Structural basis for clonal diversity of the human T-cell response to a dominant influenza virus epitope

Innate protection against intrarectal SIV acquisition by a live SHIV vaccine

Identifying immune correlates of protection is a major challenge in AIDS vaccine development. Anti-Envelope antibodies have been considered critical for protection against SIV/HIV (SHIV) acquisition. Here, we evaluated the efficacy of an SHIV vaccine against SIVmac251 challenge, where the role of antibody was excluded, as there was no cross-reactivity between SIV and SHIV envelope antibodies. After 8 low-dose intrarectal challenges with SIVmac251, 12 SHIV-vaccinated animals demonstrated efficacy, compared with 6 naive controls, suggesting protection was achieved in the absence of anti-envelope antibodies. Interestingly, CD8+ T cells (and some NK cells) were not essential for preventing viral acquisition, as none of the CD8-depleted macaques were infected by SIVmac251 challenges. Initial investigation of protective innate immunity revealed that protected animals had elevated pathways related to platelet aggregation/activation and reduced pathways related to interferon and responses to virus. Moreover, higher expression of platelet factor 4 on circulating platelet-leukocyte aggregates was associated with reduced viral acquisition. Our data highlighted the importance of innate immunity, identified mechanisms, and may provide opportunities for novel HIV vaccines or therapeutic strategy development.

Attention network for predicting T-cell receptor-peptide binding can associate attention with interpretable protein structural properties

Understanding how a T-cell receptor (TCR) recognizes its specific ligand peptide is crucial for gaining an insight into biological functions and disease mechanisms. Despite its importance, experimentally determining TCR-peptide-major histocompatibility complex (TCR-pMHC) interactions is expensive and time-consuming. To address this challenge, computational methods have been proposed, but they are typically evaluated by internal retrospective validation only, and few researchers have incorporated and tested an attention layer from language models into structural information. Therefore, in this study, we developed a machine learning model based on a modified version of Transformer, a source-target attention neural network, to predict the TCR-pMHC interaction solely from the amino acid sequences of the TCR complementarity-determining region (CDR) 3 and the peptide. This model achieved competitive performance on a benchmark dataset of the TCR-pMHC interaction, as well as on a truly new external dataset. Additionally, by analyzing the results of binding predictions, we associated the neural network weights with protein structural properties. By classifying the residues into large- and small-attention groups, we identified statistically significant properties associated with the largely attended residues such as hydrogen bonds within CDR3. The dataset that we created and the ability of our model to provide an interpretable prediction of TCR-peptide binding should increase our knowledge about molecular recognition and pave the way for designing new therapeutics.

Thioamide Analogues of MHC I Antigen Peptides

Short, synthetic peptides that are displayed by major histocompatibility complex I (MHC I) can stimulate CD8 T cells in vivo to destroy virus-infected or cancer cells. The development of such peptides as vaccines that provide protective immunity, however, is limited by rapid proteolytic degradation. Introduction of unnatural amino acid residues can suppress MHC I antigen proteolysis, but the modified peptides typically display lower affinity for MHC I and/or diminished ability to activate CD8 T cells relative to native antigen. Here, we report a new strategy for modifying MHC I antigens to enhance resistance to proteolysis while preserving MHC I affinity and T cell activation properties. This approach, replacing backbone amide groups with thioamides, was evaluated in two well-characterized antigens presented by HLA-A2, a common human MHC I. For each antigen, singly modified thioamide analogues retained affinity for HLA-A2 and activated T cells specific for the native antigen, as measured via interferon-γ secretion. In each system, we identified a highly potent triply substituted thioamide antigen ("thio-antigen") that displayed substantial resistance to proteolytic cleavage. Collectively, our results suggest that thio-antigens may represent a general and readily accessible source of potent vaccine candidates that resist degradation.

Autoimmunity-associated T cell receptors recognize HLA-B*27-bound peptides

Human leucocyte antigen B*27 (HLA-B*27) is strongly associated with inflammatory diseases of the spine and pelvis (for example, ankylosing spondylitis (AS)) and the eye (that is, acute anterior uveitis (AAU))1. How HLA-B*27 facilitates disease remains unknown, but one possible mechanism could involve presentation of pathogenic peptides to CD8+ T cells. Here we isolated orphan T cell receptors (TCRs) expressing a disease-associated public β-chain variable region-complementary-determining region 3β (BV9-CDR3β) motif2-4 from blood and synovial fluid T cells from individuals with AS and from the eye in individuals with AAU. These TCRs showed consistent α-chain variable region (AV21) chain pairing and were clonally expanded in the joint and eye. We used HLA-B*27:05 yeast display peptide libraries to identify shared self-peptides and microbial peptides that activated the AS- and AAU-derived TCRs. Structural analysis revealed that TCR cross-reactivity for peptide-MHC was rooted in a shared binding motif present in both self-antigens and microbial antigens that engages the BV9-CDR3β TCRs. These findings support the hypothesis that microbial antigens and self-antigens could play a pathogenic role in HLA-B*27-associated disease.

