Targeting insulin signaling and TRAF2/JNK pathway: a comprehensive in silico study of Uncaria tomentosa compounds

Targeting insulin signaling and TRAF2/JNK pathway: a comprehensive in silico study of Uncaria tomentosa compounds | 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 Targeting insulin signaling and TRAF2/JNK pathway: a comprehensive in silico study of Uncaria tomentosa compounds Bruna Leticia Freitas-Marchi, Shraddha Parate, Vibhu Jha, Felipe Santiago Chambergo Alcalde, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7962616/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 Type 2 diabetes (T2D) is a metabolic syndrome frequently associated with obesity and endoplasmic reticulum stress-mediated inflammation, which can lead to unfolded protein response (UPR), impaired insulin signaling, and apoptosis. In an attempt to identify potential natural therapeutic candidates, this study investigated the mechanisms of action of fourteen compounds present in Uncaria tomentosa (UT), a medicinal plant from the Amazon rainforest, using in silico modeling. The study focused on UPR, TRAF2/JNK pro-inflammatory and insulin signaling pathways, which play key roles in T2D. The UT compounds were docked against several human proteins involved in these pathways, and molecular dynamics simulations confirmed stable interactions between the target proteins (PERK, TRAF2, JNK, TNF-α, IRS-1, PI3K, AKT, GSK3β, and PPARγ) and four of the UT compounds, 5-Carboxystrictosidine , Cinchonain , Epicatechin and Mitraphylline . Additionally, ADMET property analyses were conducted for the four promising compounds, revealing favorable pharmacokinetic properties. These findings suggest that specific UT compounds may offer therapeutic potential in managing T2D by modulating signaling pathways related to the conditions UPR, inflammation, and insulin resistance. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Biological sciences/Drug discovery Unfolded protein response Insulin resistance Type 2 diabetes Uncaria tomentosa Phytochemicals In silico modelling Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. INTRODUCTION Diabetes is a non-communicable disease with a large increase in cases in the last years [ 1 ]. The number of diagnosed cases of diabetes rose from 108 million in 1980, to 422 million in 2014, with more rapid prevalence increase in low- and middle-income countries than in high-income countries [1; 2]. More than 95% of the diabetes cases are type 2 diabetes mellitus (T2D), a metabolic disorder with high mortality rates and significant healthcare expenses worldwide [3; 1; 4]. According to some authors, the global mortality rate caused by T2D and its complications was 6.7 million in 2021 as it is estimated that in 2045 there will be a significant increase to 700 million cases of T2D in the world [2; 5; 6]. Furthermore, the International Diabetes Federation published in 2021 that 3 in 4 adults in developing or non-developed countries have T2D. In total, USD 966 billion has been spent worldwide on health treatments for T2D in the last 15 years [ 2 ]. According to Parker et al . (2023), 1 in every 4 dollars spent in health care in the United States in 2022, was used for treatment of patients with T2D [ 7 ]. T2D is a chronic condition characterized by hyperglycemia and metabolic disturbances involving carbohydrates, lipids, and proteins, resulting from changes in insulin production, secretion and/or its mechanism of action [3; 8; 9]. Insulin resistance, defined as an attenuated biological response to circulating insulin, is a defect observed in both obesity and T2D [ 10 ]. Several molecular events contribute to insulin resistance, most of which are associated with oxidative stress, inflammation, or ER stress [9; 11; 12;]. Under oxidative stress, ER stress is triggered and initiates the Unfolded Protein Response (UPR) [12; 13; 14; 15]. The UPR relies on three protein sensors, of which the inositol requiring enzyme 1 (IRE1) is the most conserved. IRE1 consists of a luminal domain, a transmembrane helix, and cytosolic kinase and RNase domains. In its inactive form, the luminal domain binds the chaperone BIP, blocking its ability to dimerize [12; 16]. During the UPR, BIP dissociates, which enables dimerization, trans-autophosphorylation and activation. Once IRE1 is activated, TNF receptor-associated factor 2 (TRAF2) is recruited to bind to this stressor sensor, mediating the activation of N-terminal c-Jun kinase (JNK) [16; 17]. Particularly under the accumulation of ROS and misfolded proteins, several pathways are activated to protect cells from damage [12; 16; 18]. However, if the stress condition cannot be mitigated, the ER also signals molecular events involved in the expression of pro-inflammatory cytokines, such as tumoral necrosis factor alpha (TNF-α) [16; 19]. This cytokine leads to the expression of TRAF2 and JNK, which regulate pro-apoptotic pathways to eliminate damaged cells [16; 19]. JNK also induces the phosphorylation of Ser312 (human numbering) in insulin receptor substrate 1 (IRS-1), inhibiting its activity [20; 21; 22]. This reduces the insulin-binding capability, further worsening insulin resistance [12; 21; 23]. Therapies that include drugs targeting lipid metabolism has been suggested for the treatment of individuals with various diseases, including obesity and T2D. Indeed, the World Health Organization (WHO) has proposed strategies to incorporate complementary and alternative therapies, such as phytotherapy, into the development of new technologies and innovations as global public health tools [ 1 ]. Uncaria tomentosa (UT), popularly known as “cat's claw”, is a species of the Rubiaceae family, recognized as an herbal medicinal plant native to the Amazon rainforest [ 24 ]. It has already been demonstrated that UT extract can attenuate oxidative stress and the expression of proteins during ER stress, and that it exhibits antioxidant and anti-inflammatory properties [9; 25; 26; 27]. However, which specific compounds from the plant extract that might interact with proteins involved in the UPR or insulin signaling pathways have not yet been identified. In this study, we used in silico modeling to explore the potential interactions of fourteen UT compounds with key target proteins, including PERK, TNF-α, TRAF2, JNK, IRS-1, PI3K, AKT, GSK3β and PPARγ. 2. MATERIAL AND METHODS 2.1 Selection of compounds and target proteins The crystal structures of 13 proteins, corresponding to those involved in the UPR, ISR or insulin signaling pathway in Homo sapiens , were downloaded from the Protein Data Bank ( www.rcsb.org ) (Table 1 ). For HRI kinase, due to the lack of a crystal structure, a homology model previously developed in the group using the Yasara software, was utilized. In addition, the structures of fourteen compounds found in UT [ 28 ], were obtained from the PubChem database ( http://www.pubchem.ncbi.nlm.nih.gov ) (Table 2 ). The chemical structures of the fourteen compounds are shown in Supplementary Fig. S1 . Subsequently, the structures were transferred to Maestro software [ 29 ] for further preparation, docking, simulations and analysis. Table 1 Selected proteins and their respective codes in Protein Data Bank (PDB), used in the in silico modelling. Group 1 involves proteins of the ISR and UPR receptor pathways; Group 2 involves proteins related to the inflammation and cell survival pathway; Group 3 involves proteins of the insulin signaling pathway. Target proteins PDB code Resolution (Å) Group 1 Protein Kinase RNA-Like ER Kinase (PERK) 4M7I 2.34 Inositol-requiring enzyme 1 (IRE1) 3P23 2.70 Heme-regulated eIF2-α kinase (HRI) Homology protein - General control nonderepressible 2 (GCN2) 7QQ6 2.80 Eukaryotic translation initiation factor 2 alpha and gamma (eIF2α) 8QZZ 3.35 Group 2 TNF Receptor Associated Factor 2 (TRAF2) 1D0A 2.0 c-Jun N-terminal kinases (JNK) 4HYS 2.42 Tumor necrosis factor alpha (TNF-α) 2AZ5 2.10 Group 3 Insulin receptor substrate (IRS-1) 2Z8C 3.25 Phosphoinositide 3-kinase (PI3K) 4UWH 1.93 Protein kinase B (PKB/AKT) 1O6L 1.60 Glycogen synthase kinase 3 beta (GSK3β) 3F88 2.60 Peroxisome proliferator-activated receptor gamma (PPARγ) 1I7I 2.35 Table 2 Selected compounds present in the UT plant, and their respective codes in the Pubchem databank. UT compounds PubChem code 1 5-Carboxystrictosidine CID: 44593370 2 7-Deoxyloganic acid CID: 443322 3 Cinchonain CID: 442675 4 Epicatechin CID: 72276 5 Hirsuteine CID: 3037151 6 Hirsutine CID: 3037884 7 Isomitraphylline CID: 11726520 8 Isorhynchophylline CID: 3037048 9 Mitraphylline CID: 94160 10 Rhynchophylline CID: 5281408 11 Strictosamide CID: 10345799 12 Uncarine C (Pteropodine) CID: 10429112 13 Uncarine D (Speciophylline) CID: 168985 14 Uncarine E (Isopteropodine) CID: 9885603 2.2 Protein structure preparation All downloaded structures were prepared using the Protein Preparation Wizard tool in Maestro [ 29 ]. During the preprocessing stage, hydrogen atoms and disulfide bonds were added to the initial coordinates. All water molecules located at a distance greater than 5.0 angstroms (Å) were deleted. To determine the likely protonation states of the side chains and the energy penalties associated with alternate protonation states, a pH of 7.0 ± 0.2 was applied. The protein hydrogen bond assignments were then optimized in the H-bond Refine Tab using sample water orientations and PROPKA at pH = 7.0 [ 30 ]. For the initial restrained minimizations, the root-mean-square deviation (RMSD) for heavy atom convergence was set to 0.3 Å. The optimized potential for liquid simulations (OPLS4) force field was employed, and hydrogen atoms were minimized while allowing sufficient heavy atom movements to relax strained bonds, angles and clashes [ 31 ]. 2.3 Compound preparation and ligand docking The structures of the compounds were prepared using the LigPrep module in Maestro. The Epik module [ 32 ] was used to evaluate possible ionization states at physiological pH 7.0 ± 0.2, and the OPLS4 force field was selected for the optimization. To perform molecular docking and predict the interactions of the protein–ligand complexes (Fig. 1 ), the Induced Fit Docking (IFD) methodology in Glide was used [ 33 ]. The cubic grid box for the docking was defined at the centroid of the bound co-crystallized ligand in the active binding site, with a side length of 20 Å. All fourteen ligands were docked into the active sites of the target proteins. During the initial docking procedure, the van der Waals scaling factor was set at 0.5 for both receptors and ligands. The Prime refinement step was applied to the side chains of residues within 5.0 Å of the ligand [ 29 ]. 2.4 Molecular dynamics (MD) simulations The stabilities of the protein-ligand complexes were investigated through MD simulations, using the Desmond engine in Schrödinger [ 29 ], in NPT ensemble for 200–300 ns, when the complexes reached a relatively stable binding conformation and dynamic behavior. The TIP3P force field was used to model water molecules and periodic boundary conditions were applied with a 10 Å water buffer surrounding the protein within a cubic simulation box [ 29 ]. Na + and Cl − ions were added to neutralize the system and achieve a final NaCl concentration of 150 mM. Temperature and pressure were set to 300 k and 1 bar, using the Nose-Hoover thermostat and Martyna-Tobias-Klein barostat, respectively [35; 36]. The OPLS4 force field was employed for the proteins and ligands [ 31 ]. As a measure of protein mobility, the Root Mean Square Deviation (RMSD) of the α-carbons of the protein was calculated throughout the simulation, and the RMSD of the ligand heavy atoms during the trajectory of the simulation was also calculated, as a measure of ligand mobility [ 37 ]. The RMSD relates to the deviation of the ligand and protein α-carbon atoms, from their initial suggested docking pose during the simulations. All data analyses, including the calculation of RMSD and protein–ligand contacts, were performed using the Simulation Interaction Diagram (SID) tool in Maestro software [ 29 ]. 2.5 ADMET profiling To predict pharmacokinetic properties, SMILES (Simplified Molecular Input Line Entry Specification) codes of the selected compounds present in the UT plant were obtained and used to calculate the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties, using the SwissADME and ProTox III servers. The SwissADME platform enables the submission of SMILES files for ligands, detecting the structural fragments present in the compound. Descriptors corresponding to ADMET properties are calculated using internally implemented and validated models within the SwissADME tool. This platform incorporates several model libraries that analyze various ADMET property characteristics based on the physicochemical space of the molecule's structural groups and the database information [ 38 ]. The results are provided as graphical outputs with legends indicating compliance or non-compliance with ADMET properties [ 38 ]. For toxic substructure analysis, the ProTox III server was used. This tool evaluates structural alerts related to hepatotoxicity, mutagenicity, genotoxicity, carcinogenicity, cytotoxicity, cytochrome P450 interaction, and acute oral toxicity. The server's libraries compile toxicological safety data (both in vitro and in vivo ) from public databases and literature, with a total of 174 datasets/models. These models detect structural alerts in ligands by comparing the molecular information to known toxic groups and substructures of documented substances [ 39 ]. 3. RESULTS 3.1 Ligand docking of the UT compounds Fourteen compounds present in the UT plant were preliminarily tested to evaluate their potential affinities with proteins involved in the ISR and TRAF2/JNK pathways, as shown in Figs. 2 , 3 and 4 . In the first group of proteins, the kinase eIF2α and the stress response receptors GCN2, HRI, IRE1 and PERK were analyzed (Fig. 2 A, 2 B, 2 C, 2 D and 2 E). Among the fourteen tested compounds, none exhibited higher affinity for the eIF2α than the substrate GTP, or higher affinity for GCN2, HRI or IRE1 kinases than the substrate ATP. However, in the first group of proteins, Epicatechin (4) showed a significant interaction with PERK, exhibiting a binding energy around − 9.13 kcal/mol, significantly higher than ATP, as shown in Fig. 2 E. In the second group of proteins, relating to inflammation and cell survival pathway, TRAF2, JNK and TNF-α were analyzed (Fig. 3 A, 3 B and 3 C). For TRAF2 (Fig. 3 A), ATP was bound at the inhibition site and showed an affinity of -5.53 kcal/mol. Epicatechin (4) showed affinity for the inhibition site of the protein, with a binding energy of -5.73 kcal/mol. In addition, in the JNK protein complex (Fig. 3 B), Epicatechin (4) also demonstrated strong interaction with the protein, with an affinity of -7.23 kcal/mol, in comparison to ATP binding with an affinity of -7.66 kcal/mol. Finally, for the TNF-α cytokine (Fig. 3 C), the co-crystallized inhibitor 307, bound at the active site of the cytokine, displayed an affinity of -5.13 kcal/mol. For this system, 5- Carboxystrictosidine (1), Epicatechin (4) and Uncarine D (13) demonstrated relative affinity for the residues of the same inhibition site, with binding energies of -4.68, -4.69, and − 4.65 kcal/mol, respectively. In the third group of proteins, relating to the insulin signaling pathway, IRS-1, PI3K, AKT, GSK3β and PPARγ were analyzed (Fig. 4 A, 4 B, 4 C, 4 D and 4 E). It was found that the insulin-like growth factor 2 (IGF-2), bound in the phosphorylation complex of IR-IRS1 proteins which activates insulin signaling (Fig. 4 A), exhibited an affinity of -6.87 kcal/mol. The UT compounds 5-Carboxystrictosidine (1), 7-Deoxyloganic acid (2) and Epicatechin (4) showed affinities with residues at the same phosphorylation site of the complex, with binding energies of -6.86, -6,80 and − 7.75 kcal/mol, respectively. For the kinase PI3K (Fig. 4 B), the substrate ATP bound at the catalytic activity site, with an affinity of -9.13 kcal/mol, while Epicatechin (4) exhibited a relative affinity of -8.99 kcal/mol at the catalytic site of PI3K. None of the compounds exhibited higher affinity for the kinases AKT and GSK3β than the substrate ATP (Fig. 4 C and 4 D). For the PPARγ protein (Fig. 4 E), the prostaglandin J2 (PGJ2) bound at the protein active site, which activates PPARγ expression, showed an affinity of -8.88 kcal/mol. For this protein, only four of the 14 compounds were able to dock to the active site. In this case, 5-Carboxystrictosidine (1) exhibited a strong affinity for the active site of PPARγ, with a binding energy of -10.05 kcal/mol (Fig. 4 E). 