Martin Karplus of Harvard University in the U.S. will integrate informatics and artificial intelligence approaches to design stable, synthetic antigens based on the viral hemagglutinin (HA) protein to be used as a universal influenza vaccine. Seasonal influenza causes substantial morbidity worldwide, and the development of a universal vaccine is a global health priority. The current HA-based vaccines do not provide broad coverage against multiple strains, and must be administered annually because of the high mutability of the virus. To address this, they will use a new approach to develop vaccines that elicit broadly-neutralizing antibodies effective against many different influenza strains, including avian, swine, and human varieties. First, they will assemble the publicly-available DNA sequences of the HA gene of avian, swine, and human influenza A strains, and use homology modeling with existing protein structures as templates to create a library of antigens that together are predicted to both maintain the immune system’s memory of influenza while enabling a rapid immune response to future seasonal or pandemic strains. The antigen library will be optimized by performing affinity maturation simulations initiated from known germline precursors of broadly-neutralizing antibodies, and combining the results with machine learning, to predict which antigen cocktails will produce antibodies with the greatest breadth of reactivity. The most promising of these immunization cocktails will be efficacy-tested in small animal models.