Different T-cell and B-cell repertoire elicited by the SARS-CoV-2 inactivated vaccine and S1 subunit vaccine in rhesus macaques

Multiple types of SARS-CoV-2 vaccines have been used worldwide, but summarizing their immunologic efficacy post-vaccination remains challenging. The BCR and TCR sequencing based on single-cell sorting makes it possible to evaluate the vaccine-induced immune responses of B or T cells. In this study, we compared the repertoire diversities of B cells and T cells between a whole-virus inactivated vaccine and an S1 protein subunit vaccine in rhesus macaques. We found that the inactivated vaccine could induce a large antigen-specific-BCR repertoire with longer VH CDR3 (21 aa), while the CD3+ TCR α chains of the two vaccine groups showed a similar TCRV/J usage frequency. Detailed analysis of the TCR and BCR repertoires might be of interest for further understanding of the mechanisms of vaccine-induced immune responses.

Role of cross-reactivity in cellular immune targeting of influenza A M158-66 variant peptide epitopes

The immunologic significance of cross-reactivity of TCR recognition of peptide:MHC complexes is still poorly understood. We have described TCR cross-reactivity in a system involving polyclonal CD8 T cell recognition of the well characterized influenza viral M158-66 epitope. While M158-66 is generally conserved between influenza A isolates, error-prone transcription generates stable variant RNA during infection which could act as novel epitopes. If packaged and viable, variant genomic RNA generates an influenza quasispecies. The stable RNA variants would generate a new transmissible epitope that can select a specific repertoire, which itself should have cross-reactive properties. We tested two candidate peptides in which Thr65 is changed to Ala (A65) or Ser (S65) using recall responses to identify responding T cell clonotypes. Both peptides generated large polyclonal T cell repertoires of their own with repertoire characteristics and cross-reactivity patterns like that observed for the M158-66 repertoire. Both substitutions could be present in viral genomes or mRNA at sufficient frequency during an infection to drive immunity. Peptides from the resulting protein would be a target for CD8 cells irrespective of virus viability or transmissibility. These data support the hypothesis that cross-reactivity is important for immunity against RNA virus infections.

T cell receptors (TCRs) employ diverse strategies to target a p53 cancer neoantigen

Adoptive cell therapy with tumor-specific T cells can mediate durable cancer regression. The prime target of tumor-specific T cells are neoantigens arising from mutations in self-proteins during malignant transformation. To understand T cell recognition of cancer neoantigens at the atomic level, we studied oligoclonal T cell receptors (TCRs) that recognize a neoepitope arising from a driver mutation in the p53 oncogene (p53R175H) presented by the major histocompatibility complex class I molecule HLA-A2. We previously reported the structures of three p53R175H-specific TCRs (38-10, 12-6, and 1a2) bound to p53R175H and HLA-A2. The structures showed that these TCRs discriminate between WT and mutant p53 by forming extensive interactions with the R175H mutation. Here, we report the structure of a fourth p53R175H-specific TCR (6-11) in complex with p53R175H and HLA-A2. In contrast to 38-10, 12-6, and 1a2, TCR 6-11 makes no direct contacts with the R175H mutation, yet is still able to distinguish mutant from WT p53. Structure-based in silico mutagenesis revealed that the 60-fold loss in 6-11 binding affinity for WT p53 compared to p53R175H is mainly due to the higher energetic cost of desolvating R175 in the WT p53 peptide during complex formation than H175 in the mutant. This indirect strategy for preferential neoantigen recognition by 6-11 is fundamentally different from the direct strategies employed by other TCRs and highlights the multiplicity of solutions to recognizing p53R175H with sufficient selectivity to mediate T cell killing of tumor but not normal cells.

