and invasion. These findings provide a mechanism for the in vitro and in vivo validation of fisetin as a natural anti-inflammatory, anticancer compound.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunctional enrichment analysis suggested that fisetin could be a promising agent for the treatment of cancer, age-related diseases involving cell cycle regulation, DNA damage response, and signal transduction \u003csup\u003e44\u003c/sup\u003e. Additionally, the KEGG and Reactome pathway analyses also indicated its potential participation in prostate cancer, Th17 differentiation, and the AGE-RAGE signaling pathway in diabetic complications, which played an important role in the tumorigenesis process, autoimmune diseases, cancers, and diabetic complications \u003csup\u003e45\u003c/sup\u003e. Nevertheless, toxicity profiling indicated potential harmful effects of fisetin, such as hepatotoxicity, neurotoxicity, and estrogenic activity, thus suggesting the dose optimization and safety assessment to be carried out before clinical use \u003csup\u003e46\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe optimized geometrical parameters of fisetin obtained from DFT calculations are important structural details for understanding the antioxidant and anticancer activities. Four -OH groups favouring electron donation and radical scavenging at 3, 7 (position), 3\u0026prime;,4\u0026prime; positions, which are joined by a large conjugated aromatic system. The small HOMO-LUMO gap (0.15191 eV) supports high electronic reactivity and charge transfer, which is in agreement with its strong free radical scavenging potential and related biological interactions to cancer and oxidative stress pathways \u003csup\u003e47\u003c/sup\u003e \u003csup\u003e48\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eMolecular dynamics simulation of the fisetin-PTGS2 complex for 100 ns shows that ligand binding stabilizes the protein structure. The complex shows less RMSD (2 \u0026Aring;) and RMSF values than the free protein (3 \u0026Aring;), indicating decreased flexibility, particularly in functional areas, which is in favor of inhibition. Analysis of hydrogen bonds reveals that there are unstable, but very significant intermolecular interactions which make a contribution to the binding strength of -9.4 kcal/mol at the Hartree‐Fock level of theory. Further validation of the simulation methodology is provided by radius of gyration and solvent accessible surface area, which suggest that a more compact, eventually partially exposed protein conformation exists upon binding; stable profiles in both temperature and density lend further support to our simulations \u003csup\u003e49\u003c/sup\u003e \u003csup\u003e50\u003c/sup\u003e \u003csup\u003e51\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough this study was primarily intended to investigate fisetin\u0026rsquo;s multi-target potential, due to the high binding affinity and disease correlation with the fisetin-PTGS2 complex, MD simulation was conducted by using this complex. Hence, PTGS2 can be regarded as a typical case to describe the anti-cancer and anti-inflammatory mechanism of fisetin; other proteins from the PPI network could also display the broad regulatory role. Combining PCI, PPI, DFT calculation, molecular docking, and MD simulation approaches, this study offers a well-defined computational model for the therapeutic effect of fisetin. However, these in silico observations need to be experimentally verified in cellular and in vivo models for both effectiveness and therapeutic applicability. In the future, we could also explore coordination with other important proteins based on MD simulations and include multi-omics analyses for a comprehensive investigation of fisetin\u0026apos;s therapeutic potential.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFisetin, a bioactive flavonoid commonly found in fruits and vegetables, has therapeutic effects on many diseases such as cancer, neurodegenerative diseases, cardiovascular disease, and metabolic syndrome. In the present integrative proteomics work, its multi-target mode of action is unveiled, revealing high-affinity binding interactors among key regulatory proteins (ADAM9, PTGS2, AKT1, and TP53) and potential molecular networks. This drug might act upon to exert influence on essential pathways in cancer, inflammation, and cellular homeostasis. The high binding affinities (-6.1 to -9.0 kcal/mol), as well as the electronic reactivity calculated by DFT and stability derived from MD simulation for fisetin-PTGS2 complex, further confirm that it can be considered a potential drug candidate for cancer and inflammation. However, predicted toxicity risks involving hepatotoxicity, neurotoxicity, and estrogenic properties stress a careful preclinical optimization of dosing and formulation. Taken together, these results established a molecular basis for future structure-activity relationship studies and therapeutic potentiality analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that the data supporting the findings of this study are available within the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Authors received no external and internal funding for this work.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRiaz, M. et al. Phytobioactive compounds as therapeutic agents for human diseases: A review. \u003cem\u003eFood Sci Nutr\u003c/em\u003e. \u003cstrong\u003e11\u003c/strong\u003e, 2500-2529 (2023).\u003c/li\u003e\n\u003cli\u003eMuscolo, A. Mariateresa, O. 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The present study revealed the molecular mechanism of fisetin through the PCI and PPI interactions network analysis. The optimized geometry of fisetin, free energy, and polar response were estimated using Gaussian 9.0. AutoDock Vina was used to perform the molecular docking between fisetin and the STITCH-identified proteins. MD simulations were also performed by GROMACS for 100 ns to validate the Docking results and analyze the stability and dynamic behavior of the fisetin-PTGS2 complex under the physiological condition. This study identified 110 proteins by PCI and PPI, and also obtained 15 crucial proteins that regulate autophagy, cell growth, protein-serine kinase activity, cytokine activity, and different pathways. Docking studies revealed that fisetin strongly interacted with PTGS2 and ADAM9 with the binding affinities of -9.4 and -8.9 kcal/mol, respectively. DFT calculations and MD studies reveal that fisetin has a strong electronic reactivity and can efficiently interact with PTGS2, leading to the potential use of this compound as an antineoplastic/oxidative stress therapeutic agent. Overall, these findings describe the molecular basis for fisetin's multiple beneficial effects and suggest its further development into a health-promoting therapeutic agent.\nKeywords Fisetin, Network pharmacology, DFT calculation, MD simulation","manuscriptTitle":"Systematic In-silico Analysis of Fisetin-Proteins Interactions Revealing the PTGS2 as a Potential Therapeutic Target","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-26 07:09:16","doi":"10.21203/rs.3.rs-7955881/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":"66a8a1c9-d493-4913-bbf0-39c5248cff61","owner":[],"postedDate":"November 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58208133,"name":"Biological sciences/Biochemistry"},{"id":58208134,"name":"Biological sciences/Cancer"},{"id":58208135,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":58208136,"name":"Biological sciences/Drug discovery"}],"tags":[],"updatedAt":"2025-12-26T05:24:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-26 07:09:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7955881","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7955881","identity":"rs-7955881","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}