In-silico Network Pharmacology and Computational Modelling of Bioactive Compounds From Allium Cepa Targeting Downstream Protein Effectors in Diabetes

In-silico Network Pharmacology and Computational Modelling of Bioactive Compounds From Allium Cepa Targeting Downstream Protein Effectors in Diabetes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article In-silico Network Pharmacology and Computational Modelling of Bioactive Compounds From Allium Cepa Targeting Downstream Protein Effectors in Diabetes Aliyu Ahmad, Ani Onuabuchi Nnenna, Shamsudeen Mamman, Chika Nwokoro, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8181479/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 Network pharmacology is an emerging, cost-effective drug-development approach that uses systems biology and network theory to integrate data and reveal interactions between compounds and their biological targets. Diabetes mellitus is a widespread metabolic disease affecting millions worldwide, while traditional medicine represents knowledge of healing practices inherited across generations and indigenous communities. Allium cepa contains various nutraceutical compounds that give it notable antioxidant, antitumor, antidiabetic, and anti-inflammatory properties. Bioactive compounds of Allium cepa were identified using a phytochemical interactive database. Both the diabetic and bioactive compound target proteins were determined and screened for oral drug bioavailability potential with favorable pharmacokinetic properties. The target proteins underwent protein-protein interaction network analysis, and analyzed using molecular docking analysis. The interaction of kaempferol with PPARG, PPARA and GSK3B possesses − 8.5 kcal/mol, -8.1 kcal/mol, and − 8.0 kcal/mol, respectively. The results obtained showed that kaempferol identified with strong binding affinity with the selected diabetic target proteins and confirmed as the best bioactive compound with an overall interaction profile, antidiabetic property, and good oral drug likeness. Network Pharmacology Diabetes Mellitus Allium cepa Molecular docking Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1.0 Background of the Study Network pharmacology is the latest developmental field, which has promising approaches for a very economical drug development process (Rekha et al., 2023 ). The network pharmacology approach enhances the provision of a full or a partial understanding of pharmacological data using systems biology and network theory concepts; hence, it is presently being considered as the next model of drug discovery (Zhang et al., 2019 ). It integrates systematic data to provide interactions between compounds and their targets. Network pharmacology can include processes like screening of active components, enrichment analysis, identification and utilization of disease target databases. It considered an emergent approach in the efficient identification of phytopharmaceuticals to propose hypotheses of mechanisms that can further be validated experimentally (Jiashuo et al., 2022 ). Moreover, the capability of network pharmacology not only comprises virtual computing and high output rate data analysis but also involves network construction based on the interactions and network topological analysis. Diabetes mellitus is a prevalent metabolic disorder that has been impacting millions of people globally (Rekha et al., 2023 ). Diabetes mellitus is a serious metabolic syndrome affecting many worldwide. It is a multigenetic metabolic disease, causing devastating global health challenge, The anti-diabetic drugs currently used are unsatisfactory, presumably due to limitation caused by single target (Wang et al., 2020 ). Diabetes mellitus implicates a significant risk to human health and ranks as the 9th leading basis of death globally. It is considered as one of the rapidly emerging diseases globally. According to statistics revealed worldwide in 2019, there were approximately 463 million people diagnosed with diabetes and expected to rise to about 693 million by the year 2045. Diabetes is characterized by hyperglycemia, involving a relative lack of insulin secretion, resistance, or both, i.e., the sugars in food are not used by the body for energy and thus increase the blood glucose level in the body. This happens when the pancreas is not able to produce the required amount of insulin or even when the body is not able to properly process the glucose by resisting insulin (American Diabetes Association, 2010 ). The two primary types of diabetes are Type 1 and Type 2, each with distinct underlying factors and mechanisms. The level of blood sugar can rise as a result of inadequate secretion of insulin and lower body sensitivity to insulin, resulting by number of factors, including family history, viral infection, lifestyle and poor dietary habits (Roglic et al. , 2016). Type 1 diabetes characterized by a total lack of insulin in the body. Type 2 diabetes is characterizedby insufficient insulin secretion, insulin resistance, and beta cell destruction (Rekha etal., 2023 ; Sun et al., 2022 ). Traditional medicine composed of information and therapeutic methods from the previous generations. In this type of therapy natural herbals or spiritual methods used to treat various deseases (Haux, 2022 ), emphasizing the significance and historical benefit of traditional medicine in improving health and wellness through ancient practices (Rekha et al., 2023 ). Onion (Allium cepa) is a plant in the Liliaceae family that includes beneficial components such as flavonoids, anthocyanins, sulfur-containing compounds, saponins and phenols (Devi, Dhall and Brar, 2025 ; Nisha et al. , 2025). These compounds have been shown scientifically to exhibit therapeutic benefits such as antioxidant, anticancer, antidiabetic and anti-inflammatory properties (Jayaswall et al., 2025 ). Nutraceuticals are described as "a food or part of a food that provides health benefits beyond its basic nutritional value" (Maria et al. , 2024). This study focuses on investigating anti-diabetic effect of the bioactive compounds from Allium cepa through computational and network pharmacology techniques by interacting with diabetic target proteins. 2.0 Materials and Methods 2.1 Collection and Preparation of Phytocompounds Seven phytochemical bioactive compounds of Allium cepa were selected from 47 identified phytochemicals using the Phytochemical Interactions Database (PCIDB, 2021) ( https://www.genome.jp/dp/pcidp/kna_species ). These compounds were characterized using their Simplified Molecular Input Line Entry System (SMILES) collected from the PubChem chemical compounds database, and their drug similarity score was predicted using Lipinski's rule of five model in Swiss ADME ( https://www.swissadme.ch ). 2.2 Identification of Target Proteins and Construction The study gathered protein target genes for diabetes mellitus in humans and bioactive compounds of Allium cepa from UniProt and Swiss Target Prediction databases ( https://www.swisstargetprediction.ch ) as described by Kim et al. ( 2025 ). Duplication of predicted target proteins was eliminated during database construction in Microsoft Excel 2019 (Wang et al., 2020 ). The overlapping genes between the diabetes and bioactive target genes were determined using an online Venn diagram using bioinformatic Venny 2.0 ( https://bioinfogp.cnb.csic.es/tools/venny/ ). A complex information network was constructed based on the interaction of bioactive compounds of Allium cepa and diabetes predicted genes described by Ahmad et al. ( 2025 ). 2.3 Gene Set Enrichment Analysis The study identified biochemical pathways and protein-protein interactions linked to diabetes using ShinyGO 0.85 ( https://bioinformatics.sdstate.edu.go/ ) and String database 12.0V ( https://string-db.org/ ). The pathways significantly associated with the target overlapping genes were identified using the Kyoto Encyclopedia of Genes (KEGG) and Gene Ontology (GO) as described by Kanehisa et al. ( 2025 ), and data was transferred to Cytoscape for protein-protein interaction analysis. 