Identification of Gallagyldilacton as a Mutation-Biased EGFR Inhibitor from Punica granatum Peel via Integrative Computational Analysis

Identification of Gallagyldilacton as a Mutation-Biased EGFR Inhibitor from Punica granatum Peel via Integrative Computational Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Identification of Gallagyldilacton as a Mutation-Biased EGFR Inhibitor from Punica granatum Peel via Integrative Computational Analysis Doğacan Jeremy Edens, Özgür Cem Erkin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8583101/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Non-small cell lung cancer (NSCLC) accounts for the majority of lung cancer cases worldwide and remains a leading cause of cancer-related mortality. Aberrant activation of the epidermal growth factor receptor (EGFR), driven by overexpression or mutation, represents a central oncogenic mechanism in NSCLC and a primary target of tyrosine kinase inhibitors (TKIs). Despite substantial clinical success, the emergence of resistance-conferring EGFR mutations continues to limit the long-term efficacy of existing therapies. In this study, we employed an integrative in silico framework combining network pharmacology, molecular docking, molecular dynamics simulations, and MM-PBSA analyses to investigate the therapeutic potential of phytochemicals derived from Punica granatum peel against NSCLC-associated molecular targets. Network analysis identified EGFR, AKT1, and matrix metalloproteinases (MMPs) as key nodes within NSCLC-related signaling pathways. Among the screened compounds, gallagyldilacton and naringenin emerged as prominent candidates with distinct target interaction profiles. Gallagyldilacton exhibited computationally stable and energetically favorable binding to multiple mutant EGFR variants, including T790M and L858R, while displaying comparatively weaker affinity toward wild-type EGFR, consistent with a mutation-biased inhibitory profile. In contrast, naringenin demonstrated preferential interactions with AKT1 and select MMPs, consistent with potential modulation of proliferative and metastatic signaling pathways. Comparative analyses with approved TKIs revealed that gallagyldilacton achieves binding stability and interaction patterns comparable to established inhibitors for specific EGFR mutants. Collectively, these findings support the potential of Punica granatum peel phytochemicals as sources of selective, multitarget modulators in NSCLC and highlight gallagyldilacton as a compound exhibiting mutation-biased EGFR interactions and a candidate for further experimental investigation. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Biological sciences/Drug discovery Non-small cell lung cancer epidermal growth factor receptor mutations network pharmacology molecular docking Punica granatum. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Lung cancer remains a prime contributor to the global cancer burden 1 . With non-small cell carcinoma (NSCLC) and small-cell carcinoma (SCLC) accounting for approximately 85% and 15% of lung cancer cases, respectively 2 , the disproportionate research focus on the former is unsurprising. While relatively recent efforts to curb population-level risk behaviors—most notably cigarette smoking—have met with success in many Western nations, governments of emerging economies such as Turkey, India, and China have yet to incite comparable nationwide cessation trends. Consequently, the high prevalence of lung cancer, particularly NSCLC, persists in these regions, effectively prolonging the global impact of the disease. Contemporary treatment modalities emphasize early detection, followed by patient enrollment into chemotherapy or invasive surgical interventions, often coupled with adjuvant chemotherapy. In particular, NSCLC chemotherapy has largely centered on inhibition of the EGFR, which exhibits the highest frequency of overexpression and/or mutation among NSCLC patients, followed by aberrant signaling involving the Kirsten rat sarcoma viral oncogene (KRAS) 3 . Over the years, efforts to therapeutically target EGFR—particularly through tyrosine kinase inhibitors (TKIs)—have yielded varying degrees of success. A primary limitation of clinically deployed TKIs, such as osimertinib, as well as earlier agents including erlotinib and gefitinib, is the inevitable development of drug resistance in NSCLC, progressively diminishing therapeutic efficacy over successive treatment cycles. Both pre-existing mutations within the EGFR-encoding gene and those acquired during therapeutic intervention constitute major obstacles to sustained clinical success. Accordingly, a primary objective in the development of novel TKIs is to achieve effective inhibition across multiple mutant forms of the target. For instance, the most prevalent EGFR mutant forms arise from single-point substitutions at residues T790 and L858, in which threonine and leucine are replaced by methionine (~ 50% of patients) and arginine (~ 41% of patients), respectively 4 . The therapeutic landscape of NSCLC chemotherapy includes EGFR-TKIs such as erlotinib and gefitinib, as well as their successors, including afatinib, dacomitinib, and osimertinib. With the overarching goal of improving disease control, these agents were developed to counteract distinct classes of EGFR mutations. For example, dacomitinib, which was designed to improve upon the efficacy of first-generation agents, demonstrated enhanced activity against less common EGFR mutations, including G719X, L747X, and S768I. Emerging therapeutic strategies—including next-generation TKIs, antibody–drug conjugates, novel immunotherapeutic approaches, and targeted protein degraders—have demonstrated considerable promise in patients with progressive EGFR-mutant NSCLC following EGFR-TKI treatment, despite the persistent challenge of acquired resistance 5 . The accelerating pace of such drug development can be attributed to the increasing sophistication of contemporary methodologies alongside the implementation of novel approaches. In recent years, network pharmacology and computational drug design have played pivotal roles in advancing oncological research. Furthermore, incorporation of complementary analytical strategies—such as molecular dynamics simulations, MM-GB/PBSA calculations, quantitative structure–activity relationship (QSAR) modeling, and ADME profiling—has markedly enhanced the precision of drug discovery and design. Researchers have shown increasing interest in applying these techniques to natural compounds, particularly plant-derived flavonoids and polyphenols. Consequently, a paradigm has emerged centered on the systematic exploration of ethnopharmacologically relevant botanical sources, many of which have been traditionally utilized by communities maintaining long-standing medicinal practices. Such plants have been used for millennia within these populations, with knowledge of their beneficial effects transmitted across generations through oral tradition. Industrialization and urbanization have inevitably contributed to a global shift away from reliance on such traditional knowledge systems. Nevertheless, the wide array of ameliorative properties attributed to these plants has increasingly been substantiated, and their underlying mechanisms of action have begun to be elucidated. These properties include anti-inflammatory, antidiabetic, antibacterial, and anticancer activities 6 . Among such plants is Punica granatum L. ( P. granatum ), commonly known as pomegranate. P. granatum belongs to the Lythraceae family and is generally believed to originate from the mountainous regions of Iran and Iraq, although aspects of its taxonomy and geographic origin remain debated 6 . The pomegranate plant produces a diverse array of bioactive compounds, primarily flavonoids and anthocyanins. While all parts of the plant have piqued the interest of researchers, the most commonly investigated is the fruit itself. The fruit, being composed of peel, arils, and seeds at a ratio of 5:4:1, is where these compounds of interest are the most abundant. A variety of conditions, such as neurodegenerative diseases, cancer, and diabetes, have been reported to be ameliorated by phytochemicals such as proanthocyanidins, phenolics, ellagitannins, and complex polysaccharides from the fruit 7 , 8 . On the other hand, the peel of the fruit which generally tend to be regarded as a waste product, is proven to be a good source of bioactive compounds such as polyphenols, anthocyanins, flavonoids and tannins. Although the content and composition of compounds found in the peel can differ vastly between the varieties, gallo tannins, ellagic acid, punicalins, punicalagins and gallic acid were found to be the most abundant phytochemicals. In addition, owing to differences in P. granatum varieties, the content and composition of other biologically active compounds such as catechin, quercetin, kaempferol, chlorogenic acid and naringenin can also be found in varying amounts in the peel 9 . For instance, reports of the concentration of ellagic acid found in P. granatum differ greatly between the varieties, Zivzik and Manfalouty 9 , 10 . Furthermore, the composition of compounds found within the peel seems to vary heavily within specimens of the same cultivar based on the stage of maturation at which the fruit is harvested. Regardless, there is concrete evidence of a varying but constant presence of compounds that exhibit anticancer properties in P. granatum 9 – 16 . Compounds such as ellagic acid, naringenin, punicalagin, and pedunculagin have been reported to have efficacy on various cancer cell lines ranging from MCF-7 to A549 to HCT-116 in vitro 17 – 20 . Although the molecular composition of the peel varies, the anticancer activity remains consistent, suggesting that this effect is not necessarily due to the activities of multiple compounds but rather to stronger inhibitory activity that results from the compounds’ synergy. Lastly, the fact that the peel of the fruit is where most of the beneficial compounds are concentrated has massive implications, as industries interested in the fruit itself generally process the arils’ contents and dispose pomegranate marcs. The theoretical utilization of a waste product produced at such a massive scale as a means of introducing novel drugs for a variety of ailments holds within it the promise of an eco-friendly contribution to medicine, not to mention the profitability of such an endeavor. Many studies have assessed the in vitro cancer cell viability of P. granatum extracts or certain compounds within different parts of the fruit as potential treatment for NSCLC, and thus have created progenitor research which could potentially catalyze the development of derivative drugs 21 – 23 . Punicalagin, punicafolin, and other phytochemicals such as genistein derived from P. granatum , have shown promise as multitarget therapy agents for a variety of malignancies, including breast cancer, hepatocellular carcinoma, and non-small cell lung cancer. Key overlapping molecular targets that regularly identified by network-pharmacology analyses are AKT1, STAT3, CASP3, SRC, IL6, and NOS3, which are involved in important pathways such PI3K–AKT, JAK–STAT, AMPK, HIF-1α, and mTOR. Despite expression-level fluctuations, strong and stable binding interactions, such as those between genistein and AKT1, punicalagin and SRC/AKT1/CASP3, and punicafolin and STAT3, were supported by molecular dynamics simulations. These computational predictions were further validated by complementary actual results, such as apoptosis induction, G0/G1 arrest, ROS generation in lung cancer cells, and decreased expression of oncogenic targets in HCC. Additionally, pomegranate peel chemicals show dual potential by binding SARS-CoV-2 proteases (Mpro, PLpro), indicating antiviral importance. Together, these investigations show the therapeutic adaptability of plant-derived polyphenols as modulators of networks that drive inflammation and cancer, emphasizing network pharmacology and integrated validation approaches as potent pipelines for mechanistic discovery 24 – 27 . Therefore, by using an integrative network pharmacological method, we investigated the potential inhibitory effects of phytochemicals from P. granatum peel on encoded proteins of upregulated genes in NSCLC. Among other peel phytochemicals, gallagyldilacton promises a pan-EGFR inhibitory potential which might represent a way of powerful intervention in the treatment of EGFR mutant NSCLCs. RESULTS Compound–Target Network and Enrichment Analyses The compound–target network (Fig. 1 b) comprised 449 nodes and 6,842 edges, indicating extensive compound–protein associations. On average, each curated P. granatum compound interacted with approximately 270 predicted targets, and the overall network exhibited a mean node degree of 16.10. Hub proteins with the highest degrees included ABO (P16442), ADAM17 (P78536), and THRB (P10828), with degree values of 47, 35, and 32, respectively. The compiled list of upregulated differentially expressed genes (DEGs) from NSCLC literature consisted of 832 unique targets ( Supplementary Table S1 ). Intersection of the DEG list with PharmMapper 28 -predicted targets yielded 58 overlapping proteins (Fig. 1 a). These overlapping targets were subsequently used to construct a protein–protein interaction (PPI) network (Fig. 1 c), which contained 82 nodes and 1,204 edges. Among compound nodes, cyanidin and delphinidin exhibited the highest degrees (50 each), while gallic acid showed the lowest (degree = 36). Target proteins displayed an average degree of 22.95, with EGFR representing a highly connected node (degree = 39) and SPARC exhibiting lower connectivity (degree = 3), indicating differences in network connectivity between these proteins. Functional enrichment analysis integrated Gene Ontology (GO), KEGG 29 , WikiPathways, Reactome gene sets, and canonical pathways (Fig. 2 a). GO and KEGG analyses identified significant enrichment in carboxylic acid metabolic processes (GO:0019752, log p = − 15.61), hormonal response (GO:0009725, log p = − 15.58), and collagen catabolic processes (GO:0030574, log p = − 11.86) (Fig. 2 b). Nuclear receptor signaling (R-HSA-9006931, log p = − 13.89) was also prominently represented, alongside pathways related to extracellular matrix disassembly, proteoglycans in cancer, and regulation of cell migration. Within the carboxylic acid metabolic process cluster, monocarboxylic acid metabolism (GO:0032787) emerged as the most significant subnode (log p = − 14.73). Transcription factor enrichment analysis identified SP1 as the dominant regulator (log p = − 16), associated with a large subset of overlapping targets including CBS, EGFR, ESR1, MIF, MMP9, MMP12, OAT, and PLAU (Fig. 2 c). Protein network analysis further defined a set of core targets (Fig. 3 a). These representative core targets, except PLAU, were selected for subsequent molecular docking and simulation analyses. Molecular Docking A total of 386 ligand–protein docking simulations were performed, of which 374 produced valid docking poses. Twelve docking attempts failed due to positive binding energy values (> 0 kcal/mol). Twelve ligand–protein complexes exhibited estimated inhibition constants in the nanomolar range, with the majority involving MMP9 and MMP13. A summary of docking energies is provided in Fig. 3 b, and calculated inhibition constants are listed in Supplementary Table S5 online. When assessed