key: cord-1052833-s3wqy7ve
authors: Nag, Anish; Dhull, Nikhil; Gupta, Ashmita
title: Evaluation of tea (Camellia sinensis L.) phytochemicals as multi-disease modulators, a multidimensional in silico strategy with the combinations of network pharmacology, pharmacophore analysis, statistics and molecular docking
date: 2022-05-10
journal: Mol Divers
DOI: 10.1007/s11030-022-10437-1
sha: c20f2899d2646feecd20047833fa51929f813aa2
doc_id: 1052833
cord_uid: s3wqy7ve

Tea (Camellia sinensis L.) is considered as to be one of the most consumed beverages globally and a reservoir of phytochemicals with immense health benefits. Despite numerous advantages, tea compounds lack a robust multi-disease target study. In this work, we presented a unique in silico approach consisting of molecular docking, multivariate statistics, pharmacophore analysis, and network pharmacology approaches. Eight tea phytochemicals were identified through literature mining, namely gallic acid, catechin, epigallocatechin gallate, epicatechin, epicatechin gallate (ECG), quercetin, kaempferol, and ellagic acid, based on their richness in tea leaves. Further, exploration of databases revealed 30 target proteins related to the pharmacological properties of tea compounds and multiple associated diseases. Molecular docking experiment with eight tea compounds and all 30 proteins revealed that except gallic acid all other seven phytochemicals had potential inhibitory activities against these targets. The docking experiment was validated by comparing the binding affinities (Kcal mol(−1)) of the compounds with known drug molecules for the respective proteins. Further, with the aid of the application of statistical tools (principal component analysis and clustering), we identified two major clusters of phytochemicals based on their chemical properties and docking scores (Kcal mol(−1)). Pharmacophore analysis of these clusters revealed the functional descriptors of phytochemicals, related to the ligand–protein docking interactions. Tripartite network was constructed based on the docking scores, and it consisted of seven tea phytochemicals (gallic acid was excluded) targeting five proteins and ten associated diseases. Epicatechin gallate (ECG)-hepatocyte growth factor receptor (PDB id 1FYR) complex was found to be highest in docking performance (10 kcal mol(−1)). Finally, molecular dynamic simulation showed that ECG-1FYR could make a stable complex in the near-native physiological condition. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11030-022-10437-1.

Over the last few years, the pharmaceutical industry faced several challenges owing to the ever-rising increase in the complexities of diseases worldwide. This significant obstacle that the medical community has been facing has instigated a much-needed shift in the treatment strategies that are currently being employed. A dramatic switch that favors multi-target therapeutics and herbo-synthetic combinations as complementary or sometimes even alternative therapy is being explored [1] . There is enough literature to support the claim that secondary metabolites derived from several plants are pharmacologically active and have been quite successful in treating a wide range of diseases [2, 3] . Plant-based medication is not only readily available and affordable, but it also causes the least number of side effects. In the hunt for therapeutically beneficial novel medications, the medicinal plants that have been in use for more than a thousand years are being re-examined [4, 5] . Most of these plants and their phytochemical extracts are being used globally with little or no awareness about their benefits. One such plant extract that we consume daily is the tea prepared with the young leaves, buds, and stalks of Camellia sinensis (L.) Kuntze (Family: Theaceae) [6] .

This plant is commonly cultivated commercially in tropical and subtropical regions, primarily in the southern parts of Asia and its leaf infusion, as tea is the most consumed beverage in the world. The pharmacological activity of C. sinensis extracts has been proven to help treat common ailments like hypertension, diabetes, and obesity. It also possesses anticancer, anti-inflammatory, antioxidant, and cardiopulmonary-protective properties [7, 8] . Recently, tea was reported to have beneficial role in the modulation of human gut microbes [9] , also. Both black and green teas are rich in antioxidant polyphenols such as catechins [10] . These polyphenolic antioxidants are known to protect cells from intracellular ROS production, hence preventing the oxidative destruction cycle, which includes cell membrane rupture, DNA damage, and subsequently necrosis, mutagenesis, and cell death [11, 12] . This information can be exploited to computationally map the diseases that can be cured using these phytochemicals.

Lately, computational methods have been critical in the design and discovery of novel medicines for various illnesses [13] . The in silico technique of molecular docking is currently regarded as one of the most dependable, cost-effective, time-saving, and efficient ways for screening potential medicines. The use of this approach is extensively advocated and accepted, given the abundance of phytochemicals with potential pharmacological effects [14] . Recently, in silico biology witnessed its extensive applications for search and screening of plants drugs against various conditions such as acetylcholinesterase [15] , microbial infections [16] and COVID-19 [17, 18] . Pharmacophore approach is often integrated with the molecular docking technique to understand the chemistry of protein ligand binding interaction. Pharmacophores explain the functional groups that are involved in such docking interaction. Recently, Oluyori et al. [19] identified SARS-CoV-2 inhibitors from bitter cola by using pharmacophore modeling. Molecular docking coupled with statistical methodology may be considered as to obtain accurate and valid results. In our previous studies, we successfully applied multivariate statistical models to validate our findings [20, 21] . The limiting factor of the conventional molecular docking method is that it targets a single protein rather than multiple proteins. Network pharmacology, also known as polypharmacology method, was developed to target multiple proteins and corresponding diseases [22] . Choudhary and Singh [23] utilized network pharmacology approach for targeting various proteins by Piper longum phytochemicals. Li et al. [24] found that the phytochemicals of Shenlian extract could target 37 proteins by using a similar approach. Recently, Wu et al. [25] used this tool for three flower teas (Rosa rugosa, Chrysanthemum morifolium, and Citrus aurantium) targeting nonalcoholic steatohepatitis [25] . Overall, the network pharmacology approach is witnessing an extensive application in the field of computational biology; however, such an approach is scarce for conventional tea based on C. sinensis leaves. Therefore, we believe that the innumerable health benefits of tea warrant further study on its phytochemicals and the diseases it can be used to treat. Our study is claimed to give a new insight on the numerous health benefits of tea by combining a revised approach consisting of a combination of statistics, pharmacophore alignment, virtual library screening and molecular docking.

