Computational Analysis of Azadirachta indica plant extracts Targeting Thymidylate Synthetase against Colon cancer

Sushree Sangita Mishra¹, Sibani Sahu², Biswajit Mishra³*, Satya Narayan Sahu¹*

¹School of Biotechnology, Centurion University of Technology and Management (CUTM),

Bhubaneswar, 752050, India.

²School of Applied Sciences, Centurion University of Technology and Management (CUTM),

Bhubaneswar, 752050, India.

³Pro-Vice chancellor, Centurion University of Technology and Management (CUTM),

Bhubaneswar, 752050, India.

*Corresponding Author E-mail: satyanarayansis07@gmail.com, satyanarayan.sahu@cutm.ac.in, biswajit.mishra@cutm.ac.in

ABSTRACT:

Colon cancer is the leading cause of mortality for both men and women. Because of tumour resurgence, the medical prognosis of colon cancer remains disappointing even after several medicinal studies. Furthermore, Thymidylate Synthetase (TYMS) is a key molecular target for most chemo drugs used to treat colon cancer. As a result, the purpose of the article is to develop small biomolecule indigenous drug alternatives that target the TYMS protein to treat serious malignancies associated with various cancers. Alternative treatment combinations of medications, especially those that are more effective and less toxic, appear to be of major importance. As a result, mixing natural materials with established chemotherapeutics to create hybrids provides a fresh potential to generate new anticancer chemicals that are easier to administer. In this regard, we documented Azadirachta indica, an ancient sacred plant that is rich in antioxidant phytocompounds (Azadirachta Indica). As a result, the goal of this paper was to evaluate the anticancer potential of a range of phyto-compounds from the Azadirachta Indica plant using computational methods. In addition to this, our study intended to predict the stability of mutant protein’s structure as compared to wild type structure through molecular dynamic simulation analysis. With this we extend our study to predict the potency of phytochemicals of Azadirachta indica againstTYMS protein by using molecular docking analysis. The Azadirachta indica plant contains ninety-four compounds. From which five best-docked compounds namely, Nimbolide, nimonol, nimbiol, nimbidoil, vilasinin were chosen for further investigation based on their binding affinities against the TYMS protein. A complete data screening method based on docking, ADMET characteristics, and MD simulation techniques was used in the study, and the selected compounds were shown to have a good value against the targeted TYMS protein, indicating that they are potential and effective anticancer activity.

KEYWORDS: Colon cancer, Azadirachta indica, TYMS, Molecular Docking, MD simulation, Mutation.