at the protein level, approximately one-third of the core target proteins were inhibited by at least one ligand with nanomolar-range affinity. Representative binding modes for the top-ranked complexes are shown in Fig. 4 , and interaction profiles before and after molecular dynamics (MD) simulations are summarized in Table 1 . Docking results were used to rank ligand–protein complexes based on predicted binding affinity, and MD simulations were subsequently performed to evaluate the stability of selected interactions. Table 1 Binding interactions of selected compounds with target proteins before and after MD simulations. a) Ligand-protein interactions of chosen complexes prior to MD simulation. b) Post-MD simulation complex interactions. Table 1 a [n] indicates repetition Aromatic Interactions Target Ligand Hydrophobic Interactions Hydrogen Bonds π -stacking π -cation Other Interactions Chlorogenic Acid MMP13 L185, L218, V219 [2] L185, A186 [2] , H222 [2] Salt bridge:H222 H222, L239, Y244 E223, F241, I243 [2] T245 [2] Naringenin L185 [2] , V219, H222 S182, G183, L185 H222 A186, Y214 [2] , Q223 Y244 MMP12 V235, F237, Y240 [2] L181, A182 [2] , T215 H218 Salt bridge:H218, H228 P232, T239 AKT1 W80, T82, I84 N54, Q79 [3] , Q203 W80 K268, D292 T211, K268, Y272 OAT Y85, E230, E235 Y85 [2] , R180 [2] , I232 F177K292 Salt bridge:K292 I265, Q266 Q266, T267 Gallagyldilacton EGFR L718 [2] , V726 L718, K745, R776 Salt bridge:K745 T790, M793 [2] , C797 T854, D855 MMP3 H166, Y168 Y155 [3] , H166, A167 [2] Y155H211 Salt bridge:H166, H205 A169, N175, E202 [3] Metal complex: Zn + 2 MMP7 Y172, T180, L181 Y240 T180 [2] , A182, A184 [2] E219, Y240 H183 Salt bridge:H218, H222, H228 Metal complex: Zn + 2 Epicatechin MMP9 L188, L222, V223 H226, E227 [4] , A242 H226, Y248 [2] , R249 L243, R249 [2] Kaempferol L188, V223, H226 E227 [2] , A242, Y245 H226 Y245, Y248, R249 R249 [2] Luteolin L188, L222, H226 E227 [4] , A242, R249 [3] Y248, R249, T251 T251 Pelargonidin L188, L222, V223 [2] E227 [2] , A242, R249 H226 H226, Y248 [2] , R249 Table 1 b Aromatic Interactions Target Ligand Hydrophobic Interactions Hydrogen Bonds π -stacking π -cation Other Interactions Chlorogenic Acid MMP13 L185 L185, A186 [3] , F241 T245 [3] , H251, M253 Salt bridge:H222 Naringenin L185, V219, H222 [2] L185, A186 [2] P242 MMP12 L214 [2] , H218, V235 L181, A182 [2] , H218 Salt bridge:H218 P238, T239, V243 AKT1 W80, L210, L264 H207, V271, Y272 OAT W178, I265 [2] , Q266 G51, Y55, L58 Y55 Salt bridge:K292 G142, V143, E230 Gallagyldilacton EGFR L718 [2] , L844 M793, C797, T854 [2] D855 MMP3 D158, P160 [2] , A167 F86 Salt bridge: H166 [2] , H211 A169 MMP7 Y172 [2] Y172, D175 [2] Y172 [2] Epicatechin MMP9 Y248, R249 C99 H226 Kaempferol L222, Y248, T251 E241 Luteolin L188, V223, H226 A189 [2] , V223, R249 H226 Y238 Pelargonidin L222, L243 [2] , R249 E241, Y245, R249 Y248 T251 T247 Molecular Dynamics Simulations and MM-PBSA Calculations MD simulations identified several ligand–protein complexes that remained stable over 100 ns trajectories, including EGFR–gallagyldilacton, MMP9–luteolin, MMP12–naringenin, MMP13–naringenin, and MMP13–chlorogenic acid. MM-PBSA analysis (Table 2 ) identified chlorogenic acid–MMP13 complex, with the lowest binding free energy of ΔG_bind = − 34.47 ± 4.34 kcal/mol. RMSD analysis supported the stability of this interaction (Fig. 5 a). Table 2 Binding interactions of chosen TKIs and gallagyldilacton with variant EGFR protein before and after MD simulations . a) Table of interactions with EGFR variants in chosen TKIs along with gallagyldilacton prior to simulation b) Post-simulation interactions with EGFR variants seen in TKIs and gallagyldilacton. Table 2 a [n] indicates repetition Aromatic Interactions Target Ligand Hydrophobic Interactions Hydrogen Bonds π -stacking π -cation Other Interactions EGFR WT Afatinib L718, V726 [2] , A743 K745, M793, D855 [2] F856 K745, T790, L858 Dacomitinib V726, A743, K745 M766, T790, L844 [2] T790, M793, T854 [2] D855 F856 Gefitinib V726, A743, K745 T790, L844 [2] K745, M793, C797 Gallagyldilacton L718 [2] , V726 L718, K745, R776 T790, M793 [2] , C797 Salt bridge:K745 T854, D855 EGFR T790M Afatinib L718, V726, K745 K745, E762, D855 [2] K745 L844 Dacomitinib L718, V726 [2] , A743 L844 [2] K745 Salt bridge:855 Gefitinib L718, L792 K745, M793, D855 Gallagyldilacton L718 [2] , L844 L718, E762 [2] , M793 [2] Salt bridge:K745 C797, T854, D855 F856 EGFR L858R Afatinib E758, I759, R858 K745 [2] , E762, T854 D855 Dacomitinib L718, V726 [3] , A743 K745 [2] , L788, L844 V726, D855 [2] Salt bridge:E762 Gefitinib L844 K745, M793, G796 Salt bridge:D855 C797, D855 Gallagyldilacton F723, V726, A743 L844 S720 [3] , K745, Q791 M793, T854 [3] K745 EGFR ex19del Afatinib F723, V726, A743 K745, M793, L844 [2] A722, F723, G724 T790, T854, D855 Dacomitinib L718, A743, K745 M793 Salt bridge:D855 T790, L844, T854 Gefitinib L718, V726, A743 T790, Q791 L792, M793 Gallagyldilacton L844 E762, Q791 [2] , M793 S720, A722, F723 K745 EGFR ex20ins Afatinib L718, A743, K745 T793, M796, G799 L791, T793, M796 L847 [2] Dacomitinib L718, V726 [2] , A743 F859 E762, T793, L795 M796, L847 Gefitinib L718, V726, A743 T793, M796, G799 Halogen bond:L791 L795, L847 D858 Gallagyldilacton L718 [2] , R844 K728, M796, D803 R844, D855 [3] Table 2 b Aromatic Interactions Target Ligand Hydrophobic Interactions Hydrogen Bonds π -stacking π -cation Other Interactions EGFR WT Afatinib V726, A743, L777 M793 [2] , D855 [2] D855 L858 Dacomitinib K745 [2] , T790 T790 [2] , M793, D855 Gefitinib L718, V726, A743 K745 [2] , T790 G721, M793, C797 K745 Gallagyldilacton L718 [2] , L844 M793, C797, T854 [2] D855 EGFR T790M Afatinib V726, L844 M793, C797 Salt bridge:D800 Dacomitinib L718, L844 M793, T854 Salt bridge:855 Gefitinib L844 Q791, M793 Gallagyldilacton L718 [2] , K745, L844 S720 [2] , E762 [2] , M793 [2] Salt bridge:K745 P794, G796, D855 EGFR L858R Afatinib I878 R889 Dacomitinib R841, A859 R841 [3] R841 [2] Salt bridge:D855 Gefitinib Q791, D800 Salt bridge:D855 Gallagyldilacton L718, F723, A743 T790, C797, T854 EGFR ex19del Afatinib R889. Y891 S885 Dacomitinib V726, A743, K745 L788, T790, R841 L844 Gefitinib L718, V726, L844 M793 Salt bridge:D800 Gallagyldilacton V726 S720 [2] , K745, M793 D855 EGFR ex20ins Afatinib L718, A743, K745 Q794, M796 T793, L795, L847 Dacomitinib V726, I759 [2] , M766 T793, L861 Salt bridge:D858 L791, L861 Gefitinib L718, V726, T857 L718 Gallagyldilacton V726 S720 [2] , K745, M793 D855 Naringenin displayed stable binding to a subset of its targets, while interactions with AKT1 and OAT exhibited gradual loss of stability over the simulation period (Fig. 5 b). RMSD and RMSF analyses of MMP9 complexes revealed variable ligand stability; luteolin maintained stable binding, whereas epicatechin showed weaker interaction profiles (Figs. 5 c and 5 d, Supplementary Fig. S1 ). Consistent RMSF patterns across all MMP9 simulations confirmed the robustness of the comparative analyses, as all simulations were performed using the same protein structure. Gallagyldilacton–EGFR complexes displayed moderate overall stability (ΔG_bind = − 7.20 ± 4.53 kcal/mol) while maintaining interactions with key ATP-binding site residues, including K745, T790, and L858 (Tables 2 and 3 ). Subsequent analyses focused on EGFR-containing complexes. Table 3 MM/PBSA analysis of stable complexes throughout this study . All values represented in the table are in kcal/mol. ∆ EMM ∆ Gsol Protein Ligand ∆ EvdW ∆ Eelectrostatic ∆ GP B ∆ GSA ∆ EMM ∆ Gsol ∆ Gbind EGFR Afatinib -57.28 ± 3.16 1.44 ± 11.67 33.2 ± 11.09 -4.97 ± 0.18 -55.84 ± 12.08 28.22 ± 11.05 -27.62 ± 5.38 Dacomitinib -52.99 ± 3.2 -62.03 ± 9.43 96.08 ± 8.62 -5.71 ± 0.13 -115.02 ± 9.26 90.37 ± 8.61 -24.65 ± 4.84 Gefitinib -49.52 ± 3.1 -28.43 ± 15.78 55.18 ± 13.54 -4.95 ± 0.23 -77.95 ± 15.17 50.24 ± 13.55 -27.72 ± 4.9 Gallagyldilacton -49.36 ± 2.79 -14.98 ± 6.21 62.06 ± 7.11 -4.92 ± 0.15 -64.34 ± 6.59 57.14 ± 7.05 -7.2 ± 4.53 EGFR T790M Dacomitinib -41.64 ± 3.32 -112.54 ± 16.9 140.87 ± 19.11 -4.39 ± 0.26 -154.18 ± 17.99 136.48 ± 19.02 -17.7 ± 4.83 Gallagyldilacton -43.58 ± 4.7 -45.11 ± 6.28 63.82 ± 5.32 -4.06 ± 0.12 -88.68 ± 7.61 59.77 ± 5.25 -28.92 ± 4.47 EGFR L858R Gefitinib -50.21 ± 3.36 -81.7 ± 21.75 114.34 ± 19.55 -5.16 ± 0.18 -131.91 ± 21.54 109.18 ± 19.54 -22.73 ± 5.99 Gallagyldilacton -43.61 ± 2.75 -23.41 ± 5.79 47.22 ± 6.51 -4.38 ± 0.19 -67.01 ± 6.24 42.84 ± 6.41 -24.17 ± 3.74 EGFR ex19del Dacomitinib -52.8 ± 3.49 -85.3 ± 13.88 113.78 ± 17.92 -5.44 ± 0.24 -138.11 ± 15.05 108.34 ± 17.81 -29.76 ± 6.4 EGFR ex20ins Afatinib -54.67 ± 2.87 -107.17 ± 16.86 144.3 ± 18.34 -5.3 ± 0.17 -161.84 ± 16.93 139 ± 18.29 -22.84 ± 5.19 MMP9 Luteolin -36.87 ± 3.24 -57.51 ± 4.85 74.54 ± 6.42 -3.25 ± 0.07 -94.38 ± 4.47 71.29 ± 6.42 -23.09 ± 6.87 MMP13 Chlorogenic acid -44.17 ± 3.16 -70.02 ± 5.67 83.53 ± 4.04 -3.98 ± 0.06 -114.02 ± 5.5 79.55 ± 4.03 -34.47 ± 4.34 OAT Naringenin -42.68 ± 2.58 -42.2 ± 4.78 79.54 ± 6.92 -4.33 ± 0.08 -84.88 ± 4.31 -75.22 ± 6.91 -9.67 ± 6.02 Gallagyldilacton was compared with established EGFR TKIs (afatinib, dacomitinib, and gefitinib). All TKIs showed stable binding to EGFR WT , with RMSD values ranging from 1.7 to 2.1 Å. Gallagyldilacton showed RMSD and RMSF profiles within the same range, indicating a similar binding mode (Fig. 6 a). In simulations involving EGFR T790M and EGFR L858R , gallagyldilacton demonstrated stability comparable to dacomitinib and gefitinib, respectively (Fig. 6 b and 6 c). Reduced stability was observed for exon deletion mutants (EGFR ex19del ; Fig. 6 d), whereas binding to EGFR ex20ins was comparable to that of reference TKIs (Fig. 6 e). Post-simulation interaction analyses showed that gallagyldilacton frequently formed a greater number of hydrogen bonds with EGFR variants than the reference TKIs, while consistently engaging critical ATP-binding site residues (Tables 2 a and 2 b). MM-PBSA calculations revealed a mutation-biased binding profile: gallagyldilacton displayed weaker affinity for EGFR WT but substantially stronger binding to EGFR T790M (ΔG_bind = − 28.92 ± 4.47 kcal/mol) and EGFR L858R (ΔG_bind = − 24.17 ± 3.74 kcal/mol) (Table 3 ). Van der Waals contributions remained consistent across EGFR variants (− 43.58 to − 49.36 kcal/mol), and favorable desolvation energies contributed to the overall binding free energy. Stable ligand-protein interactions were observed with both metalloproteins and EGFR across the analyzed complexes. In line with network predictions and dynamic stability analyses, gallagyldilacton showed stable binding to EGFR variants, while naringenin exhibited broader interactions involving AKT1, MMP12/13, and OAT. DISCUSSION Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality worldwide, largely due to the high prevalence of oncogenic mutations in the epidermal growth factor receptor (EGFR) and the inevitable development of resistance to targeted therapies 30 . In this study, we employed an integrative network pharmacology and computational modeling framework to investigate the inhibitory potential of phytochemicals derived from P. granatum peel against proteins encoded by genes upregulated in NSCLC. Through this approach, gallagyldilacton emerged as a prominent candidate, exhibiting stable and preferential binding to mutant forms of EGFR that are clinically associated with therapeutic resistance. Network pharmacology analysis was first used to provide a systems-level context for target prioritization. Among the differentially expressed genes and predicted compound targets, EGFR, AKT1, and several matrix metalloproteinases (MMPs) occupied central positions within the protein–protein interaction network. Rather than implying equivalent biological or clinical relevance for all identified nodes, this analysis served to guide subsequent focused investigations. EGFR was prioritized due to its well-established role as a driver of NSCLC progression and its centrality within both oncogenic signaling and current therapeutic strategies. Docking and molecular dynamics simulations revealed that gallagyldilacton binds stably to clinically relevant EGFR mutants, particularly EGFR T790M and EGFR L858R , while displaying comparatively reduced affinity toward wild-type EGFR. This mutation-biased binding profile is notable, as resistance-conferring mutations such as T790M are a major limitation of first- and second-generation EGFR tyrosine kinase inhibitors (TKIs) 31 . To our knowledge, this study provides early computational evidence suggesting that gallagyldilacton may preferentially accommodate structural alterations introduced by common EGFR resistance mutations, supporting its potential as a mutation-focused inhibitory scaffold. At the structural level, the stability of gallagyldilacton–EGFR mutant complexes appears to arise from a dense and adaptable hydrogen-bonding network, complemented by favorable van der Waals interactions within the altered binding pockets of mutant receptors. Molecular dynamics analyses indicated that these interactions remain stable over extended simulation times, suggesting that gallagyldilacton can tolerate conformational changes associated with EGFR mutations. Rather than relying on a single dominant interaction, the compound’s binding mode reflects a distributed interaction profile, which may be advantageous in the context of structurally heterogeneous mutant targets. In addition to EGFR, other P. granatum peel phytochemicals displayed affinity toward secondary targets within the network. Notably, naringenin demonstrated stable interactions with AKT1 and selected MMPs, proteins implicated in tumor survival, invasion, and microenvironment remodeling. These findings suggest the possibility of complementary network-level modulation; however, the present results do not imply standalone therapeutic relevance for these secondary interactions in NSCLC. Instead, they highlight the broader polypharmacological landscape of P. granatum –derived compounds, within which EGFR inhibition remains the primary focus of this work. When positioned against existing EGFR-targeted therapies, gallagyldilacton does not seek to supplant approved TKIs but rather represents a distinct, mutation-biased inhibitory profile. While third-generation TKIs such as osimertinib were designed to overcome T790M-mediated resistance, the emergence of additional resistance mechanisms continues to challenge long-term efficacy 32 . The computational comparability of gallagyldilacton to known TKIs in mutant EGFR systems underscores its relevance as a lead structure for further exploration, without implying clinical readiness or superiority. Although this study primarily focuses on target engagement and molecular stability, in silico ADME profiling was performed to contextualize gallagyldilacton within a translational framework. As shown in Supplementary Table S6 online, gallagyldilacton displays acceptable physicochemical properties, including moderate solubility, permeability and intestinal absorption. While these predictions do not replace experimental pharmacokinetic evaluation, they support the feasibility of the phytochemicals as optimization-ready scaffolds and complement the observed binding stability and mutation-spanning target engagement. Several limitations of this study should be acknowledged. All findings are derived from in silico analyses, and experimental validation at the biochemical and cellular levels is required to substantiate inhibitory activity. Additionally, as detailed in the Methods section, entropy contributions were