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

Abstract

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.

Keywords: Diseases; Molecular docking; Network pharmacology; Phytochemicals; Simulation; Statistics; Tea.

Fig. 1

Schematic representation of the study

Fig. 2

Superimposed three-dimensional docking interaction between control drugs and phytochemicals to target proteins (Uniprot id), along with H bond interactions; catechin (CAT), epigallocatechin gallate (EGCG), epicatechin (EC), epicatechin gallate (ECG), quercetin (QUE), kaempferol (KAE), and ellagic acid (EA)

Fig. 3

Statistical analysis of binding affinities (kcal mol⁻¹), a principal component analysis and b heat map with clustering

Fig. 4

Molecular alignment of phytochemicals showing pharmacophores (ACC acceptor, DON donor, AR aromatic)

Fig. 5

Protein–phytochemical interacting amino acids showing pharmacophores involved: catechin (CAT), epigallocatechin gallate (EGCG), epicatechin (EC), epicatechin gallate (ECG), quercetin (QUE), kaempferol (KAE), and ellagic acid (EA)

Fig. 6

Tripartite network of phytochemicals, proteins and associated diseases: catechin (CAT), epigallocatechin gallate (EGCG), epicatechin (EC), epicatechin gallate (ECG), quercetin (QUE), kaempferol (KAE), and ellagic acid (EA)

Fig. 7

Molecular dynamic (MD) simulation of epicatechin gallate (ECG)-hepatocyte growth factor receptor (PDB id 1FYR) complex: a root-mean-square deviation (RMSD), b radius of gyration, c root-mean-square fluctuation (RMSF) and ligand–protein H bonds

Fig. 8

Free energy terms of epicatechin gallate (ECG)-hepatocyte growth factor receptor (PDB id 1FYR) complex, a ΔG_Van der Waal, b ΔG_Electrostatic, c ΔG_Polar, d ΔG_Non-Polar and e ΔG_binding