Presenting viral glycoproteins on computationally designed two-component protein nanoparticles to improve neutralizing antibody responses
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| Award date | 23-06-2021 |
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| Number of pages | 327 |
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| Abstract |
Recombinant prefusion-stabilized viral glycoproteins may serve as promising vaccine candidates as they allow easy large-scale manufacturing, are safe and harbour all or most (broadly) NAb epitopes while minimally exposing irrelevant non-NAb epitopes found on postfusion trimers. Indeed the generation of prefusion stabilized glycoproteins has enabled induction of high NAb titers against traditionally difficult targets such as human immunodeficiency virus 1 (HIV-1), and respiratory syncytial virus (RSV). Nevertheless the humoral immune system has evolved to recognize a repetitive array of glycoproteins such as those found on virus-like structures; not soluble glycoproteins. Thus, efforts to develop stable and antigenically sound prefusion glycoproteins have been paralleled by the design of nanoparticles that allow these glycoproteins to be presented in a multivalent fashion. Computationally designed two-component protein nanoparticles are an elegant platform that allow facile, controlled and scalable in vitro-assembly into a fixed geometry. In this thesis we explore the use of this nanoparticle system for the display of glycoproteins from HIV-1, Lassa virus and SARS-CoV-2, known as Env, GPC, and S protein, respectively. We describe the design and in-depth in vitro and in vivo characterization of these nanoparticle vaccine candidates, while generating insights in their immunogenicity, utility, and efficacy. In addition, we describe the isolation and characterization of GPC- and S protein-specific monoclonal (N)Abs, opening avenues for vaccine design while generating promising therapeutic candidates. This work reveals the versatility and potential of computationally designed nanoparticles as well as challenges that nanoparticle vaccines face in the context of heavily glycosylated and sequence diverse glycoproteins.
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| Document type | PhD thesis |
| Language | English |
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