4.2 MD simulations The stability of the binding between the UT compounds and target proteins was evaluated, along with the types of interactions established. For the molecular dynamics analysis of the first group, only PERK had adequate stability in interactions with Epicatechin (4) in the kinase binding site, with average RMSD of 1.5 Å ( Supplementary Fig. S2 ), in agreement with the docking study, showing that the complexes maintained RMSD values within 3.0 Å, indicating stable and consistent ligand-protein interactions. In the second group of proteins, molecular dynamics of UT compounds with TNF-α revealed that Mitraphylline (9) ( Supplementary Fig. S3 ) displayed stable interactions with the cytokine, with an RMSD of 3.0 Å. Furthermore, Mitraphylline (9) also displayed stable interactions with the inhibition site of TRAF2, with an RMSD of 2.7 Å ( Supplementary Fig. S4 ), compared to the results observed in the ligand docking (Fig. 3 A). On the other hand, TRAF2 and JNK exhibited binding affinity with Epicatechin (4) and molecular dynamics confirmed the interaction remained stable ( Supplementary Fig. S5 and S6 ), with RMSD of 3.0 and 3.5 Å, respectively. For the third group of proteins, it was demonstrated in the molecular dynamics analysis that 5-Carboxystrictosidine (1) remained stable in the binding site of IRS-1 ( Supplementary Fig. S7 ), with RMSD value of 2.5 Å. Additionally, the interaction between PI3K and Epicatechin (4) exhibited high stability with an RMSD of 1.5 Å ( Supplementary Fig. S8 ). On the other hand, the molecular dynamics of UT compounds with AKT and GSK3β revealed that Cinchonain (3) demonstrated partial stability in its interactions with the catalytic site of AKT ( Supplementary Fig. S9 ) and greater stability with the phosphorylation inhibition site of GSK3β ( Supplementary Fig. S10 ), with RMSD of 5.5 and 2.5 Å, respectively, compared to the results observed in the ligand docking (Fig. 4 C and 4 D). Finally, the interaction of 5-Carboxystrictosidine (1) with the active binding site of PPARγ was assessed (Fig. 4 E), given its role in enhancing insulin sensitivity in adipose tissue, skeletal muscle, and liver [40; 41]. The compound was found to interact stably with PPARγ, presenting RMSD of 2.0 Å ( Supplementary Fig. S11 ), which is again in agreement with the docking studies. 4.3 ADMET profiling Understanding the pharmacokinetics and safety profiles of the compounds is crucial for determining their absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, particularly when assessing their suitability as drugs. ADMET analyses were conducted during the virtual screening stage for the four compounds, 5-Carboxystrictosidine , Cinchonain , Epicatechin and Mitraphylline , that showed the strongest interactions with target proteins of the stress response, TRAF2/JNK or insulin signaling pathway, following the molecular dynamics tests. The oral bioavailability graphs shown in Fig. 5 illustrate the adjustments required to optimize the behavior of each ligand as drug candidate (pink region of the radar plots). For 5-Carboxystrictosidine (Fig. 5 A), adjustments in polarity and to some extent molecular size were indicated. In the case of Cinchonain (Fig. 5 B), improvement in polarity was also highlighted, plus in the unsaturation aspect, as suggested for Epicatechin (Fig. 5 C). Conversely, the radar plot for Mitraphylline (Fig. 5 D) demonstrated that it is structurally suitable, requiring only minor flexibility adjustments. In addition, some of the pharmacochemical ADMET properties were analyzed for the four selected compounds (Table 3 ). It was found that 5-Carboxystrictosidine has strong hydrophilic characteristics, mainly caused by the sugar moiety, leading to low absorption in the gastrointestinal tract (GIT). On the other hand, the compounds Cinchonain , Epicatechin and Mitraphylline have more lipophilic characteristics, but Cinchonain presents low absorption by the GIT, while Epicatechin and Mitraphylline have high absorption by the GIT. Most of the compounds with exception of Cinchonain showed probabilities for affinity towards P-glycoproteins (P-gp), but all of them present no significant risk of inhibiting the activity of the cytochrome P450 and isoenzymes CYP1A2, CYP2C19, CYP2C9, CYP2D6, CYP3A4, CYP2E1. In addition, the four compounds did not present any predicted risk of toxicity to hepatocytes or other cells, unless the cardiotoxicity risk, in which 5-Carboxystrictosidine presented 56% of potential stimulation. Table 3 Pharmacochemical properties and toxicity profiles. Predicted ADMET properties of the four compounds 5-Carboxystrictosidine, Epicatechin , Mitraphylline and Cinchonain obtained through the SwissADME and Pro-Tox III servers, to indicate the physicochemical characteristics, lipophilicity, water solubility, pharmacokinetic aspects, similarity to other drugs (drug-likeness) and toxicity risk. ADMET properties UT compounds 5-Carboxystrictosidine Epicatechin Mitraphylline Cinchonain Physicochemical characteristics MW (g/mol) 574,58 g/mol 290,27 g/mol 368,43 g/mol 452,41 g/mol Heavy atoms 41 21 27 33 H + acceptors 12 6 5 9 H + donors 7 5 1 6 Lipophilicity Log P (-3 to 5) -2.05 0.36 1.62 1.52 Solubility in water Log S (ESOL, -4 to 0) Very soluble (-1.68) Soluble (-2.22) Soluble (-3.18) Moderately soluble (− 4.33) Pharmacokinetics GTI absorption Low High High Low BBB permeability No No No No P-gp substrate Yes Yes Yes No CYP450 inhibitor and isoenzymes No No No No Drug-likeness Lipinski No Yes Yes Yes Toxicity risk Hepatotoxicity No = 69% No = 72% No = 75% No = 73% Cardiotoxicity Yes = 56% No = 99% No = 58% No = 79% Mutagenicity No = 54% No = 55% No = 62% No = 73% Cytotoxicity No = 65% No = 84% No = 65% No = 80% 5. DISCUSSION This study aimed to identify compounds from Uncaria tomentosa [24; 28] that interact with key proteins involved in UPR and insulin signaling pathways, such as PERK, IRS-1, PI3K, AKT, GSK3β, TNF-α, TRAF2, JNK or PPARγ, using an in silico modeling. From the ligand docking, the compounds with higher affinity for the proteins than the natural substrates or co-crystallized ligands were identified by comparing the obtained docking scores (in kcal/mol). In the first group of proteins, some of the transmembrane proteins involved in UPR, such as PERK and IRE1, and some that trigger ISR, such as HRI and GCN2, were evaluated [ 42 ]. No inhibitory effects were predicted between HRI, IRE1, or GCN2 and the 14 compounds. On the other hand, it was observed that the compound Epicatechin strongly bounds to PERK kinase. This result suggests that the compound might interact with the binding site of the kinase enough to be stable [ 42 ]. Once PERK is activated, eIF2α is phosphorylated, which attenuates proteins synthesis under ER stress conditions [ 43 ]. For eIF2α, no affinity was observed with the compounds, suggesting that the compounds present in UT do not directly attenuate protein synthesis [ 42 ]. This is further supported by the finding that PERK could also be inhibited by Epicatechin . In the second group of proteins, the interactions of the UT compounds with the cytokine TNF-α, TRAF2 or JNK, expressed in one of the autophagy pathways were evaluated [44; 45]. During the ligand docking modelling, it was verified that the pro-inflammatory cytokine TNF-α exhibited affinity for three compounds: 5-Carboxystrictosidine, Epicatechin , and Uncarine D . However, when evaluating the molecular dynamics of these compounds with the cytokine, no stable interactions with the binding site were found. This occurrence can be attributed by the fact that during molecular dynamic simulations, the ligand and protein can adopt different conformations, which may disrupt interactions initially predicted by docking. The dynamic behavior can reveal that some docking poses are not stable or energetically favorable under physiological conditions, leading to weaker or lost interactions during MD [ 39 , 42 ]. However, in a second assessment by molecular dynamics it was observed that Mitraphylline interacts stably with TNF-α, suggesting that Mitraphylline , as an anti-inflammatory compound, could interfere with the activity of the pro-inflammatory cytokine TNF-α and subsequently modulate inflammatory responses [ 25 ]. Regarding the TRAF2 protein, while the flavonoid Epicatechin , known for its antioxidant action [46; 47], demonstrated higher affinity with the binding site of TRAF2 than the natural substrate, Mitraphylline demonstrated the opposite (Fig. 5 A). However, after evaluating the dynamics between Epicatechin or Mitraphylline and TRAF2, stable interactions were observed between both compounds and the protein’s binding site. From the docking, JNK showed strong interaction with Epicatechin . When evaluating the dynamics between Epicatechin and the kinase, the interaction was also confirmed to be stable. These results suggest that Epicatechin and Mitraphylline can interact stably and potentially block the activity of TNF-α, TRAF2 and JNK regulating oxidative stress and pro-inflammatory pathways [46; 47]. To relate the results obtained for the UT compounds and to evaluate the potential for treating insulin resistance, the stability of the fourteen UT compounds was also assessed through molecular dynamics, with the binding sites of target proteins involved in the insulin signaling pathway. The compound 5-Carboxystrictosidine showed stable binding towards IRS-1, from the docking and the MD simulations. The dynamics between PI3K and Epicatechin were also stable, even the small difference presented in the docking studies between Epicatechin (-8.99 kcal/mol) and ATP (-9.13 kcal/mol). This difference of affinity could be explained by the additional interactions and conformational adjustments that can be revealed in the molecular dynamic simulations, which may compensate for differences in binding affinity suggested by limitations in the docking scores [ 42 , 69 ]. Finally, when evaluating the interactions of the kinases AKT or GSK3β with the compounds, it was found that Cinchonain displayed stable binding towards the kinases in the MD simulations. Little is known about the relationship and interaction between 5-Carboxystrictosidine, Epicatechin and Cinchonain with the proteins of the insulin signaling pathway. However, according to some authors [24; 25; 28; 46; 48], these compounds cited, isolated from the plants Uncaria tomentosa or Psychotria nuda , have antioxidant characteristics and promote attenuation of ROS production in human erythrocytes, macrophages and skeletal muscle tissue. This may be related to the attenuation of ER stress and the modulation of insulin receptor signaling [12; 49]. Furthermore, the possible interaction of the transcription factor PPARγ with the compounds was evaluated. PPARγ antagonizes the metabolic syndrome of type 2 diabetes (T2D) through the negative regulation of peripheral inflammatory processes, including the suppression of pro-inflammatory cytokines and increased insulin sensitivity in adipose tissue, skeletal muscle, and liver [40; 41; 50; 51]. When evaluating the affinity and binding stability between the UT compounds and PPARγ, it is suggested that this compound may act as a potential PPARy agonist, enhancing insulin sensitivity though the regulation of glucose and lipid metabolism [ 41 , 51 ], regarding 5-Carboxystrictosidine also showed stable binding towards IRS-1 protein, a key mediator in the insulin signaling pathway, indicating its possible role in improving insulin receptor signaling and downstream metabolic effects [ 40 , 50 , 51 ]. The interactions of the four selected compounds with the residues of the target proteins were further analyzed based on the MD simulations (Table 4 ). Table 4 Characterization of the different protein interactions with the UT compounds during the molecular dynamic simulations UT compounds Protein Hydrogen bond interactions Hydrophobic interactions Amino acids (interactions ≥ 30%) Epicatechin PERK Val651; Lys621; Gln888; Arg891; Phe955; Gly956 -- PI3K Tyr670; Asp761; Ile685; Gln683; Ser687; Glu692 Tyr670 TRAF2 Ala48; Glu60; Leu73; Asp160; Phe161 -- JNK Glu109; Met111 -- Mitraphylline TNF-α -- Tyr59; Tyr119 TRAF2 Met97; Asp160 -- 5-Carboxystrictosidine PPARγ His323; Ser289; Arg288 Leu340 IRS-1 Gln1004; Glu1047; Glu1077; Met1079; Asp1083; -- Cinchonain GSK3β Asp133; Lys85; Gln185; Asn186 -- AKT Leu158; Lys160; Ala232, Glu236; Glu279; Asp293; Thr436 Phe439 The interactions found between Epicatechin and PERK residues Val651, Gln888, Phe955, Gly956 located in the DFG- motif and ATP binding site, correspond to 4 out of the 10 key residues found in the activation site (Leu598, Arg600, Gln888, Cys890, Lys938, Ser940, Asp954, Phe955, Gly956, Thr986) [52; 53], which suggests that Epicatechin might impair the activation of the kinase and inhibit the expression under the UPR and stress responses. The interactions of the same compound Epicatechin , a polyphenol known for its antioxidant role, and the JNK residues Glu109 and Met111 located in the inhibition site of the kinase, correspond to 2 out of 3 residues that mediate the suppression of JNK phosphorylation (Glu109, Leu110, Met111) [54; 55; 56], which suggests that Epicatechin might inhibit the activation of JNK, which plays a significant role in responses to oxidative stress [ 55 ]. Regarding the anti-inflammatory response of Mitraphylline , thus was found to bind strongly to the residues Tyr59 and Tyr119, located in the inhibitory binding site of the pro-inflammatory cytokine TNF-α [57; 58]. The binding of one or more compounds to this region alters the trimmer symmetry and destabilize the cytokine, thus acting as TNF-α inhibitors [57; 58]. In relation to the insulin signaling pathway, Peasari et al . (2018) reported that IRS-1 residues Glu1077, Met1079, and Asp1083, which interacted strongly with 5-Carboxystrictosidine , are located in the active site of this tyrosine kinase and modulate its insulin receptor activity [ 59 ]. Moreover, some authors [60; 61] described that the residues Tyr670, Ile685, and Ser687, which are involved in the affinity between Epicatechin and the protein kinase PI3K, are located in the catalytic phosphorylation site and may contribute to the activation of the protein. In addition, some authors [62; 63; 64] indicated that residues Leu158, Lys160, Glu236, Glu279, Thr436, and Phe439 in the AKT protein kinase which exhibited affinity for Cinchonain , are located in the catalytic site of the kinase. For GSK3β, the residues Asp133, Gln185 and Asn186 which also interacted with the compound Cinchonain , are located in the active site of the protein kinase [65; 66], suggesting that this compound may act as a modulator of GSK3β activity. To verify whether UT compounds could also be considered as therapeutics targeting lipotoxicity-induced insulin resistance, their affinity for PPARγ was evaluated. Both His323 and Ser289 are among the residues that stabilize 5-Carboxystrictosidine binding to the protein. According to some authors [67; 68], interactions with the residues His323 and Ser289 are characteristic of PPARγ agonists and contribute to the activation of the transcription factor, which is considered as a target against hyperglycemia associated with T2D. Despite the valuable predictions presented in this study, it is not possible to fully replicate the complexity of biological systems by the computational methods. Therefore, this study serves as base for conducting complementary experimental assays to validate and confirm the therapeutic potential of the predicted interactions, as well as to achieve a comprehensive understanding of the mechanism of action of the UT compounds. 