Discovering Therapeutic Candidates for Lung Cancer via PDK3 Inhibition – A drug repurposing approach

Discovering Therapeutic Candidates for Lung Cancer via PDK3 Inhibition – A drug repurposing approach | 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 Discovering Therapeutic Candidates for Lung Cancer via PDK3 Inhibition – A drug repurposing approach Zeba Firdos Khan, Aanchal Rathi, Afreen Khan, Farah Anjum, Arunabh Chaudhury, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4795408/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Pyruvate dehydrogenase kinase (PDK) can control the catalytic activity of pyruvate decarboxylation oxidation through the mitochondrial PD complex. Additionally, glycolysis is connected to the production of ATP and the tricarboxylic acid cycle. One up-and-coming method for curing metabolic illnesses like heart failure, cancer, and diabetes is by controlling the expression or activity of PDKs. To find possible bioactive inhibitors of pyruvate dehydrogenase kinase 3 (PDK3), we used a structural-based virtual large-scale analysis of bioactive chemical compounds from the FDA-approved database. Using FDA-approved compounds for the analysis leverages existing safety and efficacy data, significantly accelerating the drug repurposing process. This screening process found two naturally occurring substances with strong affinity and specificity for the PDK3 binding site: bagrosin and dehydrocholic acid. Structural-based investigations provided a precise identification of compounds that fit the active site of PDK3, with desirable binding characteristics, optimizing drug-target interactions. Both substances interact with residues on ATP-binding sites of PDK3 preferentially. Additionally, all-atom molecular dynamic (MD) simulations were used to assess the consistency and dynamics of PDK3 interaction with bagrosin and dehydrocholic acid, and the results indicated that both complexes were stable. The findings might be used to develop innovative PDK3 inhibitors that could be used to treat severe illnesses like cancer. Compounds identified from the FDA-approved database are more likely to have known pharmacokinetics and pharmacodynamics profiles, facilitating their transition into clinical trials. PDK3 FDA-approved drugs Lung cancer Virtual screening MD simulation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Lung cancer is the most common type of cancer in the world, and it has been that way for many years [ 1 ]. In the UK alone, there were 46,403 new cases of lung cancer in 2014, which is quite surprising. Even though it's the third most common cancer overall, it causes the most deaths compared to other cancers, with 22% of all cancer-related deaths being attributed to lung cancer. Unfortunately, in 93 countries, lung cancer is the leading cause of cancer death among men because it has a high mortality rate [ 2 ]. In certain countries like China, Indonesia, and some African nations, where lung cancer rates are still increasing [ 3 ], smoking rates have recently reached their highest point or are still going up. This means that unless we take action to help people quit smoking or stop them from starting in the first place, lung cancer rates will likely keep going up in the future[ 4 ]. Life depends on energy, and animals get their energy by converting fuels into power through oxidative phosphorylation (OXPHOS) in mitochondria [ 5 ]. However, most cancer cells have a different way of getting energy. They consume a lot of glucose and produce lactate, even when there's enough oxygen [ 6 , 7 ]. Aerobic glycolysis, or the Warburg effect, helps cancer cells grow and avoid dying. It can also be used as a target for cancer treatment [ 8 ]. Lactate can affect how immune cells work, making detecting and fighting abnormal cells harder. It also helps cancer cells move and spread to other parts of the body [ 9 , 10 ]. The Warburg effect is a characteristic of cancer cells, including those in lung cancer [ 11 ]. This facilitates the transition from oxidative phosphorylation to glycolysis. This switch is necessary because cancer cells need a lot of energy and building blocks to grow quickly [ 7 ]. By using more glucose and a substance called glutamine, cancer cells can make a large amount of energy (called ATP) to build important parts of the cell like membranes, DNA, and proteins [ 12 ]. When tumor cells don't get enough oxygen (called hypoxia), they change their way of getting energy. Instead of relying on oxygen, they increase their use of glucose to make ATP [ 13 ] through a process called glycolysis. This helps them survive and keep growing, even in difficult conditions [ 14 ]. A protein called hypoxia-inducible factor-1 (HIF-1) is vital in the shift from using oxygen to using glucose for energy. It activates specific genes that help cancer cells grow blood vessels (angiogenesis), take in more glucose, and use glycolysis, supporting the survival and growth of cancer cells [ 14 ]. The mitochondrial pyruvate dehydrogenase complex (PDC) is a group of components that helps convert pyruvate into acetyl-CoA. It also plays a crucial role in connecting glycolysis to the tricarboxylic acid cycle. This process also contributes to ATP production, which is vital for energy in our cells [ 15 , 16 ]. PDC is made up of three parts: pyruvate dehydrogenase (E1), dihydrolipoamide acetyltransferase (E2), and dihydrolipoamide dehydrogenase (E3). These parts work together to convert pyruvate into acetyl-CoA [ 15 ]. The activity of PDC is controlled by a protein called pyruvate dehydrogenase kinase (PDK), which can turn PDC on or off by adding a phosphate group [ 17 , 18 ]. Recent studies have shown that PDK3, one of the types of PDK, plays an important role in regulating the metabolism of cancer cells. PDK3 has the highest activity among all the PDKs, and its activity is not affected by high levels of pyruvate. This makes PDK3 a potential target for cancer therapy. PDKs, including PDK3, inhibit the conversion of pyruvate to acetyl-CoA, which leads to a shift in cellular energy production from the mitochondria to the cytoplasm. This change in energy production is associated with cancer cell growth and drug resistance. In hypoxic conditions (low oxygen levels), PDK1 and PDK3 are induced, leading to increased lactic acid production and further inhibition of mitochondrial respiration. This metabolic switch mediated by PDK3 contributes to drug resistance in hypoxic tumors. In cancer cells with high levels of HIF-1alpha, an oxygen-sensing protein, PDKs inactivate pyruvate dehydrogenase, causing pyruvate to accumulate [ 19 ]. However, PDK3 remains active, ensuring that mitochondrial respiration stays shut down. Blocking PDK3 is essential because it helps make cancer cells more vulnerable to anticancer medications, especially when insufficient oxygen is available. By stopping PDK3, we can overcome drug resistance and improve the effectiveness of cancer treatments. Several substances studied can inhibit PDK, including dichloroacetate (DCA), AZD7545, and Radicicol [ 20 ]. DCA is a compound similar to pyruvate, which can be taken orally and binds to the N-terminal region of PDK. This binding stops PDK activity, allowing PDC to function again [ 21 ]. As a result, the body shifts from using glycolysis to metabolizing glucose, which leads to apoptosis (cell death), inhibits tumor growth, and could potentially be used as a cancer treatment. A study found that under conditions of low oxygen (hypoxia), PDK3 levels increased, causing resistance to anticancer drugs [ 19 ]. However, when PDK3 was suppressed in cells, this resistance was eliminated. Further research revealed that when both copies of the PDK3 gene were silenced, more cancer cell death occurred during hypoxic conditions, indicates that PDK3 contributes to drug resistance induced by hypoxia and suggests inhibiting PDK3 could make cancer cells more sensitive to anticancer drugs. Moreover, the expression of PDK3 not only reduces cell survival during low oxygen levels but decreases lactate production (a byproduct of glycolysis) and drug resistance. These findings suggest that PDK3 could be a promising target for cancer therapy. The term "repurposing" pertains to utilizing approved compounds for one clinical purpose in treating another disease or syndrome. The motivation behind repurposing largely stems from the substantial expenses involved in drug development and the extensive duration required to establish the safety and specificity of an utterly novel medication. It often takes several years, if not decades, for a new cancer drug to undergo sufficient clinical trials to obtain approval [ 22 ]. Repurposing drugs makes it possible to swiftly advance them into Phase II and Phase III clinical studies, thereby substantially reducing the associated development costs. Here, we screened compounds approved by the FDA to find new potential inhibitors for a protein called PDK3. We have implemented a structure-based drug design approach to discover new drugs for lung cancer therapy. Materials and Methods Virtual high-throughput screening is a highly effective method for discovering new possibilities against specific targets [ 23 ]. This computational technique involves screening a vast collection of chemical libraries to identify potential drug candidates by predicting their likely binding to proteins with high affinity [ 24 , 25 ]. For docking-based virtual screening, we used InstaDock [ 25 ], PyMOL[ 26 ], Discovery Studio Visualizer [ 27 ], and GROMACS [ 28 ] tools simulations using MD and interaction analysis. Molecular Docking Molecular docking is frequently applied to determine the most favorable receptor-ligand interaction conformation. This technique determines the ideal binding position based on a docking score determined by various parameters, such as surface area, number of polar contacts, binding energy, and binding mode. For this screening procedure, we managed to get the crystallographic structure of PDK3, resolved at 2.48 Å, from the RCSB Protein Data Bank (PDB ID: 1Y8O) [ 29 ]. The structure was further refined using PyMOL by removing water molecules and extraneous small particles mixed with the main structure. The original structure had missing residues in the amino acid sequences from positions 307–319 and 322–323. To address this, remodeling was carried out using PyMod-3, which employed MODELLER software based on the self-template 6Z45 (version 9.20). Virtual screening protocol By using the InstaDock v1.0 tool in a blind search area [ 25 ]. Structure-based virtual screening was performed on the FDA-approved library to find compounds with greater binding affinity towards PDK3. Size parameters were set to X = 71, Y = 65, and Z = 6, and a grid box was defined with X = 153.741, Y = 8.516, and Z = 21.232 as its center. Compounds were picked according to their binding affinity values to conduct further study, and the compounds with the best docking hits were selected. Multiple docked conformers were generated to analyze interactions. The visualization of close interactions between PDK3 and FDA-approved drugs within a 3.5 Å range was achieved using PyMOL by detecting the polar contacts. Subsequently, we chose the compounds that interacted with the critical residues of the PDK3-ATP binding sites. PAINS and ADMET properties of FDA-approved compounds Compounds identified through docking analysis were subsequently refined depending on their physiochemical and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) characteristics. Analyzing a chemical compound's effectiveness and therapeutic possibilities is essential in pharmaceutical research. The drug-likeness of the selected compounds was determined by calculating their ADMET characteristics and PAINS patterns. Following the docking process, those identified hits were examined using SwissADME to implement the PAINS filter and evaluate the ADMET properties of the screened compounds. The SMILES of the compounds were suitable for input on both systems. The PAINS filter was utilized to screen the compounds, efficiently discovering Pan-assay interference compounds demonstrating a significant inclination to bind to many targets. PASS Analysis: Predicting Biological Activity The identified compounds were analyzed using the PASS website to predict their biological characteristics. This service can predict many pharmacological properties for a given molecule. PASS makes predictions by using structure-activity correlations based on chemical properties [ 30 ]. To train its model, the PASS web server compares the input structure to a library of predefined biological functions. Depending on the ratio between the probability of being active (Pa) and the probability of being inactive (Pi), a comprehensive compilation of potential biological properties is generated. A higher Pa value indicates an increased probability of the compound showing a predicted biological characteristic. Interaction