Abstract
Y box binding protein 1 (YB-1) which regulates DNA and RNA associated events is a crucial oncogenic target and its amplified levels have been found in many cancers. YB-1 increases the stability of short-lived mRNAs for multiple oncogenic proteins promoting cancer cell stemness and facilitating the cell cycle's progression at the G1/S phase. The scientific reports of the past three decades strongly suggests that YB-1 may be a potential vital target for treating many drug resistance cancers including ovarian cancer. Thus, in the current study, we conducted a virtual drug repurposing study. For this aim, different computational models were constructed: (i) target-protein driven models (PDB: 6KUG); (ii) machine learning (ML)-based quantitative structure-activity relationships (QSAR) models; (iii) target-protein driven pharmacophore (E-pharmacophore) models. Constructed and statistically validated models were then used to screen FDA approved drugs and compounds in clinical investigation libraries which includes around 10.000 molecules. Successful candidates were used in docking and all-atom molecular dynamics (MD) simulations to further characterize their binding events at the active site of the YB-1 and compute average binding free energies using MM/GBSA. Steered MD (sMD) simulations were also conducted for final hits as well as positive control (SU056). To estimate the binding strength of selected hit compounds to YB-1, sMD simulations were conducted to pull the ligands away from protein at a constant velocity of 10 nm/ns along the vector pointing away from the binding interface. Subsequently, the PMF curves could be used to extract the binding free energies. In this study, the PMF curves were generated by using independent sampling windows from sMD trajectories. Our discovered potential small molecule YB-1 inhibitors can be considered as potential novel inhibitors that can target YB-1.
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