Computational design of immunogenic peptide–ligand conjugates for targeted therapy against Nipah virus infection

Abstract

Introduction Nipah virus (NiV) poses a major risk to global public health due to its high infectivity and associated mortality rates. Currently, no licensed vaccines or antiviral medications are available for NiV infection, leaving clinical management limited to supportive care. The viral receptor glycoprotein responsible for binding NiV to host cell receptors (ephrin-B2/B3) represents an ideal therapeutic target. This study proposes a novel peptide-ligand conjugate (PLC) immunotherapeutic approach that exploits pre-existing immune responses in NiV-endemic populations to selectively target and eliminate infected cells. Methods We employed biomolecular modeling (in silico) to establish binding affinities and perform docking studies using a compound library obtained from the MolProphet database. A non-cleavable oxime linker was selected to enhance physical stability and ensure robust conjugation between ligand and peptide components. The peptide was engineered to contain immunogenic minimal epitope regions derived from measles, mumps, and rubella vaccines, selected based on their high immunization rates and long-lived memory responses in individuals residing in NiV-endemic areas. Results The PLC design demonstrated selective binding capacity to a transmembrane protein present on NiV-infected cells. The oxime linker provided enhanced stability, and the peptide epitope design successfully incorporated regions associated with established long-term immunity. Discussion This PLC system represents a promising framework for antiviral therapeutic development by harnessing pre-existing immune recognition to promote selective clearance of NiV-infected cells. The findings highlight critical structural components and functional roles of PLCs in therapeutic development, including drug target screening and rational design strategies for enhancing targeting specificity and molecular stability. Future work should focus on experimental validation of the computational predictions and in vitro/in vivo efficacy studies.