A highly desirable outcome in the study of infectious disease is the rational design of vaccines capable of eliciting cross-reactive, broadly neutralizing antibodies (bnAbs) for highly mutable pathogens, such as HIV and influenza, that have, so far, defied vaccination. Understanding and controlling the adaptive immune response is critical in enabling protection against both emerging infectious diseases and other diverse pathologies, including cancer. However, this complex problem demands the use of modeling and computation to link isolated molecular measurements to mechanisms that successfully predict organism level immune response.
In this project, we explored if bnAbs can be designed by a computational approach to provide vaccines for highly mutable pathogens. We developed a novel, multi-scale computational model of the affinity maturation process within a germinal center to predict the emergence of antibodies. This approach used a spatially-resolved, stochastic, agent-based model framework. Antigen internalization probabilities were initially estimated with a statistical potential. We then developed a faster method for performing solvated molecular dynamics simulations to further check the probabilities using a large number of free-energy calculations to assess feasibility. New protocols have been developed for calculating the affinity of antibodies and antigens.
This research supported and leveraged Lawrence Livermore National Laboratory's core competencies in bioscience as well as high-performance computing, simulation, and data science. The results directly address the Laboratory's biosecurity mission area and demonstrate Livermore's commitment to provide innovative science and technology to the Department of Defense.
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