Novel scFv detection system targeting the glycoprotein of harbor porpoise rhabdovirus (HPRV) through computational and experimental validation
Year: 2025
Type: article
Abstract: Harbour porpoise rhabdovirus (HPRV), with its distinct bullet-shaped nucleocapsid and negative-sense RNA genome, has become a subject of concern owing to its cytotoxic effects on marine mammals and the potential risks associated with zoonotic transmission of a recently discovered rhabdovirus affecting Harbor porpoises . Existing diagnostic techniques for marine viruses are limited, especially for cetaceans. single-chain variable fragment (scFv) technology offers a potential alternative because of its compact size, specificity, and efficient production capability in the E. coli system. This study focused on the characterization and development of a prospective diagnostic system using scFv antibodies to identify HPRV. Through the bio-panning selection process, high affinity scFv candidates were discovered and subsequently subjected to expression optimization, purification, molecular docking and molecular dynamics simulation were conducted to evaluate their binding affinities against HPRV glycoproteins. Thus, ELISA binding ability and specificity tests validated G2 as the best candidate with significant specificity and sensitivity for HPRV antigen detection without cross-reactivity with host proteins. We demonstrated that the scFv double domain is preferable to the single domain following antigen interaction, based on our computational evaluation. This study offers essential insights into an scFv-based diagnostic tool, enhancing the detection capabilities of marine rhabdoviruses in cetaceans and facilitating potential applications in zoonotic virus control.
Source: Heliyon
Authors: Ika Agus Rini, Mario Marweslie, Misae Kiba, Didik Huswo Utomo, Taek-Kyun Lee, Sukchan Lee
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,270
Awards: G8178810903, G821810533
OpenAlex ID: W7106701518
