Predicted Cellular Immunity Population Coverage Gaps for SARS-CoV-2 Subunit Vaccines and their Augmentation by Compact Peptide Sets

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.

Preliminary Immunogenicity of a Pan-COVID-19 T Cell Vaccine in HLA-A*02:01 Mice

“…The combined model predicts which HLA molecule displayed a peptide that was observed to be immunogenic in a MIRA experiment, and uses machine learning predictions of peptide display for HLA alleles not observed or peptides not tested in MIRA data. Thus, all eight MHC class I peptides in our vaccine were previously observed to be immunogenic in data from convalescent COVID-19 patients (Liu et al, 2021a;Snyder et al, 2020). We further validated that all peptides are predicted to bind HLA-A*02:01 with high (≤ 50 nM) affinity using the NetMHCpan-4.1 (Reynisson et al, 2020) and MHCflurry 2.0 (O'Donnell et al, 2020) machine learning models.…”

“…The ultimate clinical implications of these findings are at present unclear, but they have motivated a search for vaccination methods that are resilient to new strains of SARS-CoV-2.We hypothesize that an alternative to vaccine-induced antibody-based viral neutralization is the production of a robust cellular immune response to protect an individual against symptomatic SARS-CoV-2 infection. We have proposed T cell vaccines that are predicted to produce both CD8 + and CD4 + T cell responses (Liu et al, 2020(Liu et al, , 2021a, and here we present the initial immunogenic evaluation of such a vaccine in a HLA-A*02:01 human transgenic mouse model.…”

“…We have proposed T cell vaccines that are predicted to produce both CD8 + and CD4 + T cell responses (Liu et al, 2020(Liu et al, , 2021a, and here we present the initial immunogenic evaluation of such a vaccine in a HLA-A*02:01 human transgenic mouse model.…”

Influence of HLA class II polymorphism on predicted cellular immunity against SARS-CoV-2 at the population and individual level

“…To validate the computational models we analysed publicly available datasets of experimentally determined, immunogenic SARS-CoV-2 peptides. We focused on the two largest datasets recently described by Snyder et al 22, 23 (336 HLA class II peptides) and by Mateus et al 24 (135 HLA class II peptides) that contain peptides from the entire SARS-CoV-2 proteome. We also examined two relatively small datasets encompassing 9 nucleocapsid and 25 structural protein-derived (Spike, nucleocapsid or membrane) peptides 25, 26 .…”

“…Recent computational studies investigating SARS-CoV-2 vaccine immunogenicity have based their approach for T-cell epitope selection exclusively on peptide-HLA binding affinity incorporating different thresholds (e.g. 500nM or 50nM) and identified population coverage gaps in predicted cellular immunity 15, 62, 63, 64 . This approach is affected by inherent bias of certain HLA molecules towards higher or lower mean predicted affinities; thus, we show that the 50nM binding affinity threshold, one of the most commonly used, is heavily biased towards HLA-DR as the main SARS-CoV-2 peptide presenting locus with the majority of HLA-DQ and -DP molecules showing no peptide binding.…”