Molecular docking, pharmacokinetic prediction and molecular dynamics simulations of tankyrase inhibitor compounds with the protein glucokinase, induced in the development of diabetes

Abstract GCK is a protein that plays a crucial role in the sensing and regulation of glucose homeostasis, which associates it with disorders of carbohydrate metabolism and the development of several pathologies, including gestational diabetes. This makes GCK an important therapeutic target that has aroused the interest of researchers to discover GKA that are simultaneously effective in the long term and free of side effects. TNKS is a protein that interacts directly with GCK; recent studies have shown that it inhibits GCK action, which affects glucose detection and insulin secretion. This justifies our choice of TNKS inhibitors as ligands to test their effects on the GCK-TNKS complex. For this purpose, we investigated the interaction of the GCK-TNKS complex with 13 compounds (TNKS inhibitors and their analogues) using the molecular docking approach as a first step, after which the compounds that generated the best affinity scores were evaluated for drug similarity and pharmacokinetic properties. Subsequently, we selected the six compounds that generated high affinity and that were in accordance with the parameters of the drug rules as well as pharmacokinetic properties to ensure a molecular dynamics study. The results allowed us to favor the two compounds (XAV939 and IWR-1), knowing that even the tested compounds (TNKS 22, (2215914) and (46824343)) produced good results that can also be exploited. These results are therefore interesting and encouraging, and they can be exploited experimentally to discover a treatment for diabetes, including gestational diabetes. Communicated by Ramaswamy H. Sarma


Introduction
Glucokinase (GCK) is one of the four members of the hexokinase family, and it plays a key role in the detection and regulation of glucose homeostasis.Its expression is restricted to major organs (such as the liver, pancreas, brain, and gastrointestinal tract).In the liver, glucose phosphorylation by GCK promotes glycogen synthesis, while in b-cells it leads to insulin release.
Glucose, once in the body, is phosphorylated to glucose-6-phosphate by GCK in an irreversible glucose-dependent reaction that is not inhibited by the product (Matschinsky & Wilson 2019).
GCK responds to increased circulating glucose concentrations through the signaling cascade that leads to insulin secretion by pancreatic beta cells and subsequent glucose uptake and storage in the liver.Its presence in pancreatic and hepatic cells plays a crucial role in the management of glucose homeostasis, making it an emerging target for diabetes management (Sharma et al., 2022).
GCK is a protein that is primarily associated with carbohydrate metabolism disorders.This explains its role in the emergence of several pathologies in this regard.As a result, several studies have been conducted that have demonstrated its association, primarily with Gestational diabetes, hyperglycemia, Diabetes Mellitus; it has also been linked to other pathology than hyperglycemia, namely Obesity and Dyslipidemia (Carlsson et al. 2020;Hinklin et al. 2020;Khamlich et al., 2023;Su et al., 2020;Watanabe et al., 2018;Zhou et al., 2019).Due to its crucial involvement in carbohydrate metabolism and also in the development of hyperglycemic diseases, GCK is considered an important drug target for diabetes.This has piqued the interest of scientists since 1990, and several small molecules have been discovered.
In this context, many molecules have been tested, but few have shown permanent effectiveness.They lose their effectiveness after a few months of use and are accompanied by an increased risk of hypoglycemia and hypertriglyceridemia, and there are even studies that have shown the development of dyslipidemia in patients treated by these glucokinase activators (GKA).
Otherwise, according to our knowledge, there is currently dorzagliatin, a new GKA in advanced clinical development that is an activator that acts on both the liver and the pancreas, as well as TTP399, a hepatoselective GKA that has shown a clinically significant reduction in HbA1c with a minimal risk of adverse effects (Simons et al., 2018;Thilagavathi et al., 2022).
To date, the clinical trials of anti-diabetic glucokinase still attracts the attention of researchers, indeed the GK agonists proposed until now have been disappointing, with the exception of dorzagliatine which is currently in phase III clinical trials, as well as AZD-1656 and PB-201 in second phase trials, hence the need to test and search for other molecules that can better interact and modulate GCK activity as an interesting therapeutic target for hyperglycemic diseases (Ren et al., 2022;Yeh et al., 2009).
For this work we chose Tankyrase (TNKS) as a target, which is a Golgi-associated (ADP-ribose) polymerase that regulates a variety of cellular processes including the regulation of GLUT4 trafficking in adipocytes.
Numerous studies have shown that knocking out TNKS expression by gene sequestration in mice has resulted in robust changes in energy homeostasis, suggesting that pharmacological inhibition of TNKS may improve obesity and insulin sensitivity.
TNKS interact with a number of target proteins and regulate cellular processes, including telomere maintenance.In the same sense, studies have shown more specifically that it colocalizes and interacts directly with GCK and can modulate glucose sensing function via the latter (Smith & de Lange 2000).They showed that TNKS is a physiological inhibitor of GCK in pancreatic beta cells that acts by trapping the kinase in the open (inactive) conformation.
This implied that glucose-stimulated insulin secretion is suppressed when TNKS protein is stabilized, and conversely, it is increased when TNKS protein is eliminated (Chi et al., 2021;Yeh et al., 2009).As its inhibition during pancreatic progenitor formation has been shown to enhance beta cell commitment (Poon, & Nostro, 2022).
Therefore, the present work focuses on the in-silico analysis of the impact of TNKS and its inhibitor on the GCK protein in order to test the effect of these compounds on the activity of the GCK protein and on the carbohydrate metabolism on the one hand, and therefore on the development of diabetes, especially gestational diabetes, on the other hand.
To accomplish this, we will first implement the molecular docking approach, then use the medicinal similarity method and evaluate the pharmacokinetic properties to select the compounds with the best bond affinity and that follow the medicinal rules to ensure a simulation of molecular dynamics.