2.4 Network construction and Protein-Protein Interaction (PPI) Based on the data from the enrichment analysis, an interaction network between the proteins was constructed using the Cytoscape 3.10.4 version of the software. Cytoscape software was employed to establish the PPI relationship network and perform topological analysis in order to identify the key genes associated with diabetes mellitus using degree centrality. 2.5 Molecular Docking Studies The study analyzed the 3D structures of selected target proteins for diabetes and the seven bioactive ligands of Allium cepa, downloaded from RCSB Protein Data Bank ( http://www.rcsb.org/pdb ) and PubChem, respectively. The molecular docking studies of the potential therapeutic targets of diabetes selected from Cytoscape and the bioactive ligands were executed using UCSF Chimera 1.18. The docking study was performed initially by preparing the target proteins and the ligands using the Dock Prep tab to remove water molecules and unwanted residue and adding hydrogens and Gasteiger charges using UCSF Chimera 1.18 (Shapovalov and Dunbrack, 2011 ). The bioactive ligands were docked against the active site of the selected target genes of diabetes. Using the AutoDock Vina tab in Chimera, all default parameters were set, including the grid lines and the docking was run. Molecular interactions such as hydrogen bonding, hydrophobic interaction and electrostatics were examined. The Dock Score feature was used for scoring all the ligands. Analyses have been identified for the best pose. The affinity between the ligands of Allium cepa and the diabetes target shown by the docking score in the current work and the lowest docking energy was determined. 3.0 Results 3.1 Identification of Bioactive Compounds Fouty seven bioactive phytocompounds of Allium cepa were identified from the phytochemical interaction database PCIDB, among which seven phytocompounds were randomly selected, as presented in Table 1 . Their PubChem ID and SMILES were determined from the PubChem database, which was used for the prediction analysis in the SWISS Target Prediction online database. 3.2 SWISS ADME Prediction The pharmacokinetics and drug-likeness of the seven identified phytocompounds of Allium cepa using the online SWISS ADME ( https://www.swissadme.ch ). Allicin, apocynin, cyanidin, kaempferol, jasmonic acid, and quercetin satisfied the drug-likeness criteria and displayed higher predicted gastrointestinal absorption by passing Lipinski’s rule (MW > 500, H-bond donors > 5, H-bond acceptors > 10), unlike cyanin, which showed multiple violations of Lipinski’s rule and a low predicted bioavailability score (0.17), indicating poor expected oral bioavailability. Predicted blood-brain barrier permeability was restricted to allicin, jasmonic acid, and apocynin, while kaempferol, cyanin, cyanidin, and quercetin were predicted not to be blood-brain barrier permeant. Concerning the metabolism, quercetin and kaempferol are predicted inhibitors of multiple CYP450 enzymes (notably CYP1A2, CYP3A4 and CYP2D6), while cyanidin shows moderate inhibition limited to CYP1A2. Cyanin, allicin, jasmonic acid and apocynin showed no nosignificant CYP450 inhibition in SwissADME. Toxicity screening indicated no predicted carcinogenicity for any of the seven phytocompounds as presented in Table 2 . Table 2 ADMET Properties of the identified Bioactive Compounds Compounds Molecular weight Water solubility GI Absorption BBB Penetration Bioavailability Score Lipinski (Drug-like) Toxicity Metformin (C 4 H 11 N 5 ) 129.16g/mol Soluble High yes 0.55 Yes Non-toxic Cyanin (C₂₇H₃₁O₁₆⁺) 611.53g/mol Soluble Low No 0.17 No Non-toxic Quercetin (C₁₅H₁₀O₇) 302.24g/mol Soluble High No 0.55 Yes Non-toxic Kaempferol (C₁₅H₁₀O₆) 286.24g/mol Soluble High yes 0.55 Yes Non-toxic Cyanidin (C₁₅H₁₁O₆⁺) 287.24g/mol Soluble High No 0.55 Yes Non-toxic Allicin (C₆H₁₀OS₂) 162.27g/mol Soluble High Yes 0.55 Yes Non-toxic Jasmonic acid (C₁₂H₁₈O₃) 210.27g/mol Soluble High Yes 0.85 Yes Non-toxic Apocynin (C₉H₁₀O₃) 166.17g/mol Soluble High Yes 0.55 Yes Non-toxic 3.3 Target Proteins Identification The target proteins for diabetes in humans and the selected bioactive compounds of Allium cepa obtained from the UniProt and Swiss Target Prediction databases after eliminating the duplicates are 2159 and 334 genes, respectively. Figure 1 presents the Venn diagram of the intersection between diabetes-related targets and the drug-related target of the Allium cepa, identifying 24 related overlapping genes of the intersection using online bioinformatics Venny tools. 3.4 Gene Enrichment Analysis The results for the Kyoto Encyclopedia of Genes (KEGG) and Gene Ontology (GO) were presented in Figs. 2 , 3 and 4 , showing the bar plots and the pathway graph of the overlapping genes. 3.5 Protein-Protein Interaction Network Table 2 presents the protein-protein interaction network analysis conducted using Cytoscape, identifying the genes with their degree of hierarchy. A total of 23 target genes were analyzed with 23 numbers of nodes, 49 numbers of edges. The average number of neighbors was 5.158, the network diameter was 4, the network radius was 2, the characteristic path length was 1.924, the clustering coefficient was 0.528, the network density was 0.287, the network heterogeneity was 0.766, the network centralization was 0.549, the connected components were 5, and the analysis time taken was 0.005 seconds. From the statistical analysis result, five genes (AKT1, PPARG, PPARA, GSK3B and INSR) were considered for molecular docking analysis, as presented in Fig. 7 . Figures 5 and 6 below present the cytoscape layout of the 23 genes and the five selected genes for docking analysis, respectively. Table 3 Genes with their degree of Hierarchy after Cytoscape network analysis SN GENE NAME DEGREE 1. AKT1 14 2. PPARG 12 3. PPARA 11 4. GSK3B 9 5. INSR 7 6. NR3C1 7 7. PIK3R1 7 8. GCK 6 9. AKR1B1 5 10. HDAC5 4 11. CNR1 3 12. FABP4 3 13. RET 3 14. CDK6 2 15. RPS6KA3 1 16. ADRA2A 1 17. CD38 1 18. DYRK1B 1 19. CISD1 1 20. GPR35 0 21. AVPR2 0 22. PTPN22 0 23. TGM2 0 3.6 Molecular Docking Analysis The docking results between five selected target proteins (AKT1, PPARA, PPARG, GSK3B and INSR) of diabetes mellitus and the Allium cepa phytochemical compounds (allicin, apocynin, cyanidin, cyanin, kaempferol, jasmonic acid, and quercetin) were presented in Table 3 , showing the lower binding energy of the interaction between the ligands and the respective target proteins. The lower binding energy means affinity is getting higher. The binding energy displays the protein-ligand interaction affinity via an optimized algorithm that functions as an inhibitor. Different hydrophobic groups can contribute significantly to the binding of the target proteins to the hydrophobic cavity. Table 4 Result for Docking between target proteins of diabetes with ligands from Allium cepa Target gene for Diabetes Phytocompound PubChem ID Binding Energy (kcal/mol) AKT1 Allicin CID_65036 -4.4 Apocynin CID_2214 -5.2 Cyanin CID_441688 -7.0 Cyanidin CID_128861 -6.6 Jasmonic acid CID_5281166 -6.6 Kaempferol CID_5280863 -6.7 Quercetin CID_5280343 -6.8 PPARA Allicin CID_65036 -4.5 Apocynin CID_2214 -6.1 Cyanin CID_441688 -6.9 Cyanidin CID_128861 -6.7 Jasmonic acid CID_5281166 -4.5 Kaempferol CID_5280863 -8.1 Quercetin CID_5280343 -6.8 PPARG Allicin CID_65036 -3.8 Apocynin CID_2214 -6.1 Cyanin CID_441688 -8.3 Cyanidin CID_128861 -7.7 Jasmonic acid CID_5281166 -5.5 Kaempferol CID_5280863 -8.5 Quercetin CID_5280343 -7.9 GSK3B Allicin CID_65036 -3.8 Apocynin CID_2214 -5.5 Cyanin CID_441688 -9.2 Cyanidin CID_128861 -7.4 Jasmonic acid CID_5281166 -5.6 Kaempferol CID_5280863 -8.0 Quercetin CID_5280343 -8.3 INSR Allicin CID_65036 -3.6 Apocynin CID_2214 -5.2 Cyanin CID_441688 -8.7 Cyanidin CID_128861 -8.3 Jasmonic acid CID_5281166 -5.5 Kaempferol CID_5280863 -7.6 Quercetin CID_5280343 -7.8 Figure 8 present the molecular docking results showing the interaction between the selected diabetic target proteins (PPARA, PPARG, GSK3B) and Kaempferol (ligand like compound) identified from Allium cepa. 4.0 Discussion The randomly selected bioactive compounds identified from PCID were presented in Table 1 with their respective