Computational identification of drug-like marine natural products as potential RNA polymerase inhibitors against Nipah virus

Nipah virus (NiV) has been an alarming threat to human populations in southern Asia for more than a decade. It is one of the most deadly viruses in the Mononegavirales order. Despite its high mortality rate and virulence, no chemotherapeutic agent or vaccine is publicly available. Hence, this work was conducted to computationally screen marine natural products database for drug-like potential inhibitors for the viral RNA-dependent RNA polymerase (RdRp). The structural model was subjected to molecular dynamics (MD) simulation to obtain the native ensemble of the protein. The CMNPDB dataset of marine natural products was filtered to retain only compounds following Lipinski's five rules. The molecules were energy minimized and docked into different conformers of the RdRp using AutoDock Vina. The best 35 molecules were rescored by GNINA, a deep learning-based docking software. The resulting nine compounds were evaluated for their pharmacokinetic profiles and medicinal chemistry properties. The best five compounds were subjected to MD simulation for 100 ns, followed by binding free energy estimation via Molecular Mechanics/ Generalized Born Surface Area (MM/GBSA) calculations. The results showed remarkable behavior of five hits as inferred by stable binding pose and orientation to block the exit channel of RNA synthesis products in the RdRp cavity. These hits are promising starting materials for in vitro validation and structural modifications to enhance the pharmacokinetic and medicinal chemistry properties for developing antiviral lead compounds.