A unified cross-attention model for predicting antigen binding specificity to both HLA and TCR molecules
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TCR catch bonds nonlinearly control CD8 cooperation to shape T cell specificity
Naturally evolved T-cell receptors (TCRs) exhibit remarkably high specificity in discriminating non-self antigens from self-antigens under dynamic biomechanical modulation. In contrast, engineered high-affinity TCRs often lose this specificity, leading to cross-reactivity with self-antigens and off-target toxicity. The underlying mechanism for this difference remains unclear. Our study reveals that natural TCRs exploit mechanical force to form optimal catch bonds with their cognate antigens. This process relies on a mechanically flexible TCR–pMHC binding interface, which enables force-enhanced CD8 coreceptor binding to MHC-α1α2 domains through sequential conformational changes induced by force in both the MHC and CD8. Conversely, engineered high-affinity TCRs create rigid, tightly bound interfaces with cognate pMHCs of their parental TCRs. This rigidity prevents the force-induced conformational changes necessary for optimal catch-bond formation. Paradoxically, these high-affinity TCRs can form moderate catch bonds with non-stimulatory pMHCs of their parental TCRs, leading to off-target cross-reactivity and reduced specificity. We have also developed comprehensive force-dependent TCR–pMHC kinetics-function maps capable of distinguishing functional and non-functional TCR–pMHC pairs and identifying toxic, cross-reactive TCRs. These findings elucidate the mechano-chemical basis of the specificity of natural TCRs and highlight the critical role of CD8 in targeting cognate antigens. This work provides valuable insights for engineering TCRs with enhanced specificity and potency against non-self antigens, particularly for applications in cancer immunotherapy and infectious disease treatment, while minimizing the risk of self-antigen cross-reactivity.
Deep learning enhances the prediction of HLA class I-presented CD8+ T cell epitopes in foreign pathogens
Accurate in silico determination of CD8+ T cell epitopes would greatly enhance T cell-based vaccine development, but current prediction models are not reliably successful. Here, motivated by recent successes applying machine learning to complex biology, we curated a dataset of 651,237 unique human leukocyte antigen class I (HLA-I) ligands and developed MUNIS, a deep learning model that identifies peptides presented by HLA-I alleles. MUNIS shows improved performance compared with existing models in predicting peptide presentation and CD8+ T cell epitope immunodominance hierarchies. Moreover, application of MUNIS to proteins from Epstein–Barr virus led to successful identification of both established and novel HLA-I epitopes which were experimentally validated by in vitro HLA-I-peptide stability and T cell immunogenicity assays. MUNIS performs comparably to an experimental stability assay in terms of immunogenicity prediction, suggesting that deep learning can reduce experimental burden and accelerate identification of CD8+ T cell epitopes for rapid T cell vaccine development.
T-cell receptor structures and predictive models reveal comparable alpha and beta chain structural diversity despite differing genetic complexity
T-cell receptor (TCR) structures are currently under-utilised in early-stage drug discovery and repertoire-scale informatics. Here, we leverage a large dataset of solved TCR structures from Immunocore to evaluate the current state-of-the-art for TCR structure prediction, and identify which regions of the TCR remain challenging to model. Through clustering analyses and the training of a TCR-specific model capable of large-scale structure prediction, we find that the alpha chain VJ-recombined loop (CDR3α) is as structurally diverse and correspondingly difficult to predict as the beta chain VDJ-recombined loop (CDR3β). This differentiates TCR variable domain loops from the genetically analogous antibody loops and supports the conjecture that both TCR alpha and beta chains are deterministic of antigen specificity. We hypothesise that the larger number of alpha chain joining genes compared to beta chain joining genes compensates for the lack of a diversity gene segment. We also provide over 1.5M predicted TCR structures to enable repertoire structural analysis and elucidate strategies towards improving the accuracy of future TCR structure predictors. Our observations reinforce the importance of paired TCR sequence information and capture the current state-of-the-art for TCR structure prediction, while our model and 1.5M structure predictions enable the use of structural TCR information at an unprecedented scale.
Immunotherapy targeting a leader sequence cathepsin G-derived peptide
Myeloid azurophil granules provide a rich source of intracellular leukemia antigens. Cathepsin G (CG) is a serine protease that has higher expression in acute myeloid leukemia (AML) blasts in comparison to normal myeloid progenitors. Based on the unique biology of HLA-A*0201 (HLA-A2), in which presentation of leader sequence (LS)-derived peptides is favored, we focused on the LS-CG-derived peptide CG1 (FLLPTGAEA). We previously detected CG1/HLA-A2 complexes on the surface of primary HLA-A2+ AML blasts and cell lines, and immunity targeting CG1/HLA-A2 in leukemia patients. T cell receptor (TCR)-mimic (m) antibodies are immunotherapeutic antibodies that target peptide-HLA (pHLA) complexes. Here we report on the engineering, preclinical efficacy, and safety evaluation of a novel CG1/HLA-A2-targeting, T cell-engager, bispecific antibody (CG1/A2xCD3). CG1/A2xCD3 showed high binding affinity to CG1/HLA-A2 monomers, CD3-Fc fusion protein, and to AML and T cells, with potent killing of HLA-A2+ primary AML and cell lines in vitro and in vivo. This correlated with both tumor- and CG1/A2xCD3-dependent T cell activation and cytokine secretion. Lastly, CG1/A2xCD3 had no activity against normal bone marrow. Together, these results support the targeting of LS-derived peptides and the continued clinical development of CG1/A2xCD3 in the setting of AML.
γδ T-cell autoresponses to ectopic membrane proteins: a new type of pattern recognition
T-cell receptor (TCR) γδ-expressing cells are conserved lymphocytes of innate immunity involved in first-line defense and immune surveillance. TCRγδ recognizes protein/nonprotein ligands without the help of the major histocompatibility complex (MHC), especially via direct binding to protein ligands, which is dependent primarily on the δ chain complementary determining region 3 (CDR3δ). However, the mechanism of protein‒antigen recognition by human γδ TCRs remains poorly defined. We hypothesize that γδ TCRs recognize self-proteins expressed ectopically on the cell membrane that are derived from intracellular components under stress. Here, we mapped 16 intercellular self-proteins among 21,000 proteins with a huProteinChip as putative ligands for Vδ1/Vδ2 TCRs, 13 for Vδ1 TCRs and 3 for Vδ2 TCRs. Functional tests confirmed that ectopic nucleolin (NCL) is a ligand for the Vδ1 TCR, whereas protein-glutamine γ-glutamyltransferase K (TGM1) is a ligand for the Vδ2 TCR. In the context of radiation exposure, the ectopic expression of intracellular proteins on the tumor cell surface is related to the increased antitumor cytotoxicity of γδ T cells both in vitro and in vivo. In conclusion, the recognition of intracellular proteins that are ectopically expressed on somatic cells by human γδ TCRs is a basic interaction mechanism that enables new types of immune pattern recognition and a novel γδ TCR-ligand-based strategy for tumor immunotherapy.
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