1. INTRODUCTION:

Colon cancer is a tumor of the large intestine which is the last component of the digestive system. It has been caused by the uncontrollable multiplication of aberrant cells and is the most frequent kind of cancer in the gastrointestinal tract¹. As per the World Health Organization (WHO) annual report, colon cancer is the third most deadly carcinoma in men and the second most in women. According to the International Agency for Research on Cancer (IARC), 153,020 people were diagnosed with colon cancer in 2023 and 52,550 died from it. In India, colon and rectal cancer had annual incidence rates (AARs) of 4.4 and 4.1 per 100000 in males and 3.9 per 100000 in women, respectively². Colon cancer is frequently caused by a tiny growth known as a polyp. Polyps are quite frequent, although the majority of them do not progress to malignancy. Polyps come in a variety of shapes and sizes, with some having a higher risk of turning into a cancerous tumor than others. Its frequency and death depend solely on the screening regularity¹. If the malignancy can be detected early with screening, then cancer can frequently be prevented by removing cancerous polyps via surgery. For the screening, mostly the tests have been recommended are colonoscopy and fecal occult blood (FOB)³. Out of these screening modalities, colonoscopy is widely used to detect cancer at different stages, occasionally in the early stages but frequently in the advanced stages^1,3. The risk of colon cancer can be increased by a variety of environmental and hereditary factors. Among the variables age, excess weight, low physical activity, tobacco, and alcoholic liquor consumption, diabetes mellitus, and insulin resistance, low-fiber and high-fat diet have a crucial role in the onset of cancer⁴. According to the stage-wise progression of colon cancer the tumorous cells have developed through the superficial lining (mucosa) of the colon or rectum but have not expanded beyond the colon wall or rectum on the preliminary stage. The abnormal cells have grown into or through the colon or rectum wall in stage II, but they haven't migrated to adjacent lymph nodes. The malignant cells have infiltrated adjacent lymph nodes at stage III, but they haven't spread to other regions of the body yet. Cancer has progressed to other organs, such as the liver or lungs, at stage IV⁵. Therefore, it is essential to diagnose and treat it in the early stage. For more, there are various treatment modalities used to treat colon cancer includes surgery, radiation, and chemotherapy. Amongst this chemotherapy is the principal treatment strategy used for patients with rapidly progressive colorectal cancer⁶. Furthermore, mostly chemo drugs interfere with DNA synthesis and repair by incorporating themselves or their corresponding metabolite into RNA and DNA. It also results in cytotoxic action and cellular damage by inhibiting the enzymatic action required for DNA synthesis during the S-phase of the cell cycle. As a result, the chemo medications prevent the cell cycle from continuing. The important target for certain chemo drugs, used to treat colon cancer, is thymidylate synthetase (TYMS). The protein is encoded by the human thymidylate synthase gene (TYMS). TYMS protein is employed in the manufacture of deoxythymidine monophosphate (dTMP) by methylatingdeoxyuridine monophosphate (dUMP). TYMS together with the reduced folate N-5, N-10- methylene tetrahydrofolate (CH2THF), and dUMP, forms a stable ternary complex which results in the formation of dTMP. In this step, CH2THF is a principal methylene group donor which performs in the presence of methylenetetrahydrofolate reductase (MTHFR). The dTMP ultimately forms deoxythymidine triphosphate (dTTP) which then gets incorporated into DNA in the process of DNA synthesis⁷. Formation of the ternary complex, i.e., TYMS-dUMP- CH2THF, is a time-sensitive procedure that can be interrupted by the presence of a chemo drug and results in deliberated inactivation of the TYMS⁸. This leads to deplete the level of deoxythymidine triphosphate (dTTP), due to the depletion of dTMP, causing changes in the proportion of the other deoxynucleotides (dATP, dGTP, and dCTP). Eventually, this disproportioning considered imposing damage on repair and replication of DNA^6,8. Consequently, intrinsic TYMS levels of the malignant cell are examined in particular to predict how well patients with colorectal cancer respond to treatment and how long they live⁹. Individuals with a poor expression of perivascular TYMS get slightly better performance than patients with elevated expression of TYMS¹⁰. In addition to this, it was supposed that the high intrinsic TYMS values in the cancerous cell would impart resistance to chemotherapy. Hence, it proofs that TYMS protein expression is a prognostic factor united with chemo drug sensitivity. Therapies containing adjuvant chemotherapy have been shown to substantially boost disease-free survival and overall survival only in patients with depleted TYMS expression levels. The most used chemo drug which priorly targeted TYMS is 5-fluorouracil (5-FU) and its prodrug capecitabine (Xeloda)¹¹. Nevertheless, these chemotherapy drugs increase optimal and disease-free survival of biopsied stage III colorectal cancer cases¹¹, but also associated with several side-effects such as nausea and vomiting, myelosuppression, and oral and gastrointestinal ulceration¹². Furthermore, intensified metabolism of chemo drug necessitates the constant high doses administration of the drug for maintaining its adequate concentration in serum for efficient therapeutic potency, which if indurated, can be fatal¹³. Hence, to combat chemo drug resistance and lessen its negative effects, several combinations of plant-derived medications have been proposed. These medicinal phytocompounds have high therapeutic efficacy and work in tandem with conventional drugs. In looking for alternative therapeutic combinations or drugs, particularly those which are more efficient and less toxic, appears to be a great significance. Therefore, the development of hybrids combining natural products and traditional chemotherapeutics is a novel opportunity to develop new anticancer compounds that are easier to administer and predict pharmacokinetic properties¹⁴. In this respect, we documented an ancient religious plant, enriched with several antioxidant phytocompounds that is Azadirachta Indica.

Consequently, in this article, we intended to computationally investigate the anticancer potential of a variety of phytocompounds of Azadirachta Indica plants and list out the best compound according to their binding performance with the targeted protein that is TYMS.Computational methods such as pharmacokinetic analysis, drug-likeness properties, molecular interaction study, and molecular dynamics simulation are used to screen the possible candidate phytocompound having an anticancer activity that can be further employed for pharmacological use.

