Graph Theoretical Network Analysis and Pharmacoinformatics-Based Investigation of Bioactive Compounds of Rasam (South Indian Recipe) Against Human Cancer

Spice-rich recipes are referred to as "functional foods" because they include a variety of bioactive chemicals that have health-promoting properties in addition to their nutritional value. Using pharmacoinformatics-based analysis, we explored the relevance of bioactive chemicals found in rasam (a south Indian cuisine) against oxidative stress-induced human malignancies. The rasam is composed of twelve main ingredients, each of which contains a variety of bioactive chemicals. Sixty-six bioactive compounds were found from these ingredients, and their structures were downloaded from Pubchem. To �nd the right target via graph theoretical analysis (mitogen-activated protein kinase (MAPK)) and display their signaling route, a network was built. Among the identi�ed compounds sixty-six bioactive compounds were used for in silico molecular docking study against MAPK, the top four compounds were chosen for further study based on their docking score and binding a�nities. In silico predicted ADMET characteristics of the titled compounds were used to assess their drug-likeness. Molecular dynamics (MD) simulation modelling methodology were also used to analyze the effectiveness and safety pro�le of top four selected bioactive chemicals based on the docking score, as well as to assess the stability of the MAPK-ligand complex structure. Surprisingly, the discovered docking scores against MAPK revealed that the titled bioactive chemicals distribution varied between -3.5 and -10.6 kcal/mol. MD simulation validated the stability of four chemicals at the MAPK binding pockets, including Assafoetidinol A (ASA), Naringin (NAR), Rutin (RUT), and Tomatine (TOM). According to the results obtained, �fty of the sixty-six compounds showed higher binding a�nity (-6.1 to -10.6 kcal/mol), and four of these compounds may be used as lead compounds to protect cells against oxidative stress-induced human malignancies.


Introduction
Cancer is de ned as unregulated cell or tissue growth that may spread to other parts of the bodys. It is the second greatest cause of mortality in the world, behind cardiovascular illnesses, and the number of cases continues to rise 1 . According to the GLOBOCON-2020 report, there is around 19.30 million new cancer cases diagnosed and 10.00 million cancer deaths worldwide 2 . A range of modi able health behaviours, such as high fat and simple carbohydrate diet, bad eating habits as well as poor physical activity contributes to the sudden rise in cancer incidences 3 . Several studies have shown that dysregulated nutrition and sedentary life style are key factors in the cellular redox process, resulting in unwanted by-products such as reactive oxygen species (ROS), reactive nitrogen species (RNS), and DNA reactive aldehyde [4][5][6] . In mitochondria, ROS is an unavoidable by-product of oxidative phosphorylation 7 . ROS is a two-edged sword that has both helpful (at low concentration) and harmful (at high concentration) properties. At low concentration, ROS regulates cellular activities such as cell cycle, proliferation, differentiation, migration, and death while an increased quantity of ROS may damage proteins, nucleic acids, lipids, membranes, and organelles, it also reduces cell viability and causes apoptosis 8, 9 .
The production of reactive oxygen species (ROS) in cells is inhibited by a number of antioxidant defense mechanisms. Antioxidant stress response genes serves as an important function of protecting cells and tissues from toxins and oxidative stress 10 . Oxidative stress sensitive genes accomplish ROS scavenging by secreting antioxidant enzymes including superoxide dismutase (SOD), catalase, glutathione peroxidase, peroxiredoxins, and other non-enzymatic compounds such as avonoids, carotenoids, glutathione, α-lipoic acid, iron chelators, vitamins A, C and E 11 . Furthermore, increased levels of intracellular ROS beyond a certain threshold cause down regulation of cellular antioxidant pathways and enzyme systems, resulting in malignant transformation via various molecular targets such as nuclear factor-B (NF-B), nuclear factor E2 (erythroid-derived 2)-related factor 2 (Nrf2), Kelch like-ECH-associated protein 1 (Keap1), mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase (PI3K) 12 .
Spices are used in cuisine all around the globe for their taste, avour and their health advantages 13 . Because, they contain numerous bioactive components, certain spices have been utilized in Indian traditional medicine to prevent and cure numerous ailments, including cancer 14 .
Capsaicin (red pepper) 1 , curcumin (turmeric) 15 , piperine (black pepper) 16 , lycopene (tomato) 17 , myricetin (tea) 18 , and rutin (buckwheat) 19 are a few examples of bioactive chemicals that have been shown to possess antioxidant and anticancer properties. "Rasam" is a famous South Indian spicy soup that has been made fresh every day and served with rice 20 . Tamarind, red pepper, black pepper, cumin seed, fenugreek, asafoetida, garlic, tomato, coriander, curry leaves, sesame oil, and mustard are the main avors (spices) of rasam 21 . These spices used to make rasam, a "functional food" include a plethora of bioactive chemicals that have been linked to improved tumor prognosis 22 . The synergistic activity of a mixed bioactive chemicals is always greater than that of a single component 23 . Furthermore, these bioactive chemicals function via many signaling pathways and display anticancer activity by blocking certain signaling cascades that drive unregulated cell division and proliferation 24 . Bioactive substances may also inhibit the malignant transformation by targeting pro-tumorigenic cells or the prometabolic carcinogen's conversion 25 .
By affecting numerous genes and transcription factors, cancer cells have acquired resistance to cancer treatments. These genes and transcription factors are thought to be an important targets for slowing the development of cancer 26 . The signaling networks, such as genes, proteins, and enzymes, are shown in this perspective using graph theoretical network analysis. The selection of good target signaling network may be aided by graph theoretical network analysis 27 . It also gives data on the active site and molecular interactions of bioactive substances, which may help in the in silico molecular docking study. As a result, the current research used pharmacoinformatics to examine the relevance of bioactive chemicals found in rasam spices against oxidative stress-induced human malignancies. The predicted ADMET (absorption, distribution, metabolism, excretion, and toxicity) characteristics of the bioactive compounds were also investigated. Further, molecular dynamics simulation was investigated to determine the stability and binding modes of selected bioactive compounds with an appropriate cancer receptor protein.

