A structural basis of T cell cross-reactivity to native and spliced self-antigens presented by HLA-DQ8.

Type 1 diabetes (T1D) is a T cell-mediated autoimmune disease that has a strong HLA association, where a number of self-epitopes have been implicated in disease pathogenesis. Human pancreatic islet-infiltrating CD4+ T cell clones not only respond to proinsulin C-peptide (PI40-54; GQVELGGGPGAGSLQ) but also cross-react with a hybrid insulin peptide (HIP; PI40-47-IAPP74-80; GQVELGGG-NAVEVLK) presented by HLA-DQ8. How T cell receptors recognize self-peptide and cross-react to HIPs is unclear. We investigated the cross-reactivity of the CD4+ T cell clones reactive to native PI40-54 epitope and multiple HIPs fused at the same N-terminus (PI40-54) to the degradation products of two highly expressed pancreatic islet proteins, neuropeptide Y (NPY68-74) and amyloid polypeptide (IAPP23-29 and IAPP74-80). We observed that five out of the seven selected SKW3 T cell lines expressing TCRs isolated from CD4+ T cells of people with T1D responded to multiple HIPs. Despite shared TRAV26-1-TRBV5-1 gene usage in some T cells, these clones cross-reacted to varying degrees with the PI40-54 and HIP epitopes. Crystal structures of two TRAV26-1+-TRBV5-1+ T cell receptors (TCRs) in complex with PI40-54 and HIPs bound to HLA-DQ8 revealed that the two TCRs had distinct mechanisms responsible for their differential recognition of the PI40-54 and HIP epitopes. Alanine scanning mutagenesis of the PI40-54 and HIPs determined that the P2, P7, and P8 residues in these epitopes were key determinants of TCR specificity. Accordingly, we provide a molecular basis for cross-reactivity towards native insulin and HIP epitopes presented by HLA-DQ8.

A structural-based machine learning method to classify binding affinities between TCR and peptide-MHC complexes.

The activation of T cells is triggered by the interactions of T cell receptors (TCRs) with their epitopes, which are peptides presented by major histocompatibility complex (MHC) on the surfaces of antigen presenting cells (APC). While each TCR can only recognize a specific subset from a large repertoire of peptide-MHC (pMHC) complexes, it is very often that peptides in this subset share little sequence similarity. This is known as the specificity and cross-reactivity of T cells, respectively. The binding affinities between different types of TCRs and pMHC are the major driving force to shape this specificity and cross-reactivity in T cell recognition. The binding affinities, furthermore, are determined by the sequence and structural properties at the interfaces between TCRs and pMHC. Fortunately, a wealth of data on binding and structures of TCR-pMHC interactions becomes publicly accessible in online resources, which offers us the opportunity to develop a random forest classifier for predicting the binding affinities between TCR and pMHC based on the structure of their complexes. Specifically, the structure and sequence of a given complex were projected onto a high-dimensional feature space as the input of the classifier, which was then trained by a large-scale benchmark dataset. Based on the cross-validation results, we found that our machine learning model can predict if the binding affinity of a given TCR-pMHC complex is stronger or weaker than a predefined threshold with an overall accuracy approximately around 75 %. The significance of our prediction was estimated by statistical analysis. Moreover, more than 60 % of binding affinities in the ATLAS database can be successfully classified into groups within the range of 2 kcal/mol. Additionally, we show that TCR-pMHC complexes with strong binding affinity prefer hydrophobic interactions between amino acids with large aromatic rings instead of electrostatic interactions. Our results therefore provide insights to design engineered TCRs which enhance the specificity for their targeted epitopes. Taken together, this method can serve as a useful addition to a suite of existing approaches which study binding between TCR and pMHC.

TCR/pMHC Interaction: Phenotypic Model for an Unsolved Enigma.

TCR-pMHC interaction is the keystone of the adaptive immune response. This process exhibits an impressive capacity of speed, sensitivity, and discrimination that allows detecting foreign pMHCs at very low concentration among much more abundant self-pMHC ligands. However, and despite over three decades of intensive research, the mechanisms by which this remarkable discrimination and sensitivity is attained remain controversial. In kinetic proofreading mechanisms (KPR), an increase of specificity occurs by reducing the sensitivity. To overcome this difficulty, more elaborate models including feedback processes or induced rebinding have been incorporated into the KPR scheme. Here a new approach based on the assumption that the proofreading chain behaves differently for foreign- and self-pMHC complexes has been integrated into a phenotypic model in which the complexes responsible for T cell activation stabilize (for foreign peptides) or weaken (for foreign peptides), resulting in a dramatic increase in sensitivity and specificity. Stabilization and destabilization of complexes may be caused by conformational changes, rebinding, or any other process leading to variations in the dissociation rate constants of the complexes transmitting the activation. The numerical solution and the analytical expression for the steady-state response as a function of koff(i) (i = 0, 1, …, N, where C0, C1, …, CN are the complexes in the proofreading chain) are provided. The activation chain speeds up, and larger increases in sensitivity and discrimination are obtained if the rate of activation along the proofreading chain increases for foreign pMHCs and decreases for self-ligands. Experimental implications and comparison with current models are discussed.