2. Computational methods:

The important target for certain chemo drugs, used to treat colon cancer, is thymidylate synthetase (TYMS). So, we consider TYMS protein for this computational prediction. The FASTA sequence of the TYMS amino acid with the Uniport ID P04818, encoded by the human TYMS gene using the Uniport database. The amino acid sequence length of TYMS protein was from 1 (amino terminus) to 313 (carboxy-terminal). The RCSB protein data bank (PDB) provided the x-ray crystallographic structure of 'A' chain of human thymidylate synthetase protein with id 6QXG. Afterward by using the PredictSNP online server the functional activity of mutated single nucleotide polymorphisms was examined ¹⁵. It forecasts both the location of the mutation and its deleterious nature which is the cause of the disorder. With extensive literature study, we found nine mutation positions as, E23G, D48V, V84A, K93E, D110E, P194Q, S206G, H250L, and K284N. From which the mutation at P194Q was predicted to be a deleterious mutation. We used the DUET online service to estimate the structural stability of the examined mutation sites P194Q of TYMS¹⁶. According to DUET, MCSM, and SDM prediction servers the stability change values for P194Q were - 2.21, -2.254, and -1.13kcal/mol, respectively. Hence the data concluded by the DUET server revealed that P194Q was the utmost destabilizing point.

2.1. Molecular dynamics simulations:

Using the GROMACS programme¹⁷, dynamic simulation was conducted to predict the stability of ACTN4 protein after mutation. In order to accomplish ligand parameterization (topology file preparation) CHARMM general force field was utilized (CGenFF). For the native and mutant systems, the CHARMM 36 force field was employed¹⁸. The periodic boundary effects are employed to avoid edge effect of native and mutant systems. We added four numbers of Na+ ions to keep the simulated system neutral. We employed particle mesh ewald (PME) technology¹⁹in this instance to control the long-range electrostatic force. For the energy minimization phase steepest descent algorithm was employed. Following the minimization process, three more measures were incorporated into the NVT (number of particles, volume, temperature) ensemble: balancing, heating and development phase throughout the simulation trajectory. In the NVT phase 300k of temperature were added. Followed by NVT phase we performed NPT (number of particles, pressure and temperature) phase for 20ps with constant temperature and 1.0 atm pressure. We employed linear restrict solver (LINCS) technique to constrain every connection between hydrogen atoms. The 2-fs time step has been allowed for entire simulation trajectory. At every 10ps the MD trajectories were noted for entire 20 ns of simulation paths¹⁷.

2.2. Protein and ligand preparation:

The protein’s structure preparation and the structure of ligands preparation for the in-silico study was done in AutoDockTool²⁰. Herein, the TYMS protein was taken as a rigid receptor and the 94 phytocompounds of the Azadirachta Indica plant are considered as flexible ligands. Afterwards, the PDB file format of both receptor and ligand were changed to. pdbqt through AutoDockTools-1.5.6. Followed by this virtual screening process was done by using AutoDock vina software.

2.2. Virtual screening and molecular docking study:

With the whole three-dimension structure of all the entities, we used AutoDock raccoon software²⁰ to accomplish virtual screening process of studied protein and 94 numbers of phytocompounds of Azadirachta Indica plant for the verification of variation in binding energy between them. With the use of AutoDock tools, the Kollman charges were measured and polar contact was added by the hydrogen atom^21-24. Afterward, we used the Computed Atlas of Surface Topography (CASTp 3.0) of Proteins to study the TYMS protein in order to anticipate the suitable binding pocket for the protein-ligand interaction site. It should be emphasized that the CASTp detects and calculates binding pockets using the alpha shape theory of computational geometry. To initiate the docking procedure, we positioned the grid box to enclose the TYMS protein's anticipated binding pocket. According to CASTp analysis the binding pocket includes the following residues, LYS47, ASP49, ARG50, THR51. THR55, LYS77, ARG78, VAL79, PHE80, GLY83, VAL84, GLU87, LEU88, PHE91, LYS107, ILE108, TRP109, ALA111, ASN112, PHE117, LEU121, VAL134, TYR135, GLN138, TRP139, ALA181, MET190, ALA191, LEU192, PRO193, PRO194, CYS195, HIS196, GLN214, ARG215, SER216, GLY217, ASP218, LEU221, GLY222, PRO224, PHE225, ASN226, SER229, TYR230, LEU233, ASP254, HIS256, TYR258, THR306, ILE307, LYS308, MET309, MET311, ALA312, and VAL313. A gradient optimization algorithm was applied to accomplish the docking process. In the current study, the grid box was customized with the following size as 100, 76, and 76 in the x, y, and z-axis respectively. Whereas the center size was set as 32.022, -34.525, and 19.688 in x, y, and z coordinate respectively. The Discovery studio visualization tool was used to document all of the images (Visualizer D S 2013).