Analysis Interaction analysis was performed on the chosen protein-ligand complexes to examine the interactions that occur during the binding step. Discovery Studio Visualizer and PyMOL, among other bioinformatics tools, were utilized to analyze binding poses and all potential interactions. In PyMOL close contacts within the protein-ligand complex were defined as interactions within 3.5 Å. Similarly, the types of interactions and the residual and atomic locations involved were evaluated using Discovery Studio Visualizer. Critical residues of PDK3 show specific interactions with compounds chosen for more research because of their ADMET characteristics, which included binding and active sites. Therefore, the docking data were correlated by examining the binding partners of PDK3. Molecular dynamics simulation One powerful computational tool in drug design is molecular dynamics (MD) simulation. It is known to study the structures of molecules and the conformational changes in proteins caused by ligand binding by simulating the behavior of atoms and molecules [ 23 , 31 ]. On an HP Z840 computer, all-atom MD simulations were performed at 300 K for 100 ns using the GROMACS 2020-beta simulation package [ 28 ]. MD simulations of PDK3 in combination with FDA-approved drugs were run for one hundred nanoseconds. The three systems were simulated using the GROMOS 54A7 force field and the GROMACS 2020 beta software suite for all atoms. The PRODRG server generated the topological parameters of FDA-approved compounds and then included them in the PDK3 topology to form a protein-ligand complex system. In order to create an equilibrated aquatic environment, the three systems were immersed in a cubic container filled with TIP3P water model and then neutralized by adding Na + and Cl- ions. To remove any disturbances with high energy levels from the original structures, 1500 reduction steps were performed using the steepest descent method until the systems were fully reduced. NVT and NPT ensembles were used to equilibrate the energy-minimized systems twice. Ultimately, each system underwent a 100 ns simulation, after which generated trajectories were plotted using QtGrace software and checked using the provided GROMACS capabilities. Principal Component Analysis (PCA) and free energy landscapes Principal Component Analysis (PCA) is a widely used mathematical technique in molecular dynamics (MD) analytics for reducing the dimensionality of data sets. This approach for unsupervised learning identifies principal components (PCs), which are directions that capture the most variation in the data. Principal Component Analysis (PCA) is valuable for identifying significant variations in high amplitudes within MD trajectories. To analyze the PDK3 MD trajectories, we employed PCA and the free energy landscape (FEL) analysis to assess the atomic movements, conformational sampling, and stability of the compounds bagrosin and dihydrocholic acid before and after binding with PDK3. RESULTS AND DISCUSSION Molecular Docking Virtual screening is a computational process to identify possible lead compounds by analyzing thousands of compounds over a particular protein target. As part of this process, the compounds are docked into the active site of a protein receptor complex, and an energy-scoring function is used to measure how well their binding affinities [ 24 , 32 , 33 ].This technique stands out by quickness, cost-effectiveness and resource efficiency. An FDA-approved library of 3839 compounds, all compliant with the five rules of Lipinski, were subjected to docking against PDK3. The virtual screening of these compounds against PDK3 was conducted using InstaDock, an interactive user interface for molecular docking that allows for single-click operation. The selection of the top 50 hits was determined after the docking process, considering their projected ligand efficiency and docking scores. The experimental findings revealed the 50 compounds that are under investigation displayed notable affinity towards PDK3, as evidenced by binding energies spanning from − 11.6 to -7.7 kcal/mol. The results above indicate that the chosen hits exhibit significant potential in their ability to interact with PDK3, thereby warranting more studies as potential competitive inhibitors of PDK3. PAINS and ADMET properties of compounds Following the identification of the top 50 hits identified through molecular docking, compounds displaying Pan-assay interference compounds (PAINS) were excluded (Table S1 ). These compounds demonstrated a strong affinity for binding with multiple biological targets, potentially leading to non-specific off-target side effects, making them not a good option for drug discovery. Given these concerns, PAINS filtration was conducted on the selected hits, leading to a refinement in the selection process. The compounds that exhibited the highest affinity towards PDK3 following the molecular docking process, known as top hits of FDA compounds, were subsequently subjected to further investigation to assess their ADMET characteristics. The pharmacokinetic properties of two compounds, bagrosin (-10.6 kcal/mol) and dehydrocholic acid (10.5 kcal/mol), were assessed using the swissADME webserver. The analysis revealed that both compounds did not exhibit any toxic patterns, as indicated in (Table 1 ). In conjunction with the reference molecule, the ADMET features of the compounds under investigation exhibit notable gastrointestinal absorption and water solubility characteristics while demonstrating no toxicological patterns in the AMES tests. The findings suggest that the chosen compounds exhibit drug-like features, allowing more studies as potential candidates for targeting PDK3. The PASS Analysis: Predicting Biological Activity The extensive training set of the PASS server encompasses a wide array of bioactive compounds derived from numerous preclinical and clinical studies. This dataset provides a comprehensive understanding of the correlation between the structure and activity of these compounds, as elucidated by [ 30 ]. The compounds that underwent ADMET (Table 3 ) filtration were subsequently examined for their anticipated biological activities. In this research, only bagrosin and dehydrocholic acid met the requirements of the PASS criteria for the wanted biological activity prediction. The findings of this study suggest that both compounds demonstrate notable anticancer and kinase inhibitory characteristics. When the pressure (Pa) value surpasses 0.7, it indicates a significant probability that the compound will exhibit the expected biological activity. Bagrosin and dehydrocholic acid have shown great potential in various areas, such as inhibiting superoxide dismutase, antagonizing neurotransmitters, enhancing TP53, antineoplasty inhibiting JAK2, and producing analeptic effects. The Pa values for these compounds range from 0.301 to 0.954. The findings of this study indicate that both compounds exhibit noteworthy biological potential when employed in anticancer interventions that specifically target PDK3 (Table 2 ). Table 2 PASS analysis: Prediction of biological activity of selected phytochemicals against PDK3. S. No. Compound Pa Pi Activity 1. Bagrosin 0,635 0,019 Superoxide dismutase inhibitor 0,587 0,007 Alzheimer's disease treatment 0,546 0,069 TP53 expression enhancer 0,531 0,041 Oxygen scavenger 0,518 0,034 Neurotransmitter antagonist 0,498 0,001 JAK2 expression inhibitor 0,301 0,089 Antineoplastic (solid tumors) 2. Dehydrocholic acid 0,954 0,000 Bile-salt sulfotransferase inhibitor 0,904 0,004 Protein-disulfide reductase (glutathione) inhibitor 0,771 0,008 Oxidoreductase inhibitor 0,719 0,009 Analeptic 0,520 0,016 Antimetastatic 0,543 0,059 Antineoplastic Table 3 Pharmacokinetics, drug-likeness and medicinal chemistry friendliness of selected molecules predicted through SwissADME. ADME study of Molecule Bagrosin Dehydrocholic acid Canonical Smiles CC1(C(= O)NC(= O)N1)C2 = CC3 = C(C = CC4 = CC = CC = C43)C = C2 CC(CCC(= O)O)C1CCC2C1(C(= O)CC3C2C(= O)CC4C3(CCC(= O)C4)C)C Molecular Formula C18H14N2O2 C24H34O5 Physiochemical properties Molecular weight Number of heavy atoms Number of aromatic heavy atoms Fraction Csp3 Number of rotatable bonds Number of H-bond acceptors Number of H-bond donors Molar refractivity Topological polar surface area (TPSA) 290.32 g/mol 22 14 0.11 1 2 2 92.83 58.20 Ų 402.52 g/mol 29 0 0.83 4 5 1 110.88 88.51 Ų Lipophilicity Log Po/w (iLOGP) Log Po/w (XLOGP3) Log Po/w (WLOGP) Log Po/w (MLOGP) LogPo/w(SILICOS-IT) Consensus Log Po/w 1.93 3.52 2.18 2.59 3.38 2.72 2.27 2.56 4.07 2.75 4.29 3.19 Water solubility Log S (ESOL) Log S (Ali) Log S (SILICOS-IT) - 4.26, Moderately Soluble -4.43,Moderately Soluble -6.83,Poorly Soluble - 3.68, Soluble -4.07, Moderately Soluble -4.40, Moderately Soluble Pharmacokinetics GI absorption BBB permeant P-gp substrate CYP1A2 inhibitor CYP2C19 inhibitor CYP2C9 inhibitor CYP2D6 inhibitor CYP3A4 inhibitor LogKp (skin permeation) High Yes Yes Yes Yes Yes Yes No -5.57 cm/s High No Yes No No No No No -6.94 cm/s Drug likeliness Lipinski Ghose Veber Egan Muegge Bioavailability Score Yes Yes Yes Yes Yes 0.55 Yes Yes Yes Yes Yes 0.56 Medicinal chemistry PAINS Brenk Leadlikeness Syntheticaccessibility 0 alert 2 alerts: hydantoin, polycyclic_aromatic_hydrocarbon_3 No; 1 violation: XLOGP3 > 3.5 2.34 0 alert 0 alert No; 1 violation: MW > 350 4.53 Interaction Analysis The co-crystallized structure of PDK3 with its ATP binding site. The structure reveals key interaction residues for ATP binding, highlighting important residues involved in substrate binding (Fig. 1 ). We found that bagrosin and dehydrocholic acid exhibit binding affinity towards the important residues within the ATP-binding site of PDK3, namely Gly327 and Phe324. The research results showed a notable connection between both compounds and the ATP binding pocket, which is recognized as an interaction site for various established inhibitors of PDK3 (Fig. 2 A & 2 D). Both drugs exhibit strong binding affinity to the active site of PDK3, effectively obstructing the ATP binding pocket. Bagrosin and dehydrocholic acid bind with complementarily fit when they bind to PDK3 (Fig. 2 C& 2 F). The results demonstrated that PDK3 can be pharmacologically inhibited by competitively binding ATP and stabilizing the interaction between bagrosin and dehydrocholic acid with PDK3. Subsequent research examined the detailed mechanism of interaction between bagrosin, dehydrocholic acid, and PDK3. A comprehensive study helps identify the kind and characteristics of non-covalent interactions in the protein-ligand complex. A 2D depiction was created for the docked complex, as seen in (Fig. 2 B& 2 E). The 2D diagrams illustrate the interaction between the ATP binding residue Gly327 and Phe324 of PDK3 with both molecules, bagrosin, and dehydrocholic acid, via hydrogen bonding. The chemicals also interact with several important residues around the ATP-binding site in PDK3, including Ile330, Glu247, Tyr326, Lys250, Pro124, Ala127, Gln128, Ile131, His243, and Lys250. Gly327 is involved in the ATP-binding process with PDK3 (Table 4 ). The study indicated that bagrosin and dehydrocholic acid co-occur in a location that resembles the site where ATP is co-crystallized. Based on the interaction investigation of bagrosin and dehydrocholic acid with PDK3, it was shown that they can inhibit ATP competitively. Table 4 Different interactions between Bagrosin, dehydrocholic acid and PDK3 interacting residues. S. No. Compounds Binding Free Energy (kcal/ mol) Ligand Efficiency (kcal/mol/non-H atom) Type of Interactions Hydrogen bonds van der Waals Pi-Pi Stacked/ Pi-Pi T-shaped Pi-Sigma/Pi-Alkyl 1. Bagrosin -10.6 0.4818 Gly327 Ile330, Glu247, Tyr326, Lys250, Phe324 Ala127, Pro124, 2. Dehydrocholic acid -10.5 0.3621 Gly327, Phe324 Pro124, Ala127, Gln128, Ile330, Ile131, Tyr326, Gly327, His243, Glu247, Lys250, Phe324 MD simulations Post Dynamics Analysis of Trajectories MD simulations on docked complexes can improve docking models by considering how flexible ligands and proteins are [ 34 ]. MD models can help fill in the blanks about proteins' structure and dynamic behavior when experiments don't work. It was possible to study all the atoms of bagrosin, dehydrocholic acid, PDK3, and PDK3 in their free state (Table 5 ). Molecular dynamics simulations with a duration of 100 nanoseconds. Before MD analysis, MD verification was conducted on the simulated trajectories to verify the equilibration and consistency of the simulations. The investigation of PDK3's stability and dynamics before and after binding with bagrosin and dehydrocholic acid included the analysis of many systematic and structural aspects, which will be detailed in the following sections. Table 5 Mean values for the important MD parameters calculated for Native PDK3, PDK3-bagrosin and PDK3-dehydrocholic acid. System RMSD (nm) RMSF (nm) Rg (nm) SASA (nm 2 ) #H-bonds