Exploring the Therapeutic Potential of Epigallocatechin-3-gallate (Green Tea) in Periodontitis Using Network Pharmacology and Molecular Modeling Approach

Periodontitis is a common inflammatory disease affecting the supporting structures of teeth. Epigallocatechin-3-gallate (EGCG), a polyphenol found in green tea, is known for its therapeutic properties in various diseases, including periodontitis. This study aims to identify the gene targets of EGCG and investigate its potential in modulating molecular pathways associated with periodontitis. The potential gene targets of EGCG were obtained from the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) and SwissTargetPrediction databases, while genes associated with periodontitis were sourced from GeneCards and Gene Expression Omnibus (GEO) datasets. By overlapping the two datasets, ten common target genes were identified. To explore their functional relevance, enrichment analyses such as Gene Ontology (GO) and REACTOME pathway mapping were conducted. Protein–protein interaction (PPI) networks were then generated, and further analyses involving molecular docking and molecular dynamics (MD) simulations were carried out to evaluate the binding affinity and structural stability of EGCG with the selected target proteins. Ten common genes (MMP2, MMP14, BCL2, STAT1, HIF1A, MMP9, MMP13, VEGFA, ESR1, and PPARG) were identified. PPI network and GO and pathway analyses identified the promising hub genes as ESR1, MMP2, MMP9, MMP13, and STAT1 and which highlighted roles in tissue development, extracellular matrix remodeling, and signaling pathways such as interleukin and matrix metalloproteinase activities. Molecular docking and MD simulations revealed strong binding interactions between EGCG and key proteins (ESR1, MMP2, MMP9, MMP13, and STAT1), with favorable binding energies and stable complexes. Among these, ESR1 and MMP13 exhibited the most favorable docking scores and stability in molecular dynamics simulations and MM–PBSA calculations. This study provides valuable insights into the molecular mechanisms of EGCG in periodontitis treatment. The findings suggest that ESR1 and MMP13 are the most promising targets for EGCG, supported by strong binding interactions and stable conformations in simulations. These results offer a foundation for further experimental studies and potential therapeutic applications of EGCG in managing periodontitis.