In the present study, eight important tea compounds (Epicatechin, epicatechin gallate, epigallocatechin gallate, catechin, quercetin, kaempferol, ellagic acid and gallic acid) were docked with thirty target proteins as obtained from the virtual databases. Finally, tripartite network consisting of phytochemicals, proteins and associated diseases was formed with the aid of multivariate principal component analysis (PCA) and pharmacophore-based experimentations.

Tea phytochemicals, target proteins, and related illnesses were selected after extensive literature mining. Databases such as IMPPAT and PubChem were utilized to screen out phytochemicals from Camilla chinensis. IMPPAT or Indian Medicinal Plants, Phytochemistry, and Therapeutics (https:// cb. imsc. res. in/ imppat/ home) is a curated database with material from more than 50 specialized books, and 7000 research article abstracts on Indian traditional plants of ethno botanical value [26] . The structures of the compounds can be downloaded in the sdf, mol, pdb, or pdbqt formats both two-dimensionally and three-dimensionally from this database. PubChem (https:// pubch em. ncbi. nlm. nih. gov/) is the world's largest open access repository of publicly available chemical data at the National Institutes of Health (NIH) [27] . The phytochemicals identified via these servers were further analyzed to find interacting proteins and their corresponding diseases using the PCIDB (https:// www. genome. jp/ db/ pcidb) web server. The Phytochemical Interactions Database, or PCIDB, is an open-source web server with over 100,000 records, most of them from plants and linked to different protein and disease databases [28] . The proteins that interacted with the shortlisted phytochemicals were found using the UniProt database (https:// www. unipr ot. org/), which houses freely accessible information about protein sequences and their respective functions [29] . Once these proteins were found, the associated diseases were identified using KEGG DISEASE (https:// www. genome. jp/ kegg/ disea se/) and OMIM (https:// www. ncbi. nlm. nih. gov/ omim) databases. The Kyoto Encyclopedia of Genes and Genomes DISEASE Database (KEGG) [30] and Online Mendelian Inheritance in Man (OMIM) [31] are both open-access databases that provide data regarding disease pathways and other generelated information. Finally, the drugs designated to target the selected proteins were taken from the DrugBank database (https:// go. drugb ank. com/) [32] .

The Avogadro software was primarily used for the preparation of ligand molecules before docking. This software serves as a cross-platform molecule editor and visualizer for computational chemistry, molecular modeling, bioinformatics, materials science, and related fields [33] . The three-dimensional structures of the phytochemicals selected for the study, namely gallic acid, catechin, epigallocatechin gallate, epicatechin, epicatechin gallate, quercetin, kaempferol and ellagic acid, were downloaded from the PubChem database (https:// pubch em. ncbi. nlm. nih. gov/) in the.sdf format and converted to their respective.pdb formats using the Avogadro software. Three-dimensional structures of the control drug molecules were downloaded from the DrugBank database (https:// go. drugb ank. com/) in the.pdb formats and were subjected to energy minimization and structural optimization by using Avogadro.

The SwissADME server (http:// www. swiss adme. ch/) was employed to assess the drug-likeness of the selected phytochemicals. This tool allows us to compute physicochemical descriptors and estimate ADME parameters, pharmacokinetic characteristics, drug-like nature, and medicinal chemistry friendliness of one or more small compounds. Some of the characteristics that were taken into account to predict the drug-likeness of the compounds were the topological polar surface area (TPSA, important for the estimation of brain permeability and gastrointestinal absorption of the potential drugs), gastrointestinal absorption, PGP substrate, lipophilicity (XLOGP3), and water solubility (Log S) [34] . The Lipinski's rule of 5 was also used to predict the drug-likeness of the molecules. The rule of 5 indicates that poor absorption or penetration is more probable when there are more than 5 H-bond donors and 10 H-bond acceptors, while molecular weight (MWT) is more than 500, and computed Log P (CLogP) is greater than 5 [35] .

The three-dimensional structures of the protein targets were downloaded from the RSCB PDB database (https:// www. rcsb. org/). This is a database that stores threedimensional structural data for big biological entities including proteins and nucleic acids [36] . Out of the thirty proteins that were selected for the study, the native structures of 28 were retrieved from the PDB database itself. These proteins include the carbonic anhydrase 12 The structures of the remaining two proteins U D P-g l u c u ro n o s yl t ra n s fe r a s e 1 -1 a n d U D Pglucuronosyltransferase 1-4 were unavailable, and hence, protein structure prediction was performed by homology modeling. The SWISS-MODEL server (https:// swiss model. expasy. org/) was used for homology modeling [37] . UCSF Chimera software was used to further optimize all 3D protein structures for docking [38] . The quality assessment of the modeled proteins was performed by SwissADME MolProbity tool and UCLA-DOE LAB SAVES v6.0: PROCHECK tool (https:// saves. mbi. ucla. edu/).