PDK3 0.32 0.12 2.15 181.31 278 PDK3-Bagrosin 0.28 0.13 2.18 181.35 282 PDK3-Dehydrocholic acid 0.29 0.13 2.19 186.55 278 Structural deviations in PDK3 When a small molecule interacts with the active binding cleft of a protein, it may cause substantial changes in the protein's structure[ 35 , 36 ]. The root mean square deviation (RMSD) provides useful data on a protein's conformational changes and structural variations [ 37 – 39 ]. The PMSD analysis was used to evaluate the variations in the backbone conformation of PDK3 before and after a ligand's interaction. The stability of PDK3 and its complexes with bagrosin and dehydrocholic acid was determined by tracking the time-dependent changes in RMSD throughout the simulation period. The RMSD plots of the PDK3-bagrosin and PDK3-dehydrocholic acid complexes displayed time-dependent graphs illustrating the changes and deviations in the protein backbone during the simulations. The simulation trajectory results are shown in (Fig. 3 A) and were then used to examine the stability of the complex. The RMSD figure indicates a small reduction in fluctuations in ligand-bound systems compared to the free state of PDK3[ 39 , 40 ]. All three systems reached a state of equilibrium and maintained stability during the 100-nanosecond simulation period. The observed fluctuations in RMSD are insufficient to cause significant structural deviations, although they suggest some system adaptations. The PDF plot in the lower panel (Fig. 3 C) also demonstrates a marginal reduction in the RMSD values. An analysis of root-mean-square fluctuation (RMSF) is useful for identifying the residual vibrations present in a protein molecule during MD simulation [ 41 , 42 ]). The Root Mean Square Fluctuation (RMSF) analysis of the PDK3 backbone and its interactions with bagrosin and dehydrocholic acid (Fig. 3 B & 3 D) characterize the local variations occurring throughout the simulation. The high variations in the Root Mean Square Fluctuation (RMSF) values indicate the presence of loops and unstructured areas in the protein. The average variation in local structural flexibility of each residue was analyzed in PDK3 before and after interaction with the drug. The figure shows that all systems exhibit a comparable root mean square fluctuation (RMSF) pattern with minor randomized variations. The plot shows residual fluctuations reflect a consistent and slightly elevated pattern when bagrosin and dehydrocholic acid bind, suggesting that the protein-ligand complexes are distressed yet remain stable. The residues inside the PDK3 binding pocket that engage with bagrosin and dehydrocholic acid were rather stable throughout the simulation, as shown by the RMSF study. Structural compactness The radius of gyration (R g ) is a crucial measure for studying a protein's compactness and folding behavior [ 43 ]. Rg is a frequently used metric to determine the density of proteins and protein-ligand complexes. We calculated the time evolution of the R g (radius of gyration) using the simulated trajectory of all three systems[ 39 ]. Based on the R g plot, when bagrosin and dehydrocholic acid are present, PDK3 remains consistent at a value between 2.15nm and 2.19nm during the trajectory (Fig. 4 A). The findings suggest that the binding to bagrosin and dehydocholic acid and the structural dynamics and folding of PDK3 remain consistently stable with a little increase. The R g values for PDK3 exhibit a consistent distribution both before and after binding with bagrosin and dehydocholic acid, as shown in the PDF plot (Fig. 4 C). The portion of a protein molecule that can be accessed by an adjacent solvent is referred to as the solvent-accessible surface area (SASA) [ 44 ]. SASA, or solvent-accessible surface area, is linked to the R g , or radius of gyration, and is often used to investigate the variations in protein structure and folding or unfolding [ 45 ]. SASA methods are valuable in determining the extent of protein folding and the count of native contacts [ 46 , 47 ]. The changes in the solvent-accessible surface area (SASA) of PDK3 over time have been studied regarding its binding with bagrosin and dehydrocholic acid. The figure indicates no significant changes in SASA values throughout the simulations. Based on the SASA investigation, the PDK3 structure remains stable throughout the simulation, even when exposed to bagrosin and dehydrocholic acid (Fig. 4 B). The SASA distribution exhibits a similar equilibration pattern to the R g values, with a slight increase in ligand-bound systems but not significantly impacting the overall compactness (Fig. 4 B& 4 D). Interaction dynamics in the PDK3 complexes: H-bond analysis Hydrogen bonds, as H-bonds, play an essential part in folding protein structures and assembling their conformations [ 48 ]. Intramolecular hydrogen bonding is a critical factor contributing to proteins' structural stability [ 49 ]. Intramolecular hydrogen bonds have been employed to look into the conformational changes and compactness of protein structure [ 24 , 50 , 51 ]. The intramolecular H-bond count in PDK3 has been measured over time using MD trajectories (Fig. 5 A). The outcomes allow us to analyze the intramolecular bonding consistency of PDK3 both before and after its binding to bagrosin and dehydrocholic acid. The figure demonstrates a change in the number of hydrogen bonds established inside PDK3 before and after bagrosin and dehydrocholic acid-binding. The graph indicates that the hydrogen bonds produced inside PDK3 were stable and played an essential part in determining the shape of the protein structure. The dynamic analysis shows that a little rise in intramolecular hydrogen bonds within PDK3-bagrosin complexes resulted in a somewhat more condensed structure than the free PDK3 form. On the other hand, PDK3-dehydrocholic acid exhibited a comparable amount of intramolecular hydrogen bonds. The PDF illustrating intramolecular hydrogen bonding in all systems was also shown (Fig. 5 B). The interaction between bagrosin and dehydrocholic acid with PDK3 was investigated in terms of the stability of hydrogen bonding. This was done by analyzing the changes in intermolecular hydrogen bonds over time. The bagrosin-PDK3 complex established 2 stable hydrogen bonds, whereas the dehydrocholic acid complex formed 3 (Fig. 5 C& 5 D). The PDF analysis demonstrated that the intramolecular hydrogen bonds in both systems exhibited a consistent level, with a greater PDF value corresponding to an increased number of hydrogen bonding interactions (Fig. 5 B). The bagrosin and dehydocholic acid remained stationary at their original docking site on the PDK3 protein owing to stable intermolecular hydrogen bonding, which helped stabilize the complexes between the protein and ligands. Principal component and free energy The study's authors used Principal Component Analysis (PCA) on simulated trajectories to investigate the collective movements of a protein [ 52 ]. We studied the first two principal components (PCs) derived from the PCA analysis of PDK3 and its complexes with bagrosin and dehydrocholic acid. Figure 6 illustrates the exploration of several conformations inside the fundamental subspace of PDK3, PDK3-bagrosin, and PDK3-dehydrocholic acid. The Cα atom of PDK3 determines the conformational states along the EV1 and EV2. The graph demonstrates that the PDK3-bagrosin and PDK3-dehydrocholic acid complexes have nearly the same essential region as PDK3 in its unbound form. The PDK3-bagrosin complex and free-PDK3 are seen to occupy the same subspace in both EVs. The flexibility of the PDK3-bagrosin complex is greater on EV1 compared to EV2. Simultaneously, the PDK3-dehydrocholic acid complex exhibits more flexibility on EV1 in the direction of positive projection (Fig. 7 ). The conformational behavior and folding states of FELs were analyzed by constructing them from simulated trajectories [ 53 ]. The stability of protein and protein-ligand complexes in the presence of a solvent was assessed using MD simulation trajectories. The lowest energy states and the arrangement of molecular structures of the PDK3, PDK3-bagrosin, and PDK3-dehydrocholic acid complexes were obtained by analyzing two principal components, namely PC1 and PC2. The contoured maps depicting the FELs of PDK3, PDK3-bagrosin, and PDK3-dehydrocholic acid complexes are shown in (Fig. 7 ). Based on the FELs plots, the binding of bagrosin and dehydrocholic with PDK3 has a minimal impact on the size and location of the phases limited to 1–2 stable global minima. A deeper shade of blue in the Free Energy Landscapes (FELs) indicates the existence of a conformation with lower energy close to the native states, as seen in Fig. 7 . The graph shows PDK3 is constrained to a solitary, expansive global minimum encompassing 1–2 basins. PDK3-bagrosin and PDK3-dehydrocholic acid exhibit almost comparable configurations, characterized by a prominent global minimum and 3–4 smaller local basins that vary in population (Fig. 7 A- 7 C). In summary, the MD simulation and essential dynamics analysis of PDK3 in the presence of bagrosin and dehydrocholic acid demonstrate that theay remain stable over 100 nanosecond simulations with minor changes in their conformation. Based on the data above, it can be inferred that bagrosin and dehydrocholic acid have a significant affinity and stability when binding to PDK3, resulting in a potent inhibition of the PDK3 protein. Conclusions The strategic targeting of PDK3 through the repurposing of FDA-approved compounds presents a promising avenue for modulating pathways that exhibit anticancer properties. To identify potential FDA-approved pharmaceutical interventions for the dysregulated PDK3, a comprehensive multi-tier virtual screening strategy was employed on the FDA-approved database. In this context, PDK3 has been recognized as a crucial pharmacological target owing to its pivotal function as a positive regulator of cancer progression and growth. Understanding the metabolic switching of cancer cells is vital for developing effective and targeted treatment interventions.The proposed method holds significant value in advancing anticancer therapies by leveraging natural leads as highly effective PDK3 inhibitors. Additionally, it serves as a valuable therapeutic model for metabolic reprogramming. In this study, we introduce a comprehensive virtual screening approach aimed at identifying potential inhibitors of PDK3. Two compounds, namely bagrosin and dehydrocholic acid, were selected for investigation due to their notable binding affinity, specific interactions, and potential biological characteristics. Through MD simulation, PCA, and FEL analyses, researchers uncovered the stable binding of bagrosin and dehydrocholic acid to PDK3. Based on this study, the potential use of FDA-approved drugs as PDK3 inhibitors in the future was explored using a computer-assisted drug-design technique. The study identified FDA-approved drugs that offer a valuable resource and approach for identifying potential therapeutic inhibitors of PDK3 to treat different types of cancer, following further experimental validation. Abbreviations PDK3- Pyrivate dehydrogenase kinase 3; HIF-1- Hypoxia-inducible factor-1; PDC- Pyruvate dehydrogenase complex; MD- Molecular Dynamics; Rg- Radius of gyration; RMSD- Root mean square deviation; RMSF- Root mean square fluctuation; PDF- Probability density function; SASA- Solvent accessible surface area; PCA- Principal Component Analysis; FEL- Free Energy Landscape Declarations Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding: This work is supported by Indian Council of Medical Research (Grant No. ISRM/12(22)/2020). This research was funded by Taif University, Saudi Arabia, Project Number (TU-DSPP-2024-140). Acknowledgments: The authors extend their appreciation to Taif University, Saudi Arabia for supporting this work through project number (TU-DSPP-2024-140). Data Availability Statement Data is provided within the manuscript or supplementary information files. Supplementary material Supplementary data to this article can be found online. Author's contribution statement Zeba Firdos Khan: Conceptualization, Investigation, Data curation, Validation, Writing – original draft, Aanchal Rathi: Investigation, Validation, Formal analysis, Afreen Khan : Validation, Formal analysis, Farah Anjum: Methodology, Investigation, Validation, Arunabh Chaudhury: Investigation, Validation, Formal analysis, Aaliya Taiyab : Validation, Formal analysis, Writing – review & editing, Anas Shamsi: Data curation, Validation, Formal analysis, Writing – review & editing, Md. 