TCR-pMHC Binding Research Articles

A Computational Strategy for the Rapid Identification and Ranking of Patient-Specific T Cell Receptors Bound to Neoantigens.

T cell receptor (TCR) recognition of a peptide-major histocompatibility complex (pMHC) is crucial for adaptive immune response. The identification of therapeutically relevant TCR-pMHC protein pairs is a bottleneck in the implementation of TCR-based immunotherapies. The ability to computationally design TCRs to target a specific pMHC requires automated integration of next-generation sequencing, protein-protein structure prediction, molecular dynamics, and TCR ranking. A pipeline to evaluate patient-specific, sequence-based TCRs to a target pMHC is presented. Using the three most frequently expressed TCRs from 16 colorectal cancer patients, the protein-protein structure of the TCRs to the target CEA peptide-MHC is predicted using Modeller and ColabFold. TCR-pMHC structures are compared using automated equilibration and successive analysis. ColabFold generated configurations require an ≈2.5× reduction in equilibration time of TCR-pMHC structures compared to Modeller. The structural differences between Modeller and ColabFold are demonstrated by root mean square deviation (≈0.20nm) between clusters of equilibrated configurations, which impact the number of hydrogen bonds and Lennard-Jones contacts between the TCR and pMHC. TCR ranking criteria that may prioritize TCRs for evaluation of in vitro immunogenicity are identified, and this ranking is validated by comparing to state-of-the-art machine learning-based methods trained to predict the probability of TCR-pMHC binding.

Accurate TCR-pMHC interaction prediction using a BERT-based transfer learning method.

Accurate prediction of TCR-pMHC binding is important for the development of cancer immunotherapies, especially TCR-based agents. Existing algorithms often experience diminished performance when dealing with unseen epitopes, primarily due to the complexity in TCR-pMHC recognition patterns and the scarcity of available data for training. We have developed a novel deep learning model, 'TCR Antigen Binding Recognition' based on BERT, named as TABR-BERT. Leveraging BERT's potent representation learning capabilities, TABR-BERT effectively captures essential information regarding TCR-pMHC interactions from TCR sequences, antigen epitope sequences and epitope-MHC binding. By transferring this knowledge to predict TCR-pMHC recognition, TABR-BERT demonstrated better results in benchmark tests than existing methods, particularly for unseen epitopes.

TCR-ESM: Employing protein language embeddings to predict TCR-peptide-MHC binding

Cognate target identification for T-cell receptors (TCRs) is a significant barrier in T-cell therapy development, which may be overcome by accurately predicting TCR interaction with peptide-bound major histocompatibility complex (pMHC). In this study, we have employed peptide embeddings learned from a large protein language model- Evolutionary Scale Modeling (ESM), to predict TCR-pMHC binding. The TCR-ESM model presented outperforms existing predictors. The complementarity-determining region 3 (CDR3) of the hypervariable TCR is located at the center of the paratope and plays a crucial role in peptide recognition. TCR-ESM trained on paired TCR data with both CDR3α and CDR3β chain information performs significantly better than those trained on data with only CDR3β, suggesting that both TCR chains contribute to specificity, the relative importance however depends on the specific peptide-MHC targeted. The study illuminates the importance of MHC information in TCR-peptide binding which remained inconclusive so far and was thought dependent on the dataset characteristics. TCR-ESM outperforms existing approaches on external datasets, suggesting generalizability. Overall, the potential of deep learning for predicting TCR-pMHC interactions and improving the understanding of factors driving TCR specificity are highlighted. The prediction model is available at http://tcresm.dhanjal-lab.iiitd.edu.in/ as an online tool.

Overall avidity declines in TCR repertoires during latent CMV but not EBV infection.