4. CONCLUSIONS In summary, the results presented in this study contribute to a better understanding of the mechanisms of action of the compounds present in the Uncaria tomentosa plant, within the molecular context of UPR, ER stress, insulin signaling regulation, insulin resistance and cell death. 5-Carboxystrictosidine, Cinchonain, Epicatechin and Mitraphylline were found to exhibit the strongest affinities with the proteins involved with UPR and insulin resistance conditions, demonstrating significant interactions with key residues during the in silico modelling. This further supports the possible therapeutic benefits of the plant, although future experimental validations remain necessary. Declarations AUTHOR CONTRIBUTIONS Bruna L. F. Marchi: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Validation; Software; Visualization; Writing - original draft. Shraddha Parate: Investigation; Methodology; Validation; Writing - original draft. Vibhu Jha: Investigation; Methodology; Validation; Writing - original draft. Felipe S C Alcalde: Investigation; Writing - original draft. Leif A. Eriksson: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Resources; Supervision; Validation; Visualization; Writing – review & editing. Viviane A Nunes: Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Supervision; Validation; Visualization; Writing - review & editing. DATA AVAILABILITY STATEMENT Protein structures after preparation, docked structures of the target proteins and UT compounds, plus the MD simulations, are freely available for download at zenodo.org (https://zenodo.org/records/17458126 and https://zenodo.org/records/17458126). CONFLICT OF INTEREST We declare that there is no financial or other potential conflict of interest. FUNDING DECLARATION This work was supported by National Research Council (CNPq), grant n o . 200473/2022-0 – Brazil; Sao Paulo Research Foundation (FAPESP), grant n o . 22/16702-3 – Brazil; Sven and Lilly Lawski Foundation, grant n o . N2024-0035 – Sweden. Allocation of computing time at the supercomputing center NSC provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS), which is partially funded by the Swedish Research Council through grant agreement n o . 2022–06725 - Sweden References World Health Organization (WHO). Diabetes. Fact Sheets (2023). International Diabetes Federation (IDF). Diabetes by region. Diabetes Atlas , p. 1–3, (2021). World Health Organization (WHO). Definition, Diagnosis and Classification of Diabetes Mellitus and its Complications. Report of a WHO Consultation. Part 1: Diagnosis and Classification of Diabetes Mellitus (2009). Zhou, Z. et al. Healthcare Resource Use, Costs, and Disease Progression Associated with Diabetic Nephropathy in Adults with Type 2 Diabetes: A Retrospective Observational Study. Diabetes Ther. 8 , 555–571 (2017). Zhao, Y., Barrere-Cain, R. E. & Yang, X. Nutritional systems biology of type 2 diabetes. Genes Nutr. 10 , 31 (2015). Sun, H. et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res. Clin. Pract. 183 , 109119 (2022). Parker, E. D. et al. Economic Costs of Diabetes in the U.S. in 2022. Diabetes Care 47 , 26–43 (2024). Giuffrida, F. M. A. et al. Maturity-onset diabetes of the young (MODY) in Brazil: Establishment of a national registry and appraisal of available genetic and clinical data. Diabetes Res. Clin. Pract. 123 , 134–142 (2017). Freitas-Marchi, B. L. et al. Effect of Uncaria tomentosa aqueous extract on the response to palmitate-induced lipotoxicity in cultured skeletal muscle cells. BMC Complement. Med. Ther. 23 , 412 (2023). Mirabelli, M. et al. Mediterranean Diet Nutrients to Turn the Tide against Insulin Resistance and Related Diseases. Nutrients 12 , 1066 (2020). Fung, J. & Berger, A. Hyperinsulinemia and Insulin Resistance: Scope of the Problem. J. Metab. Heal. 1 , (2016). Ly, L. D. et al. Oxidative stress and calcium dysregulation by palmitate in type 2 diabetes. Exp. Mol. Med. 49 , e291–e291 (2017). Wan, S. & Jiang, L. Erratum to: Endoplasmic reticulum (ER) stress and the unfolded protein response (UPR) in plants. Protoplasma 253 , 765–765 (2016). Dewidar, B. et al. Alterations of hepatic energy metabolism in murine models of obesity, diabetes and fatty liver diseases. eBioMedicine 94 , 104714 (2023). Kaniuka, O. et al. Upregulation of GRP78 is accompanied by decreased antioxidant response and mitophagy promotion in streptozotocin-induced type 1 diabetes in rats. Biochim. Biophys. Acta - Mol. Basis Dis. 1871 , 167531 (2025). Zhao, L. et al. Endoplasmic reticulum chaperone GRP78 participates in fluoride‐induced autophagy in LS8 cells by regulating the IRE1‐TRAF2‐JNK pathway. Environ. Toxicol. 38 , 1756–1767 (2023). Carlesso, A., Hörberg, J., Reymer, A. & Eriksson, L. A. New insights on human IRE1 tetramer structures based on molecular modeling. Sci. Rep. 10 , 17490 (2020). Pakos‐Zebrucka, K. et al. The integrated stress response. EMBO Rep. 17 , 1374–1395 (2016). Hetz, C., Zhang, K. & Kaufman, R. J. Mechanisms, regulation and functions of the unfolded protein response. Nat. Rev. Mol. Cell Biol. 21 , 421–438 (2020). Aguirre, V. et al. Phosphorylation of Ser307 in Insulin Receptor Substrate-1 Blocks Interactions with the Insulin Receptor and Inhibits Insulin Action. J. Biol. Chem. 277 , 1531–1537 (2002). Lemmer, I. L., Willemsen, N., Hilal, N. & Bartelt, A. A guide to understanding endoplasmic reticulum stress in metabolic disorders. Mol. Metab. 47 , 101169 (2021). Scarano, F. et al. Potential of Nutraceutical Supplementation in the Modulation of White and Brown Fat Tissues in Obesity-Associated Disorders: Role of Inflammatory Signalling. Int. J. Mol. Sci. 22 , 3351 (2021). Oyadomari, S. & Mori, M. Roles of CHOP/GADD153 in endoplasmic reticulum stress. Cell Death Differ. 11 , 381–389 (2004). Bors, M. et al. Studies of biological properties of Uncaria tomentosa extracts on human blood mononuclear cells. J. Ethnopharmacol. 142 , 669–678 (2012). Azevedo, B. C. et al. Aqueous extracts from Uncaria tomentosa (Willd. ex Schult.) DC. reduce bronchial hyperresponsiveness and inflammation in a murine model of asthma. J. Ethnopharmacol. 218 , 76–89 (2018). Araujo, L. C. C. et al. Uncaria tomentosa improves insulin sensitivity and inflammation in experimental NAFLD. Sci. Rep. 8 , 11013 (2018). Navarro-Hoyos, M., Alvarado-Corella, D., Moreira-Gonzalez, I., Arnaez-Serrano, E. & Monagas-Juan, M. Polyphenolic Composition and Antioxidant Activity of Aqueous and Ethanolic Extracts from Uncaria tomentosa Bark and Leaves. Antioxidants 7 , 65 (2018). Heitzman, M., Neto, C., Winiarz, E., Vaisberg, A. & Hammond, G. Ethnobotany, phytochemistry and pharmacology of (Rubiaceae). Phytochemistry 66 , 5–29 (2005). Schrödinger release 2023-2: Desmond Molecular Dynamics System. D. E. Shaw Research, New York, NY (2023). Olsson, M. H. M., Søndergaard, C. R., Rostkowski, M. & Jensen, J. H. PROPKA3: Consistent Treatment of Internal and Surface Residues in Empirical p K a Predictions. J. Chem. Theory Comput. 7 , 525–537 (2011). Lu, C. et al. OPLS4: Improving Force Field Accuracy on Challenging Regimes of Chemical Space. J. Chem. Theory Comput. 17 , 4291–4300 (2021). Shelley, J. C. et al. Epik: a software program for pK a prediction and protonation state generation for drug-like molecules. J. Comput. Aided. Mol. Des. 21 , 681–691 (2007). Friesner, R. A. et al. Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy. J. Med. Chem. 47 , 1739–1749 (2004). Land, H. & Humble, M. S. YASARA: A Tool to Obtain Structural Guidance in Biocatalytic Investigations. Methods Mol Biol. 1685 , 43–67 (2018). Nosé, S. A unified formulation of the constant temperature molecular dynamics methods. J. Chem. Phys. 81 , 511–519 (1984). Martyna, G. J., Klein, M. L. & Tuckerman, M. Nosé–Hoover chains: The canonical ensemble via continuous dynamics. J. Chem. Phys. 97 , 2635–2643 (1992). Jha, V. & Eriksson, L. A. Selectivity analysis of diaminopyrimidine-based inhibitors of MTHFD1, MTHFD2 and MTHFD2L. Sci. Rep. 14 , 21073 (2024). Daina, A., Michielin, O. & Zoete, V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep. 7 , 42717 (2017). Niazi, S. K. & Mariam, Z. Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis. Pharmaceuticals 17 , 22 (2023). Lee, K.-C. et al. Aliskiren Reduces Hepatic steatosis and Epididymal Fat Mass and Increases Skeletal Muscle Insulin Sensitivity in High-Fat Diet-Fed Mice. Sci. Rep. 6 , 18899 (2016). Villapol, S. Roles of Peroxisome Proliferator-Activated Receptor Gamma on Brain and Peripheral Inflammation. Cell. Mol. Neurobiol. 38 , 121–132 (2018). Durrant, J. D. & McCammon, J. A. Molecular dynamics simulations and drug discovery. BMC Biol. 9 , 71 (2011). Neill, G. & Masson, G. R. A stay of execution: ATF4 regulation and potential outcomes for the integrated stress response. Front. Mol. Neurosci. 16 , (2023). He, W. et al. Attenuation of TNFSF10/TRAIL-induced apoptosis by an autophagic survival pathway involving TRAF2- and RIPK1/RIP1-mediated MAPK8/JNK activation. Autophagy 8 , 1811–1821 (2012). Tsuchida, M. et al. Pro-apoptotic functions of TRAF2 in p53-mediated apoptosis induced by cisplatin. J. Toxicol. Sci. 45 , 219–226 (2020). German, I. J. S. et al. New Trends to Treat Muscular Atrophy: A Systematic Review of Epicatechin. Nutrients 16 , 326 (2024). Lan, X., Han, X., Li, Q. & Wang, J. (−)-Epicatechin, a Natural Flavonoid Compound, Protects Astrocytes Against Hemoglobin Toxicity via Nrf2 and AP-1 Signaling Pathways. Mol. Neurobiol. 54 , 7898–7907 (2017). Carvalho Junior, A. et al. Antimycobacterial and Nitric Oxide Production Inhibitory Activities of Triterpenes and Alkaloids from Psychotria nuda (Cham. & Schltdl.) Wawra. Molecules 24 , 1026 (2019). Henriksen, E. J. Improvement of insulin sensitivity by antagonism of the renin-angiotensin system. Am. J. Physiol. Integr. Comp. Physiol. 293 , R974–R980 (2007). Odegaard, J. I. et al. Macrophage-specific PPARγ controls alternative activation and improves insulin resistance. Nature 447 , 1116–1120 (2007). Heald, M. & Cawthorne, M. A. Dual Acting and Pan-PPAR Activators as Potential Anti-diabetic Therapies. Handb Exp Pharmacol . 203 , 35–51 (2011). Ung, P. M.-U. & Schlessinger, A. DFGmodel: Predicting Protein Kinase Structures in Inactive States for Structure-Based Discovery of Type-II Inhibitors. ACS Chem. Biol. 10 , 269–278 (2015). Ganz, J. et al. A novel specific PERK activator reduces toxicity and extends survival in Huntington’s disease models. Sci. Rep. 10 , 6875 (2020). Heo, Y.-S. et al. Structural basis for the selective inhibition of JNK1 by the scaffolding protein JIP1 and SP600125. EMBO J. 23 , 2185–2195 (2004). Jiao, Z. et al. Kaempferide Prevents Titanium Particle Induced Osteolysis by Suppressing JNK Activation during Osteoclast Formation. Sci. Rep. 7 , 16665 (2017). Karkehabadi, H., Sadr, S., Khoshbin, E. & Taherkhani, A. A Novel Approach for Apical Periodontitis Prevention Through RIPK3 Inhibition Using Organic Flavonoids. Avicenna J. Dent. Res. 16 , 146–154 (2024). He, M. M. et al. Small-Molecule Inhibition of TNF-α. Science. 310 , 1022–1025 (2005). Zia, K. et al. Identification of potential TNF-α inhibitors: from in silico to in vitro studies. Sci. Rep. 10 , 20974 (2020). Peasari, J. reddy, Motamarry, S. sri, Varma, K. S., Anitha, P. & Potti, R. B. Chromatographic analysis of phytochemicals in Costus igneus and computational studies of flavonoids. Informatics Med. Unlocked 13 , 34–40 (2018). Valdés-Tresanco, M. S., Valdés-Tresanco, M. E., Rubio-Carrasquilla, M., Valiente, P. A., Moreno, E. Tailored Parameterization of the LIE Method for Calculating the Binding Free Energy of Vps34–Inhibitor Complexes. ACS Omega 6 , 29525–29536 (2021). Gewinner, C. et al. Evidence that Inositol Polyphosphate 4-Phosphatase Type II Is a Tumor Suppressor that Inhibits PI3K Signaling. Cancer Cell 16 , 115–125 (2009). Huang, B. X. & Kim, H.-Y. Interdomain Conformational Changes in Akt Activation Revealed by Chemical Cross-linking and Tandem Mass Spectrometry. Mol. Cell. Proteomics 5 , 1045–1053 (2006). Voukkalis, N. et al. SRPK1 and Akt Protein Kinases Phosphorylate the RS Domain of Lamin B Receptor with Distinct Specificity: A Combined Biochemical and In Silico Approach. PLoS One 11 , e0154198 (2016). Shin, S. Y., Kim, C. G., Jung, Y. J., Lim, Y. & Lee, Y. H. The UPR inducer DPP23 inhibits the metastatic potential of MDA-MB-231 human breast cancer cells by targeting the AKT–IKK–NF-κB–MMP-9 axis. Sci. Rep. 6 , 34134 (2016). Xie, H. et al. Designing of dual inhibitors for GSK-3β and CDK5: Virtual screening and in vitro biological activities study. Oncotarget 8 , 18118–18128 (2017). Zhang, M. et al. Chromenone-based GSK-3β inhibitors as potential therapeutic targets for cardiovascular diseases: In silico study, molecular dynamics, and ADMET profiles. Arab. J. Chem. 15 , 104288 (2022). Wang, L. et al. Natural product agonists of peroxisome proliferator-activated receptor gamma (PPARγ): a review. Biochem. Pharmacol. 92 , 73–89 (2014). Useini, A., Engelberger, F., Künze, G. & Sträter, N. Structural basis of the activation of PPARγ by the plasticizer metabolites MEHP and MINCH. Environ. Int. 173 , 107822 (2023). Guendouzi, A., Belkhiri, L., Slimani, Z., Guendouzi, A. & Moroy, G. Virtual Screening of Novel Benzothiozinone Derivatives to Predict Potential Inhibitors of Mycobacterium Tuberculosis Kinases 2D-QSAR, Molecular Docking, MM-PBSA Dynamics Simulations, and ADMET Properties. Int. J. Mol. Sci. 26 , 5129 (2025). Additional Declarations No competing interests reported. Supplementary Files SupplementarymaterialUTinsilico2025099.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7962616","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":545831507,"identity":"148a16f9-5eab-4f3c-82d8-199248f64d25","order_by":0,"name":"Bruna Leticia Freitas-Marchi","email":"","orcid":"","institution":"University of Gothenburg (GU)","correspondingAuthor":false,"prefix":"","firstName":"Bruna","middleName":"Leticia","lastName":"Freitas-Marchi","suffix":""},{"id":545831508,"identity":"a3e424fe-a4a1-4c88-a6b0-05c84c57521b","order_by":1,"name":"Shraddha Parate","email":"","orcid":"","institution":"Chalmers University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Shraddha","middleName":"","lastName":"Parate","suffix":""},{"id":545831509,"identity":"b9795f72-d4bb-4ab4-a8da-db205c9281cd","order_by":2,"name":"Vibhu Jha","email":"","orcid":"","institution":"University of Bradford","correspondingAuthor":false,"prefix":"","firstName":"Vibhu","middleName":"","lastName":"Jha","suffix":""},{"id":545831510,"identity":"6b8d2b4f-6187-4c53-972d-35ec15c6572b","order_by":3,"name":"Felipe Santiago Chambergo Alcalde","email":"","orcid":"","institution":"University of Sao Paulo (EACH-USP)","correspondingAuthor":false,"prefix":"","firstName":"Felipe","middleName":"Santiago Chambergo","lastName":"Alcalde","suffix":""},{"id":545831511,"identity":"68e5ba09-8e01-4c39-ab13-a0407650fc01","order_by":4,"name":"Leif A. Eriksson","email":"","orcid":"","institution":"University of Gothenburg (GU)","correspondingAuthor":false,"prefix":"","firstName":"Leif","middleName":"A.","lastName":"Eriksson","suffix":""},{"id":545831512,"identity":"32f0511e-4225-48a0-ab0b-bcc99d6ad547","order_by":5,"name":"Viviane Abreu Nunes","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYBADOTYJCCOBaC3GpGtJbCBai8Hxs8ekCyrq0vukG9gkf+5gyDNvIKTlTF6a9Iwzh3PbZA6wSfOeYSiWOUBAi2RDjpk0b9uB3DaJBDZpxjaGxBmEHCbZ/wao5V9dOhtQi+RPYrTwS4BsaWBOAGmR4CVOyxtja55jhw3bZA42W/OekSiWIKSFjT/H8DZPTZ28/Ozmgzd/7rDJI6gFCTA2ABEpGmC6RsEoGAWjYBRgAAALoTR3KTuzVAAAAABJRU5ErkJggg==","orcid":"","institution":"University of Sao Paulo (EACH-USP)","correspondingAuthor":true,"prefix":"","firstName":"Viviane","middleName":"Abreu","lastName":"Nunes","suffix":""}],"badges":[],"createdAt":"2025-10-27 16:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7962616/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7962616/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96082434,"identity":"6ddb5c34-bf35-4f14-96e1-251c838f00e8","added_by":"auto","created_at":"2025-11-17 11:50:44","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1542283,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptUTcompounds31.10.25.docx","url":"https://assets-eu.researchsquare.com/files/rs-7962616/v1/66dfca9e8edc6a42a7aa519b.docx"},{"id":96082428,"identity":"23d74d6d-c5b5-40d8-b241-c693390118c7","added_by":"auto","created_at":"2025-11-17 11:50:44","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7917,"visible":true,"origin":"","legend":"","description":"","filename":"732c8b4b429b47b09903a98504203da3.json","url":"https://assets-eu.researchsquare.com/files/rs-7962616/v1/24c2ed86422bf66075214049.json"},{"id":96246609,"identity":"cf29fcaa-f026-4f69-9289-dedd27f244b9","added_by":"auto","created_at":"2025-11-19 07:26:22","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":13781884,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialUTinsilico20250991.docx","url":"https://assets-eu.researchsquare.com/files/rs-7962616/v1/76e5e466ed4da1494a469564.docx"},{"id":96082446,"identity":"af12ea36-0aad-4000-a027-9314156c924d","added_by":"auto","created_at":"2025-11-17 11:50:44","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":13766016,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialUTinsilico2025099.