The active sites present in the target proteins were predicted by CASTp (Computed Atlas of Surface Topography of proteins) server. CASTp (http:// sts. bioe. uic. edu/ castp/ index. html? 3trg) is a freely accessible server, and the sites predicted by the server were used to set the dimensions of the grid box while performing docking [39] . The binding grid co-ordinates of the thirty proteins are shown in Supplementary Table S3 . Protein-ligand molecular was performed by AutoDock Vina software. AutoDock Vina relies on a sophisticated gradient optimization method, followed by the prediction of the best binding orientation of the ligand within the protein cavity [40] . The eight tea phytochemicals were docked with all thirty proteins in the present study. The phytochemical-protein docking results were compared with that of the respective control drugs. Finally, the top result for each ligand was superimposed with the respective control by using BioVia Discovery Studio software, and the similarity of docking poses was examined.

Principal component analysis Minitab 18 statistical software was used to perform principal component analysis or PCA to group phytochemicals based on the docking scores (binding affinity in Kcal mol −1 ). This statistical tool allows us to reduce the difficulty that accompanies the interpretation of larger data sets. It decreases the dimensionality of such datasets, therefore improving interpretability, while minimizing data loss. By reducing the overall distance between the data and their projection onto the PC (principal components), the first PC (PC1) maximizes variance. The second and following PCs are unrelated to the first, although chosen in the same way [41, 42] .

A second confirmatory statistical test for clustering of docking scores was performed by TBtools software along with heat map generation. Pearson correlation methodology was used for the statistical clustering. TBtools (a Toolkit for Biologists integrating various biological data-handling tools) is an open access and stand-alone software suite, with various integrated tools. This software is based on simple IOS logic (input, output and start) [43] .

Structural classification of eight (8) phytochemicals of tea was performed by the ClassyFire server (http:// class yfire. wisha rtlab. com/). Classyfire performs automated structural classification of the input molecules. It relies on hierarchical chemical classification principle and provides structural information as created by ChemOnt [44] .

PharmaGist, an open-source server, was used to perform pharmacophore analysis of the eight tea compounds. Pharmacophore screening is helpful to identify the common descriptors or functional groups. Based on the clustering of multivariate statistical PCA modeling (Minitab), each of the clusters was subjected to pharmacophore analysis. PharmaGist analysis is based on the DUD (directory of useful decoys) data set, which contains 2950 active ligands for 40 different receptors, with 36 decoy compounds for each of the active ligands [45] . Three-dimensional structural rendering of the output files was performed by PyMol software to represent common descriptors.

The tool Cytoscape (https:// cytos cape. org/) allows for global datasets and functional annotations to be projected and integrated, creating strong visual mappings that span these datasets [46] . To understand the interaction among phytochemicals, protein targets and associated diseases, a tripartite network was constructed using Cytoscape v3.7.2.

MD simulation was performed as described in our previous study [20] . Briefly, GROMACS-2019.2 [47] -based biomolecular package was used to perform the molecular docking simulation of Epicatechin gallate (ECG)-hepatocyte growth factor receptor (PDB id 1FYR) as facilitated by the Simlab, the University of Arkansas for Medical Sciences (UAMS), Little Rock, USA [48] . GROMOS96 43a1 force field was used for the simulation, and a ligand topology file was generated by PRODRG software [49] . A triclinic grid box was defined for the protein-ligand complex. The molecular dynamic simulation was performed in SPC water and 0.15 M counter ions (Na + /Cl − ) environment. The system was supported with NVT/NPT ensemble temperature 300 K and 1 bar atmospheric pressure. The pressure and temperature were maintained with Parrinello-Rahmanbarostat and Parrinello-Danadio-Bussithermostat, respectively [50] . The resultant model was energy minimized by 5000 steepest descent integrator, and run time was fixed for 100 ns. Parameters, namely root-meansquare deviation (RMSD), radius of gyration (Rg), rootmean-square flexibility (RMSF) and ligand-H bonds, were evaluated for the protein-ligand complex. A comparative study was performed with above-mentioned parameters for ligand-bound and ligand-free proteins.

The free energies of epicatechin gallate (ECG)-hepatocyte growth factor receptor (PDB id 1FYR) complex (ΔG_ Vander Waal, ΔG_Electrostatic, ΔG_Polar, ΔG_Non-Polar and ΔG_Binding) were estimated by molecular mechanics-Poisson-Boltzmann solvent-accessible surface area (MM-PBSA) method using g-mmbsa package [51] .

The following equation is performed for calculating ΔG_ Bind (KJ mol −1 ):

where ΔG_Comp = the energy of protein-ligand complex, G_Prot and G_Lig = individual energy of protein and ligand, respectively. The MMPBSA calculation was performed for 5-ns trajectory.

Tea (C. sinensis) leaves are rich in compounds such as catechin, epicatechin, epicatechin gallate and epigallocatechin gallate [3] . Catechin and its derivatives generally constituted of 30% dry weight of black tea leaves [52] . Epigallocatechin gallate was also reported to be the principal constituent of green tea [53] . Furthermore, phytochemicals such as quercetin, kaempferol, ellagic acid and gallic acid were also reported in tea [54, 55] . Considering previously reported health benefits, richness and dominance of abovementioned phytochemicals in the tea leaves, these eight compounds were selected for this study. For instance, catechin-gallic acid esters epicatechin, epicatechin gallate and epigallocatechin gallate were reported to have antidiabetic and antioxidant properties [6] . Epigallocatechin gallate, in particular, also served as a hepatoprotective agent by suppressing cytotoxin-induced cell death [56] . Ellagic acid had antioxidant and anti-inflammatory properties. Gallic acid and kaempferol displayed anticancer and anti-inflammatory activities, while Quercetin had been deemed beneficial in cases of hypertension [3] . Based on these findings, eight tea phytochemicals, namely epicatechin, epicatechin gallate, epigallocatechin gallate, catechin, quercetin, kaempferol, ellagic acid and gallic acid, were selected for this study ( Table 1 ). The schematic presentation of the work is represented in Fig. 1 .

