Abstract
The emerging zoonotic Nipah virus (NiV) is a major threat to public health because of its potential to cause severe outbreaks from human-to-human transmission and lack of therapeutic options currently. Identification of effective therapeutics to combat NiV infections is needed to contain future outbreaks. This research uses in silico methods to predict putative therapeutic candidates for the NiV attachment glycoprotein G (NiV-G) from existing therapeutic agents. To do this, virtual screening of NiV-G against 1615 FDA approved drugs publicly available from the Zinc 15 database is performed using a molecular docking approach via AutoDock Vina software. Further, a molecular dynamics simulation using WebGRO server is employed to identify top NiV-G inhibitors. Most of the binding for the top three ligands–as determined by binding energy–occurs in the catalytic groove that must contain Phe458, Trp504, Gln559, and Glu579 in order to successfully inhibit NiV-G. The molecular dynamics simulation analysis validates rigidity and stability of the docked complex through the assessment of root mean square deviations, root mean square fluctuations, solvent accessible surface area, radius of gyration, and hydrogen bond analysis from simulation trajectories. Post-molecular dynamics analysis also shows that Alvimopan, Betrixaban, and Ribociclib interact with NiV-G in the same binding pocket. Therefore, Alvimopan, Betrixaban, and Ribociclib are identified as top NiV-G inhibitors that could be used to improve NiV-infected patient outcomes when an outbreak arises.