excluded from MM-PBSA calculations due to computational constraints, which may affect the absolute estimation of binding free energies. Nevertheless, relative comparisons across similar systems remain informative within this framework. Future work should focus on experimental validation, structural optimization, and evaluation of biological activity in EGFR-mutant NSCLC models. In conclusion, this study integrates network pharmacology, molecular docking, molecular dynamics simulations, and MM-PBSA calculations to identify gallagyldilacton as a promising, mutation-biased EGFR-focused inhibitor derived from P. granatum peel. By emphasizing selectivity toward resistance-associated EGFR mutants and situating these findings within a systems-level context, this work provides a rational computational foundation for the further investigation of plant-derived polyphenols as candidates for overcoming EGFR-driven therapeutic resistance in NSCLC. METHODS Data Acquisition and Preparation To compile a comprehensive list of differentially expressed genes (DEGs), a literature review of studies published between 2018 and 2023 that analyzed DEGs in NSCLC tumor tissue was conducted ( Supplementary Table S1 ). For inclusion, upregulated DEGs with a fold change greater than 2 (FC > 2) were considered significant ( Supplementary Table S2 ). Phytochemicals derived from P. granatum peel that have been reported to exhibit anticancer activity were collected from the literature, yielding a total of 24 compounds relevant to this study ( Supplementary Table S3 ). These compounds encompassed multiple chemical classes, including anthocyanins, ellagitannins, flavonoids, hydroxybenzoic acids, and hydroxycinnamic acids. Following compilation, each compound was retrieved from PubChem 33 in MOL2 format, and its geometry was optimized using Avogadro 34 . To guide subsequent analyses, a dataset of potential protein targets was generated for each compound. Accordingly, PharmMapper 28 was employed to predict and constrain the set of protein targets analyzed in this study. The parameters “Generate Conformers” and “Select Targets Set” were set to “Yes” and “Human Protein Targets Only“ (v2010, 2241), respectively. Both the compiled DEG list and the predicted targets obtained from PharmMapper were converted into UniProtKB identifiers using the Retrieve/ID mapping service provided by UniProt 35 . Network Construction and Enrichment Analyses Following target identification, a compound–target interaction network was constructed using Cytoscape (v3.10.3) 36 . Compounds exhibiting a degree value greater than one were selected and subsequently used for enrichment analyses. The compiled DEG list was overlapped with the PharmMapper-derived target list using Venny 37 . The overlapping targets were subsequently subjected to STRING analysis within Cytoscape to generate a protein–protein interaction (PPI) network, into which the active compounds were integrated to visualize their associations with the overlapping proteins 38 . Gene Ontology (GO) and KEGG 29 pathway enrichment analyses were performed using Metascape (v3.5.20250101), a web-based platform designed to integrate multiple OMICS datasets into a unified bioinformatics workflow using the overlapping target proteins identified in Cytoscape. Of relevance to this study was Metascape’s ability to perform pathway enrichment across multiple databases and to subsequently cluster enriched term. Protein network analysis was additionally performed using STRING to identify core nodes based on functional interrelatedness and contribution to molecular assemblies 39 . Protein Preparation and Docking Selected protein structures were retrieved from the Protein Data Bank (PDB) 40 ( Supplementary Table S4 ) and prepared using ChimeraX 41 . Nonstandard atoms were removed from the PDB files, while essential metal ions and solvent molecules were retained. AutoDock 4.2.6 42 was used to add hydrogen atoms, merge nonpolar hydrogens, assign Kollman charges, and define AD4 atom types. Members of the core protein list obtained via Metascape analyses were subjected to docking via AutoDock. Based on prior literature, residues defining ATP-binding sites or allosteric domains were identified, and the average coordinates of these regions were calculated for each target protein. For docking, a cubic grid box with dimensions of 60 × 60 × 60 points and a spacing of 0.5 Å was centered on the calculated coordinate average. This approach ensured that only literature-validated functional sites and domains with inhibitory potential were considered. Map types were set directly; target proteins that necessitate the presence of metal ions such as zinc, had their ions preserved and were mapped in addition to organic material. Docking simulations were performed using the Lamarckian genetic algorithm implemented in AutoDock 4.2. Each docking was executed with 10 GA runs, a population size of 150, a maximum of 2500000 evaluations, in 27000 generations, and rates of gene mutation and crossover at 0.02 and 0.8, respectively. The number of generations for picking the worst individual was set at 10, while the mean and variance of the Cauchy distribution for gene mutation were set at 0.0 and 1.0, respectively. Docking parameters were set to default, and post-docking ligand-protein interactions, such as hydrogen bonds, salt bridges, etc., were assessed using the Protein-Ligand Interaction Profiler (PLIP) 43 . Molecular Dynamics Simulations and MM-PBSA Calculations Molecular dynamics (MD) simulations were performed using GROMACS version 2024.4 44 , and ligand–protein complexes exhibiting nanomolar-range inhibition constants were selected for further investigation. Given the prevalence of metalloproteins and associated zinc ions, the CHARMM36 force field—equipped with validated metal ion parameters, including zinc 45 —and a modified TIP3P water model was employed throughout all MD simulations. Ligand topology was obtained via SwissParam, and proteins were prepared via the DockPrep tool in ChimeraX. Energy minimization (EM) was performed using a step size of 0.01 until the maximum force fell below 10 kcal/mol. Both canonical ensemble isothermal volume (NVT) equilibration and isothermal pressure (NPT) equilibration were run for 100 ps at 2 fs intervals. MD simulations were carried out for 100 ns at 2 fs intervals with the temperature set at 300 K. Subsequent RMSD, RMSF, and H-bond analyses of each MD simulation were carried out internally in GROMACS using built-in functions. Although implicit generalized Born (GB) models are commonly used for protein–ligand free energy calculations, there is consensus that simulations employing the CHARMM36 force field are better suited to Poisson–Boltzmann (PB)-based approaches. Therefore, Gibbs free energy calculations were done using gmx_MMPBSA via the PB model, which is a free energy computation tool based on AmberTools that is capable of a variety of calculations, the most relevant of which involve ligand-protein MM-PB(GB)SA determinations 46 . In our simulations,calculations took into consideration the last 20 ns of stable complexes, i.e., complexes which attained stability at values ≤ 3 Å, at a set temperature and interval of 300 K and 1, respectively. Calculations were executed based on the following formulae: ΔG = ΔH – TΔS (1) ΔH = ΔE MM + ΔG sol (2) ΔE MM = ΔE vdW + ΔE electrostatic (3) ΔG sol = ΔG PB + ΔG SA (4) ΔG bind = ΔG complex – (ΔG receptor + ΔG ligand ) (5) Amongst the gmx_MMPBSA provided methods of computing, the terms for enthalpy (TΔS) are C2, interaction entropy, normal mode (NMODE), and quasi-harmonic entropy calculations. The former two methods have low computational cost requirements with the drawback of less precise calculations, whereas the latter two methods have substantially high computational demands. Due to the substantial computational demands of quasi-harmonic entropy and NMODE calculations, the entropic term (TΔS) was excluded from MM-PBSA calculations in this study. Declarations COMPETING INTERESTS The authors declare no competing interests FUNDING DECLARATION This work was not supported by any funding resources. Author Contribution Ö.C.E. Conceptualization, Project administration, Formal analysis, Resources, Supervision, Writing-Review and Editing;D.J.E. Data Curation, Investigation, Validation, Writing-Original Draft. Data Availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. References Schabath, M. B. & Cote, M. L. Cancer progress and priorities: Lung cancer. Cancer Epidemiol. Biomarkers Prev. 28 , 1563–1579 (2019). Rudin, C. M., Brambilla, E., Faivre-Finn, C. & Sage, J. Small-cell lung cancer. Nat. Rev. Dis. Primers . 7 (1), 3, 1–20 (2021). Chevallier, M., Borgeaud, M., Addeo, A. & Friedlaender, A. Oncogenic driver mutations in non-small cell lung cancer: Past, present and future. World J. Clin. Oncol. 24 (4), 217–237 (2021). Nie, W. et al. Structural analysis of the EGFR TK domain and potential implications for EGFR targeted therapy. Int. J. Oncol. 40 , 1763–1769 (2012). Zhou, F. et al. 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Bidirectional approach of Punica granatum natural compounds: reduction in lung cancer and SARS-CoV-2 propagation. BMC Complement. Med. Ther. 25 (1), 32 (2025). Ramarajyam, G., Murugan, R. & Rajendiran, S. Network pharmacology and bioinformatics illuminates punicalagin’s pharmacological mechanisms countering drug resistance in hepatocellular carcinoma. Hum. Gene . 42 , 201328 (2024). Liu, X. et al. PharmMapper server: A web server for potential drug target identification using pharmacophore mapping approach. Nucleic Acids Res. 38 , W609–W614 (2010). Kanehisa, M., Furumichi, M., Sato, Y., Matsuura, Y. & Ishiguro-Watanabe, M. KEGG: biological systems database as a model of the real world. Nucleic Acids Res. 53 , D672–D677 (2025). Guo, Q. et al. Current treatments for non-small cell lung cancer. Front. Oncol. 12 , 945102 (2022). Abourehab, M. A. S., Alqahtani, A. M., Youssif, B. G. M. & Gouda, A. M. 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GROMACS Documentation Release 2024 GROMACS Development Team. (2024). 10.5281/zenodo.10589697 Huang, J. et al. CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat. Methods . 14 , 71–73 (2017). Valdés-Tresanco, M. S., Valdés-Tresanco, M. E., Valiente, P. A. & Moreno, E. gmx_MMPBSA: A New Tool to Perform End-State Free Energy Calculations with GROMACS. J. Chem. Theory Comput. 17 , 6281–6291 (2021). Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.xlsx SupplementaryFigure1RMSDAnalysisofMMP9withvariousligands.png 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8583101","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":602900570,"identity":"eed3c2e8-fdf5-4c11-b549-6a48297a09ff","order_by":0,"name":"Doğacan Jeremy Edens","email":"","orcid":"","institution":"Adana Alparslan Türkeş Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Doğacan","middleName":"Jeremy","lastName":"Edens","suffix":""},{"id":602900571,"identity":"a009a13f-a521-4b9e-81d7-25e9562981c2","order_by":1,"name":"Özgür Cem Erkin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYPCCBB4G9gYwi7GBeC08B8CqgQQzcVoYGCQSiNTCz3746IaPe9JkzCUfP3/Mw2Aju+EA/7EP+LRI9qSl3ZzxLIfHcnaaYTMPQ5rxhgPMzDPwaTG4wWN2m+dABY/B7QSQlsOJIC14HWZwg//b7T8gLTePfwRq+U+MFh622wwHcniADJAtBwhrAfrF7GbPgTQegzM5hTPnGCQbzzzMbIxXCzDEnt34cSDZ3uD48Q0f3lTYyfYdb3yMVwu6O4GYuJgcBaNgFIyCUYAPAAAHw0rcmAGI4AAAAABJRU5ErkJggg==","orcid":"","institution":"Adana Alparslan Türkeş Science and Technology University","correspondingAuthor":true,"prefix":"","firstName":"Özgür","middleName":"Cem","lastName":"Erkin","suffix":""}],"badges":[],"createdAt":"2026-01-12 14:54:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8583101/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8583101/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104339991,"identity":"ec63eea6-0837-486f-9a9e-3c62ca0863d6","added_by":"auto","created_at":"2026-03-10 16:30:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":770390,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntersection of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eP. granatum\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e peel compounds and DEGs identified in NSCLC a)\u003c/strong\u003e Venn diagram of genes found in the accumulated DEG list in relation to the list of potential targets obtained. \u003cstrong\u003eb)\u003c/strong\u003e The compound-target network of active components of \u003cem\u003eP. granatum\u003c/em\u003e and their potential proteins targets. Compounds are depicted in yellow, whereas target proteins are depicted in blue. \u003cstrong\u003ec)\u003c/strong\u003ePPI network of the proteins in overlap list with additional visualization of the compounds’ interactions with the target proteins.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8583101/v1/d3d92f153808e7074b9b0997.png"},{"id":104339986,"identity":"1ef36623-fbca-418a-8839-cdc433ad5ee1","added_by":"auto","created_at":"2026-03-10 16:30:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1191270,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegrated\u003c/strong\u003e \u003cstrong\u003efunctional enrichment and pathway analysis of PPI network\u003c/strong\u003e \u003cstrong\u003ea)\u003c/strong\u003e Enrichment network of the overlapping DEGs (different pathways colored differently). \u003cstrong\u003eb)\u003c/strong\u003e Pathways/biological processes found to be most enriched within the DEG set. \u003cstrong\u003ec)\u003c/strong\u003eSet of transcription regulators found to most commonly regulate the DEG list. \u003cstrong\u003eb\u003c/strong\u003e and \u003cstrong\u003ec\u003c/strong\u003e colored according to p values. \u003cstrong\u003ea-c\u003c/strong\u003e were produced using Metascape and multiple database data (http://metascape.org/gp/index.html#/main/step1)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8583101/v1/4afe369c83c9ea7a91b4edac.png"},{"id":104405568,"identity":"3ce87ac6-56ac-41f9-88f2-af9c7ad65766","added_by":"auto","created_at":"2026-03-11 12:23:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1218639,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of core target proteins and their potential tendencies towards \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eP. granatum\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e peel compounds by docking analysis\u003c/strong\u003e \u003cstrong\u003ea)\u003c/strong\u003e Core target proteins network retracted from the ligand-protein network. The pivotal role of EGFR and its interrelatedness with other members of the core network can be seen. A majority of the members of the secondary core network, represented in blue, have received comparatively recent research attention as the landscape of scientific research shifted interest towards proteins such as PIK3R1 or MAPK14. \u003cstrong\u003eb)\u003c/strong\u003e Heatmap of all relevant docking permutations which have been executed in this study. Crossed out cells are of those which have not been docked, while blank cells are of those which produced positive binding energies. The heatmap was created by using GraphPad Prism v.10.6.1.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8583101/v1/2b4d23056857921fad3cb8a0.png"},{"id":104779631,"identity":"99bb6d2a-0e57-423d-900d-7037bcae6eaa","added_by":"auto","created_at":"2026-03-17 07:43:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":406124,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe docking map of compound-target interactions.