Furthermore, we identified 30 proteins and their associated target diseases though database scouting as mentioned in the Materials and Methods. In Table 2 , we tabulated target proteins, associated diseases and control drugs of the respective proteins.

SwissADME could effectively predict the drug-like properties of eight tea phytochemicals. However, it was noted in the literature that the drug metabolism should not be assessed in one parameter. Hou et al. [83] reported that the values obtained from TPSA (total polar surface area)

did not correlate with another drug-likeliness parameter. In general, TPSA represents the bioavailability of drug candidates, and the recommended range is 20 to 140 Å 2 [82] . Except two compounds, other phytochemicals satisfied the criteria (Table 3) . Water solubility of the drug-like molecules is considered to have a significant impact on ADME (absorption, distribution, metabolism and excretion) properties, i.e., on the bioavailability of the compounds. The parameter Log S represents the intrinsic solubility of the candidate drugs in the water. The log value range within − 1 to − 5 shows the balancing between the solubility in water and lipids [83] . In our study, all the phytochemicals are found well within the range of log S (− 1.64 to − 3.70). Further, the transcellular passive diffusion is one of the major parameters for permeability and absorption of the drug-like compounds, and crossing the barrier of the gastrointestinal (GI) tract represents such property. Our study showed that except epicatechin gallate and epigallocatechin gallate, all six phytochemicals had high GI absorption capacity. XlogP3 is an automatic method and determinant of lipophilicity of drug-like molecules. The recommended range of XlogP3 is 1. 48-6.19 , and all the compounds passed the criteria. p-Glycoprotein substrate (PGP) is a representative of active transporter and generally removes the drug molecules from the cells, when positive. Except two compounds, all other six were found to be PGP negative [84] . Finally, we observed minimum Lipinski's violations (0-2). Overall, all the compounds were found to be passing one or other druglikeness parameters.

The homology modeling of two proteins (UDPglucuronosyltransferase 1-1: UniProt id P22309 and UDPglucuronosyltransferase 1-4: UniProt id P22310) were performed by SWISS-Model server, based on the templates of PDB id 6KVJ.1.A and 6O86.1.A. The quality analysis was done by the parameters MolProbity score, QMEAN and GMQE (Global Model Quality Estimate) Z scores. While QMEAN represents the degree of nativeness of the modeled structure, GMQE (Global Model Quality Estimation) shows expected accuracy of the structured models [85] . The results of these quality estimates are shown in supplementary tables S1 and S2. Finally, Ramachandran plots revealed > 85% favorable regions for both the modeled structures ( Fig.  S1 and S2) . Overall, despite the unavailability of accurate templates for our modeled proteins, we were able to obtain moderate quality models.

Molecular docking is the preferred tool for initial screening of drug molecules. It eliminates the need for tedious, [21] . In this work, all seven phytochemicals, except gallic acid, showed substantial inhibitory potential against target proteins. Out of all the phytochemicals, epigallocatechin gallate, epicatechin gallate, and quercetin have shown maximum coverage, targeting 22, 21 and 20 proteins, respectively, where the binding scores were better than those of the respective controls (Table 4 ). In the molecular docking experiment, we identified the best target protein for each of the phytochemicals in terms of binding affinity (Kcal mol −1 ). Target proteins, namely hepatocyte growth factor receptor (PDB id 1FYR), dihydrofolate reductase (PDB id 1BOZ), prostaglandin G/H synthase 2 (PDB id 5F19) and angiotensin-converting enzyme (PDB id 1O86), on an average was found to be most potential targets by the phytochemicals. Epigallocatechin gallate could inhibit the progression of tumor genes by blocking DNA methyltransferases, proteases, and dihydrofolate reductase (DHFR) activities [88] . In a recent study, Zong et al. [21] showed that this phytochemical prevented aminoglycosides-induced ototoxicity in Zebra fish, by blocking several enzymes including DHFR [89] . In our work, epigallocatechin gallate scored considerably well (− 9.8 kcal mol −1 ) against DHFR compared with other target proteins. Tea phytochemicals were earlier reported to have strong inhibiting potentials against angiotensin-converting enzyme (ACE) protein (Fauzi et al., 2018) [90] . Epicatechin and kaempferol were found to have substantial binding potentials with matrix metalloproteinase (MMP)-9 (PDB id 1GKC) compared to their respective controls, both having similar scores − 9 kcal mol −1 . Kanbarkar and Mishra (2021) [91] recently in their findings showed that tea phytochemicals could inhibit MMP protein.

Kaempferol could modulate the activity of MMP-2 and 9 proteins [92] . Anticancer activities of tea polyphenols were vastly reported in the literature [93, 94] . In their review, Cheng et al. (2020) [95] showed the modulating effect of Catechin and its derivatives against various types of cancer. The flavonoids quercetin and rutin could suppress the expressions of multiple oncogenes, including prostaglandin synthase 2 and showed antiglioma effects [96] . Consistent with these findings, among all 30 target proteins, prostaglandin G/H synthase 2 (PDB id 5F19) was the best targeted by the phytochemicals catechin and quercetin with the binding affinities − 9.2 and − 9.4 kcal mol −1 , respectively. Recently, Wang et al. (2021) [97] showed that the tea phytochemical catechin had strong binding affinity with ACE protein.

