A study of recent computational developments to analyze experimentally determined immunopeptidomes and harness these data to improve our understanding of antigen presentation and MHC binding specificities, and the ability to predict MHC ligands.
Four questions to David Gfeller:
Into what context does your latest review* in Seminars in Immunology fit?
The last 10 years witnessed major advances in MHC ligand identification and prediction, especially through the generation of proteome-scale datasets of peptides displayed on MHC molecules, the co-called immunopeptidomes.
What was the topic and the scope you set yourself going into it?
This review summarizes what we’ve learned about the main properties of immunopeptidomes and how this translated into improved predictions of MHC ligands and T-cell epitopes.
Why pay particular attention to this subject?
Despite recent clinical success in cancer immunotherapy, the set of targets seen by T cells on the surface of cancer cells remains broadly unknown. Efforts to better understand why specific peptides are displayed on the surface of cancer cells and can be targeted by T cells while others are not are key to improve our ability to predict such targets in new patients. These targets can in turn guide personalized cancer immunotherapy treatments.
What are the hypotheses/models that you put forward in this review?
The main hypothesis is that accurate and predictive models describing the set of peptides presented on MHC molecules are very useful to guide predictions of T-cell epitopes.
* Contemplating immunopeptidomes to better predict them - David Gfeller, Yan Liu, Julien Racle.