In our study, we selected the PSSMs obtained using the SMM PMBEC (Stabilized Matrix Method, Peptide:MHC Binding Energy Covariance) algorithm , and available on the Immune Epitope Database (IEDB) . Despite being simple lookup tables, a recent study demonstrated that PSSMs based on the SMM PMBEC algorithm offer accuracy comparable to existing deep learning methods . Moreover, instead of being limited to a preexisting set of peptide sequences, this allowed us to virtually score any arbitrary peptide sequences from length 8 to 11. We used the PSSMs for the human MHC class I A*0201 allele (only for 8-, 9-, 10-, and 11-mers peptides) for SOO and the A*0101 and A*2602 alleles (only for 9-mers peptides) for MOO, as they show opposite sequence binding profiles. The predictions using these PSSMs are expressed in log 10 (IC50 nM) (pIC50), where a predicted pIC50 of 0.0 corresponds to an IC50 of 1 nM and a predicted pIC50 of 3 corresponds to an IC50 of 1 µM.