\u003c/strong\u003e The 12 complexes computed to be within the nanomolar range can be seen along with the interactions between the proteins and their respective ligands. \u003cstrong\u003ea)\u003c/strong\u003e(left) AKT1-naringenin, (center) EGFR-gallagyldilacton, (right) MMP3-gallagyldilacton \u003cstrong\u003eb)\u003c/strong\u003e (left) MMP7-gallagyldilacton, (center) MMP9-epicatechin, (right) MMP9-kaempferol \u003cstrong\u003ec)\u003c/strong\u003e (left) MMP9-luteolin, (center) MMP9-pelargonidin, (right) MMP12-naringenin \u003cstrong\u003ed)\u003c/strong\u003e (left) MMP13-naringenin, (center) MMP13-chlorogenic acid, (right) OAT-naringenin.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8583101/v1/6fb7ad0aaccba8ea1d4e8505.png"},{"id":104405440,"identity":"0f8fc98f-fdc2-4ffb-895c-860e664a8c66","added_by":"auto","created_at":"2026-03-11 12:22:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":662729,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular dynamics simulations of significant compound-target complexes\u003c/strong\u003e. \u003cstrong\u003ea)\u003c/strong\u003e RMSD analyses of complexes MMP3-gallagyldilacton, MMP7-gallagydilacton, MMP13-chlorogenic acid. \u003cstrong\u003eb)\u003c/strong\u003e RMSD analyses of complexes involving naringenin. \u003cstrong\u003ec\u003c/strong\u003e) MMP9 RMSD results. \u003cstrong\u003ed)\u003c/strong\u003e RMSF analysis of MMP9 complexes.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8583101/v1/df94a68baee915e808df23cf.png"},{"id":104405840,"identity":"228969e4-d7f8-490e-b477-2f947b1cd6f8","added_by":"auto","created_at":"2026-03-11 12:23:58","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":689593,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular dynamics simulations of chosen TKIs and gallagyldilacton-EGFR variant protein complexes. \u003c/strong\u003eRMSD (left column) and RMSF (right column) analysis results of EGFR variants. \u003cstrong\u003ea)\u003c/strong\u003e EGFR\u003csup\u003eWT \u003c/sup\u003e\u0026nbsp;\u003cstrong\u003eb)\u003c/strong\u003e EGFR\u003csup\u003eT790M\u003c/sup\u003e \u003cstrong\u003ec)\u003c/strong\u003e EGFR\u003csup\u003eL858R\u003c/sup\u003e \u003cstrong\u003ed)\u003c/strong\u003e EGFR\u003csup\u003eex19del\u003c/sup\u003e \u003cstrong\u003ee)\u003c/strong\u003e EGFR\u003csup\u003eex20ins\u003c/sup\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8583101/v1/1325db7fc19067a28784de0f.png"},{"id":104783990,"identity":"b646a1d6-089d-4e86-a2a7-df00fef19a93","added_by":"auto","created_at":"2026-03-17 08:04:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6597618,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8583101/v1/859f9f40-7567-4e20-a94b-999f75114c58.pdf"},{"id":104339984,"identity":"0bcaebe3-e5e4-49d3-b5a9-ed77f2f64d7f","added_by":"auto","created_at":"2026-03-10 16:30:24","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":37090,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8583101/v1/2b487fdb8f97033eed13004c.xlsx"},{"id":104779705,"identity":"e0d2447e-882d-4d38-8594-04e9fdebeb71","added_by":"auto","created_at":"2026-03-17 07:45:00","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3032274,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1RMSDAnalysisofMMP9withvariousligands.png","url":"https://assets-eu.researchsquare.com/files/rs-8583101/v1/d2ac7fe374aa22b2677b3e98.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of Gallagyldilacton as a Mutation-Biased EGFR Inhibitor from Punica granatum Peel via Integrative Computational Analysis","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eLung cancer remains a prime contributor to the global cancer burden\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. With non-small cell carcinoma (NSCLC) and small-cell carcinoma (SCLC) accounting for approximately 85% and 15% of lung cancer cases, respectively \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, the disproportionate research focus on the former is unsurprising. While relatively recent efforts to curb population-level risk behaviors\u0026mdash;most notably cigarette smoking\u0026mdash;have met with success in many Western nations, governments of emerging economies such as Turkey, India, and China have yet to incite comparable nationwide cessation trends. Consequently, the high prevalence of lung cancer, particularly NSCLC, persists in these regions, effectively prolonging the global impact of the disease. Contemporary treatment modalities emphasize early detection, followed by patient enrollment into chemotherapy or invasive surgical interventions, often coupled with adjuvant chemotherapy. In particular, NSCLC chemotherapy has largely centered on inhibition of the EGFR, which exhibits the highest frequency of overexpression and/or mutation among NSCLC patients, followed by aberrant signaling involving the Kirsten rat sarcoma viral oncogene (KRAS) \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOver the years, efforts to therapeutically target EGFR\u0026mdash;particularly through tyrosine kinase inhibitors (TKIs)\u0026mdash;have yielded varying degrees of success. A primary limitation of clinically deployed TKIs, such as osimertinib, as well as earlier agents including erlotinib and gefitinib, is the inevitable development of drug resistance in NSCLC, progressively diminishing therapeutic efficacy over successive treatment cycles. Both pre-existing mutations within the EGFR-encoding gene and those acquired during therapeutic intervention constitute major obstacles to sustained clinical success. Accordingly, a primary objective in the development of novel TKIs is to achieve effective inhibition across multiple mutant forms of the target. For instance, the most prevalent EGFR mutant forms arise from single-point substitutions at residues T790 and L858, in which threonine and leucine are replaced by methionine (~\u0026thinsp;50% of patients) and arginine (~\u0026thinsp;41% of patients), respectively \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The therapeutic landscape of NSCLC chemotherapy includes EGFR-TKIs such as erlotinib and gefitinib, as well as their successors, including afatinib, dacomitinib, and osimertinib. With the overarching goal of improving disease control, these agents were developed to counteract distinct classes of EGFR mutations. For example, dacomitinib, which was designed to improve upon the efficacy of first-generation agents, demonstrated enhanced activity against less common EGFR mutations, including G719X, L747X, and S768I. Emerging therapeutic strategies\u0026mdash;including next-generation TKIs, antibody\u0026ndash;drug conjugates, novel immunotherapeutic approaches, and targeted protein degraders\u0026mdash;have demonstrated considerable promise in patients with progressive EGFR-mutant NSCLC following EGFR-TKI treatment, despite the persistent challenge of acquired resistance \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The accelerating pace of such drug development can be attributed to the increasing sophistication of contemporary methodologies alongside the implementation of novel approaches. In recent years, network pharmacology and computational drug design have played pivotal roles in advancing oncological research. Furthermore, incorporation of complementary analytical strategies\u0026mdash;such as molecular dynamics simulations, MM-GB/PBSA calculations, quantitative structure\u0026ndash;activity relationship (QSAR) modeling, and ADME profiling\u0026mdash;has markedly enhanced the precision of drug discovery and design.\u003c/p\u003e \u003cp\u003eResearchers have shown increasing interest in applying these techniques to natural compounds, particularly plant-derived flavonoids and polyphenols. Consequently, a paradigm has emerged centered on the systematic exploration of ethnopharmacologically relevant botanical sources, many of which have been traditionally utilized by communities maintaining long-standing medicinal practices. Such plants have been used for millennia within these populations, with knowledge of their beneficial effects transmitted across generations through oral tradition. Industrialization and urbanization have inevitably contributed to a global shift away from reliance on such traditional knowledge systems. Nevertheless, the wide array of ameliorative properties attributed to these plants has increasingly been substantiated, and their underlying mechanisms of action have begun to be elucidated. These properties include anti-inflammatory, antidiabetic, antibacterial, and anticancer activities \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAmong such plants is \u003cem\u003ePunica granatum\u003c/em\u003e L. (\u003cem\u003eP. granatum\u003c/em\u003e), commonly known as pomegranate. \u003cem\u003eP. granatum\u003c/em\u003e belongs to the \u003cem\u003eLythraceae\u003c/em\u003e family and is generally believed to originate from the mountainous regions of Iran and Iraq, although aspects of its taxonomy and geographic origin remain debated \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The pomegranate plant produces a diverse array of bioactive compounds, primarily flavonoids and anthocyanins. While all parts of the plant have piqued the interest of researchers, the most commonly investigated is the fruit itself. The fruit, being composed of peel, arils, and seeds at a ratio of 5:4:1, is where these compounds of interest are the most abundant. A variety of conditions, such as neurodegenerative diseases, cancer, and diabetes, have been reported to be ameliorated by phytochemicals such as proanthocyanidins, phenolics, ellagitannins, and complex polysaccharides from the fruit \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. On the other hand, the peel of the fruit which generally tend to be regarded as a waste product, is proven to be a good source of bioactive compounds such as polyphenols, anthocyanins, flavonoids and tannins. Although the content and composition of compounds found in the peel can differ vastly between the varieties, gallo tannins, ellagic acid, punicalins, punicalagins and gallic acid were found to be the most abundant phytochemicals. In addition, owing to differences in \u003cem\u003eP. granatum\u003c/em\u003e varieties, the content and composition of other biologically active compounds such as catechin, quercetin, kaempferol, chlorogenic acid and naringenin can also be found in varying amounts in the peel \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. For instance, reports of the concentration of ellagic acid found in \u003cem\u003eP. granatum\u003c/em\u003e differ greatly between the varieties, Zivzik and Manfalouty \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Furthermore, the composition of compounds found within the peel seems to vary heavily within specimens of the same cultivar based on the stage of maturation at which the fruit is harvested. Regardless, there is concrete evidence of a varying but constant presence of compounds that exhibit anticancer properties in \u003cem\u003eP. granatum\u003c/em\u003e \u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14 CR15\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Compounds such as ellagic acid, naringenin, punicalagin, and pedunculagin have been reported to have efficacy on various cancer cell lines ranging from MCF-7 to A549 to HCT-116 \u003cem\u003ein vitro\u003c/em\u003e \u003csup\u003e\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Although the molecular composition of the peel varies, the anticancer activity remains consistent, suggesting that this effect is not necessarily due to the activities of multiple compounds but rather to stronger inhibitory activity that results from the compounds\u0026rsquo; synergy. Lastly, the fact that the peel of the fruit is where most of the beneficial compounds are concentrated has massive implications, as industries interested in the fruit itself generally process the arils\u0026rsquo; contents and dispose pomegranate marcs. The theoretical utilization of a waste product produced at such a massive scale as a means of introducing novel drugs for a variety of ailments holds within it the promise of an eco-friendly contribution to medicine, not to mention the profitability of such an endeavor. Many studies have assessed the \u003cem\u003ein vitro\u003c/em\u003e cancer cell viability of \u003cem\u003eP. granatum\u003c/em\u003e extracts or certain compounds within different parts of the fruit as potential treatment for NSCLC, and thus have created progenitor research which could potentially catalyze the development of derivative drugs \u003csup\u003e\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Punicalagin, punicafolin, and other phytochemicals such as genistein derived from \u003cem\u003eP. granatum\u003c/em\u003e, have shown promise as multitarget therapy agents for a variety of malignancies, including breast cancer, hepatocellular carcinoma, and non-small cell lung cancer. Key overlapping molecular targets that regularly identified by network-pharmacology analyses are AKT1, STAT3, CASP3, SRC, IL6, and NOS3, which are involved in important pathways such PI3K\u0026ndash;AKT, JAK\u0026ndash;STAT, AMPK, HIF-1α, and mTOR. Despite expression-level fluctuations, strong and stable binding interactions, such as those between genistein and AKT1, punicalagin and SRC/AKT1/CASP3, and punicafolin and STAT3, were supported by molecular dynamics simulations. These computational predictions were further validated by complementary actual results, such as apoptosis induction, G0/G1 arrest, ROS generation in lung cancer cells, and decreased expression of oncogenic targets in HCC. Additionally, pomegranate peel chemicals show dual potential by binding SARS-CoV-2 proteases (Mpro, PLpro), indicating antiviral importance. Together, these investigations show the therapeutic adaptability of plant-derived polyphenols as modulators of networks that drive inflammation and cancer, emphasizing network pharmacology and integrated validation approaches as potent pipelines for mechanistic discovery \u003csup\u003e\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Therefore, by using an integrative network pharmacological method, we investigated the potential inhibitory effects of phytochemicals from \u003cem\u003eP. granatum\u003c/em\u003e peel on encoded proteins of upregulated genes in NSCLC. Among other peel phytochemicals, gallagyldilacton promises a pan-EGFR inhibitory potential which might represent a way of powerful intervention in the treatment of EGFR mutant NSCLCs.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCompound\u0026ndash;Target Network and Enrichment Analyses\u003c/h2\u003e \u003cp\u003eThe compound\u0026ndash;target network (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb) comprised 449 nodes and 6,842 edges, indicating extensive compound\u0026ndash;protein associations. On average, each curated \u003cem\u003eP. granatum\u003c/em\u003e compound interacted with approximately 270 predicted targets, and the overall network exhibited a mean node degree of 16.10. Hub proteins with the highest degrees included ABO (P16442), ADAM17 (P78536), and THRB (P10828), with degree values of 47, 35, and 32, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe compiled list of upregulated differentially expressed genes (DEGs) from NSCLC literature consisted of 832 unique targets (\u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Intersection of the DEG list with PharmMapper \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e-predicted targets yielded 58 overlapping proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). These overlapping targets were subsequently used to construct a protein\u0026ndash;protein interaction (PPI) network (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec), which contained 82 nodes and 1,204 edges. Among compound nodes, cyanidin and delphinidin exhibited the highest degrees (50 each), while gallic acid showed the lowest (degree\u0026thinsp;=\u0026thinsp;36). Target proteins displayed an average degree of 22.95, with EGFR representing a highly connected node (degree\u0026thinsp;=\u0026thinsp;39) and SPARC exhibiting lower connectivity (degree\u0026thinsp;=\u0026thinsp;3), indicating differences in network connectivity between these proteins.