Machine Learning-Enabled Design of Norovirus and Herpesvirus Vaccines - We Propose a Highly Integrated and…
FY2024 · complete year · data as of 2026-07-03
A single federal award, with both ends of the trail linked.
Award AY1AX000036 is a grant from the Department of Health and Human Services to University of Washington, $30.8M obligated, FY2024 to FY2029, for Machine Learning-Enabled Design of Norovirus and Herpesvirus Vaccines - We Propose a Highly…. The work is performed in Washington. Source
Description
Machine Learning-Enabled Design of Norovirus and Herpesvirus Vaccines - We Propose a Highly Integrated and Synergistic Program to Develop Universal Vaccine Design Approaches That Can Be Broadly Applied to Viral and Bacterial Pathogens. We Will Demonstrate the Validity of Our Approaches by Developing Vaccines for Three Human Herpesviruses That Have Remained Out of Reach Despite Efforts by Many Vaccine Developers: HSV-1, HSV-2, and Ebv. Herpesviruses Are Responsible for Substantial Morbidity and Mortality Worldwide, and Several of Them Are Strongly Linked to Chronic Illnesses Including Cancer and Neurodegenerative Diseases. Despite Their Significant Public Health Burden, There Are No Licensed Vaccines for Herpesviruses Other Than for Varicella-Zoster Virus, Which Use Approaches That Cannot Be Generalized to Other Family Members. The Failure of Earlier Vaccine Development Efforts Is Due to a Lack of Stabilized Antigens and Compatible Display Platforms, an Incomplete Understanding of the Mechanisms of Protection, and Poor Induction of Mucosal Immunity, Which May Be Required for Protection. A Distinguishing Feature of Our Program Is the Development and Application of Innovative Artificial Intelligence (Ai)-Based Computational Methods for Antigen and Protein Nanoparticle Vaccine Design. The Baker, Dimaio, King, Lee, and Veesler Labs at the University of Washington Will Integrate These Computational and Structural Methods Into General Workflows for Universal Vaccine Design and Evaluation That Can Be Applied to Many Families of Viral and Bacterial Pathogens. We Will Combine This Ability with Deep Disease-Specific Expertise and High-Throughput Pipelines for Vaccine Production and Evaluation, Harnessing Recent Innovations in Mrna Vaccine Manufacturing to Iterate Between the Computer and the Lab to Improve Vaccine Performance. The Bloom Lab at the Fred Hutchinson Cancer Center (Fred Hutch) Will Use Analyses of Herpesvirus Evolution and High-Throughput Deep Mutational Scanning to Inform Vaccine Design and Evaluation. The Georgiou and Lavinder Group at the University of Texas at Austin Are Experts in B Cell Immunology and Antibody Repertoire Analyses and Will Comprehensively Characterize Humoral Responses to Herpesvirus Infection and Vaccination. The Leib Lab at Dartmouth and the Lund and Mcguire Labs at the Fred Hutch Are Experts in Evaluating Novel Herpesvirus Vaccine Candidates and Will Use Key Animal Models Established in Their Laboratories to Develop Workflows That Enable the Rapid and Comprehensive Evaluation of Immune Responses Elicited by Our Program’s Novel Ai-Designed Vaccines. Our Program Will Define the Determinants of Protective Immunity Against Herpesvirus Infection, Identify Universal Vaccine Design Approaches and Computational Design Methodologies That Can Be Applied Across Diverse Viral Families, and Clinically DE-Risk Vaccine Candidates for Three Herpesviruses with Major Global Health Burdens. We Emphasize That the Integrated Ai-Based Vaccine Design Approaches and Experimental Workflows Developed as Part of This Program Will Broadly Generalize to Other Vaccine Design Challenges in the Future.