Molecular Docking-Based Screening of Epicatechin Gallate against Estrogen Receptor 1 (ESR1) Using Estradiol as a Standard Ligand: Insights into Female Reproductive Health
Female fertility and ovarian function are controlled by a complex network of hormonal signals, in which estrogen receptors play an important regulatory role. Among these receptors, estrogen receptor 1 (ESR1) is widely expressed in ovarian tissues and contributes to essential reproductive processes, including ovulation and maintenance of hormonal balance. In recent years, natural phytochemicals have attracted attention as potential regulators of estrogen receptor activity because they possess structures similar to natural estrogens and generally show good safety profiles. In this study, potential phytochemical candidates were evaluated for their ability to interact with estrogen receptor 1 (ESR1; UniProt ID: P03372; PDB ID: 1A52), using Estradiol as a standard reference compound. Molecular docking analysis was carried out using LibDock and CDOCKER protocols available in BIOVIA Discovery Studio 2019. In addition, structural dynamics studies were performed to assess the stability and behavior of protein-ligand complexes. The docking results showed that the selected phytochemicals demonstrated strong binding potential toward ESR1 by forming interactions with important amino acid residues within the receptor's ligand-binding region. When compared with the reference compound Estradiol, these compounds showed favorable binding patterns and stable conformations. Further structural dynamics analysis supported the stability of the formed complexes. Overall, the findings indicate that the investigated phytochemical, Epicatechin Gallate (PubChem CID: 107905), may have the potential to influence ESR1-related signaling pathways involved in female reproductive health and ovarian physiology. This computational study provides valuable preliminary evidence for further experimental research toward the development of plant-based therapeutic approaches for reproductive health management.
Authors
- Dr. Vinay Kumar Singh
- Dr. Ummat Salwat Shaumya
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-10-06
- DOI
- https://doi.org/10.5281/zenodo.23187339
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00