Decoding T Cell Receptor Cross-Reactivity

Although specificity is considered a hallmark of adaptive immunity, the vast universe of potential peptides that can be presented by an MHC protein makes T cell and TCR cross-reactivity a necessity. It has been estimated that, for a functioning immune system, any individual TCR must be able to productively recognize at least one million different peptide-MHC complexes. TCR structural adaptability, the flexibility of the peptide and MHC, and molecular mimicry can all contribute to cross-reactivity. This complexity makes it difficult to predict the cross-reactivity of TCRs from structural (much less sequence) information. Access to more detailed data about what kind of ligands any individual TCR prefers and how this relates to structural and sequence data is needed to better understand cross-reactivity and specificity.
Along with techniques such as yeast or mammalian cell display, combinatorial and positional scanning libraries (PSLs) can be used for investigating the peptides compatible with a given TCR. Analyzing results from co-culture experiments performed with positional scanning libraries allows for identification of amino acid substitutions in the peptide that are acceptable for a given TCR. Scoring these results allows for further investigation into more unique peptides that can be recognized. The recognition of a new index peptide can then be examined via another PSL. This analysis can be performed for multiple rounds, interrogating the landscape of possible peptides a given TCR can and cannot recognize.
The results presented here underscore the effectiveness of this iterative extended PSL (ePSL) approach for mapping the cross-reactivity profiles of TCRs, enabling the identification of distinct peptides with substantial divergence from the cognate sequence. By applying the ePSL with structurally well-characterized systems, we can generate datasets indicating which peptides are and are not recognized that can be directly integrated with structural and biophysical analyses. This thorough approach enhances our understanding of TCR specificity and cross-reactivity and establishes a framework for future studies aimed at predicting TCR cross-reactivity and informing the rational design of TCR-based therapeutics.