\u003c/p\u003e \u003cp\u003eFunctional enrichment analysis integrated Gene Ontology (GO), KEGG \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, WikiPathways, Reactome gene sets, and canonical pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). GO and KEGG analyses identified significant enrichment in carboxylic acid metabolic processes (GO:0019752, log p\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;15.61), hormonal response (GO:0009725, log p\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;15.58), and collagen catabolic processes (GO:0030574, log p\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;11.86) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Nuclear receptor signaling (R-HSA-9006931, log p\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;13.89) was also prominently represented, alongside pathways related to extracellular matrix disassembly, proteoglycans in cancer, and regulation of cell migration.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWithin the carboxylic acid metabolic process cluster, monocarboxylic acid metabolism (GO:0032787) emerged as the most significant subnode (log p\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;14.73). Transcription factor enrichment analysis identified SP1 as the dominant regulator (log p\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;16), associated with a large subset of overlapping targets including CBS, EGFR, ESR1, MIF, MMP9, MMP12, OAT, and PLAU (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Protein network analysis further defined a set of core targets (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). These representative core targets, except PLAU, were selected for subsequent molecular docking and simulation analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMolecular Docking\u003c/h3\u003e\n\u003cp\u003eA total of 386 ligand\u0026ndash;protein docking simulations were performed, of which 374 produced valid docking poses. Twelve docking attempts failed due to positive binding energy values (\u0026gt;\u0026thinsp;0 kcal/mol). Twelve ligand\u0026ndash;protein complexes exhibited estimated inhibition constants in the nanomolar range, with the majority involving MMP9 and MMP13. A summary of docking energies is provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, and calculated inhibition constants are listed in \u003cb\u003eSupplementary Table S5\u003c/b\u003e online.\u003c/p\u003e \u003cp\u003eWhen assessed at the protein level, approximately one-third of the core target proteins were inhibited by at least one ligand with nanomolar-range affinity. Representative binding modes for the top-ranked complexes are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and interaction profiles before and after molecular dynamics (MD) simulations are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Docking results were used to rank ligand\u0026ndash;protein complexes based on predicted binding affinity, and MD simulations were subsequently performed to evaluate the stability of selected interactions.\u003c/p\u003e \u003cp\u003eTable 1 Binding interactions of selected compounds with target proteins before and after MD simulations. a) Ligand-protein interactions of chosen complexes prior to MD simulation. b) Post-MD simulation complex interactions.\u003c/p\u003e\u003cp\u003eTable 1 a\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003e\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e[n] indicates repetition\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAromatic Interactions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTarget\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLigand\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eHydrophobic Interactions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eHydrogen Bonds\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eπ\u003c/em\u003e\u003cb\u003e-stacking\u003c/b\u003e\u003cem\u003eπ\u003c/em\u003e\u003cb\u003e-cation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eOther Interactions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChlorogenic Acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMMP13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL185, L218, V219\u003cem\u003e[2]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL185, A186\u003cem\u003e[2]\u003c/em\u003e, H222\u003cem\u003e[2]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSalt bridge:H222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH222, L239, Y244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE223, F241, I243\u003cem\u003e[2]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT245\u003cem\u003e[2]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNaringenin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL185\u003cem\u003e[2]\u003c/em\u003e, V219, H222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS182, G183, L185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA186, Y214\u003cem\u003e[2]\u003c/em\u003e, Q223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMMP12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eV235, F237, Y240\u003cem\u003e[2]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL181, A182\u003cem\u003e[2]\u003c/em\u003e, T215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSalt bridge:H218, H228\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP232, T239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAKT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eW80, T82, I84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN54, Q79\u003cem\u003e[3]\u003c/em\u003e, Q203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eW80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eK268, D292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT211, K268, Y272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY85, E230, E235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY85\u003cem\u003e[2]\u003c/em\u003e, R180\u003cem\u003e[2]\u003c/em\u003e, I232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF177K292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSalt bridge:K292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI265, Q266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ266, T267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGallagyldilacton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL718\u003cem\u003e[2]\u003c/em\u003e, V726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL718, K745, R776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSalt bridge:K745\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT790, M793\u003cem\u003e[2]\u003c/em\u003e, C797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT854, D855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMMP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH166, Y168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY155\u003cem\u003e[3]\u003c/em\u003e, H166, A167\u003cem\u003e[2]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY155H211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSalt bridge:H166, H205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA169, N175, E202\u003cem\u003e[3]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMetal complex: Zn\u0026thinsp;+\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMMP7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY172, T180, L181 Y240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT180\u003cem\u003e[2]\u003c/em\u003e, A182, A184\u003cem\u003e[2]\u003c/em\u003e E219, Y240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSalt bridge:H218, H222, H228 Metal complex: Zn\u0026thinsp;+\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpicatechin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMMP9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL188, L222, V223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH226, E227\u003cem\u003e[4]\u003c/em\u003e, A242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH226, Y248\u003cem\u003e[2]\u003c/em\u003e, R249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL243, R249\u003cem\u003e[2]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKaempferol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL188, V223, H226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE227\u003cem\u003e[2]\u003c/em\u003e, A242, Y245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY245, Y248, R249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR249\u003cem\u003e[2]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuteolin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL188, L222, H226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE227\u003cem\u003e[4]\u003c/em\u003e, A242, R249\u003cem\u003e[3]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY248, R249, T251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePelargonidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL188, L222, V223\u003cem\u003e[2]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE227\u003cem\u003e[2]\u003c/em\u003e, A242, R249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH226, Y248\u003cem\u003e[2]\u003c/em\u003e, R249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eAromatic Interactions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTarget\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLigand\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eHydrophobic Interactions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eHydrogen Bonds\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eπ\u003c/em\u003e\u003cb\u003e-stacking\u003c/b\u003e\u003cem\u003eπ\u003c/em\u003e\u003cb\u003e-cation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eOther Interactions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChlorogenic Acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMMP13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL185, A186\u003cem\u003e[3]\u003c/em\u003e, F241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT245\u003cem\u003e[3]\u003c/em\u003e, H251, M253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSalt bridge:H222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNaringenin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL185, V219, H222\u003cem\u003e[2]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL185, A186\u003cem\u003e[2]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMMP12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL214\u003cem\u003e[2]\u003c/em\u003e, H218, V235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL181, A182\u003cem\u003e[2]\u003c/em\u003e, H218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSalt bridge:H218\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP238, T239, V243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAKT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eW80, L210, L264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH207, V271, Y272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eW178, I265\u003cem\u003e[2]\u003c/em\u003e, Q266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG51, Y55, L58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSalt bridge:K292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG142, V143, E230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGallagyldilacton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL718\u003cem\u003e[2]\u003c/em\u003e, L844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM793, C797, T854\u003cem\u003e[2]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMMP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD158, P160\u003cem\u003e[2]\u003c/em\u003e, A167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSalt bridge: H166\u003cem\u003e[2]\u003c/em\u003e, H211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMMP7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY172\u003cem\u003e[2]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY172, D175\u003cem\u003e[2]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY172\u003cem\u003e[2]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpicatechin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMMP9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY248, R249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKaempferol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL222, Y248, T251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuteolin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL188, V223, H226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA189\u003cem\u003e[2]\u003c/em\u003e, V223, R249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePelargonidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL222, L243\u003cem\u003e[2]\u003c/em\u003e, R249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE241, Y245, R249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eMolecular Dynamics Simulations and MM-PBSA Calculations\u003c/h3\u003e\n\u003cp\u003eMD simulations identified several ligand\u0026ndash;protein complexes that remained stable over 100 ns trajectories, including EGFR\u0026ndash;gallagyldilacton, MMP9\u0026ndash;luteolin, MMP12\u0026ndash;naringenin, MMP13\u0026ndash;naringenin, and MMP13\u0026ndash;chlorogenic acid. MM-PBSA analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) identified chlorogenic acid\u0026ndash;MMP13 complex, with the lowest binding free energy of ΔG_bind\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;34.47\u0026thinsp;\u0026plusmn;\u0026thinsp;4.34 kcal/mol. RMSD analysis supported the stability of this interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u003cu\u003eTable 2\u003c/u\u003e\u003c/strong\u003e \u003cstrong\u003eBinding interactions of chosen TKIs and gallagyldilacton with variant EGFR protein before and after MD simulations\u003c/strong\u003e. \u003cstrong\u003ea)\u003c/strong\u003e Table of interactions with EGFR variants in chosen TKIs along with gallagyldilacton prior to simulation \u003cstrong\u003eb)\u003c/strong\u003e Post-simulation interactions with EGFR variants seen in TKIs and gallagyldilacton.