2.3. Drug-likeness property prediction:

The physiochemical and drug likeness key qualities of active compounds from Azadirachta Indica plantare predicted by using SwissADME web service²⁵. The drug likeness properties are studied by analysing the adsorption, distribution, metabolism and excretion of studied phytocompounds. Each of the phytocompounds are studied for H-bond donors (HBD) and H-bond acceptors (HBA) numbers, rotatable bond (RB) numbers, coefficient of water solubility (log S), partition coefficient (log P), total polar surface area (TPSA) and molecular weight.

3. RESULT AND DISCUSSION:

It is possible to use a variety of computational tools to analyse various chemical properties, potentially complementing experimental efforts and offering promising accuracy rates^26-27. The functional behavior of non-synonymous single nucleotide polymorphisms was investigated using PredictSNP, an internet service. It calculates the position of a polymorphism and also the mutation's intensity. We expect that all of the mutations investigated will result in harmful mutations, as indicated in Table 1.

The mutation positions P194Q and H250L were found to be most deleterious as per all the seven tools present in PredictSNP server. Followed by this all the deleterious mutation positions are taken for prediction of structural stability upon mutation of TYMS protein. Using the DUET web server, we predicted the structural stability of the TYMS protein’s mutations for further confirmation. Amongst the examined point mutations, P194Q showed the most destabilizing mutation. According to the DUET, mCSM and SDM prediction servers, the stability changes following mutation in the position P194Q of the TYMS protein were -2.21, -2.254 and -1.13kcal/mol, respectively. Table 2 shows the specifics of how structural stability varies as a result of mutations.

3.1. Molecular Dynamics Simulation:

The kinetic and microscopic characteristics were investigated using molecular dynamics (MD) simulations of wild and mutant model of TYMS protein system^27-30. 50ns molecular dynamics simulations were performed on both two systems using Gromacs 2018.6 version to explore the stability, fluctuations, and folding behaviour of the protein at the atomic level. Figure 4 shows that the two systems were showing the fluctuation after 5500ps till the end of the trajectory. The wild protein model was shown in black colour whereas the mutant was in red colour. For the wild protein model, the maximum fluctuation was found at ~0.6nm of 16000ps, while for mutant TYMS system it was at ~1.1nm of 19000ps. The average RMSD value for the 20ns trajectory was calculated using the total RMSD analysis were found to be 0.42 +/- 0.08nm and 0.6 +/- 0.1nm for wild and mutant system respectively. So, from this data we analyze that the RMSD value for wild protein model was lower than that of the mutant model system. According to the overall statistics the lesser the RMSD value, greater the stability of the protein system. The respective figure was shown in Figure 1. (a). The RMSF values of protein were determined to better understand the fluctuations of the protein residues and variations in the flexibility of the residues. The observed variations in fluctuation pattern between the two systems that is wild and mutant protein systems were measured using RMSF plots. High fluctuation was observed at ~0.38 nm for the mutant protein system. The fluctuating residues were ARG50, THR76, PHE123, SER154, LEU187, PRO184, ASN205 and ARG271. For wild protein system the notable fluctuating residues were ARG50, VAL106, PHE123, SER154, PRO184 and ASN205. The overall RMSF plot shows the more fluctuated value that is of mutant system predicts less compactiveness as compared wild protein system. The graphical representation was shown in Figure 1. (b).

