Identification of Potential p38 gamma Inhibitors via In Silico Screening, In Vitro Bioassay and Molecular Dynamics Simulation Studies

Protein kinase p38? is an attractive target against cancer because it plays a pivotal role in cancer cell proliferation by phosphorylating the retinoblastoma tumour suppressor protein. Therefore, inhibition of p38? with active small molecules represents an attractive alternative for developing anti-cancer drugs. In this work, we present a rigorous and systematic virtual screening framework to identify potential p38? inhibitors against cancer. We combined the use of machine learning-based quantitative structure activity relationship modelling with conventional computer-aided drug discovery techniques, namely molecular docking and ligand-based methods, to identify potential p38? inhibitors. The hit compounds were filtered using negative design techniques and then assessed for their binding stability with p38? through molecular dynamics simulations. To this end, we identified a promising compound that inhibits p38? activity at nanomolar concentrations and hepatocellular carcinoma cell growth in vitro in the low micromolar range. This hit compound could serve as a potential scaffold for further development of a potent p38? inhibitor against cancer.

Automated summary

Protein kinase p38? is a crucial target for cancer treatment. In this study, a virtual screening framework combining machine learning-based modeling and conventional drug discovery techniques was employed to identify potential p38? inhibitors. Through filtering and molecular dynamics simulations, a promising compound that inhibits p38? activity and hepatocellular carcinoma cell growth was discovered. This hit compound could serve as a prospective scaffold for developing potent p38? inhibitors against cancer.