In silico identification of potential HDAC3 inhibitors through machine learning, molecular docking, and molecular dynamics simulations for drug repurposing

Abstract

Background

Histone Deacetylase 3 (HDAC3) is an epigenetic enzyme that controls cell cycle progression, apoptosis, and gene expression. Overexpression of HDAC3 has been shown to be a potential contributing factor to the development and spread of breast cancer, and it has recently been identified as a promising target in breast cancer. As a result, repurposing currently approved drugs as novel HDAC3 inhibitors may reduce the labor-intensive and time-consuming process of developing new molecules.

Materials and methods

We sourced 4288 compounds from the ZINC15-approved drugs. We then employed both virtual and structure-based screening to identify and repurpose current drugs as selective inhibitors against the HDAC3 target protein. MD simulation was performed to assess the dynamic behavior and stability of the top ligand complexes for 100 ns.

Results

This computational screening obtained the top five compounds with docking scores of $-$ 10.96, $-$ 10.32, $-$ 9.83, $-$ 9.83, and $-$ 8.81 kcal/mol, respectively, in comparison with the reference ligand, BG45 (−4.18 kcal/mol), suggesting they may be more potent HDAC3 inhibitors. The MD simulation study of the top hit ligand-protein complex (HDAC3-ZINC000095618609 complex) revealed stable conformational changes. The results of pharmacokinetic and drug-likeness properties of the top-performing compounds reveal their potential to be considered viable HDAC3 inhibitors.

Conclusion

This study highlights the potential of drug repurposing as a cost-effective and faster approach to cancer treatment. here we have identified drugs have the potential to be repurposed as HDAC3 inhibitors; however, additional in vitro and in vivo studies are needed to confirm their efficacy.