Predicting T Cell Receptor Antigen Specificity From Structural Features Derived From Homology Models of Receptor-Peptide-Major Histocompatibility Complexes

The physical interaction between the T cell receptor (TCR) and its cognate antigen causes T cells to activate and participate in the immune response. Understanding this physical interaction is important in predicting TCR binding to a target epitope, as well as potential cross-reactivity. Here, we propose a way of collecting informative features of the binding interface from homology models of T cell receptor-peptide-major histocompatibility complex (TCR-pMHC) complexes. The information collected from these structures is sufficient to discriminate binding from non-binding TCR-pMHC pairs in multiple independent datasets. The classifier is limited by the number of crystal structures available for the homology modelling and by the size of the training set. However, the classifier shows comparable performance to sequence-based classifiers requiring much larger training sets.

GIANA allows computationally-efficient TCR clustering and multi-disease repertoire classification by isometric transformation

Similarity in T-cell receptor (TCR) sequences implies shared antigen specificity between receptors, and could be used to discover novel therapeutic targets. However, existing methods that cluster T-cell receptor sequences by similarity are computationally inefficient, making them impractical to use on the ever-expanding datasets of the immune repertoire. Here, we developed GIANA (Geometric Isometry-based TCR AligNment Algorithm) a computationally efficient tool for this task that provides the same level of clustering specificity as TCRdist at 600 times its speed, and without sacrificing accuracy. GIANA also allows the rapid query of large reference cohorts within minutes. Using GIANA to cluster large-scale TCR datasets provides candidate disease-specific receptors, and provides a new solution to repertoire classification. Querying unseen TCR-seq samples against an existing reference differentiates samples from patients across various cohorts associated with cancer, infectious and autoimmune disease. Our results demonstrate how GIANA could be used as the basis for a TCR-based non-invasive multi-disease diagnostic platform.

Grouping T-cell receptors (TCRs) by sequence similarity could lead to new immunological insights. Here, the authors propose a tool that allows the rapid clustering of millions of TCR sequences, identifying TCRs potentially associated with the response to cancer, infectious and autoimmune diseases.

Backbone Modifications of HLA-A2-Restricted Antigens Induce Diverse Binding and T Cell Activation Outcomes

CD8⁺ T cells express T cell receptors (TCRs) that recognize short peptide antigens in the context of major histocompatibility class I (MHC I) molecules. This recognition process produces an array of cytokine-mediated signals that help to govern immunological responses. Design of biostable MHC I peptide vaccines containing unnatural subunits is desirable, and synthetic antigens in which a native α-amino acid residue is replaced by a homologous β-amino acid residue (native side chain but extended backbone) might be useful in this regard. We have evaluated the impact of α-to-β backbone modification at a single site on T cell-mediated recognition of six clinically important viral and tumor-associated antigens bound to an MHC I. Effects of this modification on MHC I affinity and T cell activation were measured. Many of these modifications diminish or prevent T cell response. However, a number of α/β-peptide antigens were found to mimic the activity of natural antigens or to enhance maximal T cell response, as measured by interferon-γ release. Results from this broad exploratory study advance our understanding of immunological responses to antigens bearing unnatural modifications and suggest that α/β-peptides could be a source of potent and proteolytically stable variants of native antigens.

Characterization of amino acid residues of T-cell receptors interacting with HLA-A*02-restricted antigen peptides

Background

The present study aimed to explore residues’ properties interacting with HLA-A*02-restricted peptides on T-cell receptors (TCRs) and their effects on bond types of interaction and binding free energy.

Methods

We searched the crystal structures of HLA-A*02-restricted peptide-TCR complexes from the Protein Data Bank (PDB) database and subsequently collected relevant parameters. We then employed Schrodinger to analyze the bond types of interaction and Gromacs 2019 to evaluate the TCR-antigen peptide complex’s molecular dynamics simulation. Finally, we compared the changes of bond types of interaction and binding free energy before and after residue substitution to ensure consistency of the conditions before and after residue substitution.

Results

The main sites on the antigen peptides that formed the intermolecular interaction [hydrogen bond (HB) and pi stack] with TCRs were P4, P8, P2, and P6. The hydrophobicity of the amino acids inside or outside the disulfide bond of TCRs may be related to the intermolecular interaction and binding free energy between TCRs and peptides. Residues located outside the disulfide bond of TCR α or β chains and forming pi stack force played favorable roles in the complex intermolecular interaction and binding free energy. The residues of the TCR α or β chains that interacted with peptides were replaced by alanine (Ala) or glycine (Gly), and their intermolecular binding free energy of the complex had been improved. However, it had nothing to do with the formation of HB.