structures downloaded from PubChem, and their pharmacokinetics and drug likeness (ADMET) were observed and reported in Table 2 . All the bioactive compounds pass the rule of five, making them pass Lipinski’s rule except Cyanin, with a molecular weight of > 500 g/mol (611.53 g/mol). The rule of five (Ro5) predicts the bioavailability of the bioactive compounds. The ADMET properties of the bioactive compounds were compared with a standard diabetic drug (metformin), and many pharmacokinetic properties were in common, this confirms the anti-diabetic properties of the bioactive compounds identified from Allium cepa. This research work was in accordance with the work done by many researchers proving the antidiabetic properties of allicin (Li et al., 2022 ), apocynin (Sánchez-Duarte et al., 2025 ), cyanidin (Singh et al., 2022 ; Ye et al., 2024 ), kaempferol (Yang et al., 2022 ), and quercetin (Slimestad et al., 2007 ). Yan et al. ( 2023 ) reported that the antidiabetic mechanism of action involves modulation of multiple molecular targets and signaling pathways associated with the insulin resistance and the pathogenesis of diabetes and this enhances insulin sensitivity and reduces blood glucose level. In this study, analysis of the therapeutic target PPI network identified AKT1, PPARG, PPARA, GSK3B and INSR as the key core targets associated with Allium cepa’s pharmacological activity. These genes have been reported to be involved in the pathological processes of Type 2-diabetes-AKT1 (Camaya et al., 2022 , Miao et al., 2022 , Zhang et al., 2019 ), PPARG (Cooreman et al., 2024 , Frkic et al., 2021 ), PPARA (Joly et al., 2009 ), GSK3B (Teli et al. , 2023), and INSR (Chen et al., 2019 ). The result of the GO and KEGG enrichment analysis showed that active components of Allium cepa can regulate various biological processes, cellular component, molecular function, and pathways to treat Diabetes. The genes contribute to the molecular mechanisms fundamental to sensitivity of insulin and control of metabolism, hence are implicated in the advancement or prevention of metabolic syndrome like type 2 diabetes. Hong et al. (2013) affirmed that glucose homeostasis is essential, and that any disruption in this balance may result in metabolic disorder manifesting as type 2 diabetes mellitus. The response to insulin is an indication that the identified genes are involved actively in the insulin signaling cascade and its consequential effects on cell metabolism. The Cellular component category of the analysis showed that the proteins encoded by the identified genes are mostly situated in the mitochondrion, Axon, perinuclear region of the cytoplasm, and Receptor complex. Enrichment in the mitochondrion is an indication that these genes are associated with redox balance and energy metabolism which are usually compromised in Type 2 diabetes mellitus (Lowell & Shulman, 2005 ). The identified bioactive compound in Allium cepa have been reported to increase mitochondrial biogenesis, reduce reactive oxygen species (Ho et al., 2022 ), enhance insulin production and glucose metabolism (Mijgar & Deokate, 2023 , Ansari et al., 2022 ), hence, improving glucose utilization necessary in the management of Type 2 diabetes mellitus. The axon enrichment suggests involvement of the central nervous system (CNS) and neurons. The CNS functions in the regulation of insulin secretion and sensitivity (Güemes and Georgiou, 2018 ). Central Nervous system control of glucose is a drug target for diabetes mellitus (Mirzadeh et al., 2022 ). From the KEGG pathway, the drug targets of Allium cepa against diabetes mellitus were mainly linked to insulin signaling pathway, diabetes mellitus, and insulin resistance. The major highlighted nodes like P13K, IRS1/2, PPAR, AKT, AMPK, MAPK/JNK, and glycogen metabolizing enzymes were enriched prominently. This indicates defective glycogen synthesis, impaired GLUT4 translocation, improved inflammatory signaling and gluconeogenesis as presented in Fig. 4 . The docking analysis reported high binding energy (-9.2 kcal/mol) at the interaction between the diabetic target protein (GSK3B) and ligand (Cyanin) identified from Allium cepa, followed by the interaction of Cyanin with INSR and PPARG with − 8.7 kcal/mol and − 8.3 kcal/mol, respectively, as presented in Table 4 . The drug-likeness and pharmacokinetic properties of the phytocompound (cyanin) do not pass Lipinski’s rule (rule of five) with low gastrointestinal absorption; therefore, it cannot be predicted as a drug. The interaction of GSK3B with quercetin and INSR with cyanidin possesses the same binding energy of -8.3 kcal/mol. The interaction of kaempferol with PPARG, PPARA and GSK3B possesses − 8.5 kcal/mol, -8.1 kcal/mol, and − 8.0 kcal/mol, respectively. This study revealed that kaempferol fits perfectly into the hydrophobic pocket of each receptor with strong binding interactions with the three diabetic target proteins (PPARA, PPARG, GSK3B) with strong binding affinity. According to the drug-likeness and pharmacokinetics properties presented in Table 2 , kaempferol is identified as the best phytocompound to be confirmed as the drug with antidiabetic property. 5.0 Conclusion In this study, phytocompounds were identified from Allium cepa and screened for oral drug bioavailability potential with favorable pharmacokinetic properties. The results obtained showed that kaempferol, quercetin, and cyanin were identified as having a strong binding affinity with the selected diabetic target proteins (INSR, PPARA, PPARG, DSK3B), and kaempferol was confirmed as the best bioactive compound with an overall interaction profile, antidiabetic property, and good oral likeness. Declarations There is no any Ethical approval and consent applicable to this research work Consent for publication also not applicable The authors declare no any conflict of interest This research work received no external funding The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request Acknowledgment: The Authors would like to express their gratitude to Toplaholo Biogene for the training and support toward the achievement of this research work Author Contribution Aliyu and Ani Conceptualized the study, designed the research framework, performed molecular docking and interpretation of results, and drafted the initial manuscripts.Shamsudeen, Al-amin, Mustafa, Michael and James contributed to target prediction, network pharmacology and pathways enrichment analysisChika, Onwu, Mayowa, and Alexander provided the overall supervision, critically reviewed the manuscript, contributed to the interpretation of pharmacological relevance, and approved the final version for submission. 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Front Pharmacol 10. https://doi.org/10.3389/fphar.2019.00123 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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04:39:52","extension":"html","order_by":34,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":140135,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8181479/v1/b3c1ac23d3b5a16f7bb32507.html"},{"id":97141413,"identity":"66f82cfd-5656-48c1-ab0a-db7998e3e5be","added_by":"auto","created_at":"2025-12-01 10:06:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":87615,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram of the intersection of target genes of diabetes and allium cepa phytocompounds identifying the overlapping genes\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8181479/v1/c7073310a4206c7cd1410f96.png"},{"id":97105485,"identity":"4ac76448-5504-49bb-9a8f-439c8b9095e0","added_by":"auto","created_at":"2025-12-01 04:39:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":293630,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBar Plot of KEGG of the overlapping genes identified\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8181479/v1/11614604344ffa3f0d192968.png"},{"id":97105487,"identity":"69eb5263-f3bf-45ba-bfe0-17be8646d45a","added_by":"auto","created_at":"2025-12-01 04:39:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":374613,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBar Plot of