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

HeteroTCR: A heterogeneous graph neural network-based method for predicting peptide-TCR interaction

Identifying interactions between T-cell receptors (TCRs) and immunogenic peptides holds profound implications across diverse research domains and clinical scenarios. Unsupervised clustering models (UCMs) cannot predict peptide-TCR binding directly, while supervised predictive models (SPMs) often face challenges in identifying antigens previously unencountered by the immune system or possessing limited TCR binding repertoires. Therefore, we propose HeteroTCR, an SPM based on Heterogeneous Graph Neural Network (GNN), to accurately predict peptide-TCR binding probabilities. HeteroTCR captures within-type (TCR-TCR or peptide-peptide) similarity information and between-type (peptide-TCR) interaction insights for predictions on unseen peptides and TCRs, surpassing limitations of existing SPMs. Our evaluation shows HeteroTCR outperforms state-of-the-art models on independent datasets. Ablation studies and visual interpretation underscore the Heterogeneous GNN module's critical role in enhancing HeteroTCR's performance by capturing pivotal binding process features. We further demonstrate the robustness and reliability of HeteroTCR through validation using single-cell datasets, aligning with the expectation that pMHC-TCR complexes with higher predicted binding probabilities correspond to increased binding fractions.

RACER-m leverages structural features for sparse T cell specificity prediction

Reliable prediction of T cell specificity against antigenic signatures is a formidable task, complicated by the immense diversity of T cell receptor and antigen sequence space and the resulting limited availability of training sets for inferential models. Recent modeling efforts have demonstrated the advantage of incorporating structural information to overcome the need for extensive training sequence data, yet disentangling the heterogeneous TCR-antigen interface to accurately predict MHC-allele-restricted TCR-peptide interactions has remained challenging. Here, we present RACER-m, a coarse-grained structural model leveraging key biophysical information from the diversity of publicly available TCR-antigen crystal structures. Explicit inclusion of structural content substantially reduces the required number of training examples and maintains reliable predictions of TCR-recognition specificity and sensitivity across diverse biological contexts. Our model capably identifies biophysically meaningful point-mutant peptides that affect binding affinity, distinguishing its ability in predicting TCR specificity of point-mutants from alternative sequence-based methods. Its application is broadly applicable to studies involving both closely related and structurally diverse TCR-peptide pairs.

Characterisation of novel influenza-derived HLA-B*18:01-restricted epitopes

Objectives

Seasonal influenza viruses cause roughly 650 000 deaths annually despite available vaccines. CD8+ T cells typically recognise influenza-derived peptides from internal structural and non-structural influenza proteins and are an attractive avenue for future vaccine design as they could reduce the severity of disease following infection with diverse influenza strains. CD8+ T cells recognise peptides presented by the highly polymorphic Human Leukocyte Antigens class I molecules (HLA-I). Each HLA-I variant has distinct peptide binding preferences, representing a significant obstacle for designing vaccines that elicit CD8+ T cell responses across broad populations. Consequently, the rational design of a CD8+ T cell-mediated vaccine would require the identification of highly immunogenic peptides restricted to a range of different HLA molecules.

Methods

Here, we assessed the immunogenicity of six recently published novel influenza-derived peptides identified by mass-spectrometry and predicted to bind to the prevalent HLA-B*18:01 molecule.

Results

Using CD8+ T cell activation assays and protein biochemistry, we showed that 3/6 of the novel peptides were immunogenic in several HLA-B*18:01+ individuals and confirmed their HLA-B*18:01 restriction. We subsequently compared CD8+ T cell responses towards the previously identified highly immunogenic HLA-B*18:01-restricted NP219 peptide. Using X-ray crystallography, we solved the first crystal structures of HLA-B*18:01 presenting immunogenic influenza-derived peptides. Finally, we dissected the first TCR repertoires specific for HLA-B*18:01 restricted pathogen-derived peptides, identifying private and restricted repertoires against each of the four peptides.

Conclusion

Overall the characterisation of these novel immunogenic peptides provides additional HLA-B*18:01-restricted vaccine targets derived from the Matrix protein 1 and potentially the non-structural protein and the RNA polymerase catalytic subunit of influenza viruses.