Table 1: Representing the non-synonymous single nucleotide polymorphism of the TYMS gene by using PredictSNP online

Mutation	Predict SNP	MAPP	Phd-SNP	Polyphin-1	Polyphin-2	SIFT	SNAP
E23G	neutral	neutral	neutral	neutral	neutral	neutral	neutral
D48V	neutral	neutral	destructive	neutral	neutral	neutral	neutral
V84A	neutral	neutral	destructive	neutral	destructive	destructive	neutral
K93E	neutral	destructive	destructive	neutral	neutral	neutral	neutral
D110E	neutral	neutral	destructive	neutral	neutral	neutral	neutral
P194Q	destructive	destructive	destructive	destructive	destructive	destructive	destructive
S206G	neutral	neutral	neutral	neutral	neutral	neutral	neutral
H250L	destructive	destructive	destructive	neutral	neutral	destructive	destructive
K284N	neutral	destructive	neutral	neutral	neutral	neutral	neutral

Table 2. Effect of mutation on structural stability

Mutation position	Sdm prediction	Duet prediction	Mcsm prediction
E23G	0.45	0.104	-0.221
D48V	0.68	0.44	0.06
V84A	-0.69	-1.64	-1.742
K93E	0.65	0.652	0.125
D110E	-0.23	-0.303	-0.57
P194Q	-1.13	-2.21	-2.254
S206G	3.74	0.487	-0.503
H250L	1.13	-0.667	-1.002
K284N	-0.31	0.389	0.232

The solvent accessible surface area (SASA) is the surface area of a compound which is solvent susceptible and is calculated using the gmx SASA module. It is used to determine how much an amino acid is displayed to its surroundings. A compact protein structure is indicated by a lower SASA value, whereas a dispersed structure is indicated by a larger SASA value. A change in the protein's structural feature is indicated by a rise or reduction in the SASA value. The SASA values of the both systems shows that the wild protein (black) shows less surface area as compare to the mutant system(red) as shown in Figure 2(a).

The average values for wild as well as mutant were 176.11 +/- 1.42nm² and 176.20 +/- 1.19nm² respectively. From the average value our study shows that the wild protein system showing more compactness that the mutant. The Gromacs gmx h-bond module was used to calculate the hydrogen bond numbers of proteins. The module computes and analyses hydrogen bonds between all potential donors and acceptors. We used geometrical criteria to calculate the number of hydrogen bonds in this study. Throughout the simulation trajectory of 20ns the wild protein shows a greater number of hydrogen bond as compared to the mutant system as shown in Figure 2(b). The average values of the hydrogen bond for wild as well as mutant protein systems were 230.43+/- 7.24 and 228.31+/- 6.7 respectively as shown in Table 3. The higher value of hydrogen bond in wild protein system predicts the more compact structure and ultimately increased stability as compared to mutant one ³¹.

Table 3. Average value of RMSD, SASA, Number of Hydrogen bond of native and mutant model of TYMS proteins.

Systems

RMSD

(nm)

SASA

(nm²)

