Artificial intelligence-based prediction technologies have allowed definition of T-cell epitopes presented by Major Histocompatibility Complex (MHC) molecules with allele-specificity of presentation. While some have utilized these technologies on a smaller scale, recent work has expanded the workable proteome size, leveraged both classes of Major Histocompatibility (MHC) molecules, extended the range of host species assessed during comparative analysis, and incorporated pathogen genetic diversity to highlight broadly useful epitopes. A recent study focused on the zoonotic pathogen Coxiella burnetii exemplifying themes and possibilities for future analyses. These data suggest an expanding role for epitope prediction in rational vaccine design for a very broad range of pathogen and host systems.
T-cell epitope, Machine-learning, Artificial intelligence, Major histocompatibility complex, Proteome-wide