\u003c/p\u003e \u003cp\u003eTable 2 a\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003e\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e[n] indicates repetition\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAromatic Interactions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTarget\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLigand\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eHydrophobic Interactions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eHydrogen Bonds\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eπ\u003c/em\u003e\u003cb\u003e-stacking\u003c/b\u003e\u003cem\u003eπ\u003c/em\u003e\u003cb\u003e-cation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eOther Interactions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR\u003cem\u003eWT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAfatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL718, V726\u003cem\u003e[2]\u003c/em\u003e, A743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eK745, M793, D855\u003cem\u003e[2]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eK745, T790, L858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDacomitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eV726, A743, K745 M766, T790, L844\u003cem\u003e[2]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT790, M793, T854\u003cem\u003e[2]\u003c/em\u003e D855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGefitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eV726, A743, K745 T790, L844\u003cem\u003e[2]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eK745, M793, C797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGallagyldilacton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL718\u003cem\u003e[2]\u003c/em\u003e, V726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL718, K745, R776 T790, M793\u003cem\u003e[2]\u003c/em\u003e, C797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSalt bridge:K745\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT854, D855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR\u003cem\u003eT790M\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAfatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL718, V726, K745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eK745, E762, D855\u003cem\u003e[2]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eK745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDacomitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL718, V726\u003cem\u003e[2]\u003c/em\u003e, A743 L844\u003cem\u003e[2]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eK745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSalt bridge:855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGefitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL718, L792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eK745, M793, D855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGallagyldilacton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL718\u003cem\u003e[2]\u003c/em\u003e, L844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL718, E762\u003cem\u003e[2]\u003c/em\u003e, M793\u003cem\u003e[2]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSalt bridge:K745\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC797, T854, D855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR\u003cem\u003eL858R\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAfatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE758, I759, R858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eK745\u003cem\u003e[2]\u003c/em\u003e, E762, T854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDacomitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL718, V726\u003cem\u003e[3]\u003c/em\u003e, A743 K745\u003cem\u003e[2]\u003c/em\u003e, L788, L844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eV726, D855\u003cem\u003e[2]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSalt bridge:E762\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGefitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eK745, M793, G796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSalt bridge:D855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC797, D855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGallagyldilacton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF723, V726, A743 L844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS720\u003cem\u003e[3]\u003c/em\u003e, K745, Q791 M793, T854\u003cem\u003e[3]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eK745\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR\u003cem\u003eex19del\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAfatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF723, V726, A743 K745, M793, L844\u003cem\u003e[2]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA722, F723, G724 T790, T854, D855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDacomitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL718, A743, K745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSalt bridge:D855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT790, L844, T854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGefitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL718, V726, A743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT790, Q791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL792, M793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGallagyldilacton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL844\u003c/p\u003e \u003cp\u003eE762, Q791\u003cem\u003e[2]\u003c/em\u003e, M793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS720, A722, F723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eK745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR\u003cem\u003eex20ins\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAfatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL718, A743, K745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT793, M796, G799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL791, T793, M796\u003c/p\u003e \u003cp\u003eL847\u003cem\u003e[2]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDacomitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL718, V726\u003cem\u003e[2]\u003c/em\u003e, A743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE762, T793, L795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM796, L847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGefitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL718, V726, A743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT793, M796, G799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHalogen bond:L791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL795, L847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGallagyldilacton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL718\u003cem\u003e[2]\u003c/em\u003e, R844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eK728, M796, D803 R844, D855\u003cem\u003e[3]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eAromatic Interactions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTarget\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLigand\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eHydrophobic Interactions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eHydrogen Bonds\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eπ\u003c/em\u003e\u003cb\u003e-stacking\u003c/b\u003e\u003cem\u003eπ\u003c/em\u003e\u003cb\u003e-cation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eOther Interactions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR\u003cem\u003eWT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAfatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eV726, A743, L777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM793\u003cem\u003e[2]\u003c/em\u003e, D855\u003cem\u003e[2]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eD855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDacomitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eK745\u003cem\u003e[2]\u003c/em\u003e, T790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT790\u003cem\u003e[2]\u003c/em\u003e, M793, D855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGefitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL718, V726, A743 K745\u003cem\u003e[2]\u003c/em\u003e, T790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG721, M793, C797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eK745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGallagyldilacton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL718\u003cem\u003e[2]\u003c/em\u003e, L844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM793, C797, T854\u003cem\u003e[2]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR\u003cem\u003eT790M\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAfatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eV726, L844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM793, C797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSalt bridge:D800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDacomitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL718, L844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM793, T854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSalt bridge:855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGefitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ791, M793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGallagyldilacton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL718\u003cem\u003e[2]\u003c/em\u003e, K745, L844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS720\u003cem\u003e[2]\u003c/em\u003e, E762\u003cem\u003e[2]\u003c/em\u003e, M793\u003cem\u003e[2]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSalt bridge:K745\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP794, G796, D855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR\u003cem\u003eL858R\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAfatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDacomitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR841, A859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR841\u003cem\u003e[3]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR841\u003cem\u003e[2]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSalt bridge:D855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGefitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ791, D800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSalt bridge:D855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGallagyldilacton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL718, F723, A743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT790, C797, T854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR\u003cem\u003eex19del\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAfatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR889. Y891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDacomitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eV726, A743, K745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL788, T790, R841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGefitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL718, V726, L844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSalt bridge:D800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGallagyldilacton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eV726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS720\u003cem\u003e[2]\u003c/em\u003e, K745, M793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eD855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR\u003cem\u003eex20ins\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAfatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL718, A743, K745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ794, M796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT793, L795, L847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDacomitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eV726, I759\u003cem\u003e[2]\u003c/em\u003e, M766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT793, L861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSalt bridge:D858\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL791, L861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGefitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL718, V726, T857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGallagyldilacton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eV726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS720\u003cem\u003e[2]\u003c/em\u003e, K745, M793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003eNaringenin displayed stable binding to a subset of its targets, while interactions with AKT1 and OAT exhibited gradual loss of stability over the simulation period (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). RMSD and RMSF analyses of MMP9 complexes revealed variable ligand stability; luteolin maintained stable binding, whereas epicatechin showed weaker interaction profiles (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed, \u003cb\u003eSupplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Consistent RMSF patterns across all MMP9 simulations confirmed the robustness of the comparative analyses, as all simulations were performed using the same protein structure.\u003c/p\u003e \u003cp\u003eGallagyldilacton\u0026ndash;EGFR complexes displayed moderate overall stability (ΔG_bind\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;7.20\u0026thinsp;\u0026plusmn;\u0026thinsp;4.53 kcal/mol) while maintaining interactions with key ATP-binding site residues, including K745, T790, and L858 (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Subsequent analyses focused on EGFR-containing complexes.\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\u003e\u003cb\u003eMM/PBSA analysis of stable complexes throughout this study\u003c/b\u003e. All values represented in the table are in kcal/mol.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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=\"left\" 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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e∆\u003cem\u003eEMM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e∆\u003cem\u003eGsol\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProtein\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLigand\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e∆\u003cem\u003eEvdW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e∆\u003cem\u003eEelectrostatic\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e∆\u003cem\u003eGP B\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e∆\u003cem\u003eGSA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e∆\u003cem\u003eEMM\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e∆\u003cem\u003eGsol\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e∆\u003cem\u003eGbind\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAfatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-57.28\u0026thinsp;\u0026plusmn;\u0026thinsp;3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.44\u0026thinsp;\u0026plusmn;\u0026thinsp;11.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-55.84\u0026thinsp;\u0026plusmn;\u0026thinsp;12.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28.22\u0026thinsp;\u0026plusmn;\u0026thinsp;11.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-27.62\u0026thinsp;\u0026plusmn;\u0026thinsp;5.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDacomitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-52.99\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-62.03\u0026thinsp;\u0026plusmn;\u0026thinsp;9.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96.08\u0026thinsp;\u0026plusmn;\u0026thinsp;8.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-115.02\u0026thinsp;\u0026plusmn;\u0026thinsp;9.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e90.37\u0026thinsp;\u0026plusmn;\u0026thinsp;8.