Materials And Methods
Graph theoretical network analysis The graph theoretical network analysis was carried out using Cytoscape software 3.7.1 and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database 28 . The functions of numerous genes and proteins involved in the MAPK (ko04010) signaling pathways were given in the current research work (Fig. 1). Based on centrality criteria such as degree, proximity, eccentricity, eigen vector, and radiality, the network has 129 nodes and 177 edges. The measured values of degree (16), closeness (15.49), eccentricity (1), eigen vector (0.3564), radiality (9.18), and stress (1256) have shown the threshold value of all measures as well as signi cant node in the network. (Tables 1 and 2).

Protein preparation
The RCSB Protein Data Bank (PDB http://www.rcsb.org/pdb) provided the X-ray crystallographic structure of MAPK (PDB ID: 7AQB) 29 . Prior to analysis, the protein was cleaned and missing residues were inserted using Swiss-PDB Viewer v4.1.0's prepare protein process. The le was named target.pdb and saved for further analysis. We also utilized BIOVIA Discovery Studio Visualizer 4.0 software (Accelrys Software Inc., San Diego, CA) in order to determine the protein structure and amino acid position from active regions, which was then utilized for molecular docking study.

Active compounds retrieval and preparation
We found that around sixty-six bioactive components from twelve spices were used to make rasam. The identi ed 66 components were collected using the data repository (Indian Medicinal Plants, Phytochemistry, and Therapeutics (IMPPAT)), previously published studies 30 and public database PubChem (http://pubch em.ncbi.nlm.nih.gov).

Binding site identi cation
A binding site in the target is a particular location on an enzyme / protein that permits the enzyme to attach to certain molecules and perform a chemical reaction. The major strategy to treat a disease is the binding of ligands or bioactive chemicals to the speci c location of a protein / enzyme. This helps the bioactive chemicals to create enough contact sites in order to establish robust interaction with target enzymes by ensuring optimal and favourable catalytic areas. Using the Prank Web (https://prankweb.cz/) server, all possible active binding sites of targeted compounds were found for further analysis. Using the PyRx program, a receptor grid was created once the active site of protein was selected.

Molecular docking
Molecular docking approach is a crucial component of structural biology research, and it is one among the widely used technique in the process of drug design. The PyRx tool 31 and AutoDock Vina program 32 was used to accomplish the molecular docking study. The ligand was selected bioactive chemicals, and the receptor was MAPK (PDB ID: 7AQB). Polar hydrogen atoms and Kollman partial charges were introduced into the 3D structure using PyRx software. To compute docking energy a nities (kcal / mol), the receptor and ligand les were stored in .pdbqt format. For each ligand, AutoDock Vina calculated the energy a nity values of up to ten different docking positions. AutoDock Vina effects were used to calculate each complex a nity energies based on the ligand conformation at the active binding site with RMSD between the original and subsequent structures taken into consideration. The amount of hydrogen bonds and non-covalent interactions for each complex were calculated using Discovery Studio Visualizer, which produced details, compounds, and interaction pictures (2D and 3D) 33 .