The avidity of the T-cell receptor (TCR) for antigenic peptides presented by the MHC (pMHC) on cells is an essential parameter for efficient T cell-mediated immunity. Yet, whether the TCR-ligand avidity can drive the clonal evolution of virus antigen-specific CD8 T cells, and how this process is determined in latent Cytomegalovirus (CMV)- against Epstein-Barr virus (EBV)-mediated infection remains largely unknown. To address these issues, we quantified monomeric TCR-pMHC dissociation rates on CMV- and EBV-specific individual TCRαβ clonotypes and polyclonal CD8 T cell populations in healthy donors over a follow-up time of 15-18 years. The parameters involved during the long-term persistence of virus-specific T cell clonotypes were further evaluated by gene expression profiling, phenotype and functional analyses. Within CMV/pp65-specific T cell repertoires, a progressive contraction of clonotypes with high TCR-pMHC avidity and low CD8 binding dependency was observed, leading to an overall avidity decline during long-term antigen exposure. We identified a unique transcriptional signature preferentially expressed by high-avidity CMV/pp65-specific T cell clonotypes, including the inhibitory receptor LILRB1. Interestingly, T cell clonotypes of high-avidity showed higher LILRB1 expression than the low-avidity ones and LILRB1 blockade moderately increased T cell proliferation. Similar findings were made for CD8 T cell repertoires specific for the CMV/IE-1 epitope. There was a gradual in vivo loss of high-avidity T cells with time for both CMV specificities, corresponding to virus-specific CD8 T cells expressing enhanced LILRB1 levels. In sharp contrast, the EBV/BMFL1-specific T cell clonal composition and distribution, once established, displayed an exceptional stability, unrelated to TCR-pMHC binding avidity or LILRB1 expression. These findings reveal an overall long-term avidity decline of CMV- but not EBV-specific T cell clonal repertoires, highlighting the differing role played by TCR-ligand avidity over the course of these two latent herpesvirus infections. Our data further suggest that the inhibitor receptor LILRB1 potentially restricts the clonal expansion of high-avidity CMV-specific T cell clonotypes during latent infection. We propose that the mechanisms regulating the long-term outcome of CMV- and EBV-specific memory CD8 T cell clonotypes in humans are distinct.

Contact map dependence of a T-cell receptor binding repertoire.

The adaptive immune system protects the host from pathogens by discriminating between host and foreign signatures. T cells of the adaptive immune system undergo training to avoid recognition of self-antigenic signatures displayed as peptides bound to the major histocompatibility complex (pMHC) by their T-cell receptors (TCRs). Similarly, T cell responses are directed toward TCRs recognizing non-self pMHC. Reliable prediction of relevant TCR-pMHC recognition is centrally important for understanding adaptive immunity predicting optimized infections and cancer, but currently prohibited by the theoretical diversity of antigens (∼1020) and TCR signatures. We study an affinity-based model for TCR-pMHC binding that uses a crystal structure to inform a distance-based contact map that weights the pairwise amino acid interactions between TCR and pMHC. We find that the TCR-pMHC binding energy distribution depends on the number of contacts and the topology of the contact map of choice; this impacts T-cell negative selection outcome, with higher T-cell recognition probability at higher variances. In addition, we quantify the degree to which neoantigens with mutations in sites with higher contacts are recognized at a higher rate.

Naive and memory T cells TCR-HLA-binding prediction.

T cells recognize antigens through the interaction of their T cell receptor (TCR) with a peptide-major histocompatibility complex (pMHC) molecule. Following thymic-positive selection, TCRs in peripheral naive T cells are expected to bind MHC alleles of the host. Peripheral clonal selection is expected to further increase the frequency of antigen-specific TCRs that bind to the host MHC alleles. To check for a systematic preference for MHC-binding T cellsin TCR repertoires, we developed Natural Language Processing-based methods to predict TCR-MHC binding independently of the peptide presented for Class I MHC alleles. We trained a classifier on published TCR-pMHC binding pairs and obtained a high area under curve (AUC) of over 0.90 on the test set. However, when applied to TCR repertoires, the accuracy of the classifier dropped. We thus developed a two-stage prediction model, based on large-scale naive and memory TCR repertoires, denoted TCR HLA-binding predictor (CLAIRE). Since each host carries multiple human leukocyte antigen (HLA) alleles, we first computed whether a TCR on a CD8 T cell binds an MHC from any of the host Class-I HLA alleles. We then performed an iteration, where we predict the binding with the most probable allele from the first round. We show that this classifier is more precise for memory than for naïve cells. Moreover, it can be transferred between datasets. Finally, we developed a CD4-CD8 T cell classifier to apply CLAIRE to unsorted bulk sequencing datasets and showed a high AUC of 0.96 and 0.90 on large datasets. CLAIRE is available through a GitHub at: https://github.com/louzounlab/CLAIRE, and as a server at: https://claire.math.biu.ac.il/Home.

Fluctuations in T cell receptor and pMHC interactions regulate T cell activation.

