Funded Projects
Stapled, particle peptides for a universal vaccine
Funded February 2020
Submitted by Jonathan Lovell
Project Team
Jonathan Lovell
Biomedical Engineering
Bruce Davidson
Anesthesiology
Qing Lin
Chemistry
Description
Immunization is the most effective measure to prevent influenza, however, developing an effective universal influenza virus vaccine has proven elusive. Hemagglutinin (HA) is the major surface antigen of the virus. Antibodies against the conserved HA stalk domain are capable of protecting against heterologous and heterosubtypic viruses, and therefore, the HA stalk has become the major target for a universal influenza virus vaccine, which is highly sought after.
The Lovell lab has developed a liposome system that incorporates cobalt-porphyrin-phospholipid (CoPoP) and can spontaneously convert his-tagged peptides and proteins into particles, thereby greatly increasing their immunogenicity (Huang et al., Nature Nanotechnology, 2018). In preliminary data, the Lovell lab has also shown that HA stalk peptides can be combined with the CoPoP vaccine delivery system to induce antibodies that successfully recognize the wildtype HA antigen. However, when the Davidson lab challenged immunized mice with influenza, on multiple occasions, it was found that no protection from the vaccine was conferred. Since it has been reported that HA stalk antibodies can confer protection from influenza, and the CoPoP stalk peptide approach induced antibodies that recognized the wildtype HA antigen, we assume that improvements to the stalk antigen design could result in a favorable vaccine using CoPoP. The Lin lab has experience in making stapled peptides that better retain alpha helical conformation, which is the actual conformation of the HA stalk. In this blue-sky proposal, the Lin lab will produce conserved stapled alpha helical peptides and provide these to the Lovell lab for formulation and mouse immunization. Mice will be challenged with influenza in the Davidson lab and vaccine efficacy will be tested compared to control groups. If successful, this approach will lead to data that will support a future grant submission(s).
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