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7962616/v1/dbaa5ee3c6e84591be4ffad8.pdf"},{"id":96248304,"identity":"650ccc14-45a7-40fd-9563-8240733a8629","added_by":"auto","created_at":"2025-11-19 07:28:17","extension":"xml","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":160308,"visible":true,"origin":"","legend":"","description":"","filename":"732c8b4b429b47b09903a98504203da31enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7962616/v1/d9b4caa0a8d4e7bc8da3dc66.xml"},{"id":96249499,"identity":"c606bc8d-2eac-4e35-90c6-eb3dba33bb95","added_by":"auto","created_at":"2025-11-19 07:33:46","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":106143,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7962616/v1/54babcdffceb769902d1af59.jpeg"},{"id":96082437,"identity":"c8c819f9-c6e7-4eb4-9aba-f61120810e90","added_by":"auto","created_at":"2025-11-17 11:50:44","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":206868,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7962616/v1/984817672f86bcb633ac2f33.png"},{"id":96082444,"identity":"62d3328e-ae80-438d-9e7a-3b17ed5f2fb3","added_by":"auto","created_at":"2025-11-17 11:50:44","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":194150,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7962616/v1/0d693cf7b552f1ad5cca0cf1.jpeg"},{"id":96082438,"identity":"0b2c1af2-7869-4463-90fd-cc8edf680c92","added_by":"auto","created_at":"2025-11-17 11:50:44","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":202967,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7962616/v1/175ae84b9ad714ff41fb6502.png"},{"id":96082441,"identity":"1b4b69c4-8f64-4a29-b6da-810f7c1e74a9","added_by":"auto","created_at":"2025-11-17 11:50:44","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":510662,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7962616/v1/417678c9d4441be2dff1da88.png"},{"id":96082429,"identity":"c4aa151d-5541-405b-ba53-a5ac3bfa9e50","added_by":"auto","created_at":"2025-11-17 11:50:44","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":67002,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7962616/v1/7b5240635e847bc1172b811d.png"},{"id":96082439,"identity":"9913e13a-892c-4a48-9aa5-14089ea9630a","added_by":"auto","created_at":"2025-11-17 11:50:44","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":49993,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7962616/v1/5800a1a066e530d9c9ff6b5e.png"},{"id":96082440,"identity":"d53c22a6-f8c7-4001-8d9f-c82adab79507","added_by":"auto","created_at":"2025-11-17 11:50:44","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":40579,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7962616/v1/20fc1341152b3267ecbb28c2.png"},{"id":96248378,"identity":"3b8cc28c-6d61-4b47-ba3e-e567df404576","added_by":"auto","created_at":"2025-11-19 07:28:23","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":48307,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7962616/v1/ecad04a9153e99fbf667a209.png"},{"id":96246646,"identity":"bef96245-836f-4477-877a-857690b3d5c4","added_by":"auto","created_at":"2025-11-19 07:26:24","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":78463,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7962616/v1/0dd7d7cd54e2762d4c10814a.png"},{"id":96246654,"identity":"62757ca6-ec07-4eac-a098-023d5d7175e5","added_by":"auto","created_at":"2025-11-19 07:26:27","extension":"xml","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":153225,"visible":true,"origin":"","legend":"","description":"","filename":"732c8b4b429b47b09903a98504203da31structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7962616/v1/fe5bb42856583e201d6a57f4.xml"},{"id":96082449,"identity":"615b1493-c2eb-4e3d-ab76-7b57b6c900bb","added_by":"auto","created_at":"2025-11-17 11:50:45","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":177521,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7962616/v1/52ff07fdfcad2c0c1ed583bc.html"},{"id":96247451,"identity":"d1f0eedb-bb2d-47ee-a87d-c2cba926f8d9","added_by":"auto","created_at":"2025-11-19 07:27:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":147813,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic computational method of ligand docking, used to predict the binding affinity between the plant compounds and target proteins. \u003c/strong\u003eUniprot and PubChem databank were used to get the PDB of co-crystallized proteins (receptor) and UT compounds (ligands), respectively. Yasara software was used for homology protein modeling when needed [34]. After the preparation of each receptor and ligand, the docking was done using Glide in Schrodinger to predict the binding interaction.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7962616/v1/a060023181e16f72875e0a6f.png"},{"id":96082427,"identity":"e1f2dc39-2d69-4cfb-82d1-0cc41ff0eba4","added_by":"auto","created_at":"2025-11-17 11:50:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":189525,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAffinity between the proteins A) eIF2a, B) GCN2, C) HRI, D) IRE1 and E) PERK, and the compounds present in UT plant. \u003c/strong\u003eMolecular docking scores between the fourteen UT compounds and the kinase binding site of the selected proteins, represented by the negative values, in kcal/mol. Compounds numbering is shown in \u003cstrong\u003eTable 2\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7962616/v1/e218cbd486b4eefbcf1a7ce0.png"},{"id":96247465,"identity":"d75b50b9-f109-44ee-ab47-014cc9b391a7","added_by":"auto","created_at":"2025-11-19 07:27:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":221015,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAffinity between the proteins A) TRAF2, B) JNK, C) TNF-a and the UT compounds. \u003c/strong\u003eMolecular docking score between the fourteen compounds and the binding site of the selected proteins, represented by negative values, in kcal/mol. Compounds numbering is shown in \u003cstrong\u003eTable 2\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7962616/v1/4c5d8d17e97a22becae02bca.png"},{"id":96249551,"identity":"84c6e88f-e887-4175-bc28-ae3ef2fbfce6","added_by":"auto","created_at":"2025-11-19 07:33:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":188002,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAffinity between the proteins A) IRS-1, B) PI3K, C) AKT, D) GSK3b, E) PPARg and UT compounds. \u003c/strong\u003eMolecular docking score between the fourteen compounds and the binding sites of the selected proteins, represented by negative values, in kcal/mol. Compounds numbering is shown in \u003cstrong\u003eTable 2\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7962616/v1/dd4a5dd1f88344a19536b61a.png"},{"id":96248329,"identity":"17220d02-6d3a-43de-bca3-8cfd1d3854cb","added_by":"auto","created_at":"2025-11-19 07:28:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":593084,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChemical structures and main characteristics related to the oral bioavailability. \u003c/strong\u003eThe four most promising compounds, \u003cstrong\u003eA)\u003c/strong\u003e \u003cem\u003e5-Carboxystrictosidine\u003c/em\u003e; \u003cstrong\u003eB)\u003c/strong\u003e \u003cem\u003eCinchonain\u003c/em\u003e; \u003cstrong\u003eC)\u003c/strong\u003e \u003cem\u003eEpicatechin\u003c/em\u003e; \u003cstrong\u003eD)\u003c/strong\u003e \u003cem\u003eMitraphylline\u003c/em\u003e, were evaluated using bioavailability radar plots, represented by six physicochemical properties: lipophilicity (LIPO), molecular weight (SIZE), polar surface area (POLAR), solubility (INSOLU), saturation (INSATU), flexibility (FLEX)\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7962616/v1/d5732e4fb90e0f060fd0f045.png"},{"id":100406232,"identity":"2f213ae8-260f-4f2d-8805-9a38d174a85d","added_by":"auto","created_at":"2026-01-16 12:58:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2538061,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7962616/v1/a23bb7a1-6ee6-434b-a98e-aa43e61f4722.pdf"},{"id":96082448,"identity":"ba59666a-a54d-4435-bfe8-61006f593cba","added_by":"auto","created_at":"2025-11-17 11:50:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13766016,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialUTinsilico2025099.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7962616/v1/2289424b4f0139f3f27dc3c6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Targeting insulin signaling and TRAF2/JNK pathway: a comprehensive in silico study of Uncaria tomentosa compounds","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eDiabetes is a non-communicable disease with a large increase in cases in the last years [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The number of diagnosed cases of diabetes rose from 108\u0026nbsp;million in 1980, to 422\u0026nbsp;million in 2014, with more rapid prevalence increase in low- and middle-income countries than in high-income countries [1; 2]. More than 95% of the diabetes cases are type 2 diabetes mellitus (T2D), a metabolic disorder with high mortality rates and significant healthcare expenses worldwide [3; 1; 4].\u003c/p\u003e\u003cp\u003eAccording to some authors, the global mortality rate caused by T2D and its complications was 6.7\u0026nbsp;million in 2021 as it is estimated that in 2045 there will be a significant increase to 700\u0026nbsp;million cases of T2D in the world [2; 5; 6]. Furthermore, the International Diabetes Federation published in 2021 that 3 in 4 adults in developing or non-developed countries have T2D. In total, USD 966\u0026nbsp;billion has been spent worldwide on health treatments for T2D in the last 15 years [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to Parker \u003cem\u003eet al\u003c/em\u003e. (2023), 1 in every 4 dollars spent in health care in the United States in 2022, was used for treatment of patients with T2D [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eT2D is a chronic condition characterized by hyperglycemia and metabolic disturbances involving carbohydrates, lipids, and proteins, resulting from changes in insulin production, secretion and/or its mechanism of action [3; 8; 9]. Insulin resistance, defined as an attenuated biological response to circulating insulin, is a defect observed in both obesity and T2D [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSeveral molecular events contribute to insulin resistance, most of which are associated with oxidative stress, inflammation, or ER stress [9; 11; 12;]. Under oxidative stress, ER stress is triggered and initiates the Unfolded Protein Response (UPR) [12; 13; 14; 15].\u003c/p\u003e\u003cp\u003eThe UPR relies on three protein sensors, of which the inositol requiring enzyme 1 (IRE1) is the most conserved. IRE1 consists of a luminal domain, a transmembrane helix, and cytosolic kinase and RNase domains. In its inactive form, the luminal domain binds the chaperone BIP, blocking its ability to dimerize [12; 16]. During the UPR, BIP dissociates, which enables dimerization, trans-autophosphorylation and activation. Once IRE1 is activated, TNF receptor-associated factor 2 (TRAF2) is recruited to bind to this stressor sensor, mediating the activation of N-terminal c-Jun kinase (JNK) [16; 17]. Particularly under the accumulation of ROS and misfolded proteins, several pathways are activated to protect cells from damage [12; 16; 18]. However, if the stress condition cannot be mitigated, the ER also signals molecular events involved in the expression of pro-inflammatory cytokines, such as tumoral necrosis factor alpha (TNF-α) [16; 19]. This cytokine leads to the expression of TRAF2 and JNK, which regulate pro-apoptotic pathways to eliminate damaged cells [16; 19].\u003c/p\u003e\u003cp\u003eJNK also induces the phosphorylation of Ser312 (human numbering) in insulin receptor substrate 1 (IRS-1), inhibiting its activity [20; 21; 22]. This reduces the insulin-binding capability, further worsening insulin resistance [12; 21; 23].\u003c/p\u003e\u003cp\u003eTherapies that include drugs targeting lipid metabolism has been suggested for the treatment of individuals with various diseases, including obesity and T2D. Indeed, the World Health Organization (WHO) has proposed strategies to incorporate complementary and alternative therapies, such as phytotherapy, into the development of new technologies and innovations as global public health tools [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cem\u003eUncaria tomentosa\u003c/em\u003e (UT), popularly known as \u0026ldquo;cat's claw\u0026rdquo;, is a species of the Rubiaceae family, recognized as an herbal medicinal plant native to the Amazon rainforest [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. It has already been demonstrated that UT extract can attenuate oxidative stress and the expression of proteins during ER stress, and that it exhibits antioxidant and anti-inflammatory properties [9; 25; 26; 27]. However, which specific compounds from the plant extract that might interact with proteins involved in the UPR or insulin signaling pathways have not yet been identified.\u003c/p\u003e\u003cp\u003eIn this study, we used \u003cem\u003ein silico\u003c/em\u003e modeling to explore the potential interactions of fourteen UT compounds with key target proteins, including PERK, TNF-α, TRAF2, JNK, IRS-1, PI3K, AKT, GSK3β and PPARγ.\u003c/p\u003e"},{"header":"2. MATERIAL AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Selection of compounds and target proteins\u003c/h2\u003e\u003cp\u003eThe crystal structures of 13 proteins, corresponding to those involved in the UPR, ISR or insulin signaling pathway in \u003cem\u003eHomo sapiens\u003c/em\u003e, were downloaded from the \u003cem\u003eProtein Data Bank\u003c/em\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.rcsb.org\u003c/span\u003e\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For HRI kinase, due to the lack of a crystal structure, a homology model previously developed in the group using the Yasara software, was utilized. In addition, the structures of fourteen compounds found in UT [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], were obtained from the PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.pubchem.ncbi.nlm.nih.gov\u003c/span\u003e\u003cspan address=\"http://www.pubchem.ncbi.nlm.nih.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The chemical structures of the fourteen compounds are shown in \u003cb\u003eSupplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eSubsequently, the structures were transferred to Maestro software [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] for further preparation, docking, simulations and analysis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eSelected proteins and their respective codes in\u003c/b\u003e \u003cb\u003eProtein Data Bank\u003c/b\u003e \u003cb\u003e(PDB), used in the\u003c/b\u003e \u003cb\u003ein silico\u003c/b\u003e \u003cb\u003emodelling.