Adaptive immune responses depend on interactions between T cell receptors (TCRs) and peptide major histocompatibility complex (pMHC) ligands located on the surface of T cells and antigen presenting cells (APCs), respectively. As TCRs and pMHCs are often only present at low copy numbers their interactions are inherently stochastic, yet the role of stochastic fluctuations on T cell function is unclear. Here, we introduce a minimal stochastic model of T cell activation that accounts for serial TCR-pMHC engagement, reversible TCR conformational change and TCR aggregation. Analysis of this model indicates that it is not the strength of binding between the T cell and the APC cell per se that elicits an immune response, but rather the information imparted to the T cell from the encounter, as assessed by the entropy rate of the TCR-pMHC binding dynamics. This view provides an information-theoretic interpretation of T cell activation that explains a range of experimental observations. Based on this analysis, we propose that effective T cell therapeutics may be enhanced by optimizing the inherent stochasticity of TCR-pMHC binding dynamics.

Deep learning-based prediction of the T cell receptor-antigen binding specificity.

Neoantigens play a key role in the recognition of tumor cells by T cells. However, only a small proportion of neoantigens truly elicit T cell responses, and fewer clues exist as to which neoantigens are recognized by which T cell receptors (TCRs). We built a transfer learning-based model, named pMHC-TCR binding prediction network (pMTnet), to predict TCR-binding specificities of neoantigens, and T cell antigens in general, presented by class I major histocompatibility complexes (pMHCs). pMTnet was comprehensively validated by a series of analyses, and showed advance over previous work by a large margin. By applying pMTnet in human tumor genomics data, we discovered that neoantigens were generally more immunogenic than self-antigens, but HERV-E, a special type of self-antigen that is re-activated in kidney cancer, is more immunogenic than neoantigens. We further discovered that patients with more clonally expanded T cells exhibiting better affinity against truncal, rather than subclonal, neoantigens, had more favorable prognosis and treatment response to immunotherapy, in melanoma and lung cancer but not in kidney cancer. Predicting TCR-neoantigen/antigen pairs is one of the most daunting challenges in modern immunology. However, we achieved an accurate prediction of the pairing only using the TCR sequence (CDR3β), antigen sequence, and class I MHC allele, and our work revealed unique insights into the interactions of TCRs and pMHCs in human tumors using pMTnet as a discovery tool.

Single-Molecule, Super-Resolution, and Functional Analysis of G Protein-Coupled Receptor Behavior Within the T Cell Immunological Synapse.

A central process in immunity is the activation of T cells through interaction of T cell receptors (TCRs) with agonistic peptide-major histocompatibility complexes (pMHC) on the surface of antigen presenting cells (APCs). TCR-pMHC binding triggers the formation of an extensive contact between the two cells termed the immunological synapse, which acts as a platform for integration of multiple signals determining cellular outcomes, including those from multiple co-stimulatory/inhibitory receptors. Contributors to this include a number of chemokine receptors, notably CXC-chemokine receptor 4 (CXCR4), and other members of the G protein-coupled receptor (GPCR) family. Although best characterized as mediators of ligand-dependent chemotaxis, some chemokine receptors are also recruited to the synapse and contribute to signaling in the absence of ligation. How these and other GPCRs integrate within the dynamic structure of the synapse is unknown, as is how their normally migratory Gαi-coupled signaling is terminated upon recruitment. Here, we report the spatiotemporal organization of several GPCRs, focusing on CXCR4, and the G protein Gαi2 within the synapse of primary human CD4+ T cells on supported lipid bilayers, using standard- and super-resolution fluorescence microscopy. We find that CXCR4 undergoes orchestrated phases of reorganization, culminating in recruitment to the TCR-enriched center. This appears to be dependent on CXCR4 ubiquitination, and does not involve stable interactions with TCR microclusters, as viewed at the nanoscale. Disruption of this process by mutation impairs CXCR4 contributions to cellular activation. Gαi2 undergoes active exclusion from the synapse, partitioning from centrally-accumulated CXCR4. Using a CRISPR-Cas9 knockout screen, we identify several diverse GPCRs with contributions to T cell activation, most significantly the sphingosine-1-phosphate receptor S1PR1, and the oxysterol receptor GPR183. These, and other GPCRs, undergo organization similar to CXCR4; including initial exclusion, centripetal transport, and lack of receptor-TCR interactions. These constitute the first observations of GPCR dynamics within the synapse, and give insights into how these receptors may contribute to T cell activation. The observation of broad GPCR contributions to T cell activation also opens the possibility that modulating GPCR expression in response to cell status or environment may directly regulate responsiveness to pMHC.

