Improved predictions of antigen presentation and TCR recognition with MixMHCpred2.2 and PRIME2.0 reveal potent SARS-CoV-2 CD8+ T-cell epitopes. Gfeller et al

Published: 28 November 2022| Version 1 | DOI: 10.17632/2kmmjp4tmm.1
David Gfeller


Supplementary Tables. Table S1: List of HLA-I peptidomics samples considered in this work, related to Figure 1. Table S2: List of HLA-I ligands used to train MixMHCpred2.2, related to Figure 2. Table S3: List of peptides including both HLA-I ligands (i.e., positives) and random peptides (i.e., negatives) used to benchmark MixMHCpred2.2, related to Figure 2. The HLA-I ligands in the first ten datasets come from Gfeller et al. The HLA-I ligands in the last eleven datasets come from Pyke et al. Table S4: List of immunogenic and non-immunogenic peptides used to train PRIME2.0, related to Figure 3. Table S5: Information about SARS-CoV-2 peptides and donor samples used in T-cell assays, related to Figure 4. (A) List of 213 peptides from the SARS-CoV-2 proteome included in the peptide pool. (B) List of the 15 most common HLA-I alleles used to make predictions. (C) Information about the six donors used to screen SARS-CoV-2 peptides for immunogenicity. Table S6: List of TCR sequences and UMI counts (alpha and beta chains) obtained from CD8+ T cells recognizing seven SARS-CoV-2 epitopes, related to Figure 4.



Universite de Lausanne


Antigen Presentation, Epitope, Computational Biology, COVID-19


Swiss Cancer Research Foundation