KEGG of the overlapping genes identified\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8181479/v1/5ed2143b73c2cb37afbfe55c.png"},{"id":97141593,"identity":"e858bf41-4599-4f82-9192-548642e05a8a","added_by":"auto","created_at":"2025-12-01 10:06:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":328421,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKEGG Graph Pathways of the Overlapping Genes\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8181479/v1/e69d2864b4080575b8df4f4e.png"},{"id":97105490,"identity":"0f3414c7-a4eb-47c0-a8bf-8a0d75ac1001","added_by":"auto","created_at":"2025-12-01 04:39:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":188279,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCircular Layout of 23 protein-protein interaction of Overlapping Target genes in cytoscape\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8181479/v1/beb53287115404d8827ab579.png"},{"id":97105502,"identity":"f6467321-7552-45ef-8784-1834d1e4621d","added_by":"auto","created_at":"2025-12-01 04:39:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":99587,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProtein-Protein interaction of the most Hierarchical gene after Network Analysis in Cytoscape\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8181479/v1/86a1eaa8b0f83e69be4f2f75.png"},{"id":97141826,"identity":"c925e7f9-d81e-4def-a80d-57a26e9b0f11","added_by":"auto","created_at":"2025-12-01 10:07:04","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":971981,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e3D representation of (a) AKT1 Gene (PDB ID: 8R5K), (b) PPARA (PDB ID: 8RCE), (c) PPARG (PDB ID: 8WFE) (d) INSR (PDB ID: 3EKK) (e) GSK3B (PDB ID: 1I09)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8181479/v1/ec0a1945986a42ca5cee1dc8.png"},{"id":97105508,"identity":"7d7a19c4-b7cc-433f-8d3f-1aa1bb6769e5","added_by":"auto","created_at":"2025-12-01 04:39:51","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":784477,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular Docking Result Identifying (a) Kaempferol VS GSK3B, (b) Kaempferol VS PPARG (c) Kaempferol VS PPARA\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8181479/v1/1c36120842f5761d55013565.png"},{"id":104002092,"identity":"b4272bee-e279-43b5-b210-4cde5d8b0447","added_by":"auto","created_at":"2026-03-05 14:12:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4283353,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8181479/v1/1ad30a31-6569-424e-a083-9acc5aa74866.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eIn-silico Network Pharmacology and Computational Modelling of Bioactive Compounds From Allium Cepa Targeting Downstream Protein Effectors in Diabetes\u003c/p\u003e","fulltext":[{"header":"1.0 Background of the Study","content":"\u003cp\u003eNetwork pharmacology is the latest developmental field, which has promising approaches for a very economical drug development process (Rekha et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The network pharmacology approach enhances the provision of a full or a partial understanding of pharmacological data using systems biology and network theory concepts; hence, it is presently being considered as the next model of drug discovery (Zhang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It integrates systematic data to provide interactions between compounds and their targets. Network pharmacology can include processes like screening of active components, enrichment analysis, identification and utilization of disease target databases. It considered an emergent approach in the efficient identification of phytopharmaceuticals to propose hypotheses of mechanisms that can further be validated experimentally (Jiashuo et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Moreover, the capability of network pharmacology not only comprises virtual computing and high output rate data analysis but also involves network construction based on the interactions and network topological analysis.\u003c/p\u003e\u003cp\u003eDiabetes mellitus is a prevalent metabolic disorder that has been impacting millions of people globally (Rekha et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Diabetes mellitus is a serious metabolic syndrome affecting many worldwide. It is a multigenetic metabolic disease, causing devastating global health challenge, The anti-diabetic drugs currently used are unsatisfactory, presumably due to limitation caused by single target (Wang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Diabetes mellitus implicates a significant risk to human health and ranks as the 9th leading basis of death globally. It is considered as one of the rapidly emerging diseases globally. According to statistics revealed worldwide in 2019, there were approximately 463\u0026nbsp;million people diagnosed with diabetes and expected to rise to about 693\u0026nbsp;million by the year 2045.\u003c/p\u003e\u003cp\u003eDiabetes is characterized by hyperglycemia, involving a relative lack of insulin secretion, resistance, or both, i.e., the sugars in food are not used by the body for energy and thus increase the blood glucose level in the body. This happens when the pancreas is not able to produce the required amount of insulin or even when the body is not able to properly process the glucose by resisting insulin (American Diabetes Association, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The two primary types of diabetes are Type 1 and Type 2, each with distinct underlying factors and mechanisms. The level of blood sugar can rise as a result of inadequate secretion of insulin and lower body sensitivity to insulin, resulting by number of factors, including family history, viral infection, lifestyle and poor dietary habits (Roglic \u003cem\u003eet al.\u003c/em\u003e, 2016). Type 1 diabetes characterized by a total lack of insulin in the body. Type 2 diabetes is characterizedby insufficient insulin secretion, insulin resistance, and beta cell destruction (Rekha etal., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTraditional medicine composed of information and therapeutic methods from the previous generations. In this type of therapy natural herbals or spiritual methods used to treat various deseases (Haux, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), emphasizing the significance and historical benefit of traditional medicine in improving health and wellness through ancient practices (Rekha et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOnion (Allium cepa) is a plant in the Liliaceae family that includes beneficial components such as flavonoids, anthocyanins, sulfur-containing compounds, saponins and phenols (Devi, Dhall and Brar, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Nisha \u003cem\u003eet al.\u003c/em\u003e, 2025). These compounds have been shown scientifically to exhibit therapeutic benefits such as antioxidant, anticancer, antidiabetic and anti-inflammatory properties (Jayaswall et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Nutraceuticals are described as \"a food or part of a food that provides health benefits beyond its basic nutritional value\" (Maria \u003cem\u003eet al.\u003c/em\u003e, 2024).\u003c/p\u003e\u003cp\u003eThis study focuses on investigating anti-diabetic effect of the bioactive compounds from Allium cepa through computational and network pharmacology techniques by interacting with diabetic target proteins.