\u003c/b\u003e Group 1 involves proteins of the ISR and UPR receptor pathways; Group 2 involves proteins related to the inflammation and cell survival pathway; Group 3 involves proteins of the insulin signaling pathway.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTarget proteins\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePDB code\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eResolution (\u0026Aring;)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eGroup 1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProtein Kinase RNA-Like ER Kinase (PERK)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4M7I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInositol-requiring enzyme 1 (IRE1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3P23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHeme-regulated eIF2-α kinase (HRI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHomology protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGeneral control nonderepressible 2 (GCN2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7QQ6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.80\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\u003eEukaryotic translation initiation factor 2 alpha and gamma (eIF2α)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8QZZ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eGroup 2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTNF Receptor Associated Factor 2 (TRAF2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1D0A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ec-Jun N-terminal kinases (JNK)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4HYS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTumor necrosis factor alpha (TNF-α)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2AZ5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eGroup 3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInsulin receptor substrate (IRS-1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2Z8C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePhosphoinositide 3-kinase (PI3K)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4UWH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProtein\u0026nbsp;kinase B (PKB/AKT)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1O6L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGlycogen synthase kinase 3 beta (GSK3β)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3F88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePeroxisome proliferator-activated receptor gamma (PPARγ)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1I7I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSelected compounds present in the UT plant, and their respective codes in the Pubchem databank.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUT compounds\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePubChem code\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e5-Carboxystrictosidine\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID: 44593370\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e7-Deoxyloganic acid\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID: 443322\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCinchonain\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID: 442675\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eEpicatechin\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID: 72276\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eHirsuteine\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID: 3037151\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eHirsutine\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID: 3037884\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eIsomitraphylline\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID: 11726520\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eIsorhynchophylline\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID: 3037048\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMitraphylline\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID: 94160\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eRhynchophylline\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID: 5281408\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eStrictosamide\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID: 10345799\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eUncarine C (Pteropodine)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID: 10429112\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eUncarine D (Speciophylline)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID: 168985\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eUncarine E (Isopteropodine)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID: 9885603\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Protein structure preparation\u003c/h2\u003e\u003cp\u003eAll downloaded structures were prepared using the Protein Preparation Wizard tool in Maestro [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. During the preprocessing stage, hydrogen atoms and disulfide bonds were added to the initial coordinates. All water molecules located at a distance greater than 5.0 angstroms (\u0026Aring;) were deleted. To determine the likely protonation states of the side chains and the energy penalties associated with alternate protonation states, a pH of 7.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 was applied.\u003c/p\u003e\u003cp\u003eThe protein hydrogen bond assignments were then optimized in the H-bond Refine Tab using sample water orientations and PROPKA at pH\u0026thinsp;=\u0026thinsp;7.0 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. For the initial restrained minimizations, the root-mean-square deviation (RMSD) for heavy atom convergence was set to 0.3 \u0026Aring;. The optimized potential for liquid simulations (OPLS4) force field was employed, and hydrogen atoms were minimized while allowing sufficient heavy atom movements to relax strained bonds, angles and clashes [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Compound preparation and ligand docking\u003c/h2\u003e\u003cp\u003eThe structures of the compounds were prepared using the LigPrep module in Maestro. The Epik module [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] was used to evaluate possible ionization states at physiological pH 7.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2, and the OPLS4 force field was selected for the optimization.\u003c/p\u003e\u003cp\u003eTo perform molecular docking and predict the interactions of the protein\u0026ndash;ligand complexes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the Induced Fit Docking (IFD) methodology in Glide was used [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The cubic grid box for the docking was defined at the centroid of the bound co-crystallized ligand in the active binding site, with a side length of 20 \u0026Aring;. All fourteen ligands were docked into the active sites of the target proteins. During the initial docking procedure, the van der Waals scaling factor was set at 0.5 for both receptors and ligands. The Prime refinement step was applied to the side chains of residues within 5.0 \u0026Aring; of the ligand [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Molecular dynamics (MD) simulations\u003c/h2\u003e\u003cp\u003eThe stabilities of the protein-ligand complexes were investigated through MD simulations, using the Desmond engine in Schr\u0026ouml;dinger [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], in NPT ensemble for 200\u0026ndash;300 ns, when the complexes reached a relatively stable binding conformation and dynamic behavior. The TIP3P force field was used to model water molecules and periodic boundary conditions were applied with a 10 \u0026Aring; water buffer surrounding the protein within a cubic simulation box [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Na\u003csup\u003e+\u003c/sup\u003e and Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e ions were added to neutralize the system and achieve a final NaCl concentration of 150 mM. Temperature and pressure were set to 300 k and 1 bar, using the Nose-Hoover thermostat and Martyna-Tobias-Klein barostat, respectively [35; 36].\u003c/p\u003e\u003cp\u003eThe OPLS4 force field was employed for the proteins and ligands [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. As a measure of protein mobility, the Root Mean Square Deviation (RMSD) of the α-carbons of the protein was calculated throughout the simulation, and the RMSD of the ligand heavy atoms during the trajectory of the simulation was also calculated, as a measure of ligand mobility [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The RMSD relates to the deviation of the ligand and protein α-carbon atoms, from their initial suggested docking pose during the simulations. All data analyses, including the calculation of RMSD and protein\u0026ndash;ligand contacts, were performed using the Simulation Interaction Diagram (SID) tool in Maestro software [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 ADMET profiling\u003c/h2\u003e\u003cp\u003eTo predict pharmacokinetic properties, SMILES (Simplified Molecular Input Line Entry Specification) codes of the selected compounds present in the UT plant were obtained and used to calculate the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties, using the SwissADME and ProTox III servers.\u003c/p\u003e\u003cp\u003eThe SwissADME platform enables the submission of SMILES files for ligands, detecting the structural fragments present in the compound. Descriptors corresponding to ADMET properties are calculated using internally implemented and validated models within the SwissADME tool. This platform incorporates several model libraries that analyze various ADMET property characteristics based on the physicochemical space of the molecule's structural groups and the database information [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The results are provided as graphical outputs with legends indicating compliance or non-compliance with ADMET properties [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFor toxic substructure analysis, the ProTox III server was used. This tool evaluates structural alerts related to hepatotoxicity, mutagenicity, genotoxicity, carcinogenicity, cytotoxicity, cytochrome P450 interaction, and acute oral toxicity. The server's libraries compile toxicological safety data (both \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e) from public databases and literature, with a total of 174 datasets/models. These models detect structural alerts in ligands by comparing the molecular information to known toxic groups and substructures of documented substances [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Ligand docking of the UT compounds\u003c/h2\u003e\u003cp\u003eFourteen compounds present in the UT plant were preliminarily tested to evaluate their potential affinities with proteins involved in the ISR and TRAF2/JNK pathways, as shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eIn the first group of proteins, the kinase eIF2α and the stress response receptors GCN2, HRI, IRE1 and PERK were analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Among the fourteen tested compounds, none exhibited higher affinity for the eIF2α than the substrate GTP, or higher affinity for GCN2, HRI or IRE1 kinases than the substrate ATP. However, in the first group of proteins, \u003cem\u003eEpicatechin\u003c/em\u003e (4) showed a significant interaction with PERK, exhibiting a binding energy around \u0026minus;\u0026thinsp;9.13 kcal/mol, significantly higher than ATP, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the second group of proteins, relating to inflammation and cell survival pathway, TRAF2, JNK and TNF-α were analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). For TRAF2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), ATP was bound at the inhibition site and showed an affinity of -5.53 kcal/mol. \u003cem\u003eEpicatechin\u003c/em\u003e (4) showed affinity for the inhibition site of the protein, with a binding energy of -5.73 kcal/mol. In addition, in the JNK protein complex (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), \u003cem\u003eEpicatechin\u003c/em\u003e (4) also demonstrated strong interaction with the protein, with an affinity of -7.23 kcal/mol, in comparison to ATP binding with an affinity of -7.66 kcal/mol.\u003c/p\u003e\u003cp\u003eFinally, for the TNF-α cytokine (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), the co-crystallized inhibitor 307, bound at the active site of the cytokine, displayed an affinity of -5.13 kcal/mol. For this system, 5-\u003cem\u003eCarboxystrictosidine\u003c/em\u003e (1), \u003cem\u003eEpicatechin\u003c/em\u003e (4) and \u003cem\u003eUncarine D\u003c/em\u003e (13) demonstrated relative affinity for the residues of the same inhibition site, with binding energies of -4.68, -4.69, and \u0026minus;\u0026thinsp;4.65 kcal/mol, respectively.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the third group of proteins, relating to the insulin signaling pathway, IRS-1, PI3K, AKT, GSK3β and PPARγ were analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). It was found that the insulin-like growth factor 2 (IGF-2), bound in the phosphorylation complex of IR-IRS1 proteins which activates insulin signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), exhibited an affinity of -6.87 kcal/mol. The UT compounds \u003cem\u003e5-Carboxystrictosidine\u003c/em\u003e (1), \u003cem\u003e7-Deoxyloganic acid\u003c/em\u003e (2) and \u003cem\u003eEpicatechin\u003c/em\u003e (4) showed affinities with residues at the same phosphorylation site of the complex, with binding energies of -6.86, -6,80 and \u0026minus;\u0026thinsp;7.75 kcal/mol, respectively.\u003c/p\u003e\u003cp\u003eFor the kinase PI3K (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), the substrate ATP bound at the catalytic activity site, with an affinity of -9.13 kcal/mol, while \u003cem\u003eEpicatechin\u003c/em\u003e (4) exhibited a relative affinity of -8.99 kcal/mol at the catalytic site of PI3K.\u003c/p\u003e\u003cp\u003eNone of the compounds exhibited higher affinity for the kinases AKT and GSK3β than the substrate ATP (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). For the PPARγ protein (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE), the prostaglandin J2 (PGJ2) bound at the protein active site, which activates PPARγ expression, showed an affinity of -8.88 kcal/mol. For this protein, only four of the 14 compounds were able to dock to the active site. In this case, \u003cem\u003e5-Carboxystrictosidine\u003c/em\u003e (1) exhibited a strong affinity for the active site of PPARγ, with a binding energy of -10.05 kcal/mol (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.2 MD simulations\u003c/h2\u003e\u003cp\u003eThe stability of the binding between the UT compounds and target proteins was evaluated, along with the types of interactions established. For the molecular dynamics analysis of the first group, only PERK had adequate stability in interactions with \u003cem\u003eEpicatechin\u003c/em\u003e (4) in the kinase binding site, with average RMSD of 1.5 \u0026Aring; (\u003cb\u003eSupplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e), in agreement with the docking study, showing that the complexes maintained RMSD values within 3.0 \u0026Aring;, indicating stable and consistent ligand-protein interactions.\u003c/p\u003e\u003cp\u003eIn the second group of proteins, molecular dynamics of UT compounds with TNF-α revealed that \u003cem\u003eMitraphylline\u003c/em\u003e (9) (\u003cb\u003eSupplementary Fig. S3\u003c/b\u003e) displayed stable interactions with the cytokine, with an RMSD of 3.0 \u0026Aring;. Furthermore, \u003cem\u003eMitraphylline\u003c/em\u003e (9) also displayed stable interactions with the inhibition site of TRAF2, with an RMSD of 2.7 \u0026Aring; (\u003cb\u003eSupplementary Fig. S4\u003c/b\u003e), compared to the results observed in the ligand docking (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003eOn the other hand, TRAF2 and JNK exhibited binding affinity with \u003cem\u003eEpicatechin\u003c/em\u003e (4) and molecular dynamics confirmed the interaction remained stable (\u003cb\u003eSupplementary Fig. S5\u003c/b\u003e and \u003cb\u003eS6\u003c/b\u003e), with RMSD of 3.0 and 3.5 \u0026Aring;, respectively.