Computer-aided identification of lung cancer inhibitors through homology modeling and virtual screening

Abstract

Background: Lung cancer is the most often event cancer around the world and the first leading cause of cancer death in human beings. Rab39a protein is implicated in vesicular trafficking and fusion of phagosomes with lysosomes. Rab39a is overexpressed in lung cancer, which converts normal cells to abnormal cells that reproduce quickly, and resists programmed cell death that usually kills aberrant cells. Aim: In the present study, the structure-based drug discovery approach is applied to identify new lead structures as cancer drug candidates against Rab39a. Methods: A valid three-dimensional (3D) model of Rab39a generation, the prediction of protein–protein interactions (Rab39a/DENND5B) and active site identification were achieved by computational techniques. Results: Our studies suggest that the amino acid residues from PHE28 to LYS63 are important for binding with the ligand molecules. Subsequently, the virtual screening study was carried out with ligand databases against the active site of Rab39a. Conclusion: The ligand molecules with hetero amine moieties and amide group (-CONH-) have shown good value of docking score and agreeable ADME properties, so they were prioritized as potential inhibitors of Rab39a protein. Hence, Rab39a has emerged as a therapeutic target for drug development towards lung cancer.