\u003c/p\u003e"},{"header":"2.0 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Collection and Preparation of Phytocompounds\u003c/h2\u003e\u003cp\u003eSeven phytochemical bioactive compounds of Allium cepa were selected from 47 identified phytochemicals using the Phytochemical Interactions Database (PCIDB, 2021) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genome.jp/dp/pcidp/kna_species\u003c/span\u003e\u003cspan address=\"https://www.genome.jp/dp/pcidp/kna_species\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These compounds were characterized using their Simplified Molecular Input Line Entry System (SMILES) collected from the PubChem chemical compounds database, and their drug similarity score was predicted using Lipinski's rule of five model in Swiss ADME (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.swissadme.ch\u003c/span\u003e\u003cspan address=\"https://www.swissadme.ch\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Identification of Target Proteins and Construction\u003c/h2\u003e\u003cp\u003eThe study gathered protein target genes for diabetes mellitus in humans and bioactive compounds of Allium cepa from UniProt and Swiss Target Prediction databases (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.swisstargetprediction.ch\u003c/span\u003e\u003cspan address=\"https://www.swisstargetprediction.ch\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) as described by Kim et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Duplication of predicted target proteins was eliminated during database construction in Microsoft Excel 2019 (Wang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The overlapping genes between the diabetes and bioactive target genes were determined using an online Venn diagram using bioinformatic Venny 2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinfogp.cnb.csic.es/tools/venny/\u003c/span\u003e\u003cspan address=\"https://bioinfogp.cnb.csic.es/tools/venny/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A complex information network was constructed based on the interaction of bioactive compounds of Allium cepa and diabetes predicted genes described by Ahmad et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Gene Set Enrichment Analysis\u003c/h2\u003e\u003cp\u003eThe study identified biochemical pathways and protein-protein interactions linked to diabetes using ShinyGO 0.85 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinformatics.sdstate.edu.go/\u003c/span\u003e\u003cspan address=\"https://bioinformatics.sdstate.edu.go/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and String database 12.0V (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The pathways significantly associated with the target overlapping genes were identified using the Kyoto Encyclopedia of Genes (KEGG) and Gene Ontology (GO) as described by Kanehisa et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and data was transferred to Cytoscape for protein-protein interaction analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Network construction and Protein-Protein Interaction (PPI)\u003c/h2\u003e\u003cp\u003eBased on the data from the enrichment analysis, an interaction network between the proteins was constructed using the Cytoscape 3.10.4 version of the software. Cytoscape software was employed to establish the PPI relationship network and perform topological analysis in order to identify the key genes associated with diabetes mellitus using degree centrality.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Molecular Docking Studies\u003c/h2\u003e\u003cp\u003eThe study analyzed the 3D structures of selected target proteins for diabetes and the seven bioactive ligands of Allium cepa, downloaded from RCSB Protein Data Bank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.rcsb.org/pdb\u003c/span\u003e\u003cspan address=\"http://www.rcsb.org/pdb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and PubChem, respectively. The molecular docking studies of the potential therapeutic targets of diabetes selected from Cytoscape and the bioactive ligands were executed using UCSF Chimera 1.18. The docking study was performed initially by preparing the target proteins and the ligands using the Dock Prep tab to remove water molecules and unwanted residue and adding hydrogens and Gasteiger charges using UCSF Chimera 1.18 (Shapovalov and Dunbrack, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The bioactive ligands were docked against the active site of the selected target genes of diabetes. Using the AutoDock Vina tab in Chimera, all default parameters were set, including the grid lines and the docking was run. Molecular interactions such as hydrogen bonding, hydrophobic interaction and electrostatics were examined. The Dock Score feature was used for scoring all the ligands. Analyses have been identified for the best pose. The affinity between the ligands of Allium cepa and the diabetes target shown by the docking score in the current work and the lowest docking energy was determined.\u003c/p\u003e\u003c/div\u003e"},{"header":"3.0 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Identification of Bioactive Compounds\u003c/h2\u003e\u003cp\u003eFouty seven bioactive phytocompounds of Allium cepa were identified from the phytochemical interaction database PCIDB, among which seven phytocompounds were randomly selected, as presented in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Their PubChem ID and SMILES were determined from the PubChem database, which was used for the prediction analysis in the SWISS Target Prediction online database.\u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"609\" height=\"589\"\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 SWISS ADME Prediction\u003c/h2\u003e\u003cp\u003eThe pharmacokinetics and drug-likeness of the seven identified phytocompounds of Allium cepa using the online SWISS ADME (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.swissadme.ch\u003c/span\u003e\u003cspan address=\"https://www.swissadme.ch\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Allicin, apocynin, cyanidin, kaempferol, jasmonic acid, and quercetin satisfied the drug-likeness criteria and displayed higher predicted gastrointestinal absorption by passing Lipinski\u0026rsquo;s rule (MW\u0026thinsp;\u0026gt;\u0026thinsp;500, H-bond donors\u0026thinsp;\u0026gt;\u0026thinsp;5, H-bond acceptors\u0026thinsp;\u0026gt;\u0026thinsp;10), unlike cyanin, which showed multiple violations of Lipinski\u0026rsquo;s rule and a low predicted bioavailability score (0.17), indicating poor expected oral bioavailability. Predicted blood-brain barrier permeability was restricted to allicin, jasmonic acid, and apocynin, while kaempferol, cyanin, cyanidin, and quercetin were predicted not to be blood-brain barrier permeant. Concerning the metabolism, quercetin and kaempferol are predicted inhibitors of multiple CYP450 enzymes (notably CYP1A2, CYP3A4 and CYP2D6), while cyanidin shows moderate inhibition limited to CYP1A2. Cyanin, allicin, jasmonic acid and apocynin showed no nosignificant CYP450 inhibition in SwissADME. Toxicity screening indicated no predicted carcinogenicity for any of the seven phytocompounds as presented in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eADMET Properties of the identified Bioactive Compounds\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCompounds\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMolecular weight\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWater solubility\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGI Absorption\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBBB Penetration\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBioavailability Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLipinski (Drug-like)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eToxicity\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetformin\u003c/p\u003e\u003cp\u003e(C\u003csub\u003e4\u003c/sub\u003eH\u003csub\u003e11\u003c/sub\u003eN\u003csub\u003e5\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e129.16g/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoluble\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eyes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNon-toxic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCyanin (C₂₇H₃₁O₁₆⁺)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e611.53g/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoluble\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNon-toxic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuercetin (C₁₅H₁₀O₇)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e302.24g/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoluble\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNon-toxic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKaempferol (C₁₅H₁₀O₆)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e286.24g/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoluble\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eyes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNon-toxic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCyanidin (C₁₅H₁₁O₆⁺)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e287.24g/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoluble\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNon-toxic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAllicin (C₆H₁₀OS₂)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e162.27g/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoluble\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNon-toxic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJasmonic acid (C₁₂H₁₈O₃)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e210.27g/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoluble\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNon-toxic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApocynin (C₉H₁₀O₃)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e166.17g/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoluble\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNon-toxic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Target Proteins Identification\u003c/h2\u003e\u003cp\u003eThe target proteins for diabetes in humans and the selected bioactive compounds of Allium cepa obtained from the UniProt and Swiss Target Prediction databases after eliminating the duplicates are 2159 and 334 genes, respectively. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the Venn diagram of the intersection between diabetes-related targets and the drug-related target of the Allium cepa, identifying 24 related overlapping genes of the intersection using online bioinformatics Venny tools.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Gene Enrichment Analysis\u003c/h2\u003e\u003cp\u003eThe results for the Kyoto Encyclopedia of Genes (KEGG) and Gene Ontology (GO) were presented in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, showing the bar plots and the pathway graph of the overlapping genes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Protein-Protein Interaction Network\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the protein-protein interaction network analysis conducted using Cytoscape, identifying the genes with their degree of hierarchy. A total of 23 target genes were analyzed with 23 numbers of nodes, 49 numbers of edges. The average number of neighbors was 5.158, the network diameter was 4, the network radius was 2, the characteristic path length was 1.924, the clustering coefficient was 0.528, the network density was 0.287, the network heterogeneity was 0.766, the network centralization was 0.549, the connected components were 5, and the analysis time taken was 0.005 seconds. From the statistical analysis result, five genes (AKT1, PPARG, PPARA, GSK3B and INSR) were considered for molecular docking analysis, as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Figures\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e below present the cytoscape layout of the 23 genes and the five selected genes for docking analysis, respectively.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGenes with their degree of Hierarchy after Cytoscape network analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGENE NAME\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDEGREE\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAKT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePPARG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePPARA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGSK3B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eINSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNR3C1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePIK3R1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAKR1B1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHDAC5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCNR1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFABP4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCDK6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRPS6KA3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eADRA2A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e17.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCD38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDYRK1B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e19.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCISD1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGPR35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e21.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAVPR2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e22.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePTPN22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e23.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTGM2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Molecular Docking Analysis\u003c/h2\u003e\u003cp\u003eThe docking results between five selected target proteins (AKT1, PPARA, PPARG, GSK3B and INSR) of diabetes mellitus and the Allium cepa phytochemical compounds (allicin, apocynin, cyanidin, cyanin, kaempferol, jasmonic acid, and quercetin) were presented in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, showing the lower binding energy of the interaction between the ligands and the respective target proteins. The lower binding energy means affinity is getting higher. The binding energy displays the protein-ligand interaction affinity via an optimized algorithm that functions as an inhibitor. Different hydrophobic groups can contribute significantly to the binding of the target proteins to the hydrophobic cavity.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResult for Docking between target proteins of diabetes with ligands from Allium cepa\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTarget gene for Diabetes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePhytocompound\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePubChem ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBinding Energy (kcal/mol)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003e\u003cb\u003eAKT1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAllicin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_65036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-4.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eApocynin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_2214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-5.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCyanin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_441688\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCyanidin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_128861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-6.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJasmonic acid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_5281166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-6.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKaempferol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_5280863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-6.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuercetin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_5280343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-6.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003e\u003cb\u003ePPARA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAllicin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_65036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-4.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eApocynin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_2214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-6.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCyanin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_441688\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-6.