Model building and protein preparation for docking
The crystal structures of the human GCK protein and the TNKS protein were extracted from the Protein Data Bank (PDB).The GCK protein was crystallized by DIFFRACTION X-RAY 10.2210/pdb1V4S/PDB (ID 1V4S) with a resolution of 2.30 Å.The TNKS protein structure is available under the reference ID 2rf5.1 (Berman et al., 2000).
Prior to working with it, we performed validation and verification using the tools Verify 3D, Erratum, and PROCHECK (https://saves.mbi.ucla.edu/) to assess the overall quality of the protein.Then, our protein underwent a necessary preparation before proceeding to the molecular docking study by the AutoDock software, which is an automated docking package (ligand-protein) that consists of two generations: AutoDock 4 and AutoDock Vina.
This preparation of the protein included the removal of water molecules, unrelated chemical complexes, as well as HETATM groups already bound to our protein, with the addition of polar hydrogen bonds, and necessary Kollman charges and types of atoms.Finally, we saved our protein in PDB format.
These files were exported to the MarvinSketch software and then to Discovery studio to undergo an energy minimization to stabilize the molecule and an extension conversion in PDB format.The structures were then loaded in AutoDock Tools to ensure final ligand preparation and to obtain the files in PDBQT format.

Determination of active sites of tankyrase and glucokinase human proteins
The CASTp (Computed Atlas of Surface Topography of Proteins) software (http://sts.bioe.uic.edu/castp/index.html?3trg) was used to predict the binding pockets located at the structures of our protein complex.This web server uses a triangulation system (called Delaunay) to identify and measure the volume and surface of the accessible pockets.

Molecular docking
The ligand-target protein molecular docking approach is a process by which two molecules fit together in 3D space via a computational process that serves to analyze the structural complexes of the target (the GCK-TNKS complex in our case) with the proposed ligands in order to evaluate the binding energy between the two while preserving the variable and rigid conformations and poses of the ligands; the latter is considered a key means in structural biology that is used in the process of computer-aided drug design.
For this purpose, the Lamarckian genetic algorithm (LGA) of the AutoDock Vina program was used to study docking simulations between the thirteen inhibitors mentioned above and the GCK-TNKS complex, with an initial population size of 150 to produce 50 conformations and a number of evaluations equal to 2,500,000, and exhaustiveness equal to 8.For this reason, a graphical user interface program was used to determine the grid box needed to perform the simulations.
Following the docking process, the results are generated in a text file with the extension PDBQT.This file contains the atomic coordinates of the 9 best positions of the docked conformations between the ligand and the protein that were produced via the AutoDockVina scoring functions and classified according to their binding energies.Following this classification, the conformation with the most favorable free binding energy was selected to undergo a post-docking analysis of protein-ligand interactions via the Discovery Studio visualizer and PyMol (Trott & Olson 2010).

