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.

In silico structural elucidation of Nipah virus L protein and targeting RNA-dependent RNA polymerase domain by nucleoside analogs.

The large (L) protein of Mononegavirales is a multi-domain protein that performs transcription and genome replication. One of the important domains in L is the RNA-dependent RNA polymerase (RdRp), a promising target for antiviral drugs. In this work, we employed rigorous computational comparative modeling to predict the structure of L protein of Nipah virus (NiV). The RdRp domain was targeted by a panel of nucleotide analogs, previously reported to inhibit different viral RNA polymerases, using molecular docking. Best binder compounds were subjected to molecular dynamics simulation to validate their binding. Molecular mechanics/generalized-born surface area (MM/GBSA) calculations estimated the binding free energy. The predicted model of NiV L has an excellent quality as judged by physics- and knowledge-based validation tests. Galidesivir, AT-9010 and Norov-29 scored the top nucleotide analogs to bind to the RdRp. Their binding free energies obtained by MM/GBSA (-31.01 ± 3.9 to -38.37 ± 4.8 kcal/mol) ranked Norov-29 as the best potential inhibitor. Purine nucleotide analogs are expected to harbor the scaffold for an effective drug against NiV. Finally, this study is expected to provide a start point for medicinal chemistry and drug discovery campaigns toward identification of effective chemotherapeutic agent(s) against NiV.Communicated by Ramaswamy H. Sarma.