Number of hydrogen bond

Wild type

0.42 +/-

0.08

176.11 +/- 1.42

230.43 +/-

7.24

Mutant

0.6 +/-

0.1

176.20 +/- 1.19

228.31 +/-

6.7

3.2. Molecular docking and virtual screening:

In this bioinformatics work, Autodock vina was employed to determine the binding affinity of both the wild type and mutant type of TYMS protein with the phytocompound. From the virtual screening result out of 94 phytocompound of Azadirachta indica plant, we have screened total five number of active compounds that are nimbolide, nimonol, nimbiol, nimbidiol, vilasinin which have shown good result according to their binding energy and confirmation of interaction. The binding residues and binding energy of the screened photocompounds with both the wild and mutant type of TYMS protein is mentioned in Table 4 and the mode of interaction are presented in Figure 3 (a) to (j). For nimbolide binding energy was predicted -8.08 kcal/mol for the wild type model and -8.85kcal/mol for mutant variety of TYMS protein. ASN112, SER229 and GLU87 residue of wild type were forming three hydrogen bonds with protein while six hydrogen bonds were forming with mutant type protein through GLY217, GLN214, HIS196, CYS195, ASN226 residue and TP109 residue was involved in hydrophobic bond. Nimonol was showing -9.01kcal/mol binding affinity in wild type and -9.25kcal/mol binding affinity in case of mutant type of TYMS protein and the interacting residue which are involved in hydrogen bond in wild type are SER229 and ASN112 while GLU87, TYR230, HIS196 were forming three hydrophobic bonds. In the case of mutant two hydrogen bond were formed by SER229 and ASN112 and only one hydrophobic bond was formed by the GLU87 residue. Nimbiol was showing -7.66kcal/mol in wild type and -8.18 kcal/mol in mutant type of TYMS protein. This phytocompound was having all hydrophobic bond in both wild type and mutant type of TYMS protein having TRP109, LEU192, TYR135, HIS196 residue. In nimbidiol the wild type was showing -7.29kcal/mol binding affinity and showing only single hydrogen bond through ASN112 residue and four hydrophobic bonds through TRP109, TYR135, HIS196 residue. And in mutant type the binding affinity was -7.54 kcal/mol for TYMS protein and it was showing only four hydrophobic bonds through TRP109, TYR135, HIS196 residue. In vilasinin the binding energy of wild type was -7.75kcal/mol and for mutant the binding energy was -8.27kcal/mol. The wild type of TYMS protein was showing three hydrogen bonds through ILE108, SER229 residue and three hydrophobic bonds through TYR230, HIS196, GLU87 residue whereas the mutant type was showing three hydrogen bonds through PRO193, LEU192, GLU87 residue and six hydrophobic bonds through PHE225, TRP109, GLN194, PRO193 residue. From the overall binding affinity and fashion of interaction we found that nimonol is showing minimum binding energy as compared to all other phytocompounds but nimbolide forms maximum numbers of hydrogen bond with a minimum difference in binding energy against wild type and mutant model of TYMS protein. So, both nimonol and nimbolide phytocompounds of Azadirachta indica plant can be potent therapeutics against colon cancer³².

Table 4. Molecular docking study of screened phytocompounds of Azadirachta Indica plant against wild and mutant model of TYMS protein

Compound name	Wild type		Mutant
Compound name	Binding energy (kcal/mol)	Interacting residue	Binding energy (kcal/mol)	Interacting residue
Nimbolide	-8.08	ASN112, SER229, GLU87	-8.85	GLN214, HIS196, GLY217, ASN226, CYS195, TRP109
Nimonol	-9.01	ASN112, SER229, GLU87, TYR230, HIS196	-9.25	ASN112, SER229, GLU87
Nimbiol	-7.66	TRP109, LEU192, TYR135, HIS196	-8.18	TRP109, LEU192, TYR13, HIS196
Nimbidiol	-7.29	TYR135, TRP109, HIS196, ASN112	-7.54	TRP109, TYR135, HIS196
Vilasinin	-7.75	SER229, ILE108, GLU87, TYR230, HIS196	-8.27	GLU87, LEU192, PRO193, GLN194, PHE225, TRP109, PRO193, GLN194

Table 5. Prediction of drug-likeness properties of studied phytochemical

Compound name	Molecular formula	Pubchem id	Molecular weight (g/mol)	TSPA (Å²)	No. of RB	Log P	Log S	HBD	HBA
Nimbolide	CID:100017	C27H30O7	466.52	92.04	4	3.11	-3.94	7	0
Nimonol	73356511	C28H36O5	452.58	76.74	3	4.29	-3.38	5	1
Nimbiol	CID:11119228	C18H24O2	272.38	37.30	0	4.03	-4.81	2	1
Nimbidiol	CID:11334829	C17H22O3	274.35	57.53	0	3.25	-4.37	3	2
Vilasinin	CID:102090424	C26H36O5	428.56	83.06	1	2.79	-4.50	5	3

Figure 3. Diagrammatic representation of molecular docking interactions of nimbolide with wild (a) mutant (b) model of TYMS, nimonol with wild (c) mutant (d) model of TYMS, nimbiol with wild (e) mutant (f) model of TYMS, nimbidiol with wild (g) mutant (h) model of TYMS and vilasinin with wild (i) mutant (j) model of TYMS.