Drug likeliness prediction and ADMET
The determination of the physicochemical properties of proposed ligands is considered a critical step in the drug discovery process.There are several rules and guidelines that must be followed in order to grant a drug compound compliance in the preliminary screening phase.
Therefore, each molecule must meet several basic criteria to determine if it can be proposed as a 'drug' candidate, such as physicochemical descriptors and alignment with the requirements of the rules (Ghose, Veber, Egan, Muegge).
Among these rules is Lipinski's rule, which is mostly dominant and used as a ''rule of thumb''.The latter encompasses five standard parameters to be respected, which are molecular weight (with an acceptable range of 500), molar refraction (with an acceptable range of 40 -130), high lipophilicity (expressed in LogP and with an acceptable range 5), hydrogen bond acceptor (with an acceptable range 10) and hydrogen bond donor (with an acceptable range 5), for that we used the SwissADME web server which will allow us to obtain these output data http:// www.swissadme.ch/(Daina, Michielin, & Zoete, 2017).
We pursued the work by determining the pharmacokinetic properties that will be useful to predict the concept of ADMET and define the availability and accessibility of the active substance during its administration in the body, such as toxicity, mutagenic power of the substance, intestinal absorption, hemato-encephalic permeability, inhibition of the five major isoforms of cytochrome P450 and, other properties.We used the web server pkCSM for the purpose of providing this analysis: http://biosig.unimelb.edu.au/pkcsm/prediction (Pires, Blundell, & Ascher, 2015).

Simulation of molecular dynamics
At the atomistic level, molecular dynamics (MD) simulations were performed in order to carry out an in-depth study of the impact of the binding of the protein complex (GCK-TNKS) with small molecules (proposed ligands).For this reason, we used the GROningen MAchine for Chemical Simulations (GROMACS) version 4.6.5 package to perform 200 ns simulations, which will allow us to determine the degree of stability and evaluate the conformational dynamics of the complex with the six chosen ligands (TNKS 22, TNKS 49, G007-LK, IWR-1, XAV939) (Van Der Spoel et al., 2005).
For this purpose, the CHARMM 27 force field was used for all MD simulations.First, we performed a preparation of the protein topology file via the Gromacs module pdb2gmx using the CHARMM force field, as well as the ligand topology via the SwissParam tool.Then the anchored complexes were immersed in a dodecahedron box with an edge length of 1.0 nm, solvated with the three-point transferable intermolecular potential (TIP3P) water model as solvent, and the system was neutralized by adding Na þ and Cl-ions as needed.This step was necessary before ensuring an energy minimization in order to reach a negative potential energy and was optimized via the method of the steepest descent to obtain a stable system with a Fmax that did not exceed 1000 KJ/mol/nm.
Then this was followed by an equilibration step that took place in 2 phases after heating, starting with a set of NVT (characterized by a constant temperature, volume and atomic number) followed by a set of NPT (with a constant pressure).The objective is to stabilize the temperature at 300 K and the pressure at 1 bar during the 1000 ps, using the Nose-Hoover thermostat and the Parrinello-Rahman barostat for the NVT and NPT, respectively.MD simulations have been realized for 200 ns in 50000000 steps, storing the coordinated data for 2fs.All this was necessary before running the MD production cycle for each docked complex, which resulted in trajectories that were analyzed using the distribution programs gmx rmsf, gmx gyrate, gmx rms, gmx sasa, and gmx h-bond, which generated parameter values for hydrogen bond number, root-meansquare deviation (RMSD), radius of gyration (Rg), and also for solvent accessible surface area (SASA), root-mean-square fluctuation (RMSF).
The average MD trajectories of the proposed ligands were also submitted to the Bio3D library in R software for calculation (Grant et al., 2006) and the graphs of all of them were generated via the GRACE software.

