Lectures and seminars Prof. David Gfeller (Uni Lausanne): Contemplating immunopeptidomes to better predict them

T cells orchestrate the adaptive immune response against
pathogens and cancer by recognizing epitopes presented on MHC molecules. The heterogeneity of the MHC peptidome, including the high polymorphism of MHC genes, is influencing TCR repertoires and represents an important challenge towards accurate prediction and identification of T-cell epitopes in different individuals and different species.

Here we generated and curated a dataset of more than a million unique MHC-I and MHC-II ligands identified by mass spectrometry. This enabled us to precisely determine the binding motifs of >200 MHC alleles across human, mouse, cattle and chicken. Analysis of these binding specificities
combined with X-ray crystallography refined our understanding of the
molecular determinants of MHC motifs and revealed alternative binding
modes of MHC ligands. We then developed a machine learning framework to accurately predict binding specificities and ligands of any MHC allele (MixMHC(2)pred), and further integrated TCR recognition into our epitope prediction pipeline (PRIME).

Prospectively applying our tools to SARS-CoV-2 proteins identified several epitopes and TCR sequencing revealed a monoclonal response in effector/memory CD8+ T cells against one of these epitopes with cross-reactivity against the homologous peptides from other coronaviruses.

Overall, our work shows how in depth characterization of MHC motifs can help mapping the targets of T cells and understanding TCR cross-reactivity.