The IC50 value of ellagic acid was shown as 2 mM against ACE protein indicating its strong inhibitory potential [98] .

In consistent with these findings, among all other proteins ellagic acid showed the highest binding potential to ACE protein (− 8.9 kcal mol −1 ) compared with control. Epicatechin gallate scored the highest (− 10 kcal mol −1 ) against hepatocyte growth factor receptor, among all target proteins as well as phytochemicals. Hepatocyte growth factor is one of the major elements for the development of hepatocellular carcinoma [99] , and epicatechin gallate is the one of the most prominent phytochemicals in tea leaves [100, 101] . Elsewhere, it was shown that green tea and epicatechin gallate effectively prevented and managed nonalcoholic fatty liver diseases (NAFLD) [102] . Further, catechin derivatives were reported to be very effective against hepatocellular carcinoma [103] . Overall, all these findings were consistent with various wet laboratory-based studies, in turn validating the accuracy and robustness of our outcomes.

For amino acid-ligand interaction analysis and docking pose validation, the best protein-ligand complexes, Finally, ellagic acid similar to its corresponding control drug perindopril stabilized its interaction with the target angiotensin-converting enzyme through the common amino acids GLU123A, ARG124A, TYR135A and SER517A (Table 5 ).

Application of advanced statistical tool and molecular docking methodology could give insightful information on the underlying mechanism of protein-ligand biding affinities. Recently, PCA was successfully deployed for understanding the molecular interaction of Mur enzymes and gallomyricitrin [104] . In our previous studies, we used PCA tool to categorize phytochemical groups and correlated their chemical classes with their docking scores [20, 105] . Docking outputs from 8 ligands and 30 proteins along with the control docking results were taken as inputs for PCA, and the results are presented in Fig. 3a . The first principal component (PC1) and the second component (PC2) explained approximately 61.50 and 1.78% of the variance. Each of the principal components shows different dimensions of the measured dataset, and all these components are transformed into uncorrelated datasets. We observed four distinct clusters in the analysis. While cluster 4 consisted only of hydroxybenzoic acid derivative gallic acid, cluster 2 had control drugs. Ellagic acid and catechin gallate esters epigallocatechin gallate and epicatechin gallate were grouped into cluster 1, and other flavonoids quercetin, catechin, epicatechin and kaempferol were found in the cluster 3. Placement of gallic acid and control drugs in the separate cluster was indicative of comparatively weaker biding affinities to target proteins compared to other compounds. Further, to validate the finding from PCA analysis, we performed heat map generation and clustering analysis based on the Pearson correlation algorithm (Fig. 3b) . All clustering algorithms rely on the grouping principle based on the input data. Clustering principle is commonly used in gene expression experiments; however, we used this statistical methodology earlier in molecular docking study [21] . Correlation-based clustering method has an advantage over the distance-based matrices in terms of scaling irrelevance and primary focus on the relativity of output data [106] . In this study, we observed a clustering pattern similar to the PCA results. 

Pharmacophore approach is being extensively used in the computer-aided drug discovery. It is defined as the 'ensemble of various steric and electronic features' for a particular molecule. Pharmacophores of ligand entities represent functional descriptors (groups) such as aromatic, acceptor, and donor that are responsible for interaction with target proteins. Recently, we aligned three molecules, namely curcumin, piperine and chloroquine, to understand the common interacting descriptors with COVID 19 spike protein as a target [20] . In a similar study, Oluyori et al., 2022 [19] showed that standard aromatic rings of garcinia biflavonoid I, garcinia biflavonoid II, kolaflavone and amentoflavone were responsible for their interaction with the main protease and RNA-dependent-RNA polymerase of protein of SARS-CoV-2. In this study, based on the PCA/clustering outputs, we performed molecular alignment to identify functional pharmacophores of the molecules. For PCA cluster 1 (ellagic acid, epigallocatechin gallate and epicatechin gallate), three H bond acceptors, two H bond donors and one aromatic group were identified as common descriptors. On the other hand, PCA cluster 3 (quercetin, catechin, epicatechin and kaempferol) had three H bond donors, one H bond acceptor and two aromatic groups as common functional groups (Fig. 4) . Further, we observed alignment scores for PCA cluster and 2 as 26.638 and 33.237, respectively (Figs. S1 and S2). Alignment scores represent the overall alignment performance of the input molecules. Two-dimensional interaction diagrams of the best ranked ligands with their respective target proteins showed the identified descriptors as functional pharmacophores (Fig. 5 ).

Network pharmacology is an advanced technique, which enables researchers to identify multiple targets for their candidate compounds in the shortest time possible. This is an emerging methodology in drug discovery studies and is being extensively used in phytochemical-based research. In our earlier study, we successfully deployed network pharmacology approach to identify multiple disease targets of ginger phytochemicals [107] . Recently, Sarkar et al. [108] used this methodology to identify targets for mango constituent. Tea compounds were essentially reported to have multiple health benefits and diseases modulating role; however, the application of a network pharmacology approach for these tea molecules is scarce to the best of our knowledge. Nevertheless, a few such works were presented for non-Camellia sinensis tea [109, 110] . In this work, a tri-partite network consisting phytochemicals, proteins and associated diseases is constructed based on the top docking score (binding affinity Kcal mol −1 ) for each of the phytocompounds (Fig. 6) . Target proteins and their related diseases are presented in Table 2 . Gallic acid did not qualify due to overall weak binding affinities toward target proteins. While the result was analyzed, it was found that epicatechin and kaempferol both were connected with the common target (1GKC, matrix metalloproteinase-9). Matrix metalloproteinase-9 is associated with diseases such as like intervertebral disc disease and penile cancer.

