Abstract

Vaccination is the key component to overcoming the global COVID-19 pandemic. Peptide vaccines present a safe and cost-efficient alternative to traditional vaccines. They are rapidly produced and adapted for new viral variants. The vaccine's efficacy essentially depends on two components: the peptides included in the vaccine and the ability of major histocompatibility complex (MHC) molecules to bind and present these peptides to cells of the immune system. Due to the high diversity of MHC alleles and their diverging specificities in binding peptides, choosing a set of peptides that maximizes population coverage is a challenging task. Further, peptide vaccines are limited in their size allowing only for a small set of peptides to be included. Thus, they might fail to immunize a large part of the human population or protect against upcoming viral variants. Here, we present HOGVAX, a combinatorial optimization approach to select peptides that maximize population coverage. Furthermore, we exploit overlaps between peptide sequences to include a large number of peptides in a limited space and thereby also cover rare MHC alleles. We model this task as a theoretical problem, which we call the Maximal Scoring k-Superstring Problem. Additionally, HOGVAX is able to consider haplotype frequencies to take linkage disequilibrium between MHC loci into account. Our vaccine formulations contain significantly more peptides compared to vaccine sequences built from concatenated peptides. We predicted over 98% population coverage for our vaccine candidates of MHC class I and II based on single-allele and haplotype frequencies. Moreover, we predicted high numbers of per-individual presented peptides leading to a robust immunity in the face of new virus variants.

Execute HOGVAX

Stand-alone version

Open a shell in the HOGVAX folder and create the conda environment from the yaml file for proper execution of HOGVAX.

conda env create -f hogvax_env.yaml
conda activate hogvax_env

To execute HOGVAX, call the python script with the necessary arguments, see the example arguments.

python hogvax.py [arguments]