Results & discussion
The objective of our study is to predict the molecules that can potentially interact with the complex to inhibit the protein TNKS, since it is a physiological inhibitor of GCK.This suppression of TNKS will result in an amplification of the effect of GCK.For this, we proceeded by extracting these existing ligands from PubChem to test their effect as pharmacological agents that can be used as anti-diabetic treatments.
To initiate, the structure of the GCK protein as well as that of TNKS were obtained from the PDB database (Berman et al., 2000), and the physicochemical properties of the selected GCK inhibitors were extracted from the PubChem website Supplementary Table 1.Then, we used the online server CASTp to predict the active site of the proteins TNKS and GCK; the result of the pockets of each protein is represented on the table Supplementary Table 2.
To undertake the analysis, we ensured a molecular docking to the GCK protein and TNKS by the ClusPro tool, which generated a very well-founded affinity score (-987 kcal/mol) which pushed us to continue our docking of the complex with the thirteen ligands already determined, namely the TNKS 22 and its analogue, the TNKS 49 and its analogue, G007-LK, IWR-1, XAV 939 and JW55 via the AutoDock Vina software (Trott & Olson 2010), which allowed us to determine the binding affinity of these inhibitors.These binding affinities of the thirteen selected ligands have been presented in the table below Table 1 in kcal/mol.
The docked complexes were then analyzed using the PyMol tool and the Discovery Studio Visualizer, with which the best poses of the docked model were chosen, and then the analysis of the latter was carried out to determine the type of interactions, the residues involved, as well as the distance that links the ligands to the complex.The best affinity score was obtained by the analogue TNKS 22 (46824343) with À 9.3 kcal/mol and 9 bonds of hydrogen, hydrophobic, halogen, and electrostatic nature.As a second-best score, we find the TNKS 22 with an affinity score of À 9.0 kcal/mol and 9 bonds, 5 of which are hydrogen-natured and 4 of which are hydrophobic, as illustrated in Figure 1, which illustrates the hydrogen bonds and the 2D interaction diagram of the complex with the TNKS 22 and these analogues.The details of these interactions are described in Supplementary Table 5, which is followed by the inhibitor G007-LK, which has an affinity of 9.0 kcal/mol with 4 bonds 2 hydrogen at residues THR1232, GLN1248, and 2 hydrophobic bonds at residues Pro1187, Pro1235, the result of hydrogen bonding and the 2D interaction diagram of the complex with the inhibitor G007_LK as well as other inhibitors of the same class will be illustrated in Figure 2, and the details of the interactions are described in the Supplementary Table 3.
The result of hydrogen bonding and the 2D interaction diagram of the complex with TNKS 49 and these analogues are shown in Figure 3, and the details of the interactions, including the type of interaction, the residues involved, and the distance, are well described in Supplementary Table 4.
Following the molecular docking analysis, the best affinity of the compounds was selected based on the evolution of the affinities of each pose according to the 9 proposed conformations, the result of this selection is represented in Supplementary Table 6.Those tankyrase inhibitors have also shown their usefulness as cancer therapeutic agents via several studies, and some of them with promising therapeutic effects have been developed, including IWR-1, G007-LK, XAV939, and JW55.
Shih-Min A. Huang and colleagues studied the deregulation of Wnt pathway activity because it is implicated in many cancers, making it an appealing target for cancer therapies.They discovered that the inhibitor XAV939 stimulates -catenin degradation by stabilizing axin, the concentration-limiting component of the destruction complex, by inhibiting the poly-ADPribosylation TNKS enzymes (Huang et al., 2009).
Ted Lau et al. proposed in their study to identify tumor models whose growth is inhibited by tankyrase inhibitors, that b-catenin-dependent maintenance of an undifferentiated state can be blocked by tankyrase inhibition.This has been proven in vivo in a subset of colorectal cancer xenograft models.Compound G007-LK exhibits favorable pharmacokinetic properties, inhibiting tumor growth by preventing cell cycle progression, reducing colony formation, and promoting differentiation (Lau et al., 2013).
Alexander M Busch and his team provided individual treatments for a panel of human and murine lung cancer cell lines with the tankyrase inhibitors XAV939 and IWR-1 which had the effect of repressing cell growth.This inactivation stabilized the axin and reduced the growth of lung cancer cells (Busch et al., 2013).
Jo Waaler and her team were able to identify JW55, a molecule that inhibits the b-catenin signaling pathway, and found that increased nuclear accumulation of b-catenin mediates canonical Wnt signaling that is found in many tumors and is frequently associated with tumor progression and metastasis.This inhibitor works by inhibiting the PARP domain of TNKS, resulting in the stabilization of AXIN2, a member of the b-catenin destruction complex, and increased b-catenin degradation (Lau et al., 2013).
Hence the need to test these inhibitors to evaluate their capacity as anti-diabetic pharmacological agents.For this reason, we have started to use the SwissADME tool, which has allowed us to evaluate the drug characteristics by calculating the physicochemical descriptors of the thirteen ligands on the basis of the rules of Ghose, Veber, Egan, and Muegge, and above all that of Lipinski.The conformity of our ligands to these rules is represented on the table (Table 2).
In this regard, all the tested compounds present bioavailability scores of 0.55 and satisfy the Lipinski rule; all the ligands do not exceed a single violation of the rule, which is the case for analogue inhibitors TNKS 49 (2997796), analogue inhibitors TNKS 22 (46824343) and G007-LK.The thing that is acceptable as long as it respects the other Lipinski's properties (namely the molecular weight, which must be less than 500 Da so that it can easily cross cell membranes, the logP <5, which shows that they must be soluble in lipidic and aqueous solutions, as it is important that the compound is an H-bond donor <5, an H-bond acceptor <10, and an MR 130).The details of which are represented on the table (Supplementary Table 7).
Subsequently, the pkCSM tool was used to predict the central ADMET properties for drug development, which showed that all the molecules tested have a very high gastrointestinal absorption capacity, all of them representing intestinal absorbance values higher than 30%, As a result, the Blood Brain Barrier permeability value was generated, which translates the capacity of a drug to cross the blood-brain barrier, which is crucial in order to avoid side effects and toxicity.In our case, with the exception of XAV939, none of the tested compounds were able to cross the blood-brain barrier, as they have a log BB <0. 3 so they are poorly distributed in the brain.
A toxicity result was also ensured via an Ames test, which was negative for all the tested molecules, which means that all the tested compounds do not present a mutagenic potential and are not likely to act as carcinogenic elements.
All of the compounds tested have synthetic accessibility values of about 3, which means that they are simple to synthesize.Other pharmacokinetic properties of the metabolism consist of predicting if the molecule tested is likely to be an inhibitor or a substrate of cytochrome P450.This enzyme ensures the detoxification and oxidizes the xenobiotic to facilitate its excretion, so inhibiting it is not recommended.The value of clearance is also an output of the server; it includes both the hepatic and renal clearance and gives an idea of the bioavailability.All these data are detailed and approved in the table below (Table 3).
Based on previous results, we selected ligands that present the best affinity score and meet the inclusion criteria established for the pharmacokinetic study (especially that of intestinal absorption, BBB permeability, and toxicity) to carry out a study of the molecular dynamics of these compounds.