In silico pharmacokinetic properties prediction
In silico prediction of pharmacokinetic (ADME) properties of the selected bioactive chemicals plays a major role in determining its integrity and e ciency. Selected bioactive chemicals into account, properties like bioavailability, brain penetration, oral absorption, carcinogenicity, and other human intestinal absorption properties of the active bio-compounds have been determined using SwissADME (www.swissadme.ch) webserver.
The SwissADME webserver is a free tool that can predict the pharmacokinetic and drug-likeness properties of the test bioactive compounds 34 .

Toxicity prediction
Toxicity was predicted by determining the safety pro le of the intended bioactive chemicals, which must have deadly effects on people and cause organ damage. As a result, the toxicity of the chosen bioactive chemicals was assessed using ProTox-II web-based server (http://tox.charite.de/protox II) 35 .

Molecular dynamics stimulation
The molecular dynamic simulation was evaluated to determine the binding stability, con rmation and interaction modes between the selected bioactive compounds (ligands) and receptor (MAPK). The selected ligand-MAPK complex les were subjected to molecular dynamics studies using GROMACS 2019.2 software 36 . The selected ligands topology was downloaded from PRODRG server 37 . The system preparation of all the complexes were as described earlier 19 . For molecular dynamic simulation, rst vacuum was minimized using the steepest descent algorithm for 5000 steps. The complex structure was solvated in a cubic periodic box of 0.5 nm with a simple point charge (SPC) water model. The complex system was subsequently maintained with an appropriate salt concentration of 0.15M by adding a suitable amount of Na+ and Cl− counter ions. Each complex was allowed a simulation time of 50 ns from the NPT (Isothermal-Isobaric, constant number of particles, pressure, and temperature) equilibration was subjected in NPT ensemble for nal run. The trajectory analysis of root means square deviation (RMSD) and root mean square uctuation (RMSF) was performed in the GROMACS simulation package through the online server "WebGRO for Macromolecular Simulations (https://simlab.uams.edu/)".

Bioactive compounds retrieval and preparation
The accessible bioactive components of the requested spices (tamarind, red pepper, black pepper, cumin seed, fenugreek, asafoetida, garlic, tomato, coriander, curry leaves, sesame oil, and mustard) were searched using IMPART database. From the database, a list of sixty-six important bioactive compounds were selected from the desired twelve spices depicted in Table 3.

Binding site identi cation
The crystal structure of MAPK (PDB: 7AQB) included 11 binding sites, according to binding site analyses. The protein's recovered binding site residue was shown in Figure 2. Molecular docking investigations were also conducted using the obtained complex structure of the binding sites. Grid generation in molecular docking research results in more reliable ligand posture scoring. As a result, we created a receptor grid for the selected MAPK protein based on the previously acquired binding site residues to achieve more precise scoring of our ligand poses. A receptor grid with a box diameter of X = 38.6666, Y = 62.5914, and Z = 31.9740 in angstrom (Å) was created and utilized further for molecular docking experiments.

Molecular docking
The optimum intermolecular interaction between the target protein and bioactive chemicals were investigated using molecular docking analysis. To analyze their binding capability, a speci c number of bioactive chemicals (sixty-six) were docked against MAPK using PyRx tools AutoDock Vina. Twelve bioactive chemicals were shown to have a higher binding a nity (>-9 kcal/mol) with the target protein. The binding a nity of the bioactive compounds following molecular docking was found to be scattered, ranging from -3.50 to -10.60 kcal/mol, as illustrated in Fig. 3 and Table 3. The top four compounds (Assafoetidinol A (-9.80 kcal/mol), Naringin (-9.60 kcal/mol), Rutin (-9.80 kcal/mol), and Tomatine (-10.60 kcal/mol)) were chosen for future research based on their a nities with the active site aminoacid residues.
In silico Absorption, Distribution, Metabolism, and Excretion (ADME) prediction analysis Absorption, Distribution, Metabolism, and Excretion (ADME) properties was assessed through SwissADME (www.swissadme.ch) webserver, which demonstrated that key bioactive compounds from rasam, i.e. Assafoetidinol A possessed better human intestinal absorption property. Naringin, Rutin and Tomatine have moderate absorption properties. In general, moderate intestinal absorption leads to the bioactive compounds of food (rasam) might be better consumed from the gastrointestinal tract upon oral administration. The higher number of H-bonds are possibly measured to be involved during protein ligand binding. From the result, the drug-likeness properties of four compounds showed better results (+0.55 Assafoetidinol A, and +0.17 for other three compounds) thereby relating with molecular properties, these four compounds were predicted to have better chances as a possible drug-relevant candidate with anticancer potential. The ADME properties like lipophilicity (dissolve in fats, oils and nonpolar solvents), water solubility and drug-likeness of the selected compounds have been investigated and presented in Table 5.