\u003c/p\u003e\u003cp\u003eFor the third group of proteins, it was demonstrated in the molecular dynamics analysis that \u003cem\u003e5-Carboxystrictosidine\u003c/em\u003e (1) remained stable in the binding site of IRS-1 (\u003cb\u003eSupplementary Fig. S7\u003c/b\u003e), with RMSD value of 2.5 \u0026Aring;. Additionally, the interaction between PI3K and \u003cem\u003eEpicatechin\u003c/em\u003e (4) exhibited high stability with an RMSD of 1.5 \u0026Aring; (\u003cb\u003eSupplementary Fig. S8\u003c/b\u003e). On the other hand, the molecular dynamics of UT compounds with AKT and GSK3β revealed that \u003cem\u003eCinchonain\u003c/em\u003e (3) demonstrated partial stability in its interactions with the catalytic site of AKT (\u003cb\u003eSupplementary Fig. S9\u003c/b\u003e) and greater stability with the phosphorylation inhibition site of GSK3β (\u003cb\u003eSupplementary Fig. S10\u003c/b\u003e), with RMSD of 5.5 and 2.5 \u0026Aring;, respectively, compared to the results observed in the ligand docking (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eFinally, the interaction of \u003cem\u003e5-Carboxystrictosidine\u003c/em\u003e (1) with the active binding site of PPARγ was assessed (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE), given its role in enhancing insulin sensitivity in adipose tissue, skeletal muscle, and liver [40; 41]. The compound was found to interact stably with PPARγ, presenting RMSD of 2.0 \u0026Aring; (\u003cb\u003eSupplementary Fig. S11\u003c/b\u003e), which is again in agreement with the docking studies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.3 ADMET profiling\u003c/h2\u003e\u003cp\u003eUnderstanding the pharmacokinetics and safety profiles of the compounds is crucial for determining their absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, particularly when assessing their suitability as drugs.\u003c/p\u003e\u003cp\u003eADMET analyses were conducted during the virtual screening stage for the four compounds, \u003cem\u003e5-Carboxystrictosidine\u003c/em\u003e, \u003cem\u003eCinchonain\u003c/em\u003e, \u003cem\u003eEpicatechin\u003c/em\u003e and \u003cem\u003eMitraphylline\u003c/em\u003e, that showed the strongest interactions with target proteins of the stress response, TRAF2/JNK or insulin signaling pathway, following the molecular dynamics tests.\u003c/p\u003e\u003cp\u003eThe oral bioavailability graphs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrate the adjustments required to optimize the behavior of each ligand as drug candidate (pink region of the radar plots). For \u003cem\u003e5-Carboxystrictosidine\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), adjustments in polarity and to some extent molecular size were indicated. In the case of \u003cem\u003eCinchonain\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), improvement in polarity was also highlighted, plus in the unsaturation aspect, as suggested for \u003cem\u003eEpicatechin\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Conversely, the radar plot for \u003cem\u003eMitraphylline\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD) demonstrated that it is structurally suitable, requiring only minor flexibility adjustments.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn addition, some of the pharmacochemical ADMET properties were analyzed for the four selected compounds (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). It was found that \u003cem\u003e5-Carboxystrictosidine\u003c/em\u003e has strong hydrophilic characteristics, mainly caused by the sugar moiety, leading to low absorption in the gastrointestinal tract (GIT). On the other hand, the compounds \u003cem\u003eCinchonain\u003c/em\u003e, \u003cem\u003eEpicatechin\u003c/em\u003e and \u003cem\u003eMitraphylline\u003c/em\u003e have more lipophilic characteristics, but \u003cem\u003eCinchonain\u003c/em\u003e presents low absorption by the GIT, while \u003cem\u003eEpicatechin\u003c/em\u003e and \u003cem\u003eMitraphylline\u003c/em\u003e have high absorption by the GIT.\u003c/p\u003e\u003cp\u003eMost of the compounds with exception of \u003cem\u003eCinchonain\u003c/em\u003e showed probabilities for affinity towards P-glycoproteins (P-gp), but all of them present no significant risk of inhibiting the activity of the cytochrome P450 and isoenzymes CYP1A2, CYP2C19, CYP2C9, CYP2D6, CYP3A4, CYP2E1. In addition, the four compounds did not present any predicted risk of toxicity to hepatocytes or other cells, unless the cardiotoxicity risk, in which \u003cem\u003e5-Carboxystrictosidine\u003c/em\u003e presented 56% of potential stimulation.\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\u003ePharmacochemical properties and toxicity profiles.\u003c/b\u003e Predicted ADMET properties of the four compounds \u003cem\u003e5-Carboxystrictosidine, Epicatechin\u003c/em\u003e, \u003cem\u003eMitraphylline\u003c/em\u003e and \u003cem\u003eCinchonain\u003c/em\u003e obtained through the SwissADME and Pro-Tox III servers, to indicate the physicochemical characteristics, lipophilicity, water solubility, pharmacokinetic aspects, similarity to other drugs (drug-likeness) and toxicity risk.\u003c/p\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\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eADMET properties\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003eUT compounds\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5-Carboxystrictosidine\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEpicatechin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMitraphylline\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCinchonain\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003ePhysicochemical characteristics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMW (g/mol)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e574,58 g/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e290,27 g/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e368,43 g/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e452,41 g/mol\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHeavy atoms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eH\u0026thinsp;+\u0026thinsp;acceptors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eH\u0026thinsp;+\u0026thinsp;donors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLipophilicity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLog P \u003c/p\u003e\u003cp\u003e(-3 to 5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSolubility in water\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLog S \u003c/p\u003e\u003cp\u003e(ESOL, -4 to 0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVery soluble (-1.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSoluble\u003c/p\u003e\u003cp\u003e(-2.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSoluble\u003c/p\u003e\u003cp\u003e(-3.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModerately soluble (\u0026minus;\u0026thinsp;4.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003ePharmacokinetics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGTI absorption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBBB permeability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP-gp substrate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCYP450 inhibitor and isoenzymes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDrug-likeness\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLipinski\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eToxicity risk\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHepatotoxicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u0026thinsp;=\u0026thinsp;69%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u0026thinsp;=\u0026thinsp;72%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u0026thinsp;=\u0026thinsp;75%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo\u0026thinsp;=\u0026thinsp;73%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCardiotoxicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u0026thinsp;=\u0026thinsp;56%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u0026thinsp;=\u0026thinsp;99%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u0026thinsp;=\u0026thinsp;58%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo\u0026thinsp;=\u0026thinsp;79%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMutagenicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u0026thinsp;=\u0026thinsp;54%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u0026thinsp;=\u0026thinsp;55%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u0026thinsp;=\u0026thinsp;62%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo\u0026thinsp;=\u0026thinsp;73%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCytotoxicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u0026thinsp;=\u0026thinsp;65%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u0026thinsp;=\u0026thinsp;84%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u0026thinsp;=\u0026thinsp;65%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo\u0026thinsp;=\u0026thinsp;80%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. DISCUSSION","content":"\u003cp\u003eThis study aimed to identify compounds from \u003cem\u003eUncaria tomentosa\u003c/em\u003e [24; 28] that interact with key proteins involved in UPR and insulin signaling pathways, such as PERK, IRS-1, PI3K, AKT, GSK3β, TNF-α, TRAF2, JNK or PPARγ, using an \u003cem\u003ein silico\u003c/em\u003e modeling.\u003c/p\u003e\u003cp\u003eFrom the ligand docking, the compounds with higher affinity for the proteins than the natural substrates or co-crystallized ligands were identified by comparing the obtained docking scores (in kcal/mol).\u003c/p\u003e\u003cp\u003eIn the first group of proteins, some of the transmembrane proteins involved in UPR, such as PERK and IRE1, and some that trigger ISR, such as HRI and GCN2, were evaluated [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. No inhibitory effects were predicted between HRI, IRE1, or GCN2 and the 14 compounds. On the other hand, it was observed that the compound \u003cem\u003eEpicatechin\u003c/em\u003e strongly bounds to PERK kinase. This result suggests that the compound might interact with the binding site of the kinase enough to be stable [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Once PERK is activated, eIF2α is phosphorylated, which attenuates proteins synthesis under ER stress conditions [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFor eIF2α, no affinity was observed with the compounds, suggesting that the compounds present in UT do not directly attenuate protein synthesis [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. This is further supported by the finding that PERK could also be inhibited by \u003cem\u003eEpicatechin\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eIn the second group of proteins, the interactions of the UT compounds with the cytokine TNF-α, TRAF2 or JNK, expressed in one of the autophagy pathways were evaluated [44; 45].\u003c/p\u003e\u003cp\u003eDuring the ligand docking modelling, it was verified that the pro-inflammatory cytokine TNF-α exhibited affinity for three compounds: \u003cem\u003e5-Carboxystrictosidine, Epicatechin\u003c/em\u003e, and \u003cem\u003eUncarine D\u003c/em\u003e. However, when evaluating the molecular dynamics of these compounds with the cytokine, no stable interactions with the binding site were found. This occurrence can be attributed by the fact that during molecular dynamic simulations, the ligand and protein can adopt different conformations, which may disrupt interactions initially predicted by docking. The dynamic behavior can reveal that some docking poses are not stable or energetically favorable under physiological conditions, leading to weaker or lost interactions during MD [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHowever, in a second assessment by molecular dynamics it was observed that \u003cem\u003eMitraphylline\u003c/em\u003e interacts stably with TNF-α, suggesting that \u003cem\u003eMitraphylline\u003c/em\u003e, as an anti-inflammatory compound, could interfere with the activity of the pro-inflammatory cytokine TNF-α and subsequently modulate inflammatory responses [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRegarding the TRAF2 protein, while the flavonoid \u003cem\u003eEpicatechin\u003c/em\u003e, known for its antioxidant action [46; 47], demonstrated higher affinity with the binding site of TRAF2 than the natural substrate, \u003cem\u003eMitraphylline\u003c/em\u003e demonstrated the opposite (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). However, after evaluating the dynamics between \u003cem\u003eEpicatechin\u003c/em\u003e or \u003cem\u003eMitraphylline\u003c/em\u003e and TRAF2, stable interactions were observed between both compounds and the protein\u0026rsquo;s binding site.\u003c/p\u003e\u003cp\u003eFrom the docking, JNK showed strong interaction with \u003cem\u003eEpicatechin\u003c/em\u003e. When evaluating the dynamics between \u003cem\u003eEpicatechin\u003c/em\u003e and the kinase, the interaction was also confirmed to be stable. These results suggest that \u003cem\u003eEpicatechin\u003c/em\u003e and \u003cem\u003eMitraphylline\u003c/em\u003e can interact stably and potentially block the activity of TNF-α, TRAF2 and JNK regulating oxidative stress and pro-inflammatory pathways [46; 47].\u003c/p\u003e\u003cp\u003eTo relate the results obtained for the UT compounds and to evaluate the potential for treating insulin resistance, the stability of the fourteen UT compounds was also assessed through molecular dynamics, with the binding sites of target proteins involved in the insulin signaling pathway.\u003c/p\u003e\u003cp\u003eThe compound \u003cem\u003e5-Carboxystrictosidine\u003c/em\u003e showed stable binding towards IRS-1, from the docking and the MD simulations. The dynamics between PI3K and \u003cem\u003eEpicatechin\u003c/em\u003e were also stable, even the small difference presented in the docking studies between \u003cem\u003eEpicatechin\u003c/em\u003e (-8.99 kcal/mol) and ATP (-9.13 kcal/mol). This difference of affinity could be explained by the additional interactions and conformational adjustments that can be revealed in the molecular dynamic simulations, which may compensate for differences in binding affinity suggested by limitations in the docking scores [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFinally, when evaluating the interactions of the kinases AKT or GSK3β with the compounds, it was found that \u003cem\u003eCinchonain\u003c/em\u003e displayed stable binding towards the kinases in the MD simulations.\u003c/p\u003e\u003cp\u003eLittle is known about the relationship and interaction between \u003cem\u003e5-Carboxystrictosidine, Epicatechin\u003c/em\u003e and \u003cem\u003eCinchonain\u003c/em\u003e with the proteins of the insulin signaling pathway. However, according to some authors [24; 25; 28; 46; 48], these compounds cited, isolated from the plants \u003cem\u003eUncaria tomentosa\u003c/em\u003e or \u003cem\u003ePsychotria nuda\u003c/em\u003e, have antioxidant characteristics and promote attenuation of ROS production in human erythrocytes, macrophages and skeletal muscle tissue. This may be related to the attenuation of ER stress and the modulation of insulin receptor signaling [12; 49].