MD Simulation
To further investigate the results of virtual molecular docking, the six major docked ligand complexes (with the largest binding energies in kcal/mol and which exhibit the most suitable pharmacokinetic properties), including the crystalline protease complex with tankyrase inhibitors), were run for a 200 ns molecular dynamics simulation.From this MD simulation trajectory, we analyzed the root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), number of hydrogen bonds, and solvent accessible surface area (SASA) to check properties such as stability and flexibility of target-ligand conformations.

RMSD analysis
This is an indicator that demonstrates the stability of the complex, its principle is to measure the structural distance between the coordinates and, more precisely, the deviation of the alpha carbon of the main chain from the reference.We noticed from the graphical representation of the RMSD represented below (Figure 4) that the RMSD values increased progressively until 50 ns and then they converged to a constant value and therefore stabilized throughout the trajectory.From the average RMSD calculated of each anchored ligand compared to the reference, which is 3.00 nm, we can deduce that the ligand XAV939 has a higher stability than our reference, which reflects its better stability, and for the other ligands, their RMSD values varied between 0.39 nm and 0.72 nm, which reflects their stability compared to our reference, which is 0.57 nm.

RMSF analysis
This parameter indicates the mobility and residual flexibility of amino acids by measuring the average deviation of a particle over time from a reference position.
Based on the graphical representation below (Figure 5) and the average values calculated for each anchored ligand that varies between 0.14 nm and 0.19 nm except for compound G007-LK which is 0.25 nm, we deduce that their RMSF values are around the reference of 0.155 nm, which reflects a normal fluctuation and high stability for all the test compounds with a slight increase for the fluctuation of compound G007-LK.

Gyration radius analysis (Rg)
Rg is a parameter that reflects the degree of compactness, folding, and stability of the structure.It is calculated from the intrinsic dynamics of the protease-ligand complexes.
As a result, a radius of gyration value that is maintained over the simulation time reflects the stability of the stably folded protein structure, unlike in the case of an unfolded protein, where the radius of gyration value changes over time.On the other side, a higher Rg value reflects compactness and low stability.
Looking at the graphical representation of the RG illustrated below (Figure 6) we notice a stability of the trajectory throughout the simulation, which is also proven by the calculated average values that vary from 2.92 nm to 2.97 nm, so we can consider them as stable and very close to the reference, which is 3.00 nm.This shows that all the ligands were able to form compact and stable complexes with the target protein complex.

Solvent accessible surface area (SASA)
The SASA evaluates the degree of expansion of the protein volume in each system by estimating the individual MD trajectory, which is the degree of exposure of the protein to its environment (solvent).
Observing the graphical representation pictured below (Figure 7), we notice a stability of the trajectory throughout the simulation, which is also proved by the calculated average values that are stable and very close to the reference, seeing that their values ranged from 349.9 nm 2 to 353.74 nm 2 compared to our average reference value of 350.14 nm 2 .This shows that the degree of compactness (RG) and the degree of exposure (SASA) are not affected by the binding of the ligand.