Expanding the repertoire reveals recurrent, cryptic, and hematopoietic HLA class I minor histocompatibility antigens

ABSTRACT

Allogeneic stem cell transplantation (alloSCT) is a curative treatment for hematological malignancies. After HLA-matched alloSCT, antitumor immunity is caused by donor T cells recognizing polymorphic peptides, designated minor histocompatibility antigens (MiHAs), that are presented by HLA on malignant patient cells. However, T cells often target MiHAs on healthy nonhematopoietic tissues of patients, thereby inducing side effects known as graft-versus-host disease. Here, we aimed to identify the dominant repertoire of HLA-I-restricted MiHAs to enable strategies to predict, monitor or modulate immune responses after alloSCT. To systematically identify novel MiHAs by genome-wide association screening, T-cell clones were isolated from 39 transplanted patients and tested for reactivity against 191 Epstein-Barr virus transformed B cell lines of the 1000 Genomes Project. By discovering 81 new MiHAs, we more than doubled the antigen repertoire to 159 MiHAs and demonstrated that, despite many genetic differences between patients and donors, often the same MiHAs are targeted in multiple patients. Furthermore, we showed that one quarter of the antigens are cryptic, that is translated from unconventional open reading frames, for example long noncoding RNAs, showing that these antigen types are relevant targets in natural immune responses. Finally, using single cell RNA-seq data, we analyzed tissue expression of MiHA-encoding genes to explore their potential role in clinical outcome, and characterized 11 new hematopoietic-restricted MiHAs as potential targets for immunotherapy. In conclusion, we expanded the repertoire of HLA-I-restricted MiHAs and identified recurrent, cryptic and hematopoietic-restricted antigens, which are fundamental to predict, follow or manipulate immune responses to improve clinical outcome after alloSCT.

Multi-epitopevaccines, from design to expression; an in silico approach

The development of vaccines against a wide range of infectious diseases and pathogens often relies on multi-epitope strategies that can effectively stimulate both humoral and cellular immunity. Immunoinformatics tools play a pivotal role in designing such vaccines, enhancing immune response potential, and minimizing the risk of failure. This review presents a comprehensive overview of practical tools for epitope prediction and the associated immune responses. These immunoinformatics tools facilitate the selection of epitopes based on parameters such as antigenicity, absence of toxic and allergenic sequences, secondary and tertiary structures, sequence conservation, and population coverage. The chosen epitopes can be tailored for B-cells or T-cells, both of which require further assessments covered in this study. We offer a range of suitable linkers that effectively separate cytotoxic T lymphocyte and helper T lymphocyte epitopes while preserving their functionality. Additionally, we identify various adjuvants for specific purposes. We delve into the evaluation of MHC-epitope interactions, MHC clusters, and the simulation of final constructs through molecular docking techniques. We provide diverse linkers and adjuvants optimized for epitope functions to bolster immune responses through epitope attachment. By leveraging these comprehensive tools, the development of multi-epitope vaccines holds the promise of robust immunity and a significant reduction in experimental costs.

The impact of SARS-CoV-2 spike mutation on peptide presentation is HLA allomorph-specific

CD8+ T cells are crucial for viral elimination and recovery from viral infection. Nonetheless, the current understanding of the T cell response to SARS-CoV-2 at the antigen level remains limited. The Spike protein is an external structural protein that is prone to mutations, threatening the efficacy of current vaccines. Therefore, we have characterised the immune response towards the immunogenic Spike-derived peptide (S976-984, VLNDILSRL), restricted to the HLA-A*02:01 molecule, which is mutated in both Alpha (S982A) and Omicron BA.1 (L981F) variants of concern. We determined that the mutation in the Alpha variant (S982A) impacted both the stability and conformation of the peptide, bound to HLA-A*02:01, in comparison to the original S976-984. We identified a longer and overlapping immunogenic peptide (S975-984, SVLNDILSRL) that could be presented by HLA-A*02:01, HLA-A*11:01 and HLA-B*13:01 allomorphs. We showed that S975-specific CD8+ T cells were weakly cross-reactive to the mutant peptides despite their similar conformations when presented by HLA-A*11:01. Altogether, our results show that the impact of SARS-CoV-2 mutations on peptide presentation is HLA allomorph-specific, and that post vaccination there are T cells able to react and cross-react towards the variant of concern peptides.