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Anjum F, Mohammad T, Almalki AA, Akhtar O, Abdullaev B, et al. (2021) Phytoconstituents and medicinal plants for anticancer drug discovery: Computational identification of potent inhibitors of PIM1 kinase. Omics: A Journal of Integrative Biology 25: 580-590. Arcon JP, Defelipe LA, Modenutti CP, López ED, Alvarez-Garcia D, et al. (2017) Molecular dynamics in mixed solvents reveals protein–ligand interactions, improves docking, and allows accurate binding free energy predictions. Journal of chemical information and modeling 57: 846-863. Hubbard RE, Haider MK (2010) Hydrogen bonds in proteins: role and strength. eLS. Williams M, Ladbury J (2003) Hydrogen bonds in protein‐ligand complexes. Protein-ligand interactions: from molecular recognition to drug design: 137-161. Mobika J, Rajkumar M, Sibi SL, Priya VN (2021) Investigation on hydrogen bonds and conformational changes in protein/polysaccharide/ceramic based tri-component system. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 244: 118836. White SH (2005) How hydrogen bonds shape membrane protein structure. Advances in protein chemistry 72: 157-172. Maisuradze GG, Liwo A, Scheraga HA (2009) Principal component analysis for protein folding dynamics. Journal of molecular biology 385: 312-329. Altis A, Otten M, Nguyen PH, Hegger R, Stock G (2008) Construction of the free energy landscape of biomolecules via dihedral angle principal component analysis. The Journal of chemical physics 128. Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SupplymentarydataSREP2.docx Table1.docx Cite Share Download PDF Status: Published Journal Publication published 29 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 20 Sep, 2024 Reviews received at journal 16 Sep, 2024 Reviewers agreed at journal 04 Sep, 2024 Reviews received at journal 21 Aug, 2024 Reviewers agreed at journal 11 Aug, 2024 Reviewers invited by journal 30 Jul, 2024 Editor assigned by journal 30 Jul, 2024 Editor invited by journal 26 Jul, 2024 Submission checks completed at journal 25 Jul, 2024 First submitted to journal 24 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4795408","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":341710391,"identity":"f695798d-dad0-4c43-a80a-d6a969f258f9","order_by":0,"name":"Zeba Firdos Khan","email":"","orcid":"","institution":"Jamia Millia Islamia","correspondingAuthor":false,"prefix":"","firstName":"Zeba","middleName":"Firdos","lastName":"Khan","suffix":""},{"id":341710392,"identity":"94268b56-b5e2-4b40-abac-b4b6b68d3d2a","order_by":1,"name":"Aanchal Rathi","email":"","orcid":"","institution":"Jamia Millia Islamia","correspondingAuthor":false,"prefix":"","firstName":"Aanchal","middleName":"","lastName":"Rathi","suffix":""},{"id":341710393,"identity":"1cb22fd2-646e-46cc-a33a-6470f5a86fa0","order_by":2,"name":"Afreen Khan","email":"","orcid":"","institution":"Jamia Millia Islamia","correspondingAuthor":false,"prefix":"","firstName":"Afreen","middleName":"","lastName":"Khan","suffix":""},{"id":341710394,"identity":"e86a38c3-9b35-41ab-9cc9-a0a414a56d85","order_by":3,"name":"Farah Anjum","email":"","orcid":"","institution":"Taif University","correspondingAuthor":false,"prefix":"","firstName":"Farah","middleName":"","lastName":"Anjum","suffix":""},{"id":341710395,"identity":"c83ee5cd-3e85-4ead-aded-d9b9561c6877","order_by":4,"name":"Arunabh Chaudhury","email":"","orcid":"","institution":"Jamia Millia Islamia","correspondingAuthor":false,"prefix":"","firstName":"Arunabh","middleName":"","lastName":"Chaudhury","suffix":""},{"id":341710396,"identity":"b3227fee-607a-42f0-bd60-d7d69e0b11f0","order_by":5,"name":"Aaliya Taiyab","email":"","orcid":"","institution":"Jamia Millia Islamia","correspondingAuthor":false,"prefix":"","firstName":"Aaliya","middleName":"","lastName":"Taiyab","suffix":""},{"id":341710397,"identity":"c0012709-9323-431a-8c3c-5c383aa5fd13","order_by":6,"name":"Anas Shamsi","email":"","orcid":"","institution":"Ajman University","correspondingAuthor":false,"prefix":"","firstName":"Anas","middleName":"","lastName":"Shamsi","suffix":""},{"id":341710398,"identity":"9b05dbd3-829f-4394-b586-686ffac982be","order_by":7,"name":"Md. Imtaiyaz Hassan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYDACZh4Izc9wAAiBgI1oLZINRGthgGoxOECsu3TbeQ9+/PHHLs/44OnEAww1dgx80g34tZgd5kuW5m1LLjY7cHbDAYZjyQxsMgTsMzvMYyDN2MCcuA2shQ2IJBIIajH++eNPfeLmBpCWf8RpMZPgYTucuIEBqIWxjUgt1rxtxxNngByW2JfMQ1jL+TPGN3/8qU7sn3F284cP3+zk5GcQ0IIAEgcYGBLg0UQU4G8gQfEoGAWjYBSMKAAAX/lGe1i8t9IAAAAASUVORK5CYII=","orcid":"","institution":"Jamia Millia Islamia","correspondingAuthor":true,"prefix":"","firstName":"Md.","middleName":"Imtaiyaz","lastName":"Hassan","suffix":""}],"badges":[],"createdAt":"2024-07-24 12:36:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4795408/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4795408/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-78022-0","type":"published","date":"2024-11-29T15:58:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62986056,"identity":"83f1713c-c81f-4895-a92e-4a1403045cab","added_by":"auto","created_at":"2024-08-21 19:38:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":727938,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA detailed overview of PDK3 structure with the highlighted binding sites.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4795408/v1/dc7db58d0e821be2998e5dd3.png"},{"id":62985712,"identity":"f3d990ae-dea8-44cd-b95e-ce1de1ba7049","added_by":"auto","created_at":"2024-08-21 19:30:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":832802,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular docking of Bagrosin and Dehydrocholic acid with PDK3. (\u003c/strong\u003eA) Cartoon representation of Bagrosin (green) in the active site of PDK3 with residues involved in polar interactions, (B) 2D interaction map, and (C) surface view of PDK3 with bound Bagrosin (green). (D) Dehydrocholic acid (margenta) in the active site with residues involved in polar interactions (Ball and stick model), (E) 2D interaction map, and (F) surface view of PDK3 with bound Dehydrocholic acid (margenta).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4795408/v1/80d94c062b05a6073df1acb1.png"},{"id":62985709,"identity":"9579a463-1ba1-432e-8ce9-7501398aa965","added_by":"auto","created_at":"2024-08-21 19:30:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":253059,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructural Dynamics of PDK3 with Bagrosin (Red) and Dehydrocholic acid (Green). \u003c/strong\u003e(A) RMSD Plot and (B) Residual Fluctuations (RMSF) Plot of PDK3 with and without Bagrosin and Dehydrocholic acid Binding. (C) Probability Density Function plot of (C) RMSD and (D) RMSF before and after Bagrosin and Dehydrocholic acidbinding \u0026nbsp;to PDK3.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4795408/v1/be30d973833648971677ec17.png"},{"id":62985715,"identity":"36f74345-ce3f-4b56-8165-c09c654db7eb","added_by":"auto","created_at":"2024-08-21 19:30:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":277171,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvolution of (A) Radius of Gyration and (B) Solvent Accessible Surface Area Plot of PDK3 Across Time.\u003c/strong\u003eValues were derived from a 100ns MD simulation time scale. Black, red, and green denote free PDK3, Bagrosin-PDK3, and Dehydrocholic acid-PDK3 complex values, respectively. Probability Density Function plot of (C) R\u003cem\u003eg \u003c/em\u003eand (D) SASA before and after Bagrosin and Dehydrocholic acid binding to PDK3.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4795408/v1/551f44064d87d506b7ba041b.png"},{"id":62986057,"identity":"13b83825-4f22-4b81-bba5-fe89c92a72e2","added_by":"auto","created_at":"2024-08-21 19:38:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":235451,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHydrogen bonds analysis. \u003c/strong\u003e(A) Intramolecular hydrogen bonds in PDK3 and (B) Probability Density Function plot of hydrogen bonds between bagrosin,dehydrocholic acid, and PDK3.(C) Hydrogen bonds formed between PDK3 and Bagrosin (D)Hydrogen bonds formed between PDK3 and Dehydrocholic acid. All bond pairs within a 0.35nm range between bagrosin,dehydrocholic acid, and PDK3 were considered.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4795408/v1/047e29b109577f4828911a51.png"},{"id":62985719,"identity":"8b3d5aaf-5d23-49fc-a324-20d0a495101c","added_by":"auto","created_at":"2024-08-21 19:30:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":427393,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePCA Plot analysis. \u003c/strong\u003ePDK3 movements are shown in (A) free PDK3, (B) PDK3 -Bagrosin complex, and (C) PDK3-dehydrocholic acid complex to assess the complex stability before and after bagrosin (red) and dehydrocholic acid (green) binding.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4795408/v1/27c52e66474b5579b39a83fe.png"},{"id":62985716,"identity":"025d065e-c52b-4962-bf4c-59c0240df7aa","added_by":"auto","created_at":"2024-08-21 19:30:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":380062,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFree energy landscape analysis. \u003c/strong\u003eThe Gibbs energy landscape for (A) free PDK3, (B) PDK3-bagrosin complex, and (C) PDK3 dehydrocholic acid complex was acquired during a 100 ns MD simulation.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-4795408/v1/4494abdfe74fdbbb0a987911.png"},{"id":70390613,"identity":"63b7e89b-6c90-4dbf-8597-f8a31add6bcc","added_by":"auto","created_at":"2024-12-02 17:30:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4249781,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4795408/v1/ee6afaa4-2c4c-4005-acad-c70f8be6d922.pdf"},{"id":62985711,"identity":"31f892ea-985a-4750-97a3-1730c9ffc950","added_by":"auto","created_at":"2024-08-21 19:30:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19140,"visible":true,"origin":"","legend":"","description":"","filename":"SupplymentarydataSREP2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4795408/v1/8b8e2814050fc7fa8f2a4f25.docx"},{"id":62985708,"identity":"2d02191c-2351-46fb-906e-fd3e53b4d921","added_by":"auto","created_at":"2024-08-21 19:30:04","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":43461,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4795408/v1/224641b63cddc851e567f130.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Discovering Therapeutic Candidates for Lung Cancer via PDK3 Inhibition – A drug repurposing approach","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer is the most common type of cancer in the world, and it has been that way for many years [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In the UK alone, there were 46,403 new cases of lung cancer in 2014, which is quite surprising. Even though it's the third most common cancer overall, it causes the most deaths compared to other cancers, with 22% of all cancer-related deaths being attributed to lung cancer. Unfortunately, in 93 countries, lung cancer is the leading cause of cancer death among men because it has a high mortality rate [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In certain countries like China, Indonesia, and some African nations, where lung cancer rates are still increasing [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], smoking rates have recently reached their highest point or are still going up. This means that unless we take action to help people quit smoking or stop them from starting in the first place, lung cancer rates will likely keep going up in the future[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Life depends on energy, and animals get their energy by converting fuels into power through oxidative phosphorylation (OXPHOS) in mitochondria [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, most cancer cells have a different way of getting energy. They consume a lot of glucose and produce lactate, even when there's enough oxygen [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Aerobic glycolysis, or the Warburg effect, helps cancer cells grow and avoid dying. It can also be used as a target for cancer treatment [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Lactate can affect how immune cells work, making detecting and fighting abnormal cells harder. It also helps cancer cells move and spread to other parts of the body [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Warburg effect is a characteristic of cancer cells, including those in lung cancer [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This facilitates the transition from oxidative phosphorylation to glycolysis. This switch is necessary because cancer cells need a lot of energy and building blocks to grow quickly [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. By using more glucose and a substance called glutamine, cancer cells can make a large amount of energy (called ATP) to build important parts of the cell like membranes, DNA, and proteins [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. When tumor cells don't get enough oxygen (called hypoxia), they change their way of getting energy. Instead of relying on oxygen, they increase their use of glucose to make ATP [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] through a process called glycolysis. This helps them survive and keep growing, even in difficult conditions [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A protein called hypoxia-inducible factor-1 (HIF-1) is vital in the shift from using oxygen to using glucose for energy. It activates specific genes that help cancer cells grow blood vessels (angiogenesis), take in more glucose, and use glycolysis, supporting the survival and growth of cancer cells [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe mitochondrial pyruvate dehydrogenase complex (PDC) is a group of components that helps convert pyruvate into acetyl-CoA. It also plays a crucial role in connecting glycolysis to the tricarboxylic acid cycle. This process also contributes to ATP production, which is vital for energy in our cells [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. PDC is made up of three parts: pyruvate dehydrogenase (E1), dihydrolipoamide acetyltransferase (E2), and dihydrolipoamide dehydrogenase (E3). These parts work together to convert pyruvate into acetyl-CoA [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The activity of PDC is controlled by a protein called pyruvate dehydrogenase kinase (PDK), which can turn PDC on or off by adding a phosphate group [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Recent studies have shown that PDK3, one of the types of PDK, plays an important role in regulating the metabolism of cancer cells. PDK3 has the highest activity among all the PDKs, and its activity is not affected by high levels of pyruvate. This makes PDK3 a potential target for cancer therapy.