Analysis of toxicity
In silico toxicity prediction of the selected four compounds has been performed using ProTox-II web-based server. The server has identi ed drug-induced hERG toxicity, AMES toxicity, LD 50 , hepatotoxicity, skin sensitization, Tetrahymena pyriformis (TP) toxicity, and minnow toxicity which was listed in Table 6. RMSF is an another crucial parameter while examining the stability and exibility of complex systems during simulation 39 . RMSF was examined to analyse the change in behaviour of amino acid residues of target protein on binding to a ligand 40,41 . The RMSF values for Cα atoms of the protein were calculated and plotted with respect to the residues. In case of all complex, the amino acid residues showed minimal uctuations throughout the simulation. The amino acids of MAPK which interacted with ASA during docking showed minimal uctuation values during MD simulation viz. CYS28, GLY29, LYS185 and LYS229, with NAR it showed low uctuation values during MD simulation viz GLY29 and LEU192, with RUT showed minimal uctuation values during MD simulation viz. GLY29, ARG70, LYS229 and ASN269 and with TOM it showed moderate uctuation values during MD simulation viz. GLY29, LYS185, SER189, TYR266 and PRO301 (Figure 9). These results reveal that binding of both the ligands actuated no major effects on the exibility of the residues in the protein.
Further, Radius of gyration (Rg) of the complex systems were also analysed. Rg is the RMS distance of the atoms of the protein from the axis of rotation 41 . It is one among the important parameter that represents the overall change in the protein structure compactness and its dimensions during the simulation 42 . Higher Rg values characterize the protein as less compact and exible while low values depict the high compactness and rigidity 39 . Rg values of backbone atoms of protein were plotted against time to examine the changes in structural compactness. Binding of ASA decreased the backbone Rg values till 30 ns. In the time period between 31-50 ns there were no considerable uctuations and almost constant value of ~1.98 nm was maintained. Till end, the Rg values were found to be in the range between 1.95-1.99 nm. Complete analysis revealed that, in the initial stage the trajectory had shown its peak value of ~2.12 nm. Later this high value was never displayed again which shows the stability of protein in the complex (Figure 10). Binding of NAR decreased the backbone Rg values till 15 ns. In the time period between 16-45 ns there were no considerable uctuations and almost constant value of ~2.04 nm was maintained. Till end, the Rg values were found to be in the range of 2.02-2.05 nm. Complete analysis revealed that, in the initial stage, the trajectory showed its peak value of ~2.09 nm. Later, this high value was never displayed again which shows the stability of protein in the complex ( Figure 10). Binding of RUT decreased the backbone Rg values till 31 ns. In the time period between 32-50 ns there were no considerable uctuations and almost constant value of ~2.03 nm was maintained. Till end, the Rg values were found to be in the range of 2.00-2.05 nm. Complete analysis revealed that, in the initial stage, the trajectory exhibited its peak value of ~2.10 nm. Later, this high value was never displayed again which shows the stability of protein in the complex ( Figure 10). Binding of TOM decreased the backbone Rg values till 10 ns. In the time period between 11-50 ns there were no considerable uctuations and almost constant value of ~1.96 nm was maintained. Till end, the Rg values were found to be in the range of 1.94-2.00 nm. Complete analysis revealed that, in the initial stage, the trajectory had shown its peak value of ~2.10 nm. Later, this high value was never displayed again which shows the stability of protein in the complex ( Figure 10). The complete interpretation revealed that both the molecules induced no major structural changes in the protein.
Moreover, analysis of Solvent Accessible Surface Area (SASA) for all the complexes was implemented. SASA is the substantial criterion to examine the extent of exposure of receptor to the surrounding solvent molecules during simulation 39,43 . In general, binding of ligand may induce the structural changes in the receptor and hence the area in contact with the solvent also may vary 41 . SASA values of protein was plotted against time to estimate the changes in surface area. For SASA complex, the trajectory showed decrease in the values till 15 ns. Except few time intervals, minute uctuations were observed throughout the simulation period ( Figure 11). The average SASA value was found to bẽ 138 nm 2 and were in the range of 150-130 nm 2 . For NAR complex, the trajectory showed decrease in the values till 10 ns. Except few time intervals, minute uctuations were observed throughout the simulation period ( Figure 11). The average SASA value was found to be ~142 nm 2 and were in the range of 149-134 nm 2 . For RUT complex, the trajectory showed decrease in the values till 10 ns. In the time interval of 11-28 ns, minute uctuations were observed and from 29-34 ns a moderate uctuation was observed ( Figure 11). The average SASA value was found to be ~140 nm 2 and were in the range of 154-133 nm 2 . For TOM complex, the trajectory showed decrease in the values till 10 ns. Except few time intervals, minute uctuations were observed throughout the simulation period ( Figure 11). The average SASA value was found to be ~148 nm 2 and were in the range of 154-140 nm 2 . Overall, analysis revealed that the surface area of protein in both complexes were shrunken during the simulation.
To examine the binding a nity of the ligands with the target protein, the MD trajectories were analyzed to interpret the extent of hydrogen bond formation during the entire course of simulation and was depicted in Figure 12. SASA had formed good number of H-bonds with the receptor protein with a maximum of ve bonds at several time frames indicating the stronger a nity towards the target. Consistency was maintained in forming almost two hydrogen bonds for the entire simulation time which signi es the stability of the complex. For the NAR complex, the consistency was maintained in forming three hydrogen bonds with maximum of six bonds at certain time periods. For rutin complex, the consistency was maintained in forming four hydrogen bonds with maximum of nine bonds at certain time periods. For the TOM complex, the consistency was maintained in forming two hydrogen bonds with maximum of nine bonds at certain time periods. This clearly signi es that the top phytochemicals have the stronger a nity with the target protein.