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCyanidin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_128861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-6.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJasmonic acid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_5281166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-4.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKaempferol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_5280863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-8.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuercetin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_5280343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-6.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003e\u003cb\u003ePPARG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAllicin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_65036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eApocynin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_2214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-6.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCyanin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_441688\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-8.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCyanidin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_128861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJasmonic acid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_5281166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-5.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKaempferol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_5280863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-8.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuercetin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_5280343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003e\u003cb\u003eGSK3B\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAllicin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_65036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eApocynin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_2214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-5.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCyanin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_441688\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-9.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCyanidin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_128861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJasmonic acid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_5281166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-5.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKaempferol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_5280863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-8.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuercetin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_5280343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-8.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003e\u003cb\u003eINSR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAllicin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_65036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eApocynin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_2214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-5.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCyanin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_441688\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-8.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCyanidin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_128861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-8.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJasmonic acid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_5281166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-5.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKaempferol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_5280863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuercetin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID_5280343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e present the molecular docking results showing the interaction between the selected diabetic target proteins (PPARA, PPARG, GSK3B) and Kaempferol (ligand like compound) identified from Allium cepa.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4.0 Discussion","content":"\u003cp\u003eThe randomly selected bioactive compounds identified from PCID were presented in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e with their respective structures downloaded from PubChem, and their pharmacokinetics and drug likeness (ADMET) were observed and reported in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. All the bioactive compounds pass the rule of five, making them pass Lipinski\u0026rsquo;s rule except Cyanin, with a molecular weight of \u0026gt;\u0026thinsp;500 g/mol (611.53 g/mol). The rule of five (Ro5) predicts the bioavailability of the bioactive compounds. The ADMET properties of the bioactive compounds were compared with a standard diabetic drug (metformin), and many pharmacokinetic properties were in common, this confirms the anti-diabetic properties of the bioactive compounds identified from Allium cepa. This research work was in accordance with the work done by many researchers proving the antidiabetic properties of allicin (Li et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), apocynin (S\u0026aacute;nchez-Duarte et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), cyanidin (Singh et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ye et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), kaempferol (Yang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and quercetin (Slimestad et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Yan et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported that the antidiabetic mechanism of action involves modulation of multiple molecular targets and signaling pathways associated with the insulin resistance and the pathogenesis of diabetes and this enhances insulin sensitivity and reduces blood glucose level.\u003c/p\u003e\u003cp\u003eIn this study, analysis of the therapeutic target PPI network identified AKT1, PPARG, PPARA, GSK3B and INSR as the key core targets associated with Allium cepa\u0026rsquo;s pharmacological activity. These genes have been reported to be involved in the pathological processes of Type 2-diabetes-AKT1 (Camaya et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Miao et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Zhang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), PPARG (Cooreman et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Frkic et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), PPARA (Joly et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), GSK3B (Teli \u003cem\u003eet al.\u003c/em\u003e, 2023), and INSR (Chen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The result of the GO and KEGG enrichment analysis showed that active components of Allium cepa can regulate various biological processes, cellular component, molecular function, and pathways to treat Diabetes. The genes contribute to the molecular mechanisms fundamental to sensitivity of insulin and control of metabolism, hence are implicated in the advancement or prevention of metabolic syndrome like type 2 diabetes. Hong \u003cem\u003eet al.