In silico molecular docking and dynamic simulation of eugenol compounds against breast cancer

Abstract

Breast cancer is one of the most severe problems, and it is the primary cause of cancer-related death in females worldwide. The adverse effects and therapeutic resistance development are among the most potent clinical issues for potent medications for breast cancer treatment. The eugenol molecules have a significant affinity for breast cancer receptors. The aim of the study has been on the eugenol compounds, which has potent actions on Erα, PR, EGFR, CDK2, mTOR, ERBB2, c-Src, HSP90, and chemokines receptors inhibition. Initially, the drug-likeness property was examined to evaluate the anti-breast cancer activity by applying Lipinski's rule of five on 120 eugenol molecules. Further, structure-based virtual screening was performed via molecular docking, as protein-like interactions play a vital role in drug development. The 3D structure of the receptors has been acquired from the protein data bank and is docked with 87 3D PubChem and ZINC structures of eugenol compounds, and five FDA-approved anti-cancer drugs using AutoDock Vina. Then, the compounds were subjected to three replica molecular dynamic simulations run of 100 ns per system. The results were evaluated using root mean square deviation (RMSD), root mean square fluctuation (RMSF), and protein-ligand interactions to indicate protein-ligand complex stability. The results confirm that Eugenol cinnamaldehyde has the best docking score for breast cancer, followed by Aspirin eugenol ester and 4-Allyl-2-methoxyphenyl cinnamate. From the results obtained from in silico studies, we propose that the selected eugenols can be further investigated and evaluated for further lead optimization and drug development.

Keywords: AutoDock Vina; Breast cancer; Eugenol; Molecular docking; Molecular dynamic simulations.