Virtual screening of potential inhibitor against breast cancer-causing estrogen receptor alpha (ERα): molecular docking and dynamic simulations

ABSTRACT

Breast cancer (Bc_a) causes the highest rate of mortality in females owing to the out-of-control cell division in breast cells. In this work, we perform an in-silico screening based on molecular docking and molecular dynamic of curcumin derivatives against ERα. In this study, we carry out, molecular docking of fifty (50) curcumin derivatives having anticancer potential by using virtual screening tools. Ten (10) ligands were selected based on binding energy ranged from (-7.4 kcal/mol to -9 kcal/mol), lower values of inhibition constant (0.23µmol to 3.59µmol), and visualisation of intermolecular interactions. Additionally, we also assess ADMET properties of selected ligands for prediction of their toxicity and drug-likeness. The molecular dynamic simulations (MD) including RMSD, RMSF, Rg, SASA, number of H-bonds and MM-PBSA binding free energy results showed that ligand L2 and L8 bind to estrogen protein ERα more proficiently with good stability over 120 ns. These results suggest lead anticancer compounds L2 (Salicylidenecurcumin) and L8 (Curcumin difluorinated) are the most promising inhibitor against ERα of Bc_a with ∆G_bind values of (-2.939 and -4.369) kcal/mol. we expect that our findings will evoke the scientific community to further do in-vitro and in-vivo investigations for screened curcumin derivatives against ERα of Bc_a.

KEYWORDS:

• Breast cancer
• Curcumin derivatives
• molecular docking
• molecular dynamics
• MM-PBSA

Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding the work through Research Project (RGP.2/194/43). For computer time, this research used the resources of the Supercomputing Laboratory at King Abdullah University of Science & Technology (KAUST) in Thuwal, Saudi Arabia.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that supports this work are available in this paper and also details are present in the supporting information.

Additional information

Funding

This work was supported by King Khalid University [grant number RGP.2/194/43].