The computer-assisted molecular docking simulation tool is very useful in computational biology study to determine the accuracy and stability of the docked complex in a near-native physiological environment. Kushwaha et al. [111] recently exhibited the binding stability of quercetin-3-rutinoside-7-glucoside at the active site of SARS-Cov-2 M pro by using MD simulation tool. Similarly, Islam et al. [112] use MD simulation tool to evaluate the docking potential of the selected phytochemicals against the main protease of SARS-CoV-2. In our study, epicatechin gallate (ECG)-hepatocyte growth factor receptor (PDB id 1FYR) complex scored highest (− 10 kcal mol −1 ) in terms of binding affinity and was analyzed for understanding the stability and conformational dynamics of the complex using MD simulation tool. The results obtained collectively from all the four parameters, namely RMSD, RMSF, gyration and ligand-protein H bonds, clearly showed that the complex formed by the phytochemical ECG was stable and could effectively bind with the target protein hepatocyte growth factor receptor. Root mean square deviation (RMSD) is indicative of inter-residual interactions within protein. We performed a 100-ns simulation for both apo and bound states of the selected protein. We observed a gradual increment of the RMSD in both the states up to 20 ns, and thereafter, a stable RMSD was observed throughout the remaining simulation time within the range of 0.25-0.35 nm on an average. Overall, marginally higher RMSD values were seen for the bound state protein (~ 0.31 nm) than the apo state (~ 0.25 nm), possibly due to the introduction of the ligand (ECG) into the structure (Fig. 7a) . A similar observation was reported for radius of gyration (Rg). The compactness of the protein structure in the free and bound forms was analyzed in Rg analysis (Fig. 7b) . Before beginning the simulation, the Rg values of apo and bound states were calculated as 1.7 and 1.64 nm, respectively. After initial fluctuation up to 30 ns, both the structures were stabilized at ~ 1.7 and ~ 1.5 nm of Rg for apo and bound states, respectively. In the MD simulation, initial fluctuations at the start of the trajectory were commonly cited in the literature. In addition, the marginal difference of output parameter values between bound and free-state of protein was also reported elsewhere to explain stable dynamics [113, 114] . Further, to study the residual mobility of ligand-bound and ligand-unbound forms, we analyzed the root-mean-square fluctuations (RMSF) of individual amino acid residues. As shown in Fig. 7c , at the bound state, we did not observe any remarkable change in the residual flexibility when compared to the apo form. In both cases, the difference in residual flexibility was minimal. Stable hydrogen bond formation between ligand and proteins is important for the structural stability of the complex. For interaction with the protein 1FYR, ECG showed maximum 5 H bonds. We observed at least 2 H to be longlived throughout the simulation of 100 ns (Fig. 7d ).

In a recent development, MM-PBSA method is considered as an advanced methodology for the estimation of free energy. Although this method increases the computational cost significantly, it can provide more accurate results than the conventional score-based molecular docking technique [115] . The results showed that epicatechin gallate (ECG) had a very high binding affinity (− 242.09 ± 10.97 kJ mol −1 ) toward the receptor protein hepatocyte growth factor receptor (PDB id 1FYR). The dynamic stability of a protein-ligand complex was further explained by other important energy terms, namely van der Waals, electrostatic, nonpolar and polar. Among these energy terms, polar or polar solvation energy had the opposite effect, which links binding energy to the unfavorable positive value [116] . In this study, we observed that van der Waals contributed more negative energy than its electrostatic counterpart (Table 6 ). In our study, the low contribution of polar solvation energy was important to stabilize the ECG-1FYR complex. Overall, a stable energy trajectory was observed, as demonstrated by representative 5-nm snapshots on five energy terms, namely, ΔG_Van der Waal, ΔG_Electrostatic, ΔG_Polar, ΔG_Non-Polar and ΔG_binding (Fig. 8) . 

In this study, we found that tea phytochemicals were capable of targeting multiple target proteins associated with various ailments. The results supported by multiple tools like statistics, pharmacophore analysis, network pharmacology and molecular docking showed that seven tea phytochemicals (gallic acid was excluded) could altogether target five proteins and ten associated diseases at a minimum. However, this was a minimum estimate due to stringent screening applied in the present work. Further, among eight phytochemicals studied, epicatechin gallate was found to bind strongly with the target protein hepatocyte growth factor receptor (PDB id 1FYR) with the affinity as 10 kcal mol −1 . Epicatechin gallate formed a highly stable complex with the target protein in a physiological environment as found by MD simulation. 

Development of a standardized combined plant extract containing nutraceutical formulation ameliorating metabolic syndrome components

Phytochemicals and antioxidant activity of different parts of bambangan (Mangifera pajang) and tarap (Artocarpus odoratissimus)

Tea and its phytochemicals: hidden health benefits & modulation of signaling cascade by phytochemicals

The phytochemical analysis of some medicinal plants

Ethnobotany, phytochemistry, biological, and nutritional properties of genus crepis-a review

Potential bioactive components and health promotional benefits of tea (camellia sinensis)

Effect of camellia sinensis teas on left ventricular hypertrophy and insulin resistance in dyslipidemic mice

The pharmacological activity of camellia sinensis (L.) kuntze on metabolic and endocrine disorders: a systematic review

Effects of polyphenols in tea (Camellia sinensis sp.) on the modulation of gut microbiota in human trials and animal studies