\u003c/p\u003e \u003cp\u003ePDKs, including PDK3, inhibit the conversion of pyruvate to acetyl-CoA, which leads to a shift in cellular energy production from the mitochondria to the cytoplasm. This change in energy production is associated with cancer cell growth and drug resistance. In hypoxic conditions (low oxygen levels), PDK1 and PDK3 are induced, leading to increased lactic acid production and further inhibition of mitochondrial respiration. This metabolic switch mediated by PDK3 contributes to drug resistance in hypoxic tumors. In cancer cells with high levels of HIF-1alpha, an oxygen-sensing protein, PDKs inactivate pyruvate dehydrogenase, causing pyruvate to accumulate [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, PDK3 remains active, ensuring that mitochondrial respiration stays shut down. Blocking PDK3 is essential because it helps make cancer cells more vulnerable to anticancer medications, especially when insufficient oxygen is available. By stopping PDK3, we can overcome drug resistance and improve the effectiveness of cancer treatments.\u003c/p\u003e \u003cp\u003eSeveral substances studied can inhibit PDK, including dichloroacetate (DCA), AZD7545, and Radicicol [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. DCA is a compound similar to pyruvate, which can be taken orally and binds to the N-terminal region of PDK. This binding stops PDK activity, allowing PDC to function again [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. As a result, the body shifts from using glycolysis to metabolizing glucose, which leads to apoptosis (cell death), inhibits tumor growth, and could potentially be used as a cancer treatment. A study found that under conditions of low oxygen (hypoxia), PDK3 levels increased, causing resistance to anticancer drugs [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, when PDK3 was suppressed in cells, this resistance was eliminated.\u003c/p\u003e \u003cp\u003eFurther research revealed that when both copies of the PDK3 gene were silenced, more cancer cell death occurred during hypoxic conditions, indicates that PDK3 contributes to drug resistance induced by hypoxia and suggests inhibiting PDK3 could make cancer cells more sensitive to anticancer drugs. Moreover, the expression of PDK3 not only reduces cell survival during low oxygen levels but decreases lactate production (a byproduct of glycolysis) and drug resistance. These findings suggest that PDK3 could be a promising target for cancer therapy.\u003c/p\u003e \u003cp\u003eThe term \"repurposing\" pertains to utilizing approved compounds for one clinical purpose in treating another disease or syndrome. The motivation behind repurposing largely stems from the substantial expenses involved in drug development and the extensive duration required to establish the safety and specificity of an utterly novel medication. It often takes several years, if not decades, for a new cancer drug to undergo sufficient clinical trials to obtain approval [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Repurposing drugs makes it possible to swiftly advance them into Phase II and Phase III clinical studies, thereby substantially reducing the associated development costs. Here, we screened compounds approved by the FDA to find new potential inhibitors for a protein called PDK3. We have implemented a structure-based drug design approach to discover new drugs for lung cancer therapy.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eVirtual high-throughput screening is a highly effective method for discovering new possibilities against specific targets [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This computational technique involves screening a vast collection of chemical libraries to identify potential drug candidates by predicting their likely binding to proteins with high affinity [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. For docking-based virtual screening, we used InstaDock [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], PyMOL[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], Discovery Studio Visualizer [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and GROMACS [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] tools simulations using MD and interaction analysis.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Docking\u003c/h2\u003e \u003cp\u003eMolecular docking is frequently applied to determine the most favorable receptor-ligand interaction conformation. This technique determines the ideal binding position based on a docking score determined by various parameters, such as surface area, number of polar contacts, binding energy, and binding mode. For this screening procedure, we managed to get the crystallographic structure of PDK3, resolved at 2.48 \u0026Aring;, from the RCSB Protein Data Bank (PDB ID: 1Y8O) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The structure was further refined using PyMOL by removing water molecules and extraneous small particles mixed with the main structure. The original structure had missing residues in the amino acid sequences from positions 307\u0026ndash;319 and 322\u0026ndash;323. To address this, remodeling was carried out using PyMod-3, which employed MODELLER software based on the self-template 6Z45 (version 9.20).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eVirtual screening protocol\u003c/h2\u003e \u003cp\u003eBy using the InstaDock v1.0 tool in a blind search area [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Structure-based virtual screening was performed on the FDA-approved library to find compounds with greater binding affinity towards PDK3. Size parameters were set to X\u0026thinsp;=\u0026thinsp;71, Y\u0026thinsp;=\u0026thinsp;65, and Z\u0026thinsp;=\u0026thinsp;6, and a grid box was defined with X\u0026thinsp;=\u0026thinsp;153.741, Y\u0026thinsp;=\u0026thinsp;8.516, and Z\u0026thinsp;=\u0026thinsp;21.232 as its center. Compounds were picked according to their binding affinity values to conduct further study, and the compounds with the best docking hits were selected. Multiple docked conformers were generated to analyze interactions. The visualization of close interactions between PDK3 and FDA-approved drugs within a 3.5 \u0026Aring; range was achieved using PyMOL by detecting the polar contacts. Subsequently, we chose the compounds that interacted with the critical residues of the PDK3-ATP binding sites.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePAINS and ADMET properties of FDA-approved compounds\u003c/h2\u003e \u003cp\u003eCompounds identified through docking analysis were subsequently refined depending on their physiochemical and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) characteristics. Analyzing a chemical compound's effectiveness and therapeutic possibilities is essential in pharmaceutical research. The drug-likeness of the selected compounds was determined by calculating their ADMET characteristics and PAINS patterns. Following the docking process, those identified hits were examined using SwissADME to implement the PAINS filter and evaluate the ADMET properties of the screened compounds. The SMILES of the compounds were suitable for input on both systems. The PAINS filter was utilized to screen the compounds, efficiently discovering Pan-assay interference compounds demonstrating a significant inclination to bind to many targets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePASS Analysis: Predicting Biological Activity\u003c/h2\u003e \u003cp\u003eThe identified compounds were analyzed using the PASS website to predict their biological characteristics. This service can predict many pharmacological properties for a given molecule. PASS makes predictions by using structure-activity correlations based on chemical properties [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. To train its model, the PASS web server compares the input structure to a library of predefined biological functions. Depending on the ratio between the probability of being active (Pa) and the probability of being inactive (Pi), a comprehensive compilation of potential biological properties is generated. A higher Pa value indicates an increased probability of the compound showing a predicted biological characteristic.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eInteraction Analysis\u003c/h2\u003e \u003cp\u003eInteraction analysis was performed on the chosen protein-ligand complexes to examine the interactions that occur during the binding step. Discovery Studio Visualizer and PyMOL, among other bioinformatics tools, were utilized to analyze binding poses and all potential interactions. In PyMOL close contacts within the protein-ligand complex were defined as interactions within 3.5 \u0026Aring;. Similarly, the types of interactions and the residual and atomic locations involved were evaluated using Discovery Studio Visualizer. Critical residues of PDK3 show specific interactions with compounds chosen for more research because of their ADMET characteristics, which included binding and active sites. Therefore, the docking data were correlated by examining the binding partners of PDK3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMolecular dynamics simulation\u003c/h2\u003e \u003cp\u003eOne powerful computational tool in drug design is molecular dynamics (MD) simulation. It is known to study the structures of molecules and the conformational changes in proteins caused by ligand binding by simulating the behavior of atoms and molecules [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. On an HP Z840 computer, all-atom MD simulations were performed at 300 K for 100 ns using the GROMACS 2020-beta simulation package [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. MD simulations of PDK3 in combination with FDA-approved drugs were run for one hundred nanoseconds. The three systems were simulated using the GROMOS 54A7 force field and the GROMACS 2020 beta software suite for all atoms. The PRODRG server generated the topological parameters of FDA-approved compounds and then included them in the PDK3 topology to form a protein-ligand complex system.\u003c/p\u003e \u003cp\u003eIn order to create an equilibrated aquatic environment, the three systems were immersed in a cubic container filled with TIP3P water model and then neutralized by adding Na\u0026thinsp;+\u0026thinsp;and Cl- ions. To remove any disturbances with high energy levels from the original structures, 1500 reduction steps were performed using the steepest descent method until the systems were fully reduced. NVT and NPT ensembles were used to equilibrate the energy-minimized systems twice. Ultimately, each system underwent a 100 ns simulation, after which generated trajectories were plotted using QtGrace software and checked using the provided GROMACS capabilities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePrincipal Component Analysis (PCA) and free energy landscapes\u003c/h2\u003e \u003cp\u003ePrincipal Component Analysis (PCA) is a widely used mathematical technique in molecular dynamics (MD) analytics for reducing the dimensionality of data sets. This approach for unsupervised learning identifies principal components (PCs), which are directions that capture the most variation in the data. Principal Component Analysis (PCA) is valuable for identifying significant variations in high amplitudes within MD trajectories. To analyze the PDK3 MD trajectories, we employed PCA and the free energy landscape (FEL) analysis to assess the atomic movements, conformational sampling, and stability of the compounds bagrosin and dihydrocholic acid before and after binding with PDK3.