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-24.65\u0026thinsp;\u0026plusmn;\u0026thinsp;4.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGefitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-49.52\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-28.43\u0026thinsp;\u0026plusmn;\u0026thinsp;15.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.18\u0026thinsp;\u0026plusmn;\u0026thinsp;13.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-77.95\u0026thinsp;\u0026plusmn;\u0026thinsp;15.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.24\u0026thinsp;\u0026plusmn;\u0026thinsp;13.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-27.72\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGallagyldilacton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-49.36\u0026thinsp;\u0026plusmn;\u0026thinsp;2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-14.98\u0026thinsp;\u0026plusmn;\u0026thinsp;6.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62.06\u0026thinsp;\u0026plusmn;\u0026thinsp;7.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-64.34\u0026thinsp;\u0026plusmn;\u0026thinsp;6.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57.14\u0026thinsp;\u0026plusmn;\u0026thinsp;7.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR\u003cem\u003eT790M\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDacomitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-41.64\u0026thinsp;\u0026plusmn;\u0026thinsp;3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-112.54\u0026thinsp;\u0026plusmn;\u0026thinsp;16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140.87\u0026thinsp;\u0026plusmn;\u0026thinsp;19.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-154.18\u0026thinsp;\u0026plusmn;\u0026thinsp;17.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e136.48\u0026thinsp;\u0026plusmn;\u0026thinsp;19.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-17.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGallagyldilacton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-43.58\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-45.11\u0026thinsp;\u0026plusmn;\u0026thinsp;6.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63.82\u0026thinsp;\u0026plusmn;\u0026thinsp;5.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-88.68\u0026thinsp;\u0026plusmn;\u0026thinsp;7.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e59.77\u0026thinsp;\u0026plusmn;\u0026thinsp;5.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-28.92\u0026thinsp;\u0026plusmn;\u0026thinsp;4.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR\u003cem\u003eL858R\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGefitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-50.21\u0026thinsp;\u0026plusmn;\u0026thinsp;3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-81.7\u0026thinsp;\u0026plusmn;\u0026thinsp;21.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e114.34\u0026thinsp;\u0026plusmn;\u0026thinsp;19.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-131.91\u0026thinsp;\u0026plusmn;\u0026thinsp;21.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e109.18\u0026thinsp;\u0026plusmn;\u0026thinsp;19.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-22.73\u0026thinsp;\u0026plusmn;\u0026thinsp;5.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGallagyldilacton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-43.61\u0026thinsp;\u0026plusmn;\u0026thinsp;2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-23.41\u0026thinsp;\u0026plusmn;\u0026thinsp;5.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.22\u0026thinsp;\u0026plusmn;\u0026thinsp;6.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-67.01\u0026thinsp;\u0026plusmn;\u0026thinsp;6.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e42.84\u0026thinsp;\u0026plusmn;\u0026thinsp;6.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-24.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR\u003cem\u003eex19del\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDacomitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-52.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-85.3\u0026thinsp;\u0026plusmn;\u0026thinsp;13.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e113.78\u0026thinsp;\u0026plusmn;\u0026thinsp;17.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-138.11\u0026thinsp;\u0026plusmn;\u0026thinsp;15.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e108.34\u0026thinsp;\u0026plusmn;\u0026thinsp;17.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-29.76\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR\u003cem\u003eex20ins\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAfatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-54.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-107.17\u0026thinsp;\u0026plusmn;\u0026thinsp;16.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e144.3\u0026thinsp;\u0026plusmn;\u0026thinsp;18.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-161.84\u0026thinsp;\u0026plusmn;\u0026thinsp;16.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e139\u0026thinsp;\u0026plusmn;\u0026thinsp;18.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-22.84\u0026thinsp;\u0026plusmn;\u0026thinsp;5.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLuteolin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-36.87\u0026thinsp;\u0026plusmn;\u0026thinsp;3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-57.51\u0026thinsp;\u0026plusmn;\u0026thinsp;4.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74.54\u0026thinsp;\u0026plusmn;\u0026thinsp;6.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-94.38\u0026thinsp;\u0026plusmn;\u0026thinsp;4.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e71.29\u0026thinsp;\u0026plusmn;\u0026thinsp;6.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-23.09\u0026thinsp;\u0026plusmn;\u0026thinsp;6.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChlorogenic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-44.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-70.02\u0026thinsp;\u0026plusmn;\u0026thinsp;5.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.53\u0026thinsp;\u0026plusmn;\u0026thinsp;4.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-114.02\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e79.55\u0026thinsp;\u0026plusmn;\u0026thinsp;4.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-34.47\u0026thinsp;\u0026plusmn;\u0026thinsp;4.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNaringenin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-42.68\u0026thinsp;\u0026plusmn;\u0026thinsp;2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-42.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.54\u0026thinsp;\u0026plusmn;\u0026thinsp;6.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-84.88\u0026thinsp;\u0026plusmn;\u0026thinsp;4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-75.22\u0026thinsp;\u0026plusmn;\u0026thinsp;6.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-9.67\u0026thinsp;\u0026plusmn;\u0026thinsp;6.02\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\u003eGallagyldilacton was compared with established EGFR TKIs (afatinib, dacomitinib, and gefitinib). All TKIs showed stable binding to EGFR\u003csup\u003e\u003cem\u003eWT\u003c/em\u003e\u003c/sup\u003e, with RMSD values ranging from 1.7 to 2.1 \u0026Aring;. Gallagyldilacton showed RMSD and RMSF profiles within the same range, indicating a similar binding mode (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). In simulations involving EGFR\u003csup\u003e\u003cem\u003eT790M\u003c/em\u003e\u003c/sup\u003e and EGFR\u003csup\u003e\u003cem\u003eL858R\u003c/em\u003e\u003c/sup\u003e, gallagyldilacton demonstrated stability comparable to dacomitinib and gefitinib, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). Reduced stability was observed for exon deletion mutants (EGFR\u003csup\u003e\u003cem\u003eex19del\u003c/em\u003e\u003c/sup\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed), whereas binding to EGFR\u003csup\u003e\u003cem\u003eex20ins\u003c/em\u003e\u003c/sup\u003e was comparable to that of reference TKIs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePost-simulation interaction analyses showed that gallagyldilacton frequently formed a greater number of hydrogen bonds with EGFR variants than the reference TKIs, while consistently engaging critical ATP-binding site residues (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). MM-PBSA calculations revealed a mutation-biased binding profile: gallagyldilacton displayed weaker affinity for EGFR\u003csup\u003e\u003cem\u003eWT\u003c/em\u003e\u003c/sup\u003e but substantially stronger binding to EGFR\u003csup\u003e\u003cem\u003eT790M\u003c/em\u003e\u003c/sup\u003e (ΔG_bind\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;28.92\u0026thinsp;\u0026plusmn;\u0026thinsp;4.47 kcal/mol) and EGFR\u003csup\u003e\u003cem\u003eL858R\u003c/em\u003e\u003c/sup\u003e (ΔG_bind\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;24.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.74 kcal/mol) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Van der Waals contributions remained consistent across EGFR variants (\u0026minus;\u0026thinsp;43.58 to \u0026minus;\u0026thinsp;49.36 kcal/mol), and favorable desolvation energies contributed to the overall binding free energy.\u003c/p\u003e \u003cp\u003eStable ligand-protein interactions were observed with both metalloproteins and EGFR across the analyzed complexes. In line with network predictions and dynamic stability analyses, gallagyldilacton showed stable binding to EGFR variants, while naringenin exhibited broader interactions involving AKT1, MMP12/13, and OAT.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eNon-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality worldwide, largely due to the high prevalence of oncogenic mutations in the epidermal growth factor receptor (EGFR) and the inevitable development of resistance to targeted therapies \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. In this study, we employed an integrative network pharmacology and computational modeling framework to investigate the inhibitory potential of phytochemicals derived from \u003cem\u003eP. granatum\u003c/em\u003e peel against proteins encoded by genes upregulated in NSCLC. Through this approach, gallagyldilacton emerged as a prominent candidate, exhibiting stable and preferential binding to mutant forms of EGFR that are clinically associated with therapeutic resistance.\u003c/p\u003e \u003cp\u003eNetwork pharmacology analysis was first used to provide a systems-level context for target prioritization. Among the differentially expressed genes and predicted compound targets, EGFR, AKT1, and several matrix metalloproteinases (MMPs) occupied central positions within the protein\u0026ndash;protein interaction network. Rather than implying equivalent biological or clinical relevance for all identified nodes, this analysis served to guide subsequent focused investigations. EGFR was prioritized due to its well-established role as a driver of NSCLC progression and its centrality within both oncogenic signaling and current therapeutic strategies.\u003c/p\u003e \u003cp\u003eDocking and molecular dynamics simulations revealed that gallagyldilacton binds stably to clinically relevant EGFR mutants, particularly EGFR\u003csup\u003e\u003cem\u003eT790M\u003c/em\u003e\u003c/sup\u003e and EGFR\u003csup\u003e\u003cem\u003eL858R\u003c/em\u003e\u003c/sup\u003e, while displaying comparatively reduced affinity toward wild-type EGFR. This mutation-biased binding profile is notable, as resistance-conferring mutations such as T790M are a major limitation of first- and second-generation EGFR tyrosine kinase inhibitors (TKIs) \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. To our knowledge, this study provides early computational evidence suggesting that gallagyldilacton may preferentially accommodate structural alterations introduced by common EGFR resistance mutations, supporting its potential as a mutation-focused inhibitory scaffold.\u003c/p\u003e \u003cp\u003eAt the structural level, the stability of gallagyldilacton\u0026ndash;EGFR mutant complexes appears to arise from a dense and adaptable hydrogen-bonding network, complemented by favorable van der Waals interactions within the altered binding pockets of mutant receptors. Molecular dynamics analyses indicated that these interactions remain stable over extended simulation times, suggesting that gallagyldilacton can tolerate conformational changes associated with EGFR mutations. Rather than relying on a single dominant interaction, the compound\u0026rsquo;s binding mode reflects a distributed interaction profile, which may be advantageous in the context of structurally heterogeneous mutant targets.\u003c/p\u003e \u003cp\u003eIn addition to EGFR, other \u003cem\u003eP. granatum\u003c/em\u003e peel phytochemicals displayed affinity toward secondary targets within the network. Notably, naringenin demonstrated stable interactions with AKT1 and selected MMPs, proteins implicated in tumor survival, invasion, and microenvironment remodeling. These findings suggest the possibility of complementary network-level modulation; however, the present results do not imply standalone therapeutic relevance for these secondary interactions in NSCLC. Instead, they highlight the broader polypharmacological landscape of \u003cem\u003eP. granatum\u003c/em\u003e\u0026ndash;derived compounds, within which EGFR inhibition remains the primary focus of this work.\u003c/p\u003e \u003cp\u003eWhen positioned against existing EGFR-targeted therapies, gallagyldilacton does not seek to supplant approved TKIs but rather represents a distinct, mutation-biased inhibitory profile. While third-generation TKIs such as osimertinib were designed to overcome T790M-mediated resistance, the emergence of additional resistance mechanisms continues to challenge long-term efficacy \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The computational comparability of gallagyldilacton to known TKIs in mutant EGFR systems underscores its relevance as a lead structure for further exploration, without implying clinical readiness or superiority.\u003c/p\u003e \u003cp\u003eAlthough this study primarily focuses on target engagement and molecular stability, \u003cem\u003ein silico\u003c/em\u003e ADME profiling was performed to contextualize gallagyldilacton within a translational framework. As shown in \u003cb\u003eSupplementary Table S6\u003c/b\u003e online, gallagyldilacton displays acceptable physicochemical properties, including moderate solubility, permeability and intestinal absorption. While these predictions do not replace experimental pharmacokinetic evaluation, they support the feasibility of the phytochemicals as optimization-ready scaffolds and complement the observed binding stability and mutation-spanning target engagement.\u003c/p\u003e \u003cp\u003eSeveral limitations of this study should be acknowledged. All findings are derived from \u003cem\u003ein silico\u003c/em\u003e analyses, and experimental validation at the biochemical and cellular levels is required to substantiate inhibitory activity. Additionally, as detailed in the Methods section, entropy contributions were excluded from MM-PBSA calculations due to computational constraints, which may affect the absolute estimation of binding free energies. Nevertheless, relative comparisons across similar systems remain informative within this framework. Future work should focus on experimental validation, structural optimization, and evaluation of biological activity in EGFR-mutant NSCLC models.