Hydrogen bonding analysis
During the simulation, the number of hydrogen bonds formed between the ligands and our protein complex, as well as the number of donors and acceptors, were calculated, with this parameter being the main element of stabilizing interaction between two molecules, knowing that the importance of the presence of these bonds reflects the importance of the level of stability of the ligand-target protein complex.
On the basis of the graphical representations presented below (Figure 8) and the calculated average of the hydrogen bonds, we note that all the ligands that we have proposed, with the exception of the compound G007-LK, develop hydrogen bonds with the protein complexes, especially at the level of the active site, which reflects the degree of stability of the complexes.
As a result of this in silico study, TNKS inhibitors (IWR-1, XAV939) were the two tested compounds that were able to generate both high binding affinity with the complex of À 8.7, À 8.8 kcal/mol, respectively, and that comply with drug regulations in general and Lipinski in particular, as they respect the pharmacokinetic properties, which means that they have a good capacity of intestinal absorption and do not generate toxicity.In addition to other these two compounds also showed a significant good result following the molecular dynamics simulation performed, in which they showed a positive alignment with the MD parameters (RMSF, SASA, H-Bonds, Rg and RMSD) calculated along the trajectory.All these results make these two compounds promising candidates and potential drugs for gestational diabetes.
Knowing that even the other tested compounds, namely TNKS22, 2215914, and 46824343, which were analyzed in depth, gave good results regarding binding affinity, pharmaceutical compliance, and even MD simulation, means that they can also be further exploited.And for compound G007-LK, despite its better affinity and compliance with drug rules, it did not show high stability based on MD parameters, including RMSD, RMSF, and H-bonds.

Conclusion
In the present study, we tested thirteen compounds, namely TNKS inhibitors (TNKS 22,49), and their analogues, to analyze their interaction with the GCK-TNKS protein complex.For this purpose, we first used the molecular docking approach, which generated a high binding affinity between À 7.1 and 9.3 (kcal/mol).These compounds also demonstrated drug similarity and pharmacokinetic properties.Subsequently, we selected the six compounds that generated the highest affinity and agreed on the parameters of the drug rules and the pharmacokinetic properties to ensure a molecular dynamics study.The results allowed us to prioritize two compounds, namely XAV939 and IWR-1, as potential drugs, knowing that even the tested compounds (TNKS22, ( 2215914) and ( 46824343)) showed good results that can also be exploited.As for the compound G007-LK, despite its better affinity and compliance with drug rules, it did not show great stability according to MD parameters, including RMSD, RMSF, and Hbonds.These results are interesting and encourage further experimental exploration to discover a treatment for diabetes, including gestational diabetes.To this end, we note that further research will be useful to verify and prove this result experimentally in vivo and in vitro to confirm the effects of these compounds on GCK protein activity and carbohydrate metabolism and, therefore, on the development of gestational diabetes.

Figure 1 .
Figure 1.Surface area and types of possible residual interactions in 3D and 2D of the best pose obtained following molecular docking of the TNKS 22 ligand and its analogues with the complex.

Figure 2 .
Figure 2. Surface area and types of possible residual interactions in 3D and 2D of the best pose obtained following molecular docking of the tankyrase inhibitors ligands with the complex.

Figure 3 .
Figure 3. Surface area and types of possible residual interactions in 3D and 2D of the best pose obtained following molecular docking of the TNKS 49 ligand and its analogues with the complex.

Figure 6 .
Figure 6.Graphical representations of the total radius of gyration of the proposed ligands (tankyrase inhibitors) with complex (GCK-TNKS) for the time trajectory.

Figure 7 .
Figure 7. Plot of the solvent accessible surface area (SASA) of the proposed ligands (tankyrase inhibitors) with complex (GCK-TNKS) versus time.

Figure 8 .
Figure 8. Graphical representation of the number of intermolecular hydrogen bonds and the donors and acceptors between the proposed ligands (tankyrase inhibitors) with complex (GCK-TNKS) for the full-scale time course.

Table 1 .
Affinity score generated from the virtual molecular docking of the tested and selected ligands.

Table 2 .
Probability of a drug according to drug rules for the 13 proposed ligands with high binding energy.

Table 3 .
The overall pharmacokinetics properties of the thirteen main ligands with high binding energy.