3.3. ADME properties:

The physiochemical characteristics and potential ADME aspects of the phytocompounds under investigation will be covered in this study. The number of HBA, HBD, RB, log S, TPSA, logP, and molecular weight of each phytocompound were examined³⁰. Table 5 and Table 6 shows that every tested chemical meets the requirements for characteristics of drug-likeness that may be taken into account for a potential lead.

The phytocompound that was being screened and examined had a molecular weight of less than 500 Daltons. The stronger the hydrophobicity's ability to pass through the cell plasma membrane, the higher the Log P value, which indicates the hydrophobicity of the molecule or the absorption characteristic. The log S is an important parameter to take into account when analysing the pharmacokinetics of lead molecule absorption and distribution. The log S value had a limited range of -4.5 to -1 ³³. The TPSA in respect to absorption is less than 140Å2. The number of RB is always less than 10, and the smallest RB obtained from molecular modeling provides a greater level of structural confirmation. All of the properties described above were evaluated for the aforementioned phytocompounds using Lipinski's criteria, which specify the drug similarity attributes of the phytocompounds. The Lipinski rule's parameters state that the molecular weight cannot be more than 500 Daltons, the number of HBDs cannot be more than 5, the number of HBAs cannot be more than 10, and the Log P value must be greater than 1. The TPSA in respect to absorption is less than 140Å². The number of RB is always less than 10, and the smallest RB obtained from molecular modeling provides a greater level of structural confirmation. All of the properties described above were evaluated for the aforementioned phytocompounds using Lipinski's criteria, which specify the drug similarity attributes of the phytocompounds. The Lipinski rule's parameters state that the molecular weight cannot be more than 500 Daltons, the number of HBDs cannot be more than 5, the number of HBAs cannot be more than 10, and the Log P value must be greater than 1³³.

CONCLUSION:

TYMS is a enzymatic protein responsible for regulation of protein synthesis and apoptotic processes. Due to mutation of TYMS it promotes growth of the tumor, but chemo-drugs (i.e, 5-FU) helps to cure from cancer. In this study, we concluded that the result of DUETserverP194Qismoredestabilized mutation. From the molecular dynamics simulation approaches it concluded that, result between TYMS native and mutant the RMSD graph shows the wild protein model deviate lower as compared the mutant model. In RMSF graph, the mutant protein system is high fluctuated than wild. so, the mutant protein system predicts the less compactness than wild. The fluctuation of mutant proteinre siduesis ARG50, THR76, PHE123, SER154, LEU187, PRO184, ASN205 and ARG271at~0.38nm. From SASA graph, wildprotein system was showing more compactness as compared that mutant one because wild protein shows the less compact area than mutant. And from the number of Hydrogen bond graph, we concluded that wild protein system wasmore stability and predicted more compact structure than mutant system.We check the pharmacokinetic properties,drug likeliness properties and toxicity of best ten phytocompounds as per the bindingenergy. Here we performed the molecular docking result between best five docking interaction between TYMS and azadirachta indica plant extract phytocompounds that one nimonol gained best result for both wild and mutant. Our investigation concludes that the stability of TYMS protein was decrease due to mutation in the position P194Q, which causes Colon cancer and nimonol showing best inhibitory effect against mutant TYMS help to stabilize the mutant protein. Hence it may be taken into consideration for future research work.

DECLARATION OF CONFLICT OF INTEREST:

No conflict of interest.

ACKNOWLEDGEMENT:

S. Mishra,S. Sahu, B. Mishraand S.N.Sahu thankful to Centurion University of Technology and Management (CUTM), Bhubaneswar.

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Received on 10.04.2024 Revised on 18.07.2024

Accepted on 28.09.2024 Published on 10.04.2025

Available online from April 12, 2025

Research J. Pharmacy and Technology. 2025;18(4):1640-1648.

DOI: 10.52711/0974-360X.2025.00235

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.

Compound name	GI absorption	log kp	BBBP	CYP1A2	CYP2C19	CYP2D6	CYP3A4
Nimbolide	High	-7.61	No	No	No	No	No
Nimonol	High	-5.7	No	No	No	No	No
Nimbiol	High	-4.51	Yes	No	Yes	Yes	No
Nimbidiol	High	-5.03	Yes	No	No	Yes	No
Vilasinin	High	-6.71	No	No	No	No	No