Catechin and caffeine content of tea (Camellia sinensis L.) leaf significantly differ with seasonal variation: a study on popular cultivars in North East India

Polyphenols as potential antiproliferative agents: scientific trends

Catechin from green tea had the potential to decrease the chlorpyrifos induced oxidative stress in larval zebrafish (Danio rerio)

Ginger ( Zingiber officinale ) phytochemicals -gingerenone -a and shogaol inhibit SaHPPK : molecular docking, molecular dynamics simulations and in vitro approaches

Systems biology in drug discovery and development

Anti-acetylcholinesterase activity of corallocarpus epigaeus tuber: in vitro kinetics, in silico docking and molecular dynamics analysis

Diversity and in silico docking of antibacterial potent compounds in endophytic fungus chaetomium subaffine sergeeva and host heteropogon contortus

In silico evaluation of Vitis amurensis Rupr. polyphenol compounds for their inhibition potency against CoVID-19 main enzymes Mpro and RdRp

Designing of peptide aptamer targeting the receptor-binding domain of spike protein of SARS-CoV-2: an in silico study

Molecular docking, pharmacophore modelling, MD simulation and in silico ADMET study reveals bitter cola constituents as potential inhibitors of SARS-CoV-2 main protease and RNA dependent-RNA polymerase

In silico study of some selective phytochemicals against a hypothetical SARS-CoV-2 spike RBD using molecular docking tools

Piperine, an alkaloid of black pepper seeds can effectively inhibit the antiviral enzymes of dengue and ebola viruses, an in silico molecular docking study

Network pharmacology: the next paradigm in drug discovery

A census of P. longum's phytochemicals and their network pharmacological evaluation for identifying novel drug-like molecules against various diseases, with a special focus on neurological disorders

Network pharmacology analysis and experimental validation to explore the mechanism of Shenlian extract on myocardial ischemia

Network pharmacology analysis to explore mechanism of three flower tea against nonalcoholic fatty liver disease with experimental support using high-fat dietinduced rats

IMPPAT: a curated database of Indian medicinal plants

PubChem substance and compound databases

Olive Net Library presentation by

UniProt: a hub for protein information

Comprehensive gene and pathway analysis of cervical cancer progression

OMIM. org: Online Mendelian Inheritance in Man (OMIM®), an Online catalog of human genes and genetic disorders

DrugBank: A knowledgebase for drugs, drug actions and drug targets

Capillary surfaces with free boundary in a wedge

SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules

Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings

The protein data bank

SWISS-MODEL: homology modelling of protein structures and complexes

UCSF Chimera -A visualization system for exploratory research and analysis

CASTp 3.0: computed atlas of surface topography of proteins

Software news and updates gabedit -a graphical user interface for computational chemistry softwares

Principal component analysis: a review and recent developments

Points of significance: principal component analysis

TBtools: an integrative toolkit developed for interactive analyses of big biological data

Classy-Fire: automated chemical classification with a comprehensive, computable taxonomy

Cytoscape: a software environment for integrated models

More bang for your buck: improved use of GPU nodes for GROMACS 2018

Gromacs-a parallel computer for molecular-dynamics simulations

PRODRG: a tool for high-throughput crystallography of protein-ligand complexes

CHARMM36m: an improved force field for folded and intrinsically disordered proteins

g_ mmpbsa-A GROMACS tool for high-throughput MM-PBSA calculations

Phenyl-γvalerolactones and phenylvaleric acids, the main colonic metabolites of flavan-3-ols: synthesis, analysis, bioavailability, and bioactivity

Tea: a new perspective on health benefits

Genetic variation of flavonols quercetin, myricetin, and kaempferol in the Sri Lankan tea (Camellia sinensis L.) and their health-promoting aspects

Dynamic change in amino acids, catechins, alkaloids, and gallic acid in six types of tea processed from the same batch of fresh tea (Camellia sinensis L.) leaves

A comprehensive insight on the health benefits and phytoconstituents of Camellia sinensis and recent approaches for its quality control

Crystal structure of the dimeric extracellular domain of human carbonic anhydrase XII, a bitopic membrane protein overexpressed in certain cancer tumor cells

Structurebased design and synthesis of lipophilic 2, 4-diamino-6-substituted quinazolines and their evaluation as inhibitors of dihydrofolate reductases and potential antitumor agents

Crystal structure of the complex of human epidermal growth factor and receptor extracellular domains

Structure of thrombin inhibited by aeruginosin 298-A from a blue-green alga

Structure and kinetics of human carbonic anhydrase II mutants at residue Val-121

Novel mode of ligand recognition by the Erbin PDZ domain

Inhibition of human neutrophil elastase. 4. design, synthesis, x-ray crystallographic analysis, and structure-activity relationships for a series of P2-modified, orally active peptidyl pentafluoroethyl ketones

Structure of human pro-matrix metalloproteinase-2: activation mechanism revealed

Dimer formation through domain swapping in the crystal structure of the Grb2-SH2-Ac-pYVNV complex

Crystal structure of human cytochrome P450 2D6

Crystal structure of human CDK4 in complex with a D-type cyclin

Crystal structure of the human angiotensin-converting enzyme-lisinopril complex

Crystal structure of human MMP9 in complex with a reverse hydroxamate inhibitor

Insights into the molecular basis of leukocyte tethering and rolling revealed by structures of P-and E-selectin bound to SLeX and PSGL-1

Structures of recombinant native and E202Q mutant human acetylcholinesterase complexed with the snake-venom toxin fasciculin-II

Crystal structure of the secretory form of membrane-associated human carbonic anhydrase IV at 2.8-Å resolution