Conclusions

The findings of this study suggest that the hydrophobic nature of the amino acids inside or outside the disulfide bonds on the TCR may be associated with the intermolecular interaction and binding between the TCR and polypeptide. The residues located outside the TCR α or β single-chain disulfide bond and forming the pi-stack force showed a beneficial effect on the intermolecular interaction and binding of the complex. In addition, the part of the residues on the TCR α or β single chain that produced bond types of interaction with the polypeptide after being replaced by Ala or Gly, the intermolecular binding free energy of the complex was increased, regardless of whether HB was formed.

A chimeric HLA-A2:β2M:Ig fusion protein for the study of virus-specific CD8+ T-cells

INTRODUCTION

The response mediated by CD8⁺ T-cells in the context of infection and vaccination has been thoroughly investigated and represents one of the most important branches that allow for the development of immunity against intracellular pathogens and, thus, the establishment of robust antiviral responses. However, there is a lack of methods to assess antigen-specific CD8⁺ T-cells.

OBJECTIVE

Search for the ideal assays to assess the function of antigen-specific CD8⁺ T-cells.

METHODS

In the present study a chimeric HLA-A2:β2M:Ig fusion protein was produced, purified, and evaluated in functional CD8⁺ T-cell response studies using samples from Influenza A patients and humanized mice upon adenoviral vaccination.

RESULTS

The HLA-A2:β2M:Ig molecule, bound to immunodominant viral peptides by passive transfer, was able to induce robust antiviral CD8⁺ T-cell responses mediated by IFN-γ. The in vitro IFN-γ release assay using the chimeric HLA-A2:β2M:Ig fusion protein detected bona fide human CD8⁺ T-cells, demonstrating superior production of IFN-γ by human CD8⁺ T-cells induced by Influenza A immunodominant GILGFVFTL peptide. Removal of antigen-presenting cells and CD8⁺ T-cell enrichment improved significantly the IFN-γ production. The chimeric HLA-A2:β2M:Ig fusion protein also triggered HLA-A2-restricted CD8⁺ T-cell response in a humanized mouse model upon vaccination with adenovirus encoding HLA-A2-restricted HIV p24 antigen. The results strongly suggest the use of tailor-made assays for detecting HLA-A2-restricted CD8⁺ T-cell Responses in the Humanized Mouse Model.

CONCLUSION

The chimeric HLA-A2:β2M:Ig fusion protein-based assays provided a sensitive tool that may be paramount to measure virus-specific CD8⁺ T-cell response in a range of viral infections of clinical relevance.

TCR Recognition of Peptide-MHC-I: Rule Makers and Breakers

T cells are a critical part of the adaptive immune system that are able to distinguish between healthy and unhealthy cells. Upon recognition of protein fragments (peptides), activated T cells will contribute to the immune response and help clear infection. The major histocompatibility complex (MHC) molecules, or human leukocyte antigens (HLA) in humans, bind these peptides to present them to T cells that recognise them with their surface T cell receptors (TCR). This recognition event is the first step that leads to T cell activation, and in turn can dictate disease outcomes. The visualisation of TCR interaction with pMHC using structural biology has been crucial in understanding this key event, unravelling the parameters that drive this interaction and their impact on the immune response. The last five years has been the most productive within the field, wherein half of current unique TCR-pMHC-I structures to date were determined within this time. Here, we review the new insights learned from these recent TCR-pMHC-I structures and their impact on T cell activation.

Immunogenic SARS-CoV-2 Epitopes: In Silico Study Towards Better Understanding of COVID-19 Disease-Paving the Way for Vaccine Development

The emergence of the COVID-19 outbreak at the end of 2019, caused by the novel coronavirus SARS-CoV-2, has, to date, led to over 13.6 million infections and nearly 600,000 deaths. Consequently, there is an urgent need to better understand the molecular factors triggering immune defense against the virus and to develop countermeasures to hinder its spread. Using in silico analyses, we showed that human major histocompatibility complex (MHC) class I cell-surface molecules vary in their capacity for binding different SARS-CoV-2-derived epitopes, i.e., short sequences of 8-11 amino acids, and pinpointed five specific SARS-CoV-2 epitopes that are likely to be presented to cytotoxic T-cells and hence activate immune responses. The identified epitopes, each one of nine amino acids, have high sequence similarity to the equivalent epitopes of SARS-CoV virus, which are known to elicit an effective T cell response in vitro. Moreover, we give a structural explanation for the binding of SARS-CoV-2-epitopes to MHC molecules. Our data can help us to better understand the differences in outcomes of COVID-19 patients and may aid the development of vaccines against SARS-CoV-2 and possible future outbreaks of novel coronaviruses.

