Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.04.200691v1?rss=1 Authors: Liu, G., Carter, B., Gifford, D. K. Abstract: Subunit vaccines induce immunity to a pathogen by presenting a component of the pathogen and thus inherently limit the representation of pathogen peptides for cellular immunity based memory. We find that SARS-CoV-2 subunit peptides may not be robustly displayed by the Major Histocompatibility Complex (MHC) molecules in certain individuals. We introduce an augmentation strategy for subunit vaccines that adds a small number of peptides to a vaccine to improve the population coverage of pathogen peptide display. We augment a subunit vaccine by selecting additional pathogen peptides to maximize the total number of vaccine peptide hits against the distribution of MHC haplotypes in a population. For each subunit we design independent MHC class I and MHC class II peptide sets for augmentation, and alternatively design a combined set of peptides for MHC class I and class II display. We evaluate the population coverage of 9 different subunits of SARS-CoV-2, including 5 functional domains and 4 full proteins, and augment each of them to fill a predicted coverage gap. We predict that a SARS-CoV-2 receptor binding domain subunit vaccine will have fewer than six peptide-HLA hits with [≤] 50 nM binding affinity per individual in 51.31% (class I) and 32.99% (class II) of the population, and with augmentation, the uncovered population is predicted to be reduced to 0.54% (class I) and 1.46% (class II). We find that a joint set of pathogen peptides for MHC class I and class II display is predicted to produce a more compact vaccine design than using independent sets for MHC class I and class II. We provide an open source implementation of our design methods (OptiVax), vaccine evaluation tool (EvalVax), as well as the data used in our design efforts here: https://github.com/gifford-lab/optivax/tree/master/augmentation. Copy rights belong to original authors. Visit the link for more info