Jonathan Heeney of the University of Cambridge in the United Kingdom will combine computational design with high-throughput synthetic biology to deliver an effective, universal influenza vaccine candidate for clinical trials in 24 months. Influenza infection impacts public health and the global economy, yet the high mutation rate of the virus has thwarted traditional approaches to develop a broadly effective vaccine. To address this, they developed a Digital Immune Optimized and selected Synthetic Vaccine (DioSynVax) technology platform, which has been successfully used for hemorrhagic fever vaccine development. They will now apply it to influenza vaccine development, by first analyzing structural and antibody-binding data to identify highly conserved antigens from three essential viral proteins: M2, neuraminidase (NA), and hemagglutinin (HA), which will ultimately result in broader and longer-lasting immune protection. These data will then be used to generate libraries of synthetic antigens for high-throughput screening to identify those that bind the most strongly to panels of broadly reactive monoclonal antibodies, and a subset will be tested for their ability to induce an antibody response in mice. The most promising candidate for each of the three viral proteins will then be combined and tested as a vaccine for protection against multiple influenza strains in ferrets.