Epstein-Barr Virus Epitope-Major Histocompatibility Complex Interaction Combined with Convergent Recombination Drives Selection of Diverse T Cell Receptor α and β Repertoires

Recognition modes of individual T cell receptors (TCRs) are well studied, but factors driving the selection of TCR repertoires from primary through persistent human virus infections are less well understood. Using deep sequencing, we demonstrate a high degree of diversity of Epstein-Barr virus (EBV)-specific clonotypes in acute infectious mononucleosis (AIM). Only 9% of unique clonotypes detected in AIM persisted into convalescence; the majority (91%) of unique clonotypes detected in AIM were not detected in convalescence and were seeming replaced by equally diverse "de novo" clonotypes. The persistent clonotypes had a greater probability of being generated than nonpersistent clonotypes due to convergence recombination of multiple nucleotide sequences to encode the same amino acid sequence, as well as the use of shorter complementarity-determining regions 3 (CDR3s) with fewer nucleotide additions (i.e., sequences closer to germ line). Moreover, the two most immunodominant HLA-A2-restricted EBV epitopes, BRLF1109 and BMLF1280, show highly distinct antigen-specific public (i.e., shared between individuals) features. In fact, TCRα CDR3 motifs played a dominant role, while TCRβ played a minimal role, in the selection of TCR repertoire to an immunodominant EBV epitope, BRLF1. This contrasts with the majority of previously reported repertoires, which appear to be selected either on TCRβ CDR3 interactions with peptide/major histocompatibility complex (MHC) or in combination with TCRα CDR3. Understanding of how TCR-peptide-MHC complex interactions drive repertoire selection can be used to develop optimal strategies for vaccine design or generation of appropriate adoptive immunotherapies for viral infections in transplant settings or for cancer.IMPORTANCE Several lines of evidence suggest that TCRα and TCRβ repertoires play a role in disease outcomes and treatment strategies during viral infections in transplant patients and in cancer and autoimmune disease therapy. Our data suggest that it is essential that we understand the basic principles of how to drive optimum repertoires for both TCR chains, α and β. We address this important issue by characterizing the CD8 TCR repertoire to a common persistent human viral infection (EBV), which is controlled by appropriate CD8 T cell responses. The ultimate goal would be to determine if the individuals who are infected asymptomatically develop a different TCR repertoire than those that develop the immunopathology of AIM. Here, we begin by doing an in-depth characterization of both CD8 T cell TCRα and TCRβ repertoires to two immunodominant EBV epitopes over the course of AIM, identifying potential factors that may be driving their selection.

Investigation of Antigen-Specific T-Cell Receptor Clusters in Human Cancers

PURPOSE

Cancer antigen-specific T cells are key components in antitumor immune response, yet their identification in the tumor microenvironment remains challenging, as most cancer antigens are unknown. Recent advance in immunology suggests that similar T-cell receptor (TCR) sequences can be clustered to infer shared antigen specificity. This study aims to identify antigen-specific TCRs from the tumor genomics sequencing data.

EXPERIMENTAL DESIGN

We used the TRUST (Tcr Repertoire Utilities for Solid Tissue) algorithm to assemble the TCR hypervariable CDR3 regions from 9,700 bulk tumor RNA-sequencing (RNA-seq) samples, and developed a computational method, iSMART, to group similar TCRs into antigen-specific clusters. Integrative analysis on the TCR clusters with multi-omics datasets was performed to profile cancer-associated T cells and to uncover novel cancer antigens.

RESULTS

Clustered TCRs are associated with signatures of T-cell activation after antigen encounter. We further elucidated the phenotypes of clustered T cells using single-cell RNA-seq data, which revealed a novel subset of tissue-resident memory T-cell population with elevated metabolic status. An exciting application of the TCR clusters is to identify novel cancer antigens, exemplified by our identification of a candidate cancer/testis gene, HSFX1, through integrated analysis of HLA alleles and genomics data. The target was further validated using vaccination of humanized HLA-A*02:01 mice and ELISpot assay. Finally, we showed that clustered tumor-infiltrating TCRs can differentiate patients with early-stage cancer from healthy donors, using blood TCR repertoire sequencing data, suggesting potential applications in noninvasive cancer detection.