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS AND DISCUSSION","content":"\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eMolecular Docking\u003c/h2\u003e\n \u003cp\u003eVirtual screening is a computational process to identify possible lead compounds by analyzing thousands of compounds over a particular protein target. As part of this process, the compounds are docked into the active site of a protein receptor complex, and an energy-scoring function is used to measure how well their binding affinities [\u003cspan\u003e24\u003c/span\u003e, \u003cspan\u003e32\u003c/span\u003e, \u003cspan\u003e33\u003c/span\u003e].This technique stands out by quickness, cost-effectiveness and resource efficiency. An FDA-approved library of 3839 compounds, all compliant with the five rules of Lipinski, were subjected to docking against PDK3. The virtual screening of these compounds against PDK3 was conducted using InstaDock, an interactive user interface for molecular docking that allows for single-click operation. The selection of the top 50 hits was determined after the docking process, considering their projected ligand efficiency and docking scores. The experimental findings revealed the 50 compounds that are under investigation displayed notable affinity towards PDK3, as evidenced by binding energies spanning from \u0026minus;\u0026thinsp;11.6 to -7.7 kcal/mol. The results above indicate that the chosen hits exhibit significant potential in their ability to interact with PDK3, thereby warranting more studies as potential competitive inhibitors of PDK3.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003ePAINS and ADMET properties of compounds\u003c/h2\u003e\n \u003cp\u003eFollowing the identification of the top 50 hits identified through molecular docking, compounds displaying Pan-assay interference compounds (PAINS) were excluded (Table \u003cspan\u003eS1\u003c/span\u003e). These compounds demonstrated a strong affinity for binding with multiple biological targets, potentially leading to non-specific off-target side effects, making them not a good option for drug discovery. Given these concerns, PAINS filtration was conducted on the selected hits, leading to a refinement in the selection process. The compounds that exhibited the highest affinity towards PDK3 following the molecular docking process, known as top hits of FDA compounds, were subsequently subjected to further investigation to assess their ADMET characteristics. The pharmacokinetic properties of two compounds, bagrosin (-10.6 kcal/mol) and dehydrocholic acid (10.5 kcal/mol), were assessed using the swissADME webserver. The analysis revealed that both compounds did not exhibit any toxic patterns, as indicated in (Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e). In conjunction with the reference molecule, the ADMET features of the compounds under investigation exhibit notable gastrointestinal absorption and water solubility characteristics while demonstrating no toxicological patterns in the AMES tests. The findings suggest that the chosen compounds exhibit drug-like features, allowing more studies as potential candidates for targeting PDK3.\u003c/p\u003e\n \u003cdiv\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eThe PASS Analysis: Predicting Biological Activity\u003c/h2\u003e\n \u003cp\u003eThe extensive training set of the PASS server encompasses a wide array of bioactive compounds derived from numerous preclinical and clinical studies. This dataset provides a comprehensive understanding of the correlation between the structure and activity of these compounds, as elucidated by [\u003cspan\u003e30\u003c/span\u003e]. The compounds that underwent ADMET (Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e) filtration were subsequently examined for their anticipated biological activities. In this research, only bagrosin and dehydrocholic acid met the requirements of the PASS criteria for the wanted biological activity prediction. The findings of this study suggest that both compounds demonstrate notable anticancer and kinase inhibitory characteristics. When the pressure (Pa) value surpasses 0.7, it indicates a significant probability that the compound will exhibit the expected biological activity. Bagrosin and dehydrocholic acid have shown great potential in various areas, such as inhibiting superoxide dismutase, antagonizing neurotransmitters, enhancing TP53, antineoplasty inhibiting JAK2, and producing analeptic effects. The Pa values for these compounds range from 0.301 to 0.954. The findings of this study indicate that both compounds exhibit noteworthy biological potential when employed in anticancer interventions that specifically target PDK3 (Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003ePASS analysis: Prediction of biological activity of selected phytochemicals against PDK3.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS. No.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCompound\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePa\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePi\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eActivity\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"7\"\u003e\n \u003cp\u003e1.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eBagrosin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSuperoxide dismutase inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlzheimer\u0026apos;s disease treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTP53 expression enhancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOxygen scavenger\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeurotransmitter antagonist\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJAK2 expression inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAntineoplastic (solid tumors)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003e2.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eDehydrocholic acid\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBile-salt sulfotransferase inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProtein-disulfide reductase (glutathione) inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOxidoreductase inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnaleptic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAntimetastatic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAntineoplastic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003ePharmacokinetics, drug-likeness and medicinal chemistry friendliness of selected molecules predicted through SwissADME.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eADME study of Molecule\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBagrosin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDehydrocholic acid\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCanonical Smiles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC1(C(=\u0026thinsp;O)NC(=\u0026thinsp;O)N1)C2\u0026thinsp;=\u0026thinsp;CC3\u0026thinsp;=\u0026thinsp;C(C\u0026thinsp;=\u0026thinsp;CC4\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;C43)C\u0026thinsp;=\u0026thinsp;C2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC(CCC(=\u0026thinsp;O)O)C1CCC2C1(C(=\u0026thinsp;O)CC3C2C(=\u0026thinsp;O)CC4C3(CCC(=\u0026thinsp;O)C4)C)C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMolecular Formula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC18H14N2O2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC24H34O5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysiochemical properties\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMolecular weight\u003c/p\u003e\n \u003cp\u003eNumber of heavy atoms\u003c/p\u003e\n \u003cp\u003eNumber of aromatic heavy atoms\u003c/p\u003e\n \u003cp\u003eFraction Csp3\u003c/p\u003e\n \u003cp\u003eNumber of rotatable bonds\u003c/p\u003e\n \u003cp\u003eNumber of H-bond acceptors\u003c/p\u003e\n \u003cp\u003eNumber of H-bond donors\u003c/p\u003e\n \u003cp\u003eMolar refractivity\u003c/p\u003e\n \u003cp\u003eTopological polar surface area (TPSA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e290.32 g/mol\u003c/p\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e92.83\u003c/p\u003e\n \u003cp\u003e58.20 \u0026Aring;\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e402.52 g/mol\u003c/p\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e110.88\u003c/p\u003e\n \u003cp\u003e88.51 \u0026Aring;\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLipophilicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLog Po/w (iLOGP)\u003c/p\u003e\n \u003cp\u003eLog Po/w (XLOGP3)\u003c/p\u003e\n \u003cp\u003eLog Po/w (WLOGP)\u003c/p\u003e\n \u003cp\u003eLog Po/w (MLOGP)\u003c/p\u003e\n \u003cp\u003eLogPo/w(SILICOS-IT)\u003c/p\u003e\n \u003cp\u003eConsensus Log Po/w\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.93\u003c/p\u003e\n \u003cp\u003e3.52\u003c/p\u003e\n \u003cp\u003e2.18\u003c/p\u003e\n \u003cp\u003e2.59\u003c/p\u003e\n \u003cp\u003e3.38\u003c/p\u003e\n \u003cp\u003e2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.27\u003c/p\u003e\n \u003cp\u003e2.56\u003c/p\u003e\n \u003cp\u003e4.07\u003c/p\u003e\n \u003cp\u003e2.75\u003c/p\u003e\n \u003cp\u003e4.29\u003c/p\u003e\n \u003cp\u003e3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWater solubility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLog\u0026nbsp;\u003cem\u003eS\u003c/em\u003e (ESOL)\u003c/p\u003e\n \u003cp\u003eLog\u0026nbsp;\u003cem\u003eS\u003c/em\u003e (Ali)\u003c/p\u003e\n \u003cp\u003eLog\u0026nbsp;\u003cem\u003eS\u003c/em\u003e (SILICOS-IT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e4.26, Moderately Soluble\u003c/p\u003e\n \u003cp\u003e-4.43,Moderately Soluble\u003c/p\u003e\n \u003cp\u003e-6.83,Poorly Soluble\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e3.68, Soluble\u003c/p\u003e\n \u003cp\u003e-4.07, Moderately Soluble\u003c/p\u003e\n \u003cp\u003e-4.40, Moderately Soluble\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePharmacokinetics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI absorption\u003c/p\u003e\n \u003cp\u003eBBB permeant\u003c/p\u003e\n \u003cp\u003eP-gp substrate\u003c/p\u003e\n \u003cp\u003eCYP1A2 inhibitor\u003c/p\u003e\n \u003cp\u003eCYP2C19 inhibitor\u003c/p\u003e\n \u003cp\u003eCYP2C9 inhibitor\u003c/p\u003e\n \u003cp\u003eCYP2D6 inhibitor\u003c/p\u003e\n \u003cp\u003eCYP3A4 inhibitor\u003c/p\u003e\n \u003cp\u003eLogKp (skin permeation)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003e-5.57 cm/s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003e-6.94 cm/s\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrug likeliness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLipinski\u003c/p\u003e\n \u003cp\u003eGhose\u003c/p\u003e\n \u003cp\u003eVeber\u003c/p\u003e\n \u003cp\u003eEgan\u003c/p\u003e\n \u003cp\u003eMuegge\u003c/p\u003e\n \u003cp\u003eBioavailability Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedicinal chemistry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAINS\u003c/p\u003e\n \u003cp\u003eBrenk\u003c/p\u003e\n \u003cp\u003eLeadlikeness\u003c/p\u003e\n \u003cp\u003eSyntheticaccessibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 alert\u003c/p\u003e\n \u003cp\u003e2 alerts: hydantoin, polycyclic_aromatic_hydrocarbon_3\u003c/p\u003e\n \u003cp\u003eNo; 1 violation: XLOGP3\u0026thinsp;\u0026gt;\u0026thinsp;3.5\u003c/p\u003e\n \u003cp\u003e2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 alert\u003c/p\u003e\n \u003cp\u003e0 alert\u003c/p\u003e\n \u003cp\u003eNo; 1 violation: MW\u0026thinsp;\u0026gt;\u0026thinsp;350\u003c/p\u003e\n \u003cp\u003e4.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003eInteraction Analysis\u003c/h2\u003e\n \u003cp\u003eThe co-crystallized structure of PDK3 with its ATP binding site. The structure reveals key interaction residues for ATP binding, highlighting important residues involved in substrate binding (Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e). We found that bagrosin and dehydrocholic acid exhibit binding affinity towards the important residues within the ATP-binding site of PDK3, namely Gly327 and Phe324. The research results showed a notable connection between both compounds and the ATP binding pocket, which is recognized as an interaction site for various established inhibitors of PDK3 (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003eA \u0026amp; \u003cspan\u003e2\u003c/span\u003eD). Both drugs exhibit strong binding affinity to the active site of PDK3, effectively obstructing the ATP binding pocket. Bagrosin and dehydrocholic acid bind with complementarily fit when they bind to PDK3 (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003eC\u0026amp;\u003cspan\u003e2\u003c/span\u003eF). The results demonstrated that PDK3 can be pharmacologically inhibited by competitively binding ATP and stabilizing the interaction between bagrosin and dehydrocholic acid with PDK3. Subsequent research examined the detailed mechanism of interaction between bagrosin, dehydrocholic acid, and PDK3.