Immune-based mutation classification enables neoantigen prioritization and immune feature discovery in cancer immunotherapy

ABSTRACT Genetic mutations lead to the production of mutated proteins from which peptides are presented to T cells as cancer neoantigens. Evidence suggests that T cells that target neoantigens are the main mediators of effective cancer immunotherapies. Although algorithms have been used to predict neoantigens, only a minority are immunogenic. The factors that influence neoantigen immunogenicity are not completely understood. Here, we classified human neoantigen/neopeptide data into three categories based on their TCR-pMHC binding events. We observed a conservative mutant orientation of the anchor residue from immunogenic neoantigens which we termed the “NP” rule. By integrating this rule with an existing prediction algorithm, we found improved performance in neoantigen prioritization. To better understand this rule, we solved several neoantigen/MHC structures. These structures showed that neoantigens that follow this rule not only increase peptide-MHC binding affinity but also create new TCR-binding features. These molecular insights highlight the value of immune-based classification in neoantigen studies and may enable the design of more effective cancer immunotherapies.

MHC Restriction: Where Are We Now?

MHC Restriction: Where Are We Now?

Molecular Mechanisms for the Stochastic Condensation of LAT Assemblies in T Cells

T cells discriminate between self and agonist by requiring long dwelling binding events between peptide-MHC (pMHC) and the T Cell Receptor (TCR). A consequence of successful signal transduction is the formation of a multi-molecular assembly of downstream molecules that is crosslinked by the phosphorylated scaffold protein Linker for Activation of T cells (LAT). While it is known that after ligand binding Zap70 kinase recruits to the TCR and begins phosphorylating LAT, the early molecular events precipitating LAT condensation in relation to a single ligated TCR is unknown. Using supported lipid bilayer technology and TIRF microscopy, we directly measure the intracellular responses to single pMHC-TCR binding events in living primary T cells with high spatiotemporal, multi-color resolution. This provides the ability to accurately determine the order of events in TCR signal transduction in relation to a single TCR, as opposed to bulk stimulation methods. We observe that after a pMHC binds to a TCR that LAT condensation occurs after an extended delay. While this timing is likely set, in part, by a competition of kinase/phosphatase pressure and LAT diffusion, the phosphorylation of LAT has a similarly extended delay. This suggests that diffusive phospho-LAT requires an additional event to trigger aggregation. These data demonstrate novel roles for key signaling proteins and, in contrast to ensemble methods, are the first to visualize the decisive steps leading to LAT condensation and completion of kinetic proofreading.

The Formation of Lat Protein Condensates in Response to Single pMHC-TCR Binding Events

The Formation of Lat Protein Condensates in Response to Single pMHC-TCR Binding Events

High Peptide Dose Vaccination Promotes the Early Selection of Tumor Antigen-Specific CD8 T-Cells of Enhanced Functional Competence.

CD8 T-cell response efficiency critically depends on the TCR binding strength to peptide-MHC, i.e., the TCR binding avidity. A current challenge in onco-immunology lies in the evaluation of vaccine protocols selecting for tumor-specific T-cells of highest avidity, offering maximal immune protection against tumor cells and clinical benefit. Here, we investigated the impact of peptide and CpG/adjuvant doses on the quality of vaccine-induced CD8 T-cells in relation to binding avidity and functional responses in treated melanoma patients. Using TCR-pMHC binding avidity measurements combined to phenotype and functional assays, we performed a comprehensive study on representative tumor antigen-specific CD8 T-cell clones (n = 454) from seven patients vaccinated with different doses of Melan-A/ELA peptide (0.1 mg vs. 0.5 mg) and CpG-B adjuvant (1–1.3 mg vs. 2.6 mg). Vaccination with high peptide dose favored the early and strong in vivo expansion and differentiation of Melan-A-specific CD8 T-cells. Consistently, T-cell clones generated from those patients showed increased TCR binding avidity (i.e., slow off-rates and CD8 binding independency) readily after 4 monthly vaccine injections (4v). In contrast, the use of low peptide or high CpG-B doses required 8 monthly vaccine injections (8v) for the enrichment of anti-tumor T-cells with high TCR binding avidity and low CD8 binding dependency. Importantly, the CD8 binding-independent vaccine-induced CD8 T-cells displayed enhanced functional avidity, reaching a plateau of maximal function. Thus, T-cell functional potency following peptide/CpG/IFA vaccination may not be further improved beyond a certain TCR binding avidity limit. Our results also indicate that while high peptide dose vaccination induced the early selection of Melan-A-specific CD8 T-cells of increased functional competence, continued serial vaccinations also promoted such high-avidity T-cells. Overall, the systematic assessment of T-cell binding avidity may contribute to optimize vaccine design for improving clinical efficacy.