Murine MHC-Deficient Nonobese Diabetic Mice Carrying Human HLA-DQ8 Develop Severe Myocarditis and Myositis in Response to Anti-PD-1 Immune Checkpoint Inhibitor Cancer Therapy

Myocarditis has emerged as an immune-related adverse event of immune checkpoint inhibitor (ICI) cancer therapy associated with significant mortality. To ensure patients continue to safely benefit from life-saving cancer therapy, an understanding of fundamental immunological phenomena underlying ICI myocarditis is essential. We recently developed the NOD-cMHCI/II-/-.DQ8 mouse model that spontaneously develops myocarditis with lower mortality than observed in previous HLA-DQ8 NOD mouse strains. Our strain was rendered murine MHC class I and II deficient using CRISPR/Cas9 technology, making it a genetically clean platform for dissecting CD4+ T cell-mediated myocarditis in the absence of classically selected CD8+ T cells. These mice are highly susceptible to myocarditis and acute heart failure following anti-PD-1 ICI-induced treatment. Additionally, anti-PD-1 administration accelerates skeletal muscle myositis. Using histology, flow cytometry, adoptive transfers, and RNA sequencing analyses, we performed a thorough characterization of cardiac and skeletal muscle T cells, identifying shared and unique characteristics of both populations. Taken together, this report details a mouse model with features of a rare, but highly lethal clinical presentation of overlapping myocarditis and myositis following ICI therapy. This study sheds light on underlying immunological mechanisms in ICI myocarditis and provides the basis for further detailed analyses of diagnostic and therapeutic strategies.

Identification of Enhanced Vaccine Mimotopes for the p15E Murine Cancer Antigen

Mimotopes of short CD8+ T-cell epitopes generally comprise one or more mutated residues, and can increase the immunogenicity and function of peptide cancer vaccines. We recently developed a two-step approach to generate enhanced mimotopes using positional peptide microlibraries and herein applied this strategy to the broadly used H-2Kb-restricted murine leukemia p15E tumor rejection epitope. The wild-type p15E epitope (sequence: KSPWFTTL) was poorly immunogenic in mice, even when combined with a potent peptide nanoparticle vaccine system and did not delay p15E-expressing MC38 tumor growth. Following positional microlibrary functional screening of over 150 mimotope candidates, two were identified, both with mutations at residue 3 (p15E-P3C; "3C," and p15E-P3M; "3M") that better induced p15E-specific CD8+ T cells and led to tumor rejection. Although 3M was more immunogenic, 3C effectively delayed tumor growth in a therapeutic setting relative to the wild-type p15E. As 3C had less H-2Kb affinity relative to both p15E and 3M, 15 additional mimotope candidates (all that incorporated the 3C mutation) were assessed that maintained or improved predicted MHC-I affinity. Valine substitution at position 2 (3C2V, sequence: KVCWFTTL) led to improved p15E-specific immunogenicity, tumor rejection, and subsequent long-term antitumor immunity. 3C, 3M, and 3C2V mimotopes were more effective than p15E in controlling MC38 and B16-F10 tumors. T-cell receptor (TCR) sequencing revealed unique TCR transcripts for mimotopes, but there were no major differences in clonality. These results provide new p15E mimotopes for further vaccine use and illustrate considerations for MHC-I affinity, immunogenicity, and functional efficacy in mimotope design.

SIGNIFICANCE

The MHC-I-restricted p15E tumor rejection epitope is expressed in multiple murine cancer lines and is used as a marker of antitumor cellular immunity, but has seen limited success as a vaccine immunogen. An in vivo screening approach based on a positional peptide microlibraries is used to identify enhanced p15E mimotopes bearing amino acid mutations that induce significantly improved functional immunogenicity relative to vaccination with the wild-type epitope.

Research progress of immunotherapy against anaplastic thyroid cancer

Anaplastic thyroid cancer (ATC) is the most aggressive type of thyroid cancer. While ATC is rare, its mortality is high. Standard treatments, such as surgery, radiotherapy, and chemotherapy, have demonstrated limited efficacy in managing ATC. However, the advent of immunotherapy has significantly improved the prognosis for patients with ATC. Immunotherapy effectively targets and eliminates tumor cells by using the power of the body's immune cells. The neoantigen is an atypical protein generated by somatic mutation, is exclusively observed in neoplastic cells, and is devoid of central tolerance. Neoantigens exhibit enhanced specificity towards tumor cells and display robust immunogenic properties. Currently, neoantigen therapy is primarily applied in immune checkpoint inhibitors and cellular immunotherapy, encompassing adoptive immunotherapy and tumor vaccines. This study discusses the mechanism, tumor microenvironment, clinical trials, adverse events, limitations and future directions associated with ATC immunotherapy.

