Synergistic approach utilizing bioinformatics, machine learning, and traditional screening for the identification of novel CSK inhibitors targeting hepatocellular carcinoma
摘要
The overexpression or activation of C-terminal Src kinase (CSK) has been recognized as a pivotal factor in the progression of hepatocellular carcinoma (HCC), positioning CSK as a promising therapeutic target. Despite this potential, no CSK-specific inhibitors have been developed for HCC treatment to date. Addressing this gap, our study established a robust virtual screening protocol that integrates energy-based screening techniques with machine learning methodologies. Through this systematic approach, we identified a novel compound, 6, exhibiting potent CSK inhibitory activity, as evidenced by an IC50 value of 675 nM in a homogeneous time-resolved fluorescence (HTRF) bioassay. Notably, this compound demonstrated significant growth inhibition in Huh-7 and Huh-6 cell lines, along with the suppression of clone formation. To elucidate the underlying mechanism, we conducted molecular dynamics simulations, which revealed critical binding interactions between compound 6 and CSK. Specifically, residues Phe333 and Met269 were found to play essential roles in mediating these interactions, providing valuable insights into the compound’s mode of action.