Among various vaccination strategies, peptide-based vaccines appear as excellent candidates because they are cheap to produce, are highly stable and harbor low toxicity MESHD
. However, predicting which MHC-I Associated Peptide (MAP) will ultimately reach cell surface remains challenging, due to high false discovery rates. Previously, we demonstrated that synonymous codon arrangement (usage and placement) is predictive of, and modulates MAP presentation. Here, we apply CAMAP (Codon Arrangement MAP Predictor), the artificial neural network we used to unveil the role of codon arrangement in MAP presentation, to predict SARS-CoV MAPs MESHD
. We report that experimentally identified SARS-CoV-1 and SARS-CoV-2 MAPs MESHD
are associated with significantly higher CAMAP scores. Based on CAMAP scores and binding affinity, we identified 48 non-overlapping MAP candidates for a peptide-based vaccine, ensuring coverage for a high proportion of HLA haplotypes in the US population (>78%) and SARS-CoV-2 strains (detected in >98% of SARS-CoV-2 strains present in the GISAID database). Finally, we built an interactive web portal (https://www.epitopes.world) where researchers can freely explore CAMAP predictions for SARS-CoV-1/2 viruses. Collectively, we present an analysis framework that can be generalizable to empower the rapid identification of virus-specific MAPs, including in the context of an emergent virus, to help accelerate target identification for peptide-based vaccine designs that could be critical in safely attaining group immunity in the context of a global pandemic.