Targeting Nipah Virus Matrix Protein (NiV-M):_x000B_QSAR and Docking-Driven Screening Coupled with MD and Metadynamics

Lu Li

College: Hennings College of Science Mathematics and Technology

Major: BS.BIO/CELL/MOLEC

Faculty Research Mentor: Kar, Supratik

Abstract:

Nipah virus (NiV) is a highly lethal zoonotic pathogen with recurrent outbreaks and no approved antiviral therapeutics, motivating the exploration of alternative drug discovery strategies. In this work, we applied an integrated computer-aided drug discovery pipeline to identify and prioritize small-molecule ligands targeting the NiV matrix protein (NiV-M), a critical regulator of viral assembly and budding. A curated dataset of reported NiV inhibitors was first used to construct and validate a quantitative structure-activity relationship (QSAR) model, which enabled ligand-based prioritization of compounds within a defined applicability domain. In parallel, a large antiviral-focused compound library was subjected to structure-based virtual screening against the NiV-M crystal structure, followed by consensus ranking using docking and QSAR predictions. Top-ranked candidates were further filtered using in silico ADMET and toxicity profiling to enrich for pharmaceutically viable molecules. Selected ligands were then evaluated using long-timescale molecular dynamics simulations to assess binding stability, protein–ligand interaction persistence, and conformational effects on NiV-M. Binding energetics were quantified using MM-GBSA calculations, and advanced metadynamics analyses were employed to characterize ligand-induced changes in the free-energy landscape of the protein. Across multiple computational criteria, PubChem CID 6474310 (isochlorogenic acid A), consistently demonstrated superior performance, including stable binding behavior, favorable binding free energy, and pronounced restriction of protein conformational flexibility. Isochlorogenic acid A corresponds to a naturally occurring polyphenol with previously reported antiviral activity in unrelated viral systems. While the present findings are entirely predictive and do not establish antiviral efficacy against NiV, they provide a mechanistically grounded computational rationale for experimental follow-up. Overall, this study highlights the utility of combining QSAR, molecular docking, and advanced simulation techniques to accelerate the identification of promising antiviral leads against high-risk emerging pathogens.