Transfer learning improves pMHC kinetic stability and immunogenicity predictions

The cellular immune response comprises several processes, with the most notable ones being the binding of the peptide to the Major Histocompability Complex (MHC), the peptide-MHC (pMHC) presentation to the surface of the cell, and the recognition of the pMHC by the T-Cell Receptor. Identifying the most potent peptide targets for MHC binding, presentation and T-cell recognition is vital for developing peptide-based vaccines and T-cell-based immunotherapies. Data-driven tools that predict each of these steps have been developed, and the availability of mass spectrometry (MS) datasets has facilitated the development of accurate Machine Learning (ML) methods for class-I pMHC binding prediction. However, the accuracy of ML-based tools for pMHC kinetic stability prediction and peptide immunogenicity prediction is uncertain, as stability and immunogenicity datasets are not abundant. Here, we use transfer learning techniques to improve stability and immunogenicity predictions, by taking advantage of a large number of binding affinity and MS datasets. The resulting models, TLStab and TLImm, exhibit comparable or better performance than state-of-the-art approaches on different stability and immunogenicity test sets respectively. Our approach demonstrates the promise of learning from the task of peptide binding to improve predictions on downstream tasks. The source code of TLStab and TLImm is publicly available at https://github.com/KavrakiLab/TL-MHC.

Zika virus precursor membrane peptides induce immune response in peripheral blood mononuclear cells

Zika virus is a re-merging flavivirus allied to serious mental health conditions in the fetuses. There is currently no preventives or treatment available for Zika infection. In this work, we have extended the in silico analysis by performing the molecular docking of previous reported three conserved Zika virus precursor membrane (prM) peptides (MP1, MP2 and MP3) with HLA complex (pHLA) and T cell receptors (TCR) and also evaluated the peptide specific immune response in human peripheral blood mononuclear cells (PBMC). Most of the CD8+ and CD4+ T cell peptides-HLA complexes demonstrated good binding energies (ΔG) and HADDOCK scores in molecular docking analysis. Immunogenic response of peptides is measured as human peripheral blood mononuclear cell (PBMC) proliferation and interferon-gamma (IFN-γ) production using a 3-(4,5- dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay and a sandwich enzyme-linked immunosorbent assay (ELISA) respectively on ten different healthy blood samples. Peptide MP3 exhibited significant results in eight (cell proliferation) and seven (IFN-γ secretion) healthy volunteers' blood samples out of ten. Additionally, peptides MP1 and MP2 presented significant cell proliferation and IFN-γ release in six healthy blood samples. Thus, the outcomes from in silico and in vitro studies showed the immunogenic potential of peptides which need to validated in different experimental system before considering as candidate vaccine against Zika virus infection.

Fighting flu: novel CD8+ T-cell targets are required for future influenza vaccines

Seasonal influenza viruses continue to cause severe medical and financial complications annually. Although there are many licenced influenza vaccines, there are billions of cases of influenza infection every year, resulting in the death of over half a million individuals. Furthermore, these figures can rise in the event of a pandemic, as seen throughout history, like the 1918 Spanish influenza pandemic (50 million deaths) and the 1968 Hong Kong influenza pandemic (~4 million deaths). In this review, we have summarised many of the currently licenced influenza vaccines available across the world and current vaccines in clinical trials. We then briefly discuss the important role of CD8+ T cells during influenza infection and why future influenza vaccines should consider targeting CD8+ T cells. Finally, we assess the current landscape of known immunogenic CD8+ T-cell epitopes and highlight the knowledge gaps required to be filled for the design of rational future influenza vaccines that incorporate CD8+ T cells.

copepodTCR: Identification of Antigen-Specific T Cell Receptors with combinatorial peptide pooling

T cell receptor (TCR) repertoire diversity enables the orchestration of antigen-specific immune responses against the vast space of possible pathogens. Identifying TCR/antigen binding pairs from the large TCR repertoire and antigen space is crucial for biomedical research. Here, we introduce copepodTCR, an open-access tool for the design and interpretation of high-throughput experimental assays to determine TCR specificity. copepodTCR implements a combinatorial peptide pooling scheme for efficient experimental testing of T cell responses against large overlapping peptide libraries, useful for "deorphaning" TCRs of unknown specificity. The scheme detects experimental errors and, coupled with a hierarchical Bayesian model for unbiased results interpretation, identifies the response-eliciting peptide for a TCR of interest out of hundreds of peptides tested using a simple experimental set-up. We experimentally validated our approach on a library of 253 overlapping peptides covering the SARS-CoV-2 spike protein. We provide experimental guides for efficient design of larger screens covering thousands of peptides which will be crucial for the identification of antigen-specific T cells and their targets from limited clinical material.

