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Principal Investigator: Neil King
Organization: UNIVERSITY OF WASHINGTON
Fiscal Year: 2024
Award: $4,642,108
Funding agency: National Institute of Allergy and Infectious Diseases
PROJECT SUMMARY – PROJECT 4: PROTEIN NANOPARTICLE VACCINES
Project 4 focuses on developing generalizable approaches to the design, production, and evaluation of
two-component protein nanoparticle vaccines for arenaviruses, phenuiviruses, and paramyxoviruses. These
approaches will be enabled by powerful new machine learning-based tools for protein modeling and design. In
Aim 1, we will develop generalizable approaches to designing de novo protein nanoparticle immunogens that
display oligomeric antigens in specific geometrical arrangements with atomic-level accuracy. These methods
and approaches will be used to design nanoparticle immunogens that present trimeric paramyxovirus fusion
(F) and arenavirus glycoprotein complex (GPC) antigens, as well as tetrameric paramyxovirus receptor binding
protein (RBP) antigens. The designed nanoparticle immunogens will present each antigen in various
symmetries and valencies, with precisely varied antigen-antigen spacing and orientation, each an important
structural determinant of immunogenicity. In Aim 2, we will develop generalizable approaches to designing de
novo protein nanoparticle immunogens that display monomeric and heterodimeric antigens in specific
geometric arrangements with atomic-level accuracy. The orientation of these antigens—each of which has the
full set of 6 rotational and translational degrees of freedom since they lack internal symmetry—will be precisely
controlled to maximize the accessibility of epitopes that are targeted by neutralizing antibodies. We will design
nanoparticles in which the monomeric or heterodimeric antigens are presented in various symmetries and
valencies, with precisely varied antigen-antigen spacing and orientation. In Aim 3, we will produce the
designed vaccine candidates; intensively characterize their biochemical, biophysical, antigenic, and structural
properties; and rigorously evaluate their performance in immunogenicity studies and challenge studies in small
and large animal models. We will build on the Institute for Protein Design’s extensive infrastructure for protein
production and characterization to rapidly screen the large numbers of vaccine candidates we design using our
machine learning-based methods. Structural characterization at UW and deep mutational scanning at the Fred
Hutchinson Cancer Center will both inform and provide rigorous assessment of the accuracy of our design
methods. Finally, we will leverage the deep expertise and new animal models developed at UTMB Galveston to
rigorously evaluate our designed nanoparticle vaccines and identify lead candidates for further preclinical and
clinical development.
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