\u003c/p\u003e\u003cp\u003eFurthermore, the possible interaction of the transcription factor PPARγ with the compounds was evaluated. PPARγ antagonizes the metabolic syndrome of type 2 diabetes (T2D) through the negative regulation of peripheral inflammatory processes, including the suppression of pro-inflammatory cytokines and increased insulin sensitivity in adipose tissue, skeletal muscle, and liver [40; 41; 50; 51]. When evaluating the affinity and binding stability between the UT compounds and PPARγ, it is suggested that this compound may act as a potential PPARy agonist, enhancing insulin sensitivity though the regulation of glucose and lipid metabolism [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], regarding \u003cem\u003e5-Carboxystrictosidine\u003c/em\u003e also showed stable binding towards IRS-1 protein, a key mediator in the insulin signaling pathway, indicating its possible role in improving insulin receptor signaling and downstream metabolic effects [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe interactions of the four selected compounds with the residues of the target proteins were further analyzed based on the MD simulations (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacterization of the different protein interactions with the UT compounds during the molecular dynamic simulations\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eUT compounds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eProtein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHydrogen bond interactions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHydrophobic interactions\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eAmino acids (interactions\u0026thinsp;\u0026ge;\u0026thinsp;30%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cem\u003eEpicatechin\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePERK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVal651; Lys621; Gln888; Arg891; Phe955; Gly956\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePI3K\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTyr670; Asp761; Ile685; Gln683; Ser687; Glu692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTyr670\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTRAF2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAla48; Glu60; Leu73; Asp160; Phe161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJNK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGlu109; Met111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eMitraphylline\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTNF-α\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTyr59; Tyr119\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTRAF2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMet97; Asp160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003e5-Carboxystrictosidine\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePPARγ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHis323; Ser289; Arg288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLeu340\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIRS-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGln1004; Glu1047; Glu1077; Met1079;\u003c/p\u003e\u003cp\u003eAsp1083;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eCinchonain\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGSK3β\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAsp133; Lys85; Gln185; Asn186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAKT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLeu158; Lys160;\u003c/p\u003e\u003cp\u003eAla232, Glu236;\u003c/p\u003e\u003cp\u003eGlu279; Asp293;\u003c/p\u003e\u003cp\u003eThr436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePhe439\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\u003eThe interactions found between \u003cem\u003eEpicatechin\u003c/em\u003e and PERK residues Val651, Gln888, Phe955, Gly956 located in the DFG-\u003cem\u003emotif\u003c/em\u003e and ATP binding site, correspond to 4 out of the 10 key residues found in the activation site (Leu598, Arg600, Gln888, Cys890, Lys938, Ser940, Asp954, Phe955, Gly956, Thr986) [52; 53], which suggests that \u003cem\u003eEpicatechin\u003c/em\u003e might impair the activation of the kinase and inhibit the expression under the UPR and stress responses.\u003c/p\u003e\u003cp\u003eThe interactions of the same compound \u003cem\u003eEpicatechin\u003c/em\u003e, a polyphenol known for its antioxidant role, and the JNK residues Glu109 and Met111 located in the inhibition site of the kinase, correspond to 2 out of 3 residues that mediate the suppression of JNK phosphorylation (Glu109, Leu110, Met111) [54; 55; 56], which suggests that \u003cem\u003eEpicatechin\u003c/em\u003e might inhibit the activation of JNK, which plays a significant role in responses to oxidative stress [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRegarding the anti-inflammatory response of \u003cem\u003eMitraphylline\u003c/em\u003e, thus was found to bind strongly to the residues Tyr59 and Tyr119, located in the inhibitory binding site of the pro-inflammatory cytokine TNF-α [57; 58]. The binding of one or more compounds to this region alters the trimmer symmetry and destabilize the cytokine, thus acting as TNF-α inhibitors [57; 58].\u003c/p\u003e\u003cp\u003eIn relation to the insulin signaling pathway, Peasari \u003cem\u003eet al\u003c/em\u003e. (2018) reported that IRS-1 residues Glu1077, Met1079, and Asp1083, which interacted strongly with \u003cem\u003e5-Carboxystrictosidine\u003c/em\u003e, are located in the active site of this tyrosine kinase and modulate its insulin receptor activity [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Moreover, some authors [60; 61] described that the residues Tyr670, Ile685, and Ser687, which are involved in the affinity between \u003cem\u003eEpicatechin\u003c/em\u003e and the protein kinase PI3K, are located in the catalytic phosphorylation site and may contribute to the activation of the protein.\u003c/p\u003e\u003cp\u003eIn addition, some authors [62; 63; 64] indicated that residues Leu158, Lys160, Glu236, Glu279, Thr436, and Phe439 in the AKT protein kinase which exhibited affinity for \u003cem\u003eCinchonain\u003c/em\u003e, are located in the catalytic site of the kinase. For GSK3β, the residues Asp133, Gln185 and Asn186 which also interacted with the compound \u003cem\u003eCinchonain\u003c/em\u003e, are located in the active site of the protein kinase [65; 66], suggesting that this compound may act as a modulator of GSK3β activity.\u003c/p\u003e\u003cp\u003eTo verify whether UT compounds could also be considered as therapeutics targeting lipotoxicity-induced insulin resistance, their affinity for PPARγ was evaluated. Both His323 and Ser289 are among the residues that stabilize \u003cem\u003e5-Carboxystrictosidine\u003c/em\u003e binding to the protein. According to some authors [67; 68], interactions with the residues His323 and Ser289 are characteristic of PPARγ agonists and contribute to the activation of the transcription factor, which is considered as a target against hyperglycemia associated with T2D.\u003c/p\u003e\u003cp\u003eDespite the valuable predictions presented in this study, it is not possible to fully replicate the complexity of biological systems by the computational methods. Therefore, this study serves as base for conducting complementary experimental assays to validate and confirm the therapeutic potential of the predicted interactions, as well as to achieve a comprehensive understanding of the mechanism of action of the UT compounds.\u003c/p\u003e"},{"header":"4. CONCLUSIONS","content":"\u003cp\u003eIn summary, the results presented in this study contribute to a better understanding of the mechanisms of action of the compounds present in the \u003cem\u003eUncaria tomentosa\u003c/em\u003e plant, within the molecular context of UPR, ER stress, insulin signaling regulation, insulin resistance and cell death. \u003cem\u003e5-Carboxystrictosidine, Cinchonain, Epicatechin\u003c/em\u003e and \u003cem\u003eMitraphylline\u003c/em\u003e were found to exhibit the strongest affinities with the proteins involved with UPR and insulin resistance conditions, demonstrating significant interactions with key residues during the \u003cem\u003ein silico\u003c/em\u003e modelling. This further supports the possible therapeutic benefits of the plant, although future experimental validations remain necessary.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBruna L. F. Marchi:\u003c/em\u003e Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Validation; Software; Visualization; Writing - original draft. \u003cem\u003eShraddha Parate:\u003c/em\u003e Investigation; Methodology; Validation; Writing - original draft. \u003cem\u003eVibhu Jha:\u003c/em\u003e Investigation; Methodology; Validation; Writing - original draft. \u003cem\u003eFelipe S C Alcalde:\u003c/em\u003e Investigation; Writing - original draft. \u003cem\u003eLeif A. Eriksson:\u003c/em\u003e Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Resources; Supervision; Validation; Visualization; Writing \u0026ndash; review \u0026amp; editing. \u003cem\u003eViviane A Nunes:\u003c/em\u003e Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Supervision; Validation; Visualization; Writing - review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProtein structures after preparation, docked structures of the target proteins and UT compounds, plus the MD simulations, are freely available for download at zenodo.org (https://zenodo.org/records/17458126 and https://zenodo.org/records/17458126).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare that there is no financial or other potential conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING DECLARATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Research Council (CNPq), grant n\u003csup\u003eo\u003c/sup\u003e. 200473/2022-0 \u0026ndash; Brazil; Sao Paulo Research Foundation (FAPESP), grant n\u003csup\u003eo\u003c/sup\u003e.\u0026nbsp;22/16702-3\u0026nbsp;\u0026ndash; Brazil; Sven and Lilly Lawski Foundation, grant n\u003csup\u003eo\u003c/sup\u003e. N2024-0035 \u0026ndash; Sweden. Allocation of computing time at the supercomputing center NSC provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS), which is partially funded by the Swedish Research Council through grant agreement n\u003csup\u003eo\u003c/sup\u003e. 2022\u0026ndash;06725 - Sweden\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization (WHO). Diabetes. \u003cem\u003eFact Sheets\u003c/em\u003e (2023).\u003c/li\u003e\n\u003cli\u003eInternational Diabetes Federation (IDF). Diabetes by region. \u003cem\u003eDiabetes Atlas\u003c/em\u003e, p. 1\u0026ndash;3, (2021).\u003c/li\u003e\n\u003cli\u003eWorld Health Organization (WHO). Definition, Diagnosis and Classification of Diabetes Mellitus and its Complications. \u003cem\u003eReport of a WHO Consultation. Part 1: Diagnosis and Classification of Diabetes Mellitus\u003c/em\u003e (2009).\u003c/li\u003e\n\u003cli\u003eZhou, Z. \u003cem\u003eet al.\u003c/em\u003e Healthcare Resource Use, Costs, and Disease Progression Associated with Diabetic Nephropathy in Adults with Type 2 Diabetes: A Retrospective Observational Study. \u003cem\u003eDiabetes Ther.\u003c/em\u003e\u003cstrong\u003e8\u003c/strong\u003e, 555\u0026ndash;571 (2017).\u003c/li\u003e\n\u003cli\u003eZhao, Y., Barrere-Cain, R. E. \u0026amp; Yang, X. Nutritional systems biology of type 2 diabetes. \u003cem\u003eGenes Nutr.\u003c/em\u003e\u003cstrong\u003e10\u003c/strong\u003e, 31 (2015).\u003c/li\u003e\n\u003cli\u003eSun, H. \u003cem\u003eet al.\u003c/em\u003e IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. \u003cem\u003eDiabetes Res. Clin. Pract.\u003c/em\u003e\u003cstrong\u003e183\u003c/strong\u003e, 109119 (2022).\u003c/li\u003e\n\u003cli\u003eParker, E. D. \u003cem\u003eet al.\u003c/em\u003e Economic Costs of Diabetes in the U.S. in 2022. \u003cem\u003eDiabetes Care\u003c/em\u003e\u003cstrong\u003e47\u003c/strong\u003e, 26\u0026ndash;43 (2024).\u003c/li\u003e\n\u003cli\u003eGiuffrida, F. M. A. \u003cem\u003eet al.\u003c/em\u003e Maturity-onset diabetes of the young (MODY) in Brazil: Establishment of a national registry and appraisal of available genetic and clinical data. \u003cem\u003eDiabetes Res. Clin. Pract.\u003c/em\u003e\u003cstrong\u003e123\u003c/strong\u003e, 134\u0026ndash;142 (2017).\u003c/li\u003e\n\u003cli\u003eFreitas-Marchi, B. L. \u003cem\u003eet al.\u003c/em\u003e Effect of Uncaria tomentosa aqueous extract on the response to palmitate-induced lipotoxicity in cultured skeletal muscle cells. \u003cem\u003eBMC Complement. Med. Ther.\u003c/em\u003e\u003cstrong\u003e23\u003c/strong\u003e, 412 (2023).\u003c/li\u003e\n\u003cli\u003eMirabelli, M. \u003cem\u003eet al.\u003c/em\u003e Mediterranean Diet Nutrients to Turn the Tide against Insulin Resistance and Related Diseases. \u003cem\u003eNutrients\u003c/em\u003e\u003cstrong\u003e12\u003c/strong\u003e, 1066 (2020).\u003c/li\u003e\n\u003cli\u003eFung, J. \u0026amp; Berger, A. Hyperinsulinemia and Insulin Resistance: Scope of the Problem. \u003cem\u003eJ. Metab. Heal.\u003c/em\u003e\u003cstrong\u003e1\u003c/strong\u003e, (2016).\u003c/li\u003e\n\u003cli\u003eLy, L. D. \u003cem\u003eet al.\u003c/em\u003e Oxidative stress and calcium dysregulation by palmitate in type 2 diabetes. \u003cem\u003eExp. Mol. Med.\u003c/em\u003e\u003cstrong\u003e49\u003c/strong\u003e, e291\u0026ndash;e291 (2017).\u003c/li\u003e\n\u003cli\u003eWan, S. \u0026amp; Jiang, L. Erratum to: Endoplasmic reticulum (ER) stress and the unfolded protein response (UPR) in plants. \u003cem\u003eProtoplasma\u003c/em\u003e\u003cstrong\u003e253\u003c/strong\u003e, 765\u0026ndash;765 (2016).\u003c/li\u003e\n\u003cli\u003eDewidar, B. \u003cem\u003eet al.\u003c/em\u003e Alterations of hepatic energy metabolism in murine models of obesity, diabetes and fatty liver diseases. \u003cem\u003eeBioMedicine\u003c/em\u003e\u003cstrong\u003e94\u003c/strong\u003e, 104714 (2023).\u003c/li\u003e\n\u003cli\u003eKaniuka, O. \u003cem\u003eet al.\u003c/em\u003e Upregulation of GRP78 is accompanied by decreased antioxidant response and mitophagy promotion in streptozotocin-induced type 1 diabetes in rats. \u003cem\u003eBiochim. Biophys. Acta - Mol. Basis Dis.\u003c/em\u003e\u003cstrong\u003e1871\u003c/strong\u003e, 167531 (2025).\u003c/li\u003e\n\u003cli\u003eZhao, L. \u003cem\u003eet al.\u003c/em\u003e Endoplasmic reticulum chaperone GRP78 participates in fluoride‐induced autophagy in LS8 cells by regulating the IRE1‐TRAF2‐JNK pathway. \u003cem\u003eEnviron. Toxicol.\u003c/em\u003e\u003cstrong\u003e38\u003c/strong\u003e, 1756\u0026ndash;1767 (2023).\u003c/li\u003e\n\u003cli\u003eCarlesso, A., H\u0026ouml;rberg, J., Reymer, A. \u0026amp; Eriksson, L. A. New insights on human IRE1 tetramer structures based on molecular modeling. \u003cem\u003eSci. Rep.\u003c/em\u003e\u003cstrong\u003e10\u003c/strong\u003e, 17490 (2020).\u003c/li\u003e\n\u003cli\u003ePakos‐Zebrucka, K. \u003cem\u003eet al.\u003c/em\u003e The integrated stress response. \u003cem\u003eEMBO Rep.\u003c/em\u003e\u003cstrong\u003e17\u003c/strong\u003e, 1374\u0026ndash;1395 (2016).\u003c/li\u003e\n\u003cli\u003eHetz, C., Zhang, K. \u0026amp; Kaufman, R. J. Mechanisms, regulation and functions of the unfolded protein response. \u003cem\u003eNat. Rev. Mol. Cell Biol.\u003c/em\u003e\u003cstrong\u003e21\u003c/strong\u003e, 421\u0026ndash;438 (2020).\u003c/li\u003e\n\u003cli\u003eAguirre, V. \u003cem\u003eet al.\u003c/em\u003e Phosphorylation of Ser307 in Insulin Receptor Substrate-1 Blocks Interactions with the Insulin Receptor and Inhibits Insulin Action. \u003cem\u003eJ. Biol. Chem.\u003c/em\u003e\u003cstrong\u003e277\u003c/strong\u003e, 1531\u0026ndash;1537 (2002).\u003c/li\u003e\n\u003cli\u003eLemmer, I. L., Willemsen, N., Hilal, N. \u0026amp; Bartelt, A. A guide to understanding endoplasmic reticulum stress in metabolic disorders. \u003cem\u003eMol. Metab.\u003c/em\u003e\u003cstrong\u003e47\u003c/strong\u003e, 101169 (2021).\u003c/li\u003e\n\u003cli\u003eScarano, F. \u003cem\u003eet al.\u003c/em\u003e Potential of Nutraceutical Supplementation in the Modulation of White and Brown Fat Tissues in Obesity-Associated Disorders: Role of Inflammatory Signalling. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e\u003cstrong\u003e22\u003c/strong\u003e, 3351 (2021).