Universal Influenza Vaccine

Rationale: The currently available flu vaccines must be readministered on a yearly basis because of the high mutability of the circulating flu strains. Furthermore, the long time lag in producing the vaccine relative to the rate of antigenic drift and the high number of circulating strains generally result in a low level of protective immunity (20% – 70%).

Methods overview: We will use a combination of all-atom physics-based computer modeling of antigens based on known structures, machine learning analysis of available influenza (flu) type A and B viral sequences, and affinity maturation simulations to design and optimize hemagglutinin-based vaccination cocktails and administration protocols to elicit broadly-neutralizing influenza antibodies (bnAbs). The predictions will be tested experimentally in mice by our collabrators. An overview of the approach is illustrated in the image below.

The essential idea of the proposed research is to design antigen vaccination cocktails and their administration schedules to bridge the gap between an antigen that activates a germline and antigens that cover the antigenic space of known viral strains. The inter-antigen distance in the designed cocktail will be kept sufficiently small to reinforce the memory of the adaptive immunity against previously encountered antigens, yet large enough to guide it to explore the space of diverse influenza strains. This strategy maximizes the probability that the immunized host will be able to develop a more rapid response to an emerging seasonal or a pandemic strain than is possible with current seasonal vaccines, and, if successful, will be a step toward a universal permanent vaccine. The approach is organized into three steps:

1. Design of initial antigen cocktails using influenza sequence databases.

2. Optimization of antigen cocktails by affinity maturation (AM) antibody simulations.

3. Testing of proposed vaccination cocktails will be done by experimental collaborators in mice and ferrets.