\u003c/em\u003e (2013) affirmed that glucose homeostasis is essential, and that any disruption in this balance may result in metabolic disorder manifesting as type 2 diabetes mellitus. The response to insulin is an indication that the identified genes are involved actively in the insulin signaling cascade and its consequential effects on cell metabolism. The Cellular component category of the analysis showed that the proteins encoded by the identified genes are mostly situated in the mitochondrion, Axon, perinuclear region of the cytoplasm, and Receptor complex. Enrichment in the mitochondrion is an indication that these genes are associated with redox balance and energy metabolism which are usually compromised in Type 2 diabetes mellitus (Lowell \u0026amp; Shulman, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The identified bioactive compound in Allium cepa have been reported to increase mitochondrial biogenesis, reduce reactive oxygen species (Ho et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), enhance insulin production and glucose metabolism (Mijgar \u0026amp; Deokate, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Ansari et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), hence, improving glucose utilization necessary in the management of Type 2 diabetes mellitus. The axon enrichment suggests involvement of the central nervous system (CNS) and neurons. The CNS functions in the regulation of insulin secretion and sensitivity (G\u0026uuml;emes and Georgiou, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Central Nervous system control of glucose is a drug target for diabetes mellitus (Mirzadeh et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). From the KEGG pathway, the drug targets of Allium cepa against diabetes mellitus were mainly linked to insulin signaling pathway, diabetes mellitus, and insulin resistance. The major highlighted nodes like P13K, IRS1/2, PPAR, AKT, AMPK, MAPK/JNK, and glycogen metabolizing enzymes were enriched prominently. This indicates defective glycogen synthesis, impaired GLUT4 translocation, improved inflammatory signaling and gluconeogenesis as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe docking analysis reported high binding energy (-9.2 kcal/mol) at the interaction between the diabetic target protein (GSK3B) and ligand (Cyanin) identified from Allium cepa, followed by the interaction of Cyanin with INSR and PPARG with \u0026minus;\u0026thinsp;8.7 kcal/mol and \u0026minus;\u0026thinsp;8.3 kcal/mol, respectively, as presented in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The drug-likeness and pharmacokinetic properties of the phytocompound (cyanin) do not pass Lipinski\u0026rsquo;s rule (rule of five) with low gastrointestinal absorption; therefore, it cannot be predicted as a drug. The interaction of GSK3B with quercetin and INSR with cyanidin possesses the same binding energy of -8.3 kcal/mol. The interaction of kaempferol with PPARG, PPARA and GSK3B possesses \u0026minus;\u0026thinsp;8.5 kcal/mol, -8.1 kcal/mol, and \u0026minus;\u0026thinsp;8.0 kcal/mol, respectively. This study revealed that kaempferol fits perfectly into the hydrophobic pocket of each receptor with strong binding interactions with the three diabetic target proteins (PPARA, PPARG, GSK3B) with strong binding affinity. According to the drug-likeness and pharmacokinetics properties presented in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, kaempferol is identified as the best phytocompound to be confirmed as the drug with antidiabetic property.\u003c/p\u003e"},{"header":"5.0 Conclusion","content":"\u003cp\u003eIn this study, phytocompounds were identified from Allium cepa and screened for oral drug bioavailability potential with favorable pharmacokinetic properties. The results obtained showed that kaempferol, quercetin, and cyanin were identified as having a strong binding affinity with the selected diabetic target proteins (INSR, PPARA, PPARG, DSK3B), and kaempferol was confirmed as the best bioactive compound with an overall interaction profile, antidiabetic property, and good oral likeness.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThere is no any Ethical approval and consent applicable to this research work\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent for publication also not applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no any conflict of interest\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis research work received no external funding\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAcknowledgment: \u003c/h2\u003e\n\u003cp\u003eThe Authors would like to express their gratitude to Toplaholo Biogene \u0026nbsp; for the training and support toward the achievement of this research work\u0026nbsp;\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAliyu and Ani Conceptualized the study, designed the research framework, performed molecular docking and interpretation of results, and drafted the initial manuscripts.Shamsudeen, Al-amin, Mustafa, Michael and James contributed to target prediction, network pharmacology and pathways enrichment analysisChika, Onwu, Mayowa, and Alexander provided the overall supervision, critically reviewed the manuscript, contributed to the interpretation of pharmacological relevance, and approved the final version for submission.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmad S, da Costa Gonzales LJ, Bowler-Barnett EH, Rice DL, Kim M, Wijerathne S, Luciani A, Kandasaamy S, Luo J, Watkins X, Turner E, Martin MJ (2025) UniProt Consortium The UniProt website API: facilitating programmatic access to protein knowledge Nucleic Acids Research, Steven Xijin Ge, Dongmin Jung, Runan Yao, ShinyGO: a graphical gene-set enrichment tool for animals and plants, Bioinformatics, Volume 36, Issue 8, April 2020, Pages 2628\u0026ndash;2629. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/bioinformatics/btz931\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btz931\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmerican Diabetes Association (2010) Diagnosis and classification of diabetes mellitus. 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Front Pharmacol 10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fphar.2019.00123\u003c/span\u003e\u003cspan address=\"10.3389/fphar.2019.00123\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Network Pharmacology, Diabetes Mellitus, Allium cepa, Molecular docking","lastPublishedDoi":"10.21203/rs.3.rs-8181479/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8181479/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNetwork pharmacology is an emerging, cost-effective drug-development approach that uses systems biology and network theory to integrate data and reveal interactions between compounds and their biological targets. Diabetes mellitus is a widespread metabolic disease affecting millions worldwide, while traditional medicine represents knowledge of healing practices inherited across generations and indigenous communities. Allium cepa contains various nutraceutical compounds that give it notable antioxidant, antitumor, antidiabetic, and anti-inflammatory properties. Bioactive compounds of Allium cepa were identified using a phytochemical interactive database. Both the diabetic and bioactive compound target proteins were determined and screened for oral drug bioavailability potential with favorable pharmacokinetic properties. The target proteins underwent protein-protein interaction network analysis, and analyzed using molecular docking analysis. The interaction of kaempferol with PPARG, PPARA and GSK3B possesses \u0026minus;\u0026thinsp;8.5 kcal/mol, -8.1 kcal/mol, and \u0026minus;\u0026thinsp;8.0 kcal/mol, respectively. The results obtained showed that kaempferol identified with strong binding affinity with the selected diabetic target proteins and confirmed as the best bioactive compound with an overall interaction profile, antidiabetic property, and good oral drug likeness.\u003c/p\u003e","manuscriptTitle":"In-silico Network Pharmacology and Computational Modelling of Bioactive Compounds From Allium Cepa Targeting Downstream Protein Effectors in Diabetes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 04:39:46","doi":"10.21203/rs.3.rs-8181479/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":"1cfe906a-63cf-4448-863b-7e6a09e7823d","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-05T14:09:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-01 04:39:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8181479","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8181479","identity":"rs-8181479","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}