\u003c/li\u003e\n\u003cli\u003eOyadomari, S. \u0026amp; Mori, M. Roles of CHOP/GADD153 in endoplasmic reticulum stress. \u003cem\u003eCell Death Differ.\u003c/em\u003e\u003cstrong\u003e11\u003c/strong\u003e, 381\u0026ndash;389 (2004).\u003c/li\u003e\n\u003cli\u003eBors, M. \u003cem\u003eet al.\u003c/em\u003e Studies of biological properties of Uncaria tomentosa extracts on human blood mononuclear cells. \u003cem\u003eJ. Ethnopharmacol.\u003c/em\u003e\u003cstrong\u003e142\u003c/strong\u003e, 669\u0026ndash;678 (2012).\u003c/li\u003e\n\u003cli\u003eAzevedo, B. C. \u003cem\u003eet al.\u003c/em\u003e Aqueous extracts from Uncaria tomentosa (Willd. ex Schult.) DC. reduce bronchial hyperresponsiveness and inflammation in a murine model of asthma. \u003cem\u003eJ. Ethnopharmacol.\u003c/em\u003e\u003cstrong\u003e218\u003c/strong\u003e, 76\u0026ndash;89 (2018).\u003c/li\u003e\n\u003cli\u003eAraujo, L. C. C. \u003cem\u003eet al.\u003c/em\u003e Uncaria tomentosa improves insulin sensitivity and inflammation in experimental NAFLD. \u003cem\u003eSci. Rep.\u003c/em\u003e\u003cstrong\u003e8\u003c/strong\u003e, 11013 (2018).\u003c/li\u003e\n\u003cli\u003eNavarro-Hoyos, M., Alvarado-Corella, D., Moreira-Gonzalez, I., Arnaez-Serrano, E. \u0026amp; Monagas-Juan, M. Polyphenolic Composition and Antioxidant Activity of Aqueous and Ethanolic Extracts from Uncaria tomentosa Bark and Leaves. \u003cem\u003eAntioxidants\u003c/em\u003e\u003cstrong\u003e7\u003c/strong\u003e, 65 (2018).\u003c/li\u003e\n\u003cli\u003eHeitzman, M., Neto, C., Winiarz, E., Vaisberg, A. \u0026amp; Hammond, G. Ethnobotany, phytochemistry and pharmacology of (Rubiaceae). \u003cem\u003ePhytochemistry\u003c/em\u003e\u003cstrong\u003e66\u003c/strong\u003e, 5\u0026ndash;29 (2005).\u003c/li\u003e\n\u003cli\u003eSchr\u0026ouml;dinger release 2023-2: Desmond Molecular Dynamics System. D. E. Shaw Research, New York, NY (2023).\u003c/li\u003e\n\u003cli\u003eOlsson, M. H. M., S\u0026oslash;ndergaard, C. R., Rostkowski, M. \u0026amp; Jensen, J. H. PROPKA3: Consistent Treatment of Internal and Surface Residues in Empirical p K a Predictions. \u003cem\u003eJ. Chem. Theory Comput.\u003c/em\u003e\u003cstrong\u003e7\u003c/strong\u003e, 525\u0026ndash;537 (2011).\u003c/li\u003e\n\u003cli\u003eLu, C. \u003cem\u003eet al.\u003c/em\u003e OPLS4: Improving Force Field Accuracy on Challenging Regimes of Chemical Space. \u003cem\u003eJ. Chem. Theory Comput.\u003c/em\u003e\u003cstrong\u003e17\u003c/strong\u003e, 4291\u0026ndash;4300 (2021).\u003c/li\u003e\n\u003cli\u003eShelley, J. C. \u003cem\u003eet al.\u003c/em\u003e Epik: a software program for pK a prediction and protonation state generation for drug-like molecules. \u003cem\u003eJ. Comput. Aided. Mol. Des.\u003c/em\u003e\u003cstrong\u003e21\u003c/strong\u003e, 681\u0026ndash;691 (2007).\u003c/li\u003e\n\u003cli\u003eFriesner, R. A. \u003cem\u003eet al.\u003c/em\u003e Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy. \u003cem\u003eJ. Med. Chem.\u003c/em\u003e\u003cstrong\u003e47\u003c/strong\u003e, 1739\u0026ndash;1749 (2004).\u003c/li\u003e\n\u003cli\u003eLand, H. \u0026amp; Humble, M. S. YASARA: A Tool to Obtain Structural Guidance in Biocatalytic Investigations. \u003cem\u003eMethods Mol Biol.\u003c/em\u003e\u003cstrong\u003e1685\u003c/strong\u003e, 43\u0026ndash;67 (2018). \u003c/li\u003e\n\u003cli\u003eNos\u0026eacute;, S. A unified formulation of the constant temperature molecular dynamics methods. \u003cem\u003eJ. Chem. Phys.\u003c/em\u003e\u003cstrong\u003e81\u003c/strong\u003e, 511\u0026ndash;519 (1984).\u003c/li\u003e\n\u003cli\u003eMartyna, G. J., Klein, M. L. \u0026amp; Tuckerman, M. Nos\u0026eacute;\u0026ndash;Hoover chains: The canonical ensemble via continuous dynamics. \u003cem\u003eJ. Chem. Phys.\u003c/em\u003e\u003cstrong\u003e97\u003c/strong\u003e, 2635\u0026ndash;2643 (1992).\u003c/li\u003e\n\u003cli\u003eJha, V. \u0026amp; Eriksson, L. A. Selectivity analysis of diaminopyrimidine-based inhibitors of MTHFD1, MTHFD2 and MTHFD2L. \u003cem\u003eSci. Rep.\u003c/em\u003e\u003cstrong\u003e14\u003c/strong\u003e, 21073 (2024).\u003c/li\u003e\n\u003cli\u003eDaina, A., Michielin, O. \u0026amp; Zoete, V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. \u003cem\u003eSci. Rep.\u003c/em\u003e\u003cstrong\u003e7\u003c/strong\u003e, 42717 (2017).\u003c/li\u003e\n\u003cli\u003eNiazi, S. K. \u0026amp; Mariam, Z. Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis. \u003cem\u003ePharmaceuticals\u003c/em\u003e\u003cstrong\u003e17\u003c/strong\u003e, 22 (2023).\u003c/li\u003e\n\u003cli\u003eLee, K.-C. \u003cem\u003eet al.\u003c/em\u003e Aliskiren Reduces Hepatic steatosis and Epididymal Fat Mass and Increases Skeletal Muscle Insulin Sensitivity in High-Fat Diet-Fed Mice. \u003cem\u003eSci. Rep.\u003c/em\u003e\u003cstrong\u003e6\u003c/strong\u003e, 18899 (2016).\u003c/li\u003e\n\u003cli\u003eVillapol, S. Roles of Peroxisome Proliferator-Activated Receptor Gamma on Brain and Peripheral Inflammation. \u003cem\u003eCell. Mol. Neurobiol.\u003c/em\u003e\u003cstrong\u003e38\u003c/strong\u003e, 121\u0026ndash;132 (2018).\u003c/li\u003e\n\u003cli\u003eDurrant, J. D. \u0026amp; McCammon, J. A. Molecular dynamics simulations and drug discovery. \u003cem\u003eBMC Biol.\u003c/em\u003e\u003cstrong\u003e9\u003c/strong\u003e, 71 (2011).\u003c/li\u003e\n\u003cli\u003eNeill, G. \u0026amp; Masson, G. R. A stay of execution: ATF4 regulation and potential outcomes for the integrated stress response. \u003cem\u003eFront. Mol. Neurosci.\u003c/em\u003e\u003cstrong\u003e16\u003c/strong\u003e, (2023). \u003c/li\u003e\n\u003cli\u003eHe, W. \u003cem\u003eet al.\u003c/em\u003e Attenuation of TNFSF10/TRAIL-induced apoptosis by an autophagic survival pathway involving TRAF2- and RIPK1/RIP1-mediated MAPK8/JNK activation. \u003cem\u003eAutophagy\u003c/em\u003e\u003cstrong\u003e8\u003c/strong\u003e, 1811\u0026ndash;1821 (2012).\u003c/li\u003e\n\u003cli\u003eTsuchida, M. \u003cem\u003eet al.\u003c/em\u003e Pro-apoptotic functions of TRAF2 in p53-mediated apoptosis induced by cisplatin. \u003cem\u003eJ. Toxicol. Sci.\u003c/em\u003e\u003cstrong\u003e45\u003c/strong\u003e, 219\u0026ndash;226 (2020).\u003c/li\u003e\n\u003cli\u003eGerman, I. J. S. \u003cem\u003eet al.\u003c/em\u003e New Trends to Treat Muscular Atrophy: A Systematic Review of Epicatechin. \u003cem\u003eNutrients\u003c/em\u003e\u003cstrong\u003e16\u003c/strong\u003e, 326 (2024).\u003c/li\u003e\n\u003cli\u003eLan, X., Han, X., Li, Q. \u0026amp; Wang, J. (\u0026minus;)-Epicatechin, a Natural Flavonoid Compound, Protects Astrocytes Against Hemoglobin Toxicity via Nrf2 and AP-1 Signaling Pathways. \u003cem\u003eMol. Neurobiol.\u003c/em\u003e\u003cstrong\u003e54\u003c/strong\u003e, 7898\u0026ndash;7907 (2017).\u003c/li\u003e\n\u003cli\u003eCarvalho Junior, A. \u003cem\u003eet al.\u003c/em\u003e Antimycobacterial and Nitric Oxide Production Inhibitory Activities of Triterpenes and Alkaloids from Psychotria nuda (Cham. \u0026amp; Schltdl.) Wawra. \u003cem\u003eMolecules\u003c/em\u003e\u003cstrong\u003e24\u003c/strong\u003e, 1026 (2019).\u003c/li\u003e\n\u003cli\u003eHenriksen, E. J. Improvement of insulin sensitivity by antagonism of the renin-angiotensin system. \u003cem\u003eAm. J. Physiol. Integr. Comp. Physiol.\u003c/em\u003e\u003cstrong\u003e293\u003c/strong\u003e, R974\u0026ndash;R980 (2007). \u003c/li\u003e\n\u003cli\u003eOdegaard, J. I. \u003cem\u003eet al.\u003c/em\u003e Macrophage-specific PPAR\u0026gamma; controls alternative activation and improves insulin resistance. \u003cem\u003eNature\u003c/em\u003e\u003cstrong\u003e447\u003c/strong\u003e, 1116\u0026ndash;1120 (2007). \u003c/li\u003e\n\u003cli\u003eHeald, M. \u0026amp; Cawthorne, M. A. Dual Acting and Pan-PPAR Activators as Potential Anti-diabetic Therapies. \u003cem\u003eHandb Exp Pharmacol\u003c/em\u003e. \u003cstrong\u003e203\u003c/strong\u003e, 35\u0026ndash;51 (2011).\u003c/li\u003e\n\u003cli\u003eUng, P. M.-U. \u0026amp; Schlessinger, A. DFGmodel: Predicting Protein Kinase Structures in Inactive States for Structure-Based Discovery of Type-II Inhibitors. \u003cem\u003eACS Chem. Biol.\u003c/em\u003e\u003cstrong\u003e10\u003c/strong\u003e, 269\u0026ndash;278 (2015).\u003c/li\u003e\n\u003cli\u003eGanz, J. \u003cem\u003eet al.\u003c/em\u003e A novel specific PERK activator reduces toxicity and extends survival in Huntington\u0026rsquo;s disease models. \u003cem\u003eSci. Rep.\u003c/em\u003e\u003cstrong\u003e10\u003c/strong\u003e, 6875 (2020).\u003c/li\u003e\n\u003cli\u003eHeo, Y.-S. \u003cem\u003eet al.\u003c/em\u003e Structural basis for the selective inhibition of JNK1 by the scaffolding protein JIP1 and SP600125. \u003cem\u003eEMBO J.\u003c/em\u003e\u003cstrong\u003e23\u003c/strong\u003e, 2185\u0026ndash;2195 (2004).\u003c/li\u003e\n\u003cli\u003eJiao, Z. \u003cem\u003eet al.\u003c/em\u003e Kaempferide Prevents Titanium Particle Induced Osteolysis by Suppressing JNK Activation during Osteoclast Formation. \u003cem\u003eSci. Rep.\u003c/em\u003e\u003cstrong\u003e7\u003c/strong\u003e, 16665 (2017). \u003c/li\u003e\n\u003cli\u003eKarkehabadi, H., Sadr, S., Khoshbin, E. \u0026amp; Taherkhani, A. A Novel Approach for Apical Periodontitis Prevention Through RIPK3 Inhibition Using Organic Flavonoids. \u003cem\u003eAvicenna J. Dent. Res.\u003c/em\u003e\u003cstrong\u003e16\u003c/strong\u003e, 146\u0026ndash;154 (2024).\u003c/li\u003e\n\u003cli\u003eHe, M. M. \u003cem\u003eet al.\u003c/em\u003e Small-Molecule Inhibition of TNF-\u0026alpha;. \u003cem\u003eScience.\u003c/em\u003e\u003cstrong\u003e310\u003c/strong\u003e, 1022\u0026ndash;1025 (2005).\u003c/li\u003e\n\u003cli\u003eZia, K. \u003cem\u003eet al.\u003c/em\u003e Identification of potential TNF-\u0026alpha; inhibitors: from in silico to in vitro studies. \u003cem\u003eSci. Rep.\u003c/em\u003e\u003cstrong\u003e10\u003c/strong\u003e, 20974 (2020). \u003c/li\u003e\n\u003cli\u003ePeasari, J. reddy, Motamarry, S. sri, Varma, K. S., Anitha, P. \u0026amp; Potti, R. B. Chromatographic analysis of phytochemicals in Costus igneus and computational studies of flavonoids. \u003cem\u003eInformatics Med. Unlocked\u003c/em\u003e\u003cstrong\u003e13\u003c/strong\u003e, 34\u0026ndash;40 (2018).\u003c/li\u003e\n\u003cli\u003eVald\u0026eacute;s-Tresanco, M. S., Vald\u0026eacute;s-Tresanco, M. E., Rubio-Carrasquilla, M., Valiente, P. A., Moreno, E. Tailored Parameterization of the LIE Method for Calculating the Binding Free Energy of Vps34\u0026ndash;Inhibitor Complexes. \u003cem\u003eACS Omega\u003c/em\u003e\u003cstrong\u003e6\u003c/strong\u003e, 29525\u0026ndash;29536 (2021). \u003c/li\u003e\n\u003cli\u003eGewinner, C. \u003cem\u003eet al.\u003c/em\u003e Evidence that Inositol Polyphosphate 4-Phosphatase Type II Is a Tumor Suppressor that Inhibits PI3K Signaling. \u003cem\u003eCancer Cell\u003c/em\u003e\u003cstrong\u003e16\u003c/strong\u003e, 115\u0026ndash;125 (2009).\u003c/li\u003e\n\u003cli\u003eHuang, B. X. \u0026amp; Kim, H.-Y. Interdomain Conformational Changes in Akt Activation Revealed by Chemical Cross-linking and Tandem Mass Spectrometry. \u003cem\u003eMol. Cell. Proteomics\u003c/em\u003e\u003cstrong\u003e5\u003c/strong\u003e, 1045\u0026ndash;1053 (2006).\u003c/li\u003e\n\u003cli\u003eVoukkalis, N. \u003cem\u003eet al.\u003c/em\u003e SRPK1 and Akt Protein Kinases Phosphorylate the RS Domain of Lamin B Receptor with Distinct Specificity: A Combined Biochemical and In Silico Approach. \u003cem\u003ePLoS One\u003c/em\u003e\u003cstrong\u003e11\u003c/strong\u003e, e0154198 (2016).\u003c/li\u003e\n\u003cli\u003eShin, S. Y., Kim, C. G., Jung, Y. J., Lim, Y. \u0026amp; Lee, Y. H. The UPR inducer DPP23 inhibits the metastatic potential of MDA-MB-231 human breast cancer cells by targeting the AKT\u0026ndash;IKK\u0026ndash;NF-\u0026kappa;B\u0026ndash;MMP-9 axis. \u003cem\u003eSci. Rep.\u003c/em\u003e\u003cstrong\u003e6\u003c/strong\u003e, 34134 (2016).\u003c/li\u003e\n\u003cli\u003eXie, H. \u003cem\u003eet al.\u003c/em\u003e Designing of dual inhibitors for GSK-3\u0026beta; and CDK5: Virtual screening and in vitro biological activities study. \u003cem\u003eOncotarget\u003c/em\u003e\u003cstrong\u003e8\u003c/strong\u003e, 18118\u0026ndash;18128 (2017).\u003c/li\u003e\n\u003cli\u003eZhang, M. \u003cem\u003eet al.\u003c/em\u003e Chromenone-based GSK-3\u0026beta; inhibitors as potential therapeutic targets for cardiovascular diseases: In silico study, molecular dynamics, and ADMET profiles. \u003cem\u003eArab. J. Chem.\u003c/em\u003e\u003cstrong\u003e15\u003c/strong\u003e, 104288 (2022).\u003c/li\u003e\n\u003cli\u003eWang, L. \u003cem\u003eet al.\u003c/em\u003e Natural product agonists of peroxisome proliferator-activated receptor gamma (PPAR\u0026gamma;): a review. \u003cem\u003eBiochem. Pharmacol.\u003c/em\u003e\u003cstrong\u003e92\u003c/strong\u003e, 73\u0026ndash;89 (2014).\u003c/li\u003e\n\u003cli\u003eUseini, A., Engelberger, F., K\u0026uuml;nze, G. \u0026amp; Str\u0026auml;ter, N. Structural basis of the activation of PPAR\u0026gamma; by the plasticizer metabolites MEHP and MINCH. \u003cem\u003eEnviron. Int.\u003c/em\u003e\u003cstrong\u003e173\u003c/strong\u003e, 107822 (2023).\u003c/li\u003e\n\u003cli\u003eGuendouzi, A., Belkhiri, L., Slimani, Z., Guendouzi, A. \u0026amp; Moroy, G. Virtual Screening of Novel Benzothiozinone Derivatives to Predict Potential Inhibitors of Mycobacterium Tuberculosis Kinases 2D-QSAR, Molecular Docking, MM-PBSA Dynamics Simulations, and ADMET Properties. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e\u003cstrong\u003e26\u003c/strong\u003e, 5129 (2025).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Unfolded protein response, Insulin resistance, Type 2 diabetes, Uncaria tomentosa, Phytochemicals, In silico modelling","lastPublishedDoi":"10.21203/rs.3.rs-7962616/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7962616/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eType 2 diabetes (T2D) is a metabolic syndrome frequently associated with obesity and endoplasmic reticulum stress-mediated inflammation, which can lead to unfolded protein response (UPR), impaired insulin signaling, and apoptosis. In an attempt to identify potential natural therapeutic candidates, this study investigated the mechanisms of action of fourteen compounds present in \u003cem\u003eUncaria tomentosa\u003c/em\u003e (UT), a medicinal plant from the Amazon rainforest, using \u003cem\u003ein silico\u003c/em\u003e modeling. The study focused on UPR, TRAF2/JNK pro-inflammatory and insulin signaling pathways, which play key roles in T2D. The UT compounds were docked against several human proteins involved in these pathways, and molecular dynamics simulations confirmed stable interactions between the target proteins (PERK, TRAF2, JNK, TNF-α, IRS-1, PI3K, AKT, GSK3β, and PPARγ) and four of the UT compounds, \u003cem\u003e5-Carboxystrictosidine\u003c/em\u003e, \u003cem\u003eCinchonain\u003c/em\u003e, \u003cem\u003eEpicatechin\u003c/em\u003e and \u003cem\u003eMitraphylline\u003c/em\u003e. Additionally, ADMET property analyses were conducted for the four promising compounds, revealing favorable pharmacokinetic properties. These findings suggest that specific UT compounds may offer therapeutic potential in managing T2D by modulating signaling pathways related to the conditions UPR, inflammation, and insulin resistance.\u003c/p\u003e","manuscriptTitle":"Targeting insulin signaling and TRAF2/JNK pathway: a comprehensive in silico study of Uncaria tomentosa compounds","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-17 11:50:39","doi":"10.21203/rs.3.rs-7962616/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":"04f0ccd3-ee45-486a-90a3-98816d690d80","owner":[],"postedDate":"November 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58068632,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":58068633,"name":"Health sciences/Diseases"},{"id":58068634,"name":"Biological sciences/Drug discovery"}],"tags":[],"updatedAt":"2026-01-10T09:39:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-17 11:50:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7962616","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7962616","identity":"rs-7962616","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}