\u003c/p\u003e \u003cp\u003eIn conclusion, this study integrates network pharmacology, molecular docking, molecular dynamics simulations, and MM-PBSA calculations to identify gallagyldilacton as a promising, mutation-biased EGFR-focused inhibitor derived from \u003cem\u003eP. granatum\u003c/em\u003e peel. By emphasizing selectivity toward resistance-associated EGFR mutants and situating these findings within a systems-level context, this work provides a rational computational foundation for the further investigation of plant-derived polyphenols as candidates for overcoming EGFR-driven therapeutic resistance in NSCLC.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData Acquisition and Preparation\u003c/h2\u003e \u003cp\u003eTo compile a comprehensive list of differentially expressed genes (DEGs), a literature review of studies published between 2018 and 2023 that analyzed DEGs in NSCLC tumor tissue was conducted (\u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). For inclusion, upregulated DEGs with a fold change greater than 2 (FC\u0026thinsp;\u0026gt;\u0026thinsp;2) were considered significant (\u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). Phytochemicals derived from \u003cem\u003eP. granatum\u003c/em\u003e peel that have been reported to exhibit anticancer activity were collected from the literature, yielding a total of 24 compounds relevant to this study (\u003cb\u003eSupplementary Table S3\u003c/b\u003e). These compounds encompassed multiple chemical classes, including anthocyanins, ellagitannins, flavonoids, hydroxybenzoic acids, and hydroxycinnamic acids. Following compilation, each compound was retrieved from PubChem \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e in MOL2 format, and its geometry was optimized using Avogadro \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. To guide subsequent analyses, a dataset of potential protein targets was generated for each compound. Accordingly, PharmMapper \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e was employed to predict and constrain the set of protein targets analyzed in this study. The parameters \u0026ldquo;Generate Conformers\u0026rdquo; and \u0026ldquo;Select Targets Set\u0026rdquo; were set to \u0026ldquo;Yes\u0026rdquo; and \u0026ldquo;Human Protein Targets Only\u0026ldquo; (v2010, 2241), respectively. Both the compiled DEG list and the predicted targets obtained from PharmMapper were converted into UniProtKB identifiers using the Retrieve/ID mapping service provided by UniProt \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNetwork Construction and Enrichment Analyses\u003c/h3\u003e\n\u003cp\u003eFollowing target identification, a compound\u0026ndash;target interaction network was constructed using Cytoscape (v3.10.3) \u003csup\u003e36\u003c/sup\u003e. Compounds exhibiting a degree value greater than one were selected and subsequently used for enrichment analyses. The compiled DEG list was overlapped with the PharmMapper-derived target list using Venny \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. The overlapping targets were subsequently subjected to STRING analysis within Cytoscape to generate a protein\u0026ndash;protein interaction (PPI) network, into which the active compounds were integrated to visualize their associations with the overlapping proteins \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGene Ontology (GO) and KEGG \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e pathway enrichment analyses were performed using Metascape (v3.5.20250101), a web-based platform designed to integrate multiple OMICS datasets into a unified bioinformatics workflow using the overlapping target proteins identified in Cytoscape. Of relevance to this study was Metascape\u0026rsquo;s ability to perform pathway enrichment across multiple databases and to subsequently cluster enriched term. Protein network analysis was additionally performed using STRING to identify core nodes based on functional interrelatedness and contribution to molecular assemblies \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eProtein Preparation and Docking\u003c/h3\u003e\n\u003cp\u003eSelected protein structures were retrieved from the Protein Data Bank (PDB) \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e (\u003cb\u003eSupplementary Table S4\u003c/b\u003e) and prepared using ChimeraX \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Nonstandard atoms were removed from the PDB files, while essential metal ions and solvent molecules were retained. AutoDock 4.2.6 \u003csup\u003e42\u003c/sup\u003e was used to add hydrogen atoms, merge nonpolar hydrogens, assign Kollman charges, and define AD4 atom types. Members of the core protein list obtained via Metascape analyses were subjected to docking via AutoDock. Based on prior literature, residues defining ATP-binding sites or allosteric domains were identified, and the average coordinates of these regions were calculated for each target protein. For docking, a cubic grid box with dimensions of 60 \u0026times; 60 \u0026times; 60 points and a spacing of 0.5 \u0026Aring; was centered on the calculated coordinate average. This approach ensured that only literature-validated functional sites and domains with inhibitory potential were considered. Map types were set directly; target proteins that necessitate the presence of metal ions such as zinc, had their ions preserved and were mapped in addition to organic material. Docking simulations were performed using the Lamarckian genetic algorithm implemented in AutoDock 4.2. Each docking was executed with 10 GA runs, a population size of 150, a maximum of 2500000 evaluations, in 27000 generations, and rates of gene mutation and crossover at 0.02 and 0.8, respectively. The number of generations for picking the worst individual was set at 10, while the mean and variance of the Cauchy distribution for gene mutation were set at 0.0 and 1.0, respectively. Docking parameters were set to default, and post-docking ligand-protein interactions, such as hydrogen bonds, salt bridges, etc., were assessed using the Protein-Ligand Interaction Profiler (PLIP) \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Dynamics Simulations and MM-PBSA Calculations\u003c/h2\u003e \u003cp\u003eMolecular dynamics (MD) simulations were performed using GROMACS version 2024.4 \u003csup\u003e44\u003c/sup\u003e, and ligand\u0026ndash;protein complexes exhibiting nanomolar-range inhibition constants were selected for further investigation. Given the prevalence of metalloproteins and associated zinc ions, the CHARMM36 force field\u0026mdash;equipped with validated metal ion parameters, including zinc \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e\u0026mdash;and a modified TIP3P water model was employed throughout all MD simulations. Ligand topology was obtained via SwissParam, and proteins were prepared via the DockPrep tool in ChimeraX. Energy minimization (EM) was performed using a step size of 0.01 until the maximum force fell below 10 kcal/mol. Both canonical ensemble isothermal volume (NVT) equilibration and isothermal pressure (NPT) equilibration were run for 100 ps at 2 fs intervals. MD simulations were carried out for 100 ns at 2 fs intervals with the temperature set at 300 K. Subsequent RMSD, RMSF, and H-bond analyses of each MD simulation were carried out internally in GROMACS using built-in functions.\u003c/p\u003e \u003cp\u003eAlthough implicit generalized Born (GB) models are commonly used for protein\u0026ndash;ligand free energy calculations, there is consensus that simulations employing the CHARMM36 force field are better suited to Poisson\u0026ndash;Boltzmann (PB)-based approaches. Therefore, Gibbs free energy calculations were done using gmx_MMPBSA via the PB model, which is a free energy computation tool based on AmberTools that is capable of a variety of calculations, the most relevant of which involve ligand-protein MM-PB(GB)SA determinations \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. In our simulations,calculations took into consideration the last 20 ns of stable complexes, i.e., complexes which attained stability at values\u0026thinsp;\u0026le;\u0026thinsp;3 \u0026Aring;, at a set temperature and interval of 300 K and 1, respectively.\u003c/p\u003e \u003cp\u003eCalculations were executed based on the following formulae:\u003c/p\u003e \u003cp\u003e \u003cem\u003eΔG\u0026thinsp;=\u0026thinsp;ΔH \u0026ndash; TΔS\u003c/em\u003e (1)\u003c/p\u003e \u003cp\u003e \u003cem\u003eΔH\u0026thinsp;=\u0026thinsp;ΔE\u003c/em\u003e \u003csub\u003e \u003cem\u003eMM\u003c/em\u003e \u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;ΔG\u003c/em\u003e\u003csub\u003e\u003cem\u003esol\u003c/em\u003e\u003c/sub\u003e (2)\u003c/p\u003e \u003cp\u003e \u003cem\u003eΔE\u003c/em\u003e \u003csub\u003e \u003cem\u003eMM\u003c/em\u003e \u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;ΔE\u003c/em\u003e\u003csub\u003e\u003cem\u003evdW\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;ΔE\u003c/em\u003e\u003csub\u003e\u003cem\u003eelectrostatic\u003c/em\u003e\u003c/sub\u003e (3)\u003c/p\u003e \u003cp\u003e \u003cem\u003eΔG\u003c/em\u003e \u003csub\u003e \u003cem\u003esol\u003c/em\u003e \u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;ΔG\u003c/em\u003e\u003csub\u003e\u003cem\u003ePB\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;ΔG\u003c/em\u003e\u003csub\u003e\u003cem\u003eSA\u003c/em\u003e\u003c/sub\u003e (4)\u003c/p\u003e \u003cp\u003e \u003cem\u003eΔG\u003c/em\u003e \u003csub\u003e \u003cem\u003ebind\u003c/em\u003e \u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;ΔG\u003c/em\u003e\u003csub\u003e\u003cem\u003ecomplex\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e\u0026ndash; (ΔG\u003c/em\u003e\u003csub\u003e\u003cem\u003ereceptor\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;ΔG\u003c/em\u003e\u003csub\u003e\u003cem\u003eligand\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e)\u003c/em\u003e (5)\u003c/p\u003e \u003cp\u003eAmongst the gmx_MMPBSA provided methods of computing, the terms for enthalpy (TΔS) are C2, interaction entropy, normal mode (NMODE), and quasi-harmonic entropy calculations. The former two methods have low computational cost requirements with the drawback of less precise calculations, whereas the latter two methods have substantially high computational demands. Due to the substantial computational demands of quasi-harmonic entropy and NMODE calculations, the entropic term (TΔS) was excluded from MM-PBSA calculations in this study.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCOMPETING INTERESTS\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFUNDING\u003c/h2\u003e \u003cp\u003eDECLARATION\u003c/p\u003e \u003cp\u003eThis work was not supported by any funding resources.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e\u0026Ouml;.C.E. Conceptualization, Project administration, Formal analysis, Resources, Supervision, Writing-Review and Editing;D.J.E. Data Curation, Investigation, Validation, Writing-Original Draft.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSchabath, M. B. \u0026amp; Cote, M. L. Cancer progress and priorities: Lung cancer. \u003cem\u003eCancer Epidemiol. Biomarkers Prev.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e, 1563\u0026ndash;1579 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRudin, C. M., Brambilla, E., Faivre-Finn, C. \u0026amp; Sage, J. Small-cell lung cancer. \u003cem\u003eNat. Rev. Dis. Primers\u003c/em\u003e. \u003cb\u003e7\u003c/b\u003e (1), 3, 1\u0026ndash;20 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChevallier, M., Borgeaud, M., Addeo, A. \u0026amp; Friedlaender, A. 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Theory Comput.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 6281\u0026ndash;6291 (2021).\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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Non-small cell lung cancer, epidermal growth factor receptor mutations, network pharmacology, molecular docking, Punica granatum.","lastPublishedDoi":"10.21203/rs.3.rs-8583101/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8583101/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNon-small cell lung cancer (NSCLC) accounts for the majority of lung cancer cases worldwide and remains a leading cause of cancer-related mortality. Aberrant activation of the epidermal growth factor receptor (EGFR), driven by overexpression or mutation, represents a central oncogenic mechanism in NSCLC and a primary target of tyrosine kinase inhibitors (TKIs). Despite substantial clinical success, the emergence of resistance-conferring EGFR mutations continues to limit the long-term efficacy of existing therapies. In this study, we employed an integrative \u003cem\u003ein silico\u003c/em\u003e framework combining network pharmacology, molecular docking, molecular dynamics simulations, and MM-PBSA analyses to investigate the therapeutic potential of phytochemicals derived from \u003cem\u003ePunica granatum\u003c/em\u003e peel against NSCLC-associated molecular targets. Network analysis identified EGFR, AKT1, and matrix metalloproteinases (MMPs) as key nodes within NSCLC-related signaling pathways. Among the screened compounds, gallagyldilacton and naringenin emerged as prominent candidates with distinct target interaction profiles. Gallagyldilacton exhibited computationally stable and energetically favorable binding to multiple mutant EGFR variants, including T790M and L858R, while displaying comparatively weaker affinity toward wild-type EGFR, consistent with a mutation-biased inhibitory profile. In contrast, naringenin demonstrated preferential interactions with AKT1 and select MMPs, consistent with potential modulation of proliferative and metastatic signaling pathways. Comparative analyses with approved TKIs revealed that gallagyldilacton achieves binding stability and interaction patterns comparable to established inhibitors for specific EGFR mutants. Collectively, these findings support the potential of \u003cem\u003ePunica granatum\u003c/em\u003e peel phytochemicals as sources of selective, multitarget modulators in NSCLC and highlight gallagyldilacton as a compound exhibiting mutation-biased EGFR interactions and a candidate for further experimental investigation.\u003c/p\u003e","manuscriptTitle":"Identification of Gallagyldilacton as a Mutation-Biased EGFR Inhibitor from Punica granatum Peel via Integrative Computational Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-10 16:30:20","doi":"10.21203/rs.3.rs-8583101/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":"c9d815d0-2abf-4839-9e24-3b9ba66abecf","owner":[],"postedDate":"March 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":64149465,"name":"Biological sciences/Cancer"},{"id":64149466,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":64149467,"name":"Biological sciences/Drug discovery"}],"tags":[],"updatedAt":"2026-03-10T16:30:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-10 16:30:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8583101","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8583101","identity":"rs-8583101","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}