Geometrical characterization of T cell receptor binding modes reveals class-specific binding to maximize access to antigen.

Recognition of antigenic peptides bound to major histocompatibility complex (MHC) proteins by αβ T cell receptors (TCRs) is a hallmark of T cell mediated immunity. Recent data suggest that variations in TCR binding geometry may influence T cell signaling, which could help explain outliers in relationships between physical parameters such as TCR-pMHC binding affinity and T cell function. Traditionally, TCR binding geometry has been described with simple descriptors such as the crossing angle, which quantifies what has become known as the TCR's diagonal binding mode. However, these descriptors often fail to reveal distinctions in binding geometry that are apparent through visual inspection. To provide a better framework for relating TCR structure to T cell function, we developed a comprehensive system for quantifying the geometries of how TCRs bind peptide/MHC complexes. We show that our system can discern differences not clearly revealed by more common methods. As an example of its potential to impact biology, we used it to reveal differences in how TCRs bind class I and class II peptide/MHC complexes, which we show allow the TCR to maximize access to and "read out" the peptide antigen. We anticipate our system will be of use in not only exploring these and other details of TCR-peptide/MHC binding interactions, but also addressing questions about how TCR binding geometry relates to T cell function, as well as modeling structural properties of class I and class II TCR-peptide/MHC complexes from sequence information. The system is available at https://tcr3d.ibbr.umd.edu/tcr_com or for download as a script.

Mechano-regulation of Peptide-MHC Class I Conformations Determines TCR Antigen Recognition

TCRs recognize cognate pMHCs to initiate Tcell signaling and adaptive immunity. Mechanical force strengthens TCR-pMHC interactions to elicit agonist-specific catch bonds to trigger TCR signaling, but the underlying dynamic structural mechanism is unclear. We combined steered molecular dynamics (SMD) simulation, single-molecule biophysical approaches, and functional assays to collectively demonstrate that mechanical force induces conformational changes in pMHCs to enhance pre-existing contacts and activates new interactions at the TCR-pMHC binding interface to resist bond dissociation under force, resulting in TCR-pMHC catch bondsand Tcell activation. Intriguingly, cancer-associatedsomatic mutations in HLA-A2 that may restrict these conformational changes suppressed TCR-pMHC catch bonds. Structural analysis also indicated that HLA polymorphism might alter the equilibrium of these conformational changes. Our findings not only reveal critical roles of force-induced conformational changes in pMHCs for activating TCR-pMHC catch bonds but also have implications for Tcell-based immunotherapy.

Mapping the stochastic sequence of individual ligand-receptor binding events to cellular activation: T cells act on the rare events.

T cell receptor (TCR) binding to agonist peptide major histocompatibility complex (pMHC) triggers signaling events that initiate T cell responses. This system is remarkably sensitive, requiring only a few binding events to successfully activate a cellular response. On average, activating pMHC ligands exhibit mean dwell times of at least a few seconds when bound to the TCR. However, a T cell accumulates pMHC-TCR interactions as a stochastic series of discrete, single-molecule binding events whose individual dwell times are broadly distributed. With activation occurring in response to only a handful of such binding events, individual cells are unlikely to experience the average binding time. Here, we mapped the ensemble of pMHC-TCR binding events in space and time while simultaneously monitoring cellular activation. Our findings revealed that T cell activation hinges on rare, long-dwell time binding events that are an order of magnitude longer than the average agonist pMHC-TCR dwell time. Furthermore, we observed that short pMHC-TCR binding events that were spatially correlated and temporally sequential led to cellular activation. These observations indicate that T cell antigen discrimination likely occurs by sensing the tail end of the pMHC-TCR binding dwell time distribution rather than its average properties.