Discussion
The purpose of this research work was to look at the cancer-preventive impact of bioactive chemicals found in the south Indian cuisine rasam by using graph theoretical network and pharmacoinformatics analysis. Pharmacoinformatics is a collection of in silico molecular modeling tools for screening the bioactive substances based on their binding a nities, pharmacokinetics, and pharmacodynamic features 44 . By enabling researchers to narrow down the biological and synthetic research impacts, pharmacoinformatics has sped up the discovery of bioactive substances. Several substances have their positive effects predicted using pharmacoinformatics research, which were then validated by in vitro and in vivo activities. Understanding how chemicals bind, interact, and inhibit / stimulate a certain protein might help researchers nd therapeutic options for certain disease conditions.
Initially, a graph theoretical network was developed using centrality metrics, and it was suggested for metabolic networks that included enzymatic cascades and synergistic ligand-enzyme interactions. Biological networks, which are made up of a number of vertices (or nodes) linked in a pattern by a set of edges (or connections), are designed to mimic the structure of genuine biological systems. MAPK was identi ed as a receptor (target) for ligand (bioactive substances) binding in ROS-induced oxidative stress that leads to malignancies, according to the network analysis study. MAPK pathways have been shown to be impacted not just by receptor ligand interactions, but also by various cell stresses. Furthermore, since MAPK pathways regulate both mitogen-and stress-activated signals, the regulation of both pathways by ROS has piqued researchers' attention 45 . The goal of the current research work was to look into the detoxi cation / neutralization of ROS by employing bioactive chemicals found in rasam spices to protect cells against cancer. A total of sixty-six bioactive compounds were chosen from twelve spices using the IMPART database, as well as previously published publications on their effects against different human malignancies. All the chemicals chosen were docked against MAPK protein kinases, with binding a nity ranging from − 3.50 kcal/mol to -10.60 kcal/mol. Four compounds (Assafoetidinol A (-9.80 kcal/mol), Naringin (-9.60 kcal/mol), Rutin (-9.80 kcal/mol), and Tomatine (-10.60 kcal/mol)) have been chosen for future analysis based on their signi cant binding a nity, strong hydrophobic and hydrogen bonding interactions with amino acid residues present in the active site of MAPK protein.
A substance's bioactivity is largely governed by its absorption, distribution, metabolism, and excretion (ADME) characteristics, all of which are connected to its pharmacokinetic characteristics. The bioavailability of dietary phytochemicals to target cells, as well as their absorption and metabolism in the human body are certain key aspects in promoting their bioactivity and maintaining body health 46 . The small intestine absorbs some, but not all, of the components of dietary phytochemicals into the circulatory system. Some phytochemical compounds that were absorbed by the colon and altered by the gut microbiota and microbial metabolites were released back into the circulation and showed signi cant activity 47 . In order for any molecule to permeate the membrane, phytochemicals / test substance must break hydrogen bonds in the aqueous environment and partition across the membrane 48 . The polar surface area (PSA) of a chemical is connected to its hydrogen-bonding potential, whereas molecular mass and lipophilicity are associated to membrane permeability 49 . As a consequence, the ADME properties must be assessed at the earlier stages of drug design and discovery process in order to pass the standard clinical studies required to be considered as prospective therapeutic candidate 50 . In this study, all the discovered phytoconstituents were con rmed in terms of usual pharmacokinetic properties using multiple bioinformatics methods. Phytochemicals are naturally derived from variety of plants that are often consumed by humans and are usually considered safe to consume. While most phytochemicals are not regulated by the Food and Drug Administration (FDA) in the United States, their potential toxicity is unknown.
Phytochemicals are utilized as supplements in conjunction with illness therapy all around the world, but do not necessarily inform to their physicians of their use 51 . Substance toxicity refers to the property of any compound to be poisonous and to cause harm to an organism.
Toxicity testing of a substance necessitates in vitro and in vivo animal experiments, which is time-consuming, expensive, and complicated technique. Because there are no animal trials, precision, accessibility, and speed. Hence, in silico toxicity assessment has become very popular in recent times and it can offer information on any synthetic or natural molecule. In this work, in silico approaches were used to estimate the toxicity levels of four chemicals. The non-carcinogenic and non-skin irritating properties of four substances were determined using in silico testing. Three compounds, Assafoetidinol A, Rutin, and Tomatine were shown to be negative in Ames testing. Toxicity tests revealed that the four phytochemicals chosen had no negative side effects (hERG). The LD 50 (median fatal dosage) indicates the immediate or acute toxicity of substances that were determined to be the most effective in the investigation. Hence, the complexes of these compounds were subjected to molecular dynamics simulations and the results were analysed with the results of apo form of MAPK (7AQB). The complexes were validated by interpreting the RMSD, RMSF, Rg, SASA and the lead phytochemical complexes were found to be stable during the simulations.