\u003c/p\u003e\n \u003cp\u003eA comprehensive study helps identify the kind and characteristics of non-covalent interactions in the protein-ligand complex. A 2D depiction was created for the docked complex, as seen in (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003eB\u0026amp;\u003cspan\u003e2\u003c/span\u003eE). The 2D diagrams illustrate the interaction between the ATP binding residue Gly327 and Phe324 of PDK3 with both molecules, bagrosin, and dehydrocholic acid, via hydrogen bonding. The chemicals also interact with several important residues around the ATP-binding site in PDK3, including Ile330, Glu247, Tyr326, Lys250, Pro124, Ala127, Gln128, Ile131, His243, and Lys250. Gly327 is involved in the ATP-binding process with PDK3 (Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e). The study indicated that bagrosin and dehydrocholic acid co-occur in a location that resembles the site where ATP is co-crystallized. Based on the interaction investigation of bagrosin and dehydrocholic acid with PDK3, it was shown that they can inhibit ATP competitively.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDifferent interactions between Bagrosin, dehydrocholic acid and PDK3 interacting residues.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eS. No.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCompounds\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eBinding Free Energy\u003c/p\u003e\n \u003cp\u003e(kcal/ mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLigand Efficiency (kcal/mol/non-H atom)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eType of Interactions\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHydrogen bonds\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003evan der Waals\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePi-Pi Stacked/ Pi-Pi T-shaped\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePi-Sigma/Pi-Alkyl\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBagrosin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGly327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIle330,\u003c/p\u003e\n \u003cp\u003eGlu247,\u003c/p\u003e\n \u003cp\u003eTyr326,\u003c/p\u003e\n \u003cp\u003eLys250,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhe324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAla127,\u003c/p\u003e\n \u003cp\u003ePro124,\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDehydrocholic acid\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGly327,\u003c/p\u003e\n \u003cp\u003ePhe324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePro124,\u003c/p\u003e\n \u003cp\u003eAla127,\u003c/p\u003e\n \u003cp\u003eGln128,\u003c/p\u003e\n \u003cp\u003eIle330,\u003c/p\u003e\n \u003cp\u003eIle131,\u003c/p\u003e\n \u003cp\u003eTyr326,\u003c/p\u003e\n \u003cp\u003eGly327,\u003c/p\u003e\n \u003cp\u003eHis243,\u003c/p\u003e\n \u003cp\u003eGlu247,\u003c/p\u003e\n \u003cp\u003eLys250,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhe324\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003eMD simulations\u003c/h2\u003e\n \u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003ePost Dynamics Analysis of Trajectories\u003c/h2\u003e\n \u003cp\u003eMD simulations on docked complexes can improve docking models by considering how flexible ligands and proteins are [\u003cspan\u003e34\u003c/span\u003e]. MD models can help fill in the blanks about proteins\u0026apos; structure and dynamic behavior when experiments don\u0026apos;t work. It was possible to study all the atoms of bagrosin, dehydrocholic acid, PDK3, and PDK3 in their free state (Table\u0026nbsp;\u003cspan\u003e5\u003c/span\u003e). Molecular dynamics simulations with a duration of 100 nanoseconds. Before MD analysis, MD verification was conducted on the simulated trajectories to verify the equilibration and consistency of the simulations. The investigation of PDK3\u0026apos;s stability and dynamics before and after binding with bagrosin and dehydrocholic acid included the analysis of many systematic and structural aspects, which will be detailed in the following sections.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMean values for the important MD parameters calculated for Native PDK3, PDK3-bagrosin and PDK3-dehydrocholic acid.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSystem\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSD (nm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSF (nm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRg (nm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSASA (nm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e#H-bonds\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePDK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e181.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e278\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePDK3-Bagrosin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e181.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e282\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePDK3-Dehydrocholic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e186.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e278\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003eStructural deviations in PDK3\u003c/h2\u003e\n \u003cp\u003eWhen a small molecule interacts with the active binding cleft of a protein, it may cause substantial changes in the protein\u0026apos;s structure[\u003cspan\u003e35\u003c/span\u003e, \u003cspan\u003e36\u003c/span\u003e]. The root mean square deviation (RMSD) provides useful data on a protein\u0026apos;s conformational changes and structural variations [\u003cspan\u003e37\u003c/span\u003e\u0026ndash;\u003cspan\u003e39\u003c/span\u003e]. The PMSD analysis was used to evaluate the variations in the backbone conformation of PDK3 before and after a ligand\u0026apos;s interaction. The stability of PDK3 and its complexes with bagrosin and dehydrocholic acid was determined by tracking the time-dependent changes in RMSD throughout the simulation period. The RMSD plots of the PDK3-bagrosin and PDK3-dehydrocholic acid complexes displayed time-dependent graphs illustrating the changes and deviations in the protein backbone during the simulations. The simulation trajectory results are shown in (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eA) and were then used to examine the stability of the complex. The RMSD figure indicates a small reduction in fluctuations in ligand-bound systems compared to the free state of PDK3[\u003cspan\u003e39\u003c/span\u003e, \u003cspan\u003e40\u003c/span\u003e]. All three systems reached a state of equilibrium and maintained stability during the 100-nanosecond simulation period. The observed fluctuations in RMSD are insufficient to cause significant structural deviations, although they suggest some system adaptations. The PDF plot in the lower panel (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eC) also demonstrates a marginal reduction in the RMSD values.\u003c/p\u003e\n \u003cp\u003eAn analysis of root-mean-square fluctuation (RMSF) is useful for identifying the residual vibrations present in a protein molecule during MD simulation [\u003cspan\u003e41\u003c/span\u003e, \u003cspan\u003e42\u003c/span\u003e]). The Root Mean Square Fluctuation (RMSF) analysis of the PDK3 backbone and its interactions with bagrosin and dehydrocholic acid (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eB \u0026amp; \u003cspan\u003e3\u003c/span\u003eD) characterize the local variations occurring throughout the simulation. The high variations in the Root Mean Square Fluctuation (RMSF) values indicate the presence of loops and unstructured areas in the protein. The average variation in local structural flexibility of each residue was analyzed in PDK3 before and after interaction with the drug. The figure shows that all systems exhibit a comparable root mean square fluctuation (RMSF) pattern with minor randomized variations. The plot shows residual fluctuations reflect a consistent and slightly elevated pattern when bagrosin and dehydrocholic acid bind, suggesting that the protein-ligand complexes are distressed yet remain stable. The residues inside the PDK3 binding pocket that engage with bagrosin and dehydrocholic acid were rather stable throughout the simulation, as shown by the RMSF study.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\"\u003e\n \u003ch2\u003eStructural compactness\u003c/h2\u003e\n \u003cp\u003eThe radius of gyration (R\u003cem\u003eg\u003c/em\u003e) is a crucial measure for studying a protein\u0026apos;s compactness and folding behavior [\u003cspan\u003e43\u003c/span\u003e]. Rg is a frequently used metric to determine the density of proteins and protein-ligand complexes. We calculated the time evolution of the R\u003cem\u003eg\u003c/em\u003e (radius of gyration) using the simulated trajectory of all three systems[\u003cspan\u003e39\u003c/span\u003e]. Based on the R\u003cem\u003eg\u003c/em\u003e plot, when bagrosin and dehydrocholic acid are present, PDK3 remains consistent at a value between 2.15nm and 2.19nm during the trajectory (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eA). The findings suggest that the binding to bagrosin and dehydocholic acid and the structural dynamics and folding of PDK3 remain consistently stable with a little increase. The R\u003cem\u003eg\u003c/em\u003e values for PDK3 exhibit a consistent distribution both before and after binding with bagrosin and dehydocholic acid, as shown in the PDF plot (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eC). The portion of a protein molecule that can be accessed by an adjacent solvent is referred to as the solvent-accessible surface area (SASA) [\u003cspan\u003e44\u003c/span\u003e]. SASA, or solvent-accessible surface area, is linked to the R\u003cem\u003eg\u003c/em\u003e, or radius of gyration, and is often used to investigate the variations in protein structure and folding or unfolding [\u003cspan\u003e45\u003c/span\u003e]. SASA methods are valuable in determining the extent of protein folding and the count of native contacts [\u003cspan\u003e46\u003c/span\u003e, \u003cspan\u003e47\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eThe changes in the solvent-accessible surface area (SASA) of PDK3 over time have been studied regarding its binding with bagrosin and dehydrocholic acid. The figure indicates no significant changes in SASA values throughout the simulations. Based on the SASA investigation, the PDK3 structure remains stable throughout the simulation, even when exposed to bagrosin and dehydrocholic acid (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eB). The SASA distribution exhibits a similar equilibration pattern to the R\u003cem\u003eg\u003c/em\u003e values, with a slight increase in ligand-bound systems but not significantly impacting the overall compactness (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eB\u0026amp;\u003cspan\u003e4\u003c/span\u003eD).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\"\u003e\n \u003ch2\u003eInteraction dynamics in the PDK3 complexes: H-bond analysis\u003c/h2\u003e\n \u003cp\u003eHydrogen bonds, as H-bonds, play an essential part in folding protein structures and assembling their conformations [\u003cspan\u003e48\u003c/span\u003e]. Intramolecular hydrogen bonding is a critical factor contributing to proteins\u0026apos; structural stability [\u003cspan\u003e49\u003c/span\u003e]. Intramolecular hydrogen bonds have been employed to look into the conformational changes and compactness of protein structure [\u003cspan\u003e24\u003c/span\u003e, \u003cspan\u003e50\u003c/span\u003e, \u003cspan\u003e51\u003c/span\u003e]. The intramolecular H-bond count in PDK3 has been measured over time using MD trajectories (Fig.\u0026nbsp;\u003cspan\u003e5\u003c/span\u003eA). The outcomes allow us to analyze the intramolecular bonding consistency of PDK3 both before and after its binding to bagrosin and dehydrocholic acid. The figure demonstrates a change in the number of hydrogen bonds established inside PDK3 before and after bagrosin and dehydrocholic acid-binding. The graph indicates that the hydrogen bonds produced inside PDK3 were stable and played an essential part in determining the shape of the protein structure. The dynamic analysis shows that a little rise in intramolecular hydrogen bonds within PDK3-bagrosin complexes resulted in a somewhat more condensed structure than the free PDK3 form. On the other hand, PDK3-dehydrocholic acid exhibited a comparable amount of intramolecular hydrogen bonds. The PDF illustrating intramolecular hydrogen bonding in all systems was also shown (Fig.