Unveiling the ESR1 Conformational Stability and Screening Potent
Inhibitors for Breast Cancer Treatment

Khushboo      Sharma; Umesh      Panwar; Maddala      Madhavi; Isha      Joshi; Ishita      Chopra; Lovely      Soni; Arshiya      Khan; Anushka      Bhrdwaj; Abhyuday   Singh   Parihar; Vineeth   Pazharathu   Mohan; Leena      Prajapati; Rashmi      Sharma; Shweta      Agrawal; Tajamul      Hussain; Anuraj      Nayarisseri; Sanjeev   Kumar   Singh
Abstract

Background: The current study recognizes the significance of estrogen receptor alpha (ERα) as a member of the nuclear receptor protein family, which holds a central role in the pathophysiology of breast cancer. ERα serves as a valuable prognostic marker, with its established relevance in predicting disease outcomes and treatment responses.
Methods: In this study, computational methods are utilized to search for suitable drug-like compounds that demonstrate analogous ligand binding kinetics to ERα.
Results: Docking-based simulation screened out the top 5 compounds - ZINC13377936, NCI35753, ZINC35465238, ZINC14726791, and NCI663569 against the targeted protein. Further, their dynamics studies reveal that the compounds ZINC13377936 and NCI35753 exhibit the highest binding stability and affinity.
Conclusion: Anticipating the competitive inhibition of ERα protein expression in breast cancer, we envision that both ZINC13377936 and NCI35753 compounds hold substantial promise as potential therapeutic agents. These candidates warrant thorough consideration for rigorous In vitro and In vivo evaluations within the context of clinical trials. The findings from this current investigation carry significant implications for the advancement of future diagnostic and therapeutic approaches for breast cancer.
Keywords: Cancer, ESR1, Erα, virtual screening, docking based simulation, binding affinity, R programming.
Graphical Abstract

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Cite As
Medicinal Chemistry

Unveiling the ESR1 Conformational Stability and Screening Potent Inhibitors for Breast Cancer Treatment

Abstract

Graphical Abstract