Conclusion
Traditionally, home-cooked meals have been shown to help avoid chronic illnesses, improve health, and save treatment costs while also boosting quality of life. This study looked at the antioxidant properties of bioactive chemicals found in the south Indian cuisine rasam against oxidative stress-induced human malignancies. In the human body, ROS is a metabolic by-product of cellular respiration. Oxidative stress and overexpression of MAPK protein are caused by an increase in ROS levels. MAPK overexpression causes a cascade of events in cells, including mutations and carcinogenesis. Through a thorough pharmacoinformatics-based molecular docking investigation of bioactive substances against MAPK, the antioxidant potential of rasam has been proven in the current work. In silico molecular docking investigations found that the four lead phytochemicals (Assafoetidinol A, Naringin, Rutin, and Tomatine) may suppress MAPK expression. In addition, MD stimulation tests and in silico pharmacokinetic prediction analyses gives the safety pro le of four lead compounds as well as the stability of the protein-ligand complex, although, in order to determine the rasam's effectiveness, further in vitro and in vivo animal research work will be necessary.

Consent for Publication
Not applicable.

Funding
None.

Con ict of Interest
The authors declare no con ict of interest, nancial or otherwise.  The signaling pathway of MAPK Showing active site and correspondence binding site of MAPK.

Figure 3
Showing the range of docking score distribution of sixty-six phytochemicals presence in the Rasam.

Figure 4
Depicted the interaction between the compound CID: 12041593 (Assafoetidinol A) and MAPK. Left side representing 3D and the right side representing 2D complex protein-ligand interaction.

Figure 5
Depicted the interaction between the compound CID: 442428 (Naringin) and MAPK. Left side representing 3D and the right side representing 2D complex protein-ligand interaction.

Figure 6
Depicted the interaction between the compound CID: 5280805 (Rutin) and MAPK. Left side representing 3D and the right side representing 2D complex protein-ligand interaction.

Figure 7
Depicted the interaction between the compound CID: 28523 (Tomatine) and MAPK. Left side representing 3D and the right side representing 2D complex protein-ligand interaction.