\u0026nbsp;\u003cspan\u003e5\u003c/span\u003eB).\u003c/p\u003e\n \u003cp\u003eThe interaction between bagrosin and dehydrocholic acid with PDK3 was investigated in terms of the stability of hydrogen bonding. This was done by analyzing the changes in intermolecular hydrogen bonds over time. The bagrosin-PDK3 complex established 2 stable hydrogen bonds, whereas the dehydrocholic acid complex formed 3 (Fig.\u0026nbsp;\u003cspan\u003e5\u003c/span\u003eC\u0026amp;\u003cspan\u003e5\u003c/span\u003eD). The PDF analysis demonstrated that the intramolecular hydrogen bonds in both systems exhibited a consistent level, with a greater PDF value corresponding to an increased number of hydrogen bonding interactions (Fig.\u0026nbsp;\u003cspan\u003e5\u003c/span\u003eB). The bagrosin and dehydocholic acid remained stationary at their original docking site on the PDK3 protein owing to stable intermolecular hydrogen bonding, which helped stabilize the complexes between the protein and ligands.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\"\u003e\n \u003ch2\u003ePrincipal component and free energy\u003c/h2\u003e\n \u003cp\u003eThe study\u0026apos;s authors used Principal Component Analysis (PCA) on simulated trajectories to investigate the collective movements of a protein [\u003cspan\u003e52\u003c/span\u003e]. We studied the first two principal components (PCs) derived from the PCA analysis of PDK3 and its complexes with bagrosin and dehydrocholic acid. Figure\u0026nbsp;\u003cspan\u003e6\u003c/span\u003e illustrates the exploration of several conformations inside the fundamental subspace of PDK3, PDK3-bagrosin, and PDK3-dehydrocholic acid. The C\u0026alpha; atom of PDK3 determines the conformational states along the EV1 and EV2. The graph demonstrates that the PDK3-bagrosin and PDK3-dehydrocholic acid complexes have nearly the same essential region as PDK3 in its unbound form. The PDK3-bagrosin complex and free-PDK3 are seen to occupy the same subspace in both EVs. The flexibility of the PDK3-bagrosin complex is greater on EV1 compared to EV2. Simultaneously, the PDK3-dehydrocholic acid complex exhibits more flexibility on EV1 in the direction of positive projection (Fig.\u0026nbsp;\u003cspan\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe conformational behavior and folding states of FELs were analyzed by constructing them from simulated trajectories [\u003cspan\u003e53\u003c/span\u003e]. The stability of protein and protein-ligand complexes in the presence of a solvent was assessed using MD simulation trajectories. The lowest energy states and the arrangement of molecular structures of the PDK3, PDK3-bagrosin, and PDK3-dehydrocholic acid complexes were obtained by analyzing two principal components, namely PC1 and PC2. The contoured maps depicting the FELs of PDK3, PDK3-bagrosin, and PDK3-dehydrocholic acid complexes are shown in (Fig.\u0026nbsp;\u003cspan\u003e7\u003c/span\u003e). Based on the FELs plots, the binding of bagrosin and dehydrocholic with PDK3 has a minimal impact on the size and location of the phases limited to 1\u0026ndash;2 stable global minima.\u003c/p\u003e\n \u003cp\u003eA deeper shade of blue in the Free Energy Landscapes (FELs) indicates the existence of a conformation with lower energy close to the native states, as seen in Fig.\u0026nbsp;\u003cspan\u003e7\u003c/span\u003e. The graph shows PDK3 is constrained to a solitary, expansive global minimum encompassing 1\u0026ndash;2 basins. PDK3-bagrosin and PDK3-dehydrocholic acid exhibit almost comparable configurations, characterized by a prominent global minimum and 3\u0026ndash;4 smaller local basins that vary in population (Fig.\u0026nbsp;\u003cspan\u003e7\u003c/span\u003eA-\u003cspan\u003e7\u003c/span\u003eC). In summary, the MD simulation and essential dynamics analysis of PDK3 in the presence of bagrosin and dehydrocholic acid demonstrate that theay remain stable over 100 nanosecond simulations with minor changes in their conformation. Based on the data above, it can be inferred that bagrosin and dehydrocholic acid have a significant affinity and stability when binding to PDK3, resulting in a potent inhibition of the PDK3 protein.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe strategic targeting of PDK3 through the repurposing of FDA-approved compounds presents a promising avenue for modulating pathways that exhibit anticancer properties. To identify potential FDA-approved pharmaceutical interventions for the dysregulated PDK3, a comprehensive multi-tier virtual screening strategy was employed on the FDA-approved database. In this context, PDK3 has been recognized as a crucial pharmacological target owing to its pivotal function as a positive regulator of cancer progression and growth. Understanding the metabolic switching of cancer cells is vital for developing effective and targeted treatment interventions.The proposed method holds significant value in advancing anticancer therapies by leveraging natural leads as highly effective PDK3 inhibitors.\u003c/p\u003e \u003cp\u003eAdditionally, it serves as a valuable therapeutic model for metabolic reprogramming. In this study, we introduce a comprehensive virtual screening approach aimed at identifying potential inhibitors of PDK3. Two compounds, namely bagrosin and dehydrocholic acid, were selected for investigation due to their notable binding affinity, specific interactions, and potential biological characteristics. Through MD simulation, PCA, and FEL analyses, researchers uncovered the stable binding of bagrosin and dehydrocholic acid to PDK3. Based on this study, the potential use of FDA-approved drugs as PDK3 inhibitors in the future was explored using a computer-assisted drug-design technique. The study identified FDA-approved drugs that offer a valuable resource and approach for identifying potential therapeutic inhibitors of PDK3 to treat different types of cancer, following further experimental validation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003ePDK3-\u003c/strong\u003ePyrivate dehydrogenase kinase 3; \u003cstrong\u003eHIF-1-\u003c/strong\u003eHypoxia-inducible factor-1;\u0026nbsp;\u003cstrong\u003ePDC-\u003c/strong\u003ePyruvate dehydrogenase complex;\u0026nbsp;\u003cstrong\u003eMD-\u0026nbsp;\u003c/strong\u003eMolecular Dynamics; \u003cstrong\u003eRg-\u003c/strong\u003eRadius of gyration; \u003cstrong\u003eRMSD-\u003c/strong\u003eRoot mean square deviation; \u003cstrong\u003eRMSF-\u003c/strong\u003eRoot mean square fluctuation; \u003cstrong\u003ePDF-\u003c/strong\u003eProbability density function;\u003cstrong\u003e\u0026nbsp;SASA-\u003c/strong\u003eSolvent accessible surface area; \u003cstrong\u003ePCA-\u0026nbsp;\u003c/strong\u003ePrincipal Component Analysis; \u003cstrong\u003eFEL-\u0026nbsp;\u003c/strong\u003eFree Energy Landscape\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work is supported by\u0026nbsp;Indian Council of Medical Research (Grant No. ISRM/12(22)/2020). This research was funded by Taif University, Saudi Arabia, Project Number (TU-DSPP-2024-140).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e The authors extend their appreciation to Taif University, Saudi Arabia for supporting this work through project number (TU-DSPP-2024-140).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary data to this article can be found online.\u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026apos;s contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZeba Firdos Khan:\u003c/strong\u003e Conceptualization, Investigation, Data curation, Validation, Writing \u0026ndash; original draft, \u003cstrong\u003eAanchal Rathi:\u003c/strong\u003e Investigation, Validation, Formal analysis,\u0026nbsp;\u003cstrong\u003eAfreen Khan\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Validation, Formal analysis, \u003cstrong\u003eFarah Anjum:\u0026nbsp;\u003c/strong\u003eMethodology, Investigation, Validation,\u0026nbsp;\u003cstrong\u003eArunabh Chaudhury:\u003c/strong\u003e Investigation, Validation, Formal analysis,\u0026nbsp;\u003cstrong\u003eAaliya Taiyab\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Validation, Formal analysis, Writing \u0026ndash; review \u0026amp; editing, \u003cstrong\u003eAnas Shamsi:\u0026nbsp;\u003c/strong\u003eData curation, Validation, Formal analysis, Writing \u0026ndash; review \u0026amp; editing, \u003cstrong\u003eMd. Imtaiyaz Hassan:\u003c/strong\u003e Conceptualization, Funding acquisition, Supervision, Project administration, Writing \u0026ndash; review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, et al. (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians 71: 209-249.\u003c/li\u003e\n\u003cli\u003eArnold M, Rutherford MJ, Bardot A, Ferlay J, Andersson TM, et al. (2019) Progress in cancer survival, mortality, and incidence in seven high-income countries 1995\u0026ndash;2014 (ICBP SURVMARK-2): a population-based study. The Lancet Oncology 20: 1493-1505.\u003c/li\u003e\n\u003cli\u003eJha P (2009) Avoidable global cancer deaths and total deaths from smoking. 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The Journal of chemical physics 128.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"PDK3, FDA-approved drugs, Lung cancer, Virtual screening, MD simulation","lastPublishedDoi":"10.21203/rs.3.rs-4795408/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4795408/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePyruvate dehydrogenase kinase (PDK) can control the catalytic activity of pyruvate decarboxylation oxidation through the mitochondrial PD complex. Additionally, glycolysis is connected to the production of ATP and the tricarboxylic acid cycle. One up-and-coming method for curing metabolic illnesses like heart failure, cancer, and diabetes is by controlling the expression or activity of PDKs. To find possible bioactive inhibitors of pyruvate dehydrogenase kinase 3 (PDK3), we used a structural-based virtual large-scale analysis of bioactive chemical compounds from the FDA-approved database. Using FDA-approved compounds for the analysis leverages existing safety and efficacy data, significantly accelerating the drug repurposing process. This screening process found two naturally occurring substances with strong affinity and specificity for the PDK3 binding site: bagrosin and dehydrocholic acid. Structural-based investigations provided a precise identification of compounds that fit the active site of PDK3, with desirable binding characteristics, optimizing drug-target interactions. Both substances interact with residues on ATP-binding sites of PDK3 preferentially. Additionally, all-atom molecular dynamic (MD) simulations were used to assess the consistency and dynamics of PDK3 interaction with bagrosin and dehydrocholic acid, and the results indicated that both complexes were stable. The findings might be used to develop innovative PDK3 inhibitors that could be used to treat severe illnesses like cancer. Compounds identified from the FDA-approved database are more likely to have known pharmacokinetics and pharmacodynamics profiles, facilitating their transition into clinical trials.\u003c/p\u003e","manuscriptTitle":"Discovering Therapeutic Candidates for Lung Cancer via PDK3 Inhibition – A drug repurposing approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-21 19:29:59","doi":"10.21203/rs.3.rs-4795408/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-20T06:59:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-16T07:47:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111163747043300635720700027058241319120","date":"2024-09-04T12:21:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-22T00:24:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3357038773820086769490155731951200091","date":"2024-08-11T19:20:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-30T23:52:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-30T23:49:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-26T12:53:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-25T07:03:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-07-24T12:35:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a65f747c-15ad-4ad1-a6d9-230d0cc28863","owner":[],"postedDate":"August 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-02T17:27:29+00:00","versionOfRecord":{"articleIdentity":"rs-4795408","link":"https://doi.org/10.1038/s41598-024-78022-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-11-29 15:58:22","publishedOnDateReadable":"November 29th, 2024"},"versionCreatedAt":"2024-08-21 19:29:59","video":"","vorDoi":"10.1038/s41598-024-78022-0","vorDoiUrl":"https://doi.org/10.1038/s41598-024-78022-0","workflowStages":[]},"version":"v1","identity":"rs-4795408","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4795408","identity":"rs-4795408","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}