CONCLUSIONS

Our analysis on the antigen-specific TCR clusters provides a unique resource for alternative antigen discovery and biomarker identification for cancer immunotherapies.

TCRpMHCmodels: Structural modelling of TCR-pMHC class I complexes

The interaction between the class I major histocompatibility complex (MHC), the peptide presented by the MHC and the T-cell receptor (TCR) is a key determinant of the cellular immune response. Here, we present TCRpMHCmodels, a method for accurate structural modelling of the TCR-peptide-MHC (TCR-pMHC) complex. This TCR-pMHC modelling pipeline takes as input the amino acid sequence and generates models of the TCR-pMHC complex, with a median Cα RMSD of 2.31 Å. TCRpMHCmodels significantly outperforms TCRFlexDock, a specialised method for docking pMHC and TCR structures. TCRpMHCmodels is simple to use and the modelling pipeline takes, on average, only two minutes. Thanks to its ease of use and high modelling accuracy, we expect TCRpMHCmodels to provide insights into the underlying mechanisms of TCR and pMHC interactions and aid in the development of advanced T-cell-based immunotherapies and rational design of vaccines. The TCRpMHCmodels tool is available at http://www.cbs.dtu.dk/services/TCRpMHCmodels/.

Quantitative Prediction of the Landscape of T Cell Epitope Immunogenicity in Sequence Space

Immunodominant T cell epitopes preferentially targeted in multiple individuals are the critical element of successful vaccines and targeted immunotherapies. However, the underlying principles of this “convergence” of adaptive immunity among different individuals remain poorly understood. To quantitatively describe epitope immunogenicity, here we propose a supervised machine learning framework generating probabilistic estimates of immunogenicity, termed “immunogenicity scores,” based on the numerical features computed through sequence-based simulation approximating the molecular scanning process of peptides presented onto major histocompatibility complex (MHC) by the human T cell receptor (TCR) repertoire. Notably, overlapping sets of intermolecular interaction parameters were commonly utilized in MHC-I and MHC-II prediction. Moreover, a similar simulation of individual TCR-peptide interaction using the same set of interaction parameters yielded correlates of TCR affinity. Pathogen-derived epitopes and tumor-associated epitopes with positive T cell reactivity generally had higher immunogenicity scores than non-immunogenic counterparts, whereas thymically expressed self-epitopes were assigned relatively low scores regardless of their immunogenicity annotation. Immunogenicity score dynamics among single amino acid mutants delineated the landscape of position- and residue-specific mutational impacts. Simulation of position-specific immunogenicity score dynamics detected residues with high escape potential in multiple epitopes, consistent with known escape mutations in the literature. This study indicates that targeting of epitopes by human adaptive immunity is to some extent directed by defined thermodynamic principles. The proposed framework also has a practical implication in that it may enable to more efficiently prioritize epitope candidates highly prone to T cell recognition in multiple individuals, warranting prospective validation across different cohorts.

A cloning and expression system to probe T-cell receptor specificity and assess functional avidity to neoantigens

Recent studies have highlighted the promise of targeting tumor neoantigens to generate potent antitumor immune responses and provide strong motivation for improving our understanding of antigen-T-cell receptor (TCR) interactions. Advances in single-cell sequencing technologies have opened the door for detailed investigation of the TCR repertoire, providing paired information from TCRα and TCRβ, which together determine specificity. However, a need remains for efficient methods to assess the specificity of discovered TCRs. We developed a streamlined approach for matching TCR sequences with cognate antigen through on-demand cloning and expression of TCRs and screening against candidate antigens. Here, we first demonstrate the system's capacity to identify viral-antigen-specific TCRs and compare the functional avidity of TCRs specific for a given antigen target. We then apply this system to identify neoantigen-specific TCR sequences from patients with melanoma treated with personalized neoantigen vaccines and characterize functional avidity of neoantigen-specific TCRs. Furthermore, we use a neoantigen-prediction pipeline to show that an insertion-deletion mutation in a putative chronic lymphocytic leukemia (CLL) driver gives rise to an immunogenic neoantigen mut-MGA, and use this approach to identify the mut-MGA-specific TCR sequence. This approach provides a means to identify and express TCRs, and then rapidly assess antigen specificity and functional avidity of a reconstructed TCR, which can be applied for monitoring antigen-specific T-cell responses, and potentially for guiding the design of effective T-cell-based immunotherapies.