Nicholas Hayden: No financial relationships to disclose
Introduction/Rationale: Each T cell is able to recognize multiple unique peptides presented on MHC molecules in a phenomenon referred to as Cross-Reactivity. Cross-reactivity is important in heterologous immunity and autoimmunity and can be both beneficial and deleterious in both contexts. For example, infection can prime T cells to recognize different pathogens that have similar proteins, which can provide protection against disease, or to recognize similar self-proteins, which can induce autoimmune disease. To elicit cross-reactivity from the same T cell, two peptides need to be chemically similar enough to both bind the same MHC and be recognized by the same T cell receptor; however, in certain instances peptides that appear chemically different can elicit a cross-reactive response.
Methods: We sought out to develop an approach that can predict cross-reactivity using only sequence information about the peptides involved. To develop this approach, we leverage structural information to create an algorithm that emphasizes key epitope residues for homology comparison.
Results: We show that combining TCR-pMHC structural information with machine learning MHC binding predictions provides a reliable method of predicting cross-reactivity. We tested our algorithm using datasets measuring T cell cross-reactivity between viral peptides in cases both where the presenting MHC is known and in cases where it is not known. Our prediction approach, Cross-reactive T cell Epitope Predictor (XTEP), is TCR-sequence independent and MHC agnostic.
Conclusion: Our approach can be used to predict if a T cell epitope will be cross-reactive, which can be important in contexts such as vaccine design. Additionally, XTEP can predict epitopes that can be used to show cross-reactivity is occurring –for example when evaluating similarity between self-epitopes and viral peptides in autoimmunity. Our findings imply this approach is more predictive of cross-reactivity than predicting MHC binding alone or comparing full peptide similarity.
