RASSF2, potential tumor suppressor gene, acts as a KRAS-specific effectors protein and may promote apoptosis and cell cycle arrest. It stabilizes STK3/MST2 by protecting it from proteasomal degradation. RASSF2 plays a significant role against the inhibition of cancer. MODELLER (9v15) and online servers (I-Tasser, SwissModel, 3D-JigSaw, ModWeb) were utilized to generate 3D structures of the RASSF2 based on homology modeling. A comparison between models predicted by MODELLER (9v15) and Web servers had been checked through utilized evaluation tools. The most potent model for RASSF2 was analyzed and selected for molecular docking studies. The binding pockets were revealed for binding studies through Site Hound. AutoDock Vina and AutoDock4 were utilized for molecular docking, and the attempt of this experiment was to identify the ligands for RASSF2. The selected compounds may act as regulators and regulate the normal activity of RASSF2. It was also analyzed and observed that the selected compounds showed least binding energy and high-affinity binding in predicted top binding domain. The determination of protein function is based on accurate identification of binding sites in protein structures. The binding site is known, and it may allow the ligand type and protein function to be determined by performing in silico and experimental procedures. The detection, comparison, and analysis of binding pockets are pivotal to drug discovery. It proposed that predicted structure is reliable for the structural insights and functional studies. The predicted binding pockets may lead to further analysis (drug discovery), used against cancer study.
Keywords: Binding domains; Cancer; Homology modeling; Molecular docking analyses; RASSF2.