The structure and function of human dipeptidyl peptidase IV, possessing a unique eight-bladed β-propeller fold

Crystal structure of aspirin-acetylated human cyclooxygenase-2: insight into the formation of products with reversed stereochemistry

The structural basis for autoinhibition of FLT3 by the juxtamembrane domain

The structure of a human p110α/p85α complex elucidates the effects of oncogenic PI3Kα mutations

Structural biology of RNA polymerase III: subcomplex C17/25 X-ray structure and 11 subunit enzyme model

Structural basis for inhibition of the cyclin-dependent kinase Cdk6 by the tumour suppressor p16INK4a

Catalytic domain crystal structure of protein kinase C-θ (PKC θ)

Crystal structure and functional analysis of the HERG potassium channel N terminus: a eukaryotic PAS domain

Structures of closed and open conformations of dimeric human ATM

Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties

Structure-ADME relationship: still a long way to go?

Lipid formulation strategies for enhancing intestinal transport and absorption of P-glycoprotein (P-gp) substrate drugs: in vitro/in vivo case studies

Binding site identification of COVID-19 main protease 3D structure by homology modeling

AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading

Evaluation of AutoDock and AutoDock Vina on the CASF-2013 benchmark

Intracellular signaling network as a prime chemotherapy target of green tea catechin, (-)-Epigallocatechin-3-gallate. Nutrition Diet and Cancer

Epigallocatechin-3-gallate (EGCG) prevents aminoglycosides-induced ototoxicity via anti-oxidative and anti-apoptotic pathways

Interaction between green tea and perindopril reduces inhibition of angiotensin-converting enzyme activity

Matrix metalloproteinase inhibitors identified from Camellia sinensis for COVID-19 prophylaxis: an in silico approach

Potential role of phytochemicals against matrix metalloproteinase induced breast cancer; an explanatory review. Front Chem

Anticancer effects of green tea and the underlying molecular mechanisms in bladder cancer

Antioxidative, antiinflammatory, and anticancer effects of purified flavonol glycosides and aglycones in green tea

A review on anti-cancer effect of green tea catechins

The flavonoid rutin and its aglycone quercetin modulate the microglia inflammatory profile improving antiglioma activity

Comprehensive understanding of the relationship between bioactive compounds of black tea and its angiotensin converting enzyme (ACE) inhibition and antioxidant activity

antihypertensive potential of plant foods: research progress and prospect of plant-derived angiotensin-converting enzyme inhibition compounds

Targeting hepatocyte growth factor/c-mesenchymal-epithelial transition factor axis in hepatocellular carcinoma: Rationale and therapeutic strategies

A comparison of the phenolic composition of old and young tea leaves reveals a decrease in flavanols and phenolic acids and an increase in flavonols upon tea leaf maturation

Effects of epigallocatechin gallate, epigallocatechin and epicatechin gallate on the chemical and cell-based antioxidant activity, sensory properties, and cytotoxicity of a catechin-free model beverage

Green tea and epigallocatechin gallate (EGCG) for the management of nonalcoholic fatty liver diseases (NAFLD): Insights into the role of oxidative stress and antioxidant mechanism

Epigallocatechin-3-gallate in the prevention and treatment of hepatocellular carcinoma: experimental findings and translational perspectives

Exploring the interaction mechanism between potential inhibitor and multi-target Mur enzymes of mycobacterium tuberculosis using molecular docking, molecular dynamics simulation, principal component analysis, free energy landscape, dynamic cross-correlation matrices, vector movements, and binding free energy calculation

Phytochemicals as potential drug candidates for targeting SARS CoV 2 proteins, an in silico study

Impact of similarity metrics on single-cell RNA-seq data clustering

Network pharmacological evaluation for identifying novel drug-like molecules from ginger (Zingiber officinale Rosc.) against multiple disease targets, a computational biotechnology approach

Phytochemical characterization, antioxidant, anti-inflammatory, anti-diabetic properties, molecular docking, pharmacokinetic profiling, and network pharmacology analysis of the major phytoconstituents of raw and differently dried mangifera indica (himsagar cultivar): an in vitro and in silico investigations

Integrating network pharmacology and component analysis study on anti-atherosclerotic mechanisms of total flavonoids of engelhardia roxburghiana leaves in mice

Cyclocarya paliurus leaves tea improves dyslipidemia in diabetic mice: a lipidomics-based network pharmacology study

Phytochemicals present in Indian ginseng possess potential to inhibit SARS-CoV-2 virulence: A molecular docking and MD simulation study

Microb Pathog

A molecular modeling approach to identify effective antiviral phytochemicals against the main protease of SARS-CoV-2

Identification of phytochemicals targeting c-Met kinase domain using consensus docking and molecular dynamics simulation studies

Molecular insight into binding behavior of polyphenol (rutin) with beta lactoglobulin: Spectroscopic, molecular docking and MD simulation studies

Study of the binding energies between unnatural amino acids and engineered orthogonal tyrosyl-tRNA synthetases

Discovery of potential drugs for human-infecting H7N9 virus containing R294K mutation

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The authors would like to acknowledge Centre for Research Project, CHRIST (Deemed to be University), Bangalore, for providing financial support toward a minor research project entitled "Effects of storage on polyphenols and sensory qualities of tea beverage" (MIRPDSC -1904, Dt. 01/10/2019). We extend our acknowledgement to Centre for Academic and Professional Support (CAPS), CHRIST (Deemed to be University), Bangalore, for refining the manuscript as per professional language requirement.

The authors have no relevant financial or non-financial interests to disclose.