Modular Surface Engineering of Feline Parvovirus Virus-Like Particles for Antigen Presentation and Receptor-Mediated Cell Targeting
Naskalska, A.; Biela, A.; Rozycki, J.; Fic, K.; Kus, E.; Nowak, J.
Show abstract
Virus-like particles (VLPs) are non-infectious, multiprotein nanostructures that mimic the architecture of viruses while lacking genetic material. Their structural versatility and safety profile make them promising platforms for biomedical applications, including vaccine development, targeted drug delivery, and diagnostics. Among various functionalization strategies, click-chemistry has emerged as a precise and efficient method for attaching bioactive molecules to VLP surfaces. In this study, we report the production and functionalization of VLPs derived from the feline panleukopenia virus (FPV), a parvovirus closely related to the well-characterized canine parvovirus (CPV). We demonstrate successful expression, assembly, and purification of FPV VLPs, followed by their modification with fluorophores, peptides, oligonucleotides, and proteins using copper-free click-chemistry. Furthermore, using cryo-EM, we structurally characterize FPV VLP functionalized with antigenic peptides, providing detailed insights for further development of such vaccine prototype. Finally, we demonstrate that our FPV VLP specifically transduce feline and human cells expressing transferrin receptor type 1 (TfR), thus validating their potential as a unique platform for future nanotechnological and therapeutic applications.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Heterologous Prime-Boost with Immunologically Orthogonal Protein Nanoparticles for Peptide Immunofocusing 94%
- Graphene-enabled, spatially controlled electroporation of adherent cells for live-cell super-resolution microscopy 94%
- Controlling wireframe DNA origami nuclease degradation with minor groove binders 93%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.