TCR-Ligand Dissociation-Rate, a Reliable and Determining Parameter, Identifying Tumor-Reactive CD8 T Cells of High Avidity and Functional Potency

Background & Aims: Kinetics of T-cell receptor (TCR) binding to peptide-MHC (pMHC) contribute to T-cell activation and various cellular responses. However, due to technical reasons, the characterization of T-cells is usually only done with functional assays, without assessment of TCR-pMHC binding kinetics, thus limiting the information on the overall quality and clinical relevance of TCR-ligand binding properties. With the aim to broadly evaluate possible correlations between T-cell function and TCR binding kinetics in humans, we undertook a large-scale analysis of combined multiple functional readouts (i.e. CD107a degranulation, cytokine production and proliferation) and off-rate measurements by NTAmers (ref. 1) to characterize tumor- and virus-specific CD8 T-cell clones (n=400) isolated from melanoma patients and healthy donors.Results: Our results show that, within an antigen-specific repertoire, TCR-ligand off-rates accurately predicted single-functional and poly-functional avidities (i.e. ligand potency) as well as co-receptors expression/modulation of CD8 T-cells. Importantly, the TCR-pMHC dissociation-rate represented a constant and highly reliable bio-physical parameter, contrasting to functional avidity which depended on the activation status of the T-cells following re-stimulation. Remarkably, we found that the TCR-ligand off-rate repertoire depended on the antigenic-specificity, as CD8 T-cells specific for the cancer-testis antigen NY-ESO-1157-165 displayed longer TCR-ligand interactions than T-cells specific for the tumor/differentiation antigen Melan-A26-35, and both these tumor-specific TCRs exhibited significantly lower avidities than those specific for persistent herpes virus antigens (CMV/pp65495-503, EBV/BMFL1259-267).Conclusions: Our data from a large library of human T-cell clones demonstrate that the TCR-ligand dissociation-rate is a stable/reliable and determining parameter of T-cell responsiveness, arguing that it should be systematically assessed for meaningful monitoring of human T-cell responses. Furthermore, our TCR-ligand off-rate comparisons highlighted strong binding differences between non-self/virus- and self/tumor-specific repertoires. Our data from individual clones suggest that the tumor-specific repertoires contains rare but nevertheless promising TCRs with sufficiently slow off-rate and high functional avidity to potentially confer protective immune responses against cancer.(ref. 1) Hebeisen et al., Cancer Res, 75(10):1983, 2015 DisclosuresNo relevant conflicts of interest to declare.

Diffusion Analysis of Lymphocyte Specific Kinase Reveals Immobilisation Hot Spots in Live T-Cell Plasma Membranes

T-Lymphocytes initiate an adaptive immune response after specific binding of the T-cell receptor (TCR) to an antigenic peptide bound to the major histocompatibility complex (peptide-MHC) on an antigen presenting cell. Key events upon TCR-pMHC binding involve the phosphorylation of intracellular tyrosine residues of the TCR and recruitment of adapter molecules, which results in downstream signalling. Initial TCR phosphorylation is primarily carried out by the membrane associated protein lymphocyte specific kinase (Lck) making it a central molecule for T-cell signalling. Recent work on Lck, using superresolution microscopy techniques, has shown the organisation of Lck in nanoclusters in fixed as well as in live cells [1]. However, neither the structural mechanism nor the functional role of this non-random Lck distribution in the plasma membrane are well understood. Possible explanations for Lck clustering may be local lipid heterogeneities as well as interactions with other plasma membrane proteins or the cortical actin cytoskeleton. In order to acquire a better understanding of Lck dynamics in live cells, we carried out superresolution experiments on Lck-mEOS3.2, stably expressed in JCaM 1.6 cells. We used single molecule tracking to elucidate the diffusional behaviour of Lck. Based on the reported existence of nanoclusters, we implemented a simple and practical method to separately analyse Lck diffusion inside and outside these structures. Surprisingly, only Lck-molecules inside clusters were immobilised, whereas molecules outside of clusters exhibited pure Brownian motion. We further found that disruption of the actin cytoskeleton did not influence Lck immobilisations. [1] Rossy, J., et al., Conformational states of the kinase Lck regulate clustering in early T cell signalling. Nature immunology 14, 82-89 (2013). This work was supported by the Austrian Science Fund/FWF project P26337-B21 and P27941-B28.

Relevance of the T cell Receptor-Ligand Avidity for Immunity to Infection

Cytotoxic lymphocytes are critical for fighting viral and certain bacterial infections. Therefore, assessing the quality of cytotoxic T cell responses might have important clinical implications. TCR-pMHC binding (avidity) is a key determinant of T cell quality. Here we review currently available technologies for the measurement of TCR-pMHC avidity and their potential relevance for translational applications.