Molecular mechanism of specific HLA-A mRNA recognition by the RNA-binding-protein hMEX3B to promote tumor immune escape

Immunotherapy, including immune checkpoint inhibitors and adoptive cell transfer, has obtained great progress, but their efficiencies vary among patients due to the genetic and epigenetic differences. Human MEX3B (hMEX3B) protein is an RNA-binding protein that contains two KH domains at the N-terminus and a RING domain at its C-terminus, which has the activity of E3 ubiquitin ligase and is essential for RNA degradation. Current evidence suggests that hMEX3B is involved in many important biological processes, including tumor immune evasion and HLA-A regulation, but the sequence of substrate RNA recognized by hMEX3B and the functional molecular mechanisms are unclear. Here, we first screened the optimized hMEX3B binding sequence on the HLA-A mRNA and reported that the two tandem KH domains can bind with their substrate one hundred times more than the individual KH domains. We systematically investigated the binding characteristics between the two KH domains and their RNA substrates by nuclear magnetic resonance (NMR). Based on this information and the small-angle X-ray scattering (SAXS) data, we used molecular dynamics simulations to obtain structural models of KH domains in complex with their corresponding RNAs. By analyzing the models, we noticed that on the KH domains' variable loops, there were two pairs of threonines and arginines that can disrupt the recognition of the RNA completely, and this influence had also been verified both in vitro and in vivo. Finally, we presented a functional model of the hMEX3B protein, which indicated that hMEX3B regulated the degradation of its substrate mRNAs in many biological processes. Taken together, our research illustrated how the hMEX3B protein played a key role in translation inhibition during the immune response to tumor cells and provided an idea and a lead for the study of the molecular mechanism and function of other MEX3 family proteins.

DNA Vaccines: Their Formulations, Engineering and Delivery

The concept of DNA vaccination was introduced in the early 1990s. Since then, advancements in the augmentation of the immunogenicity of DNA vaccines have brought this technology to the market, especially in veterinary medicine, to prevent many diseases. Along with the successful COVID mRNA vaccines, the first DNA vaccine for human use, the Indian ZyCovD vaccine against SARS-CoV-2, was approved in 2021. In the current review, we first give an overview of the DNA vaccine focusing on the science, including adjuvants and delivery methods. We then cover some of the emerging science in the field of DNA vaccines, notably efforts to optimize delivery systems, better engineer delivery apparatuses, identify optimal delivery sites, personalize cancer immunotherapy through DNA vaccination, enhance adjuvant science through gene adjuvants, enhance off-target and heritable immunity through epigenetic modification, and predict epitopes with bioinformatic approaches. We also discuss the major limitations of DNA vaccines and we aim to address many theoretical concerns.

Optical sensing and control of T cell signaling pathways

T cells regulate adaptive immune responses through complex signaling pathways mediated by T cell receptor (TCR). The functional domains of the TCR are combined with specific antibodies for the development of chimeric antigen receptor (CAR) T cell therapy. In this review, we first overview current understanding on the T cell signaling pathways as well as traditional methods that have been widely used for the T cell study. These methods, however, are still limited to investigating dynamic molecular events with spatiotemporal resolutions. Therefore, genetically encoded biosensors and optogenetic tools have been developed to study dynamic T cell signaling pathways in live cells. We review these cutting-edge technologies that revealed dynamic and complex molecular mechanisms at each stage of T cell signaling pathways. They have been primarily applied to the study of dynamic molecular events in TCR signaling, and they will further aid in understanding the mechanisms of CAR activation and function. Therefore, genetically encoded biosensors and optogenetic tools offer powerful tools for enhancing our understanding of signaling mechanisms in T cells and CAR-T cells.

Molecular Characteristics, Functional Definitions, and Regulatory Mechanisms for Cross-Presentation Mediated by the Major Histocompatibility Complex: A Comprehensive Review

The major histocompatibility complexes of vertebrates play a key role in the immune response. Antigen-presenting cells are loaded on MHC I molecules, which mainly present endogenous antigens; when MHC I presents exogenous antigens, this is called cross-presentation. The discovery of cross-presentation provides an important theoretical basis for the study of exogenous antigens. Cross-presentation is a complex process in which MHC I molecules present antigens to the cell surface to activate CD8+ T lymphocytes. The process of cross-representation includes many components, and this article briefly outlines the origins and development of MHC molecules, gene structures, functions, and their classical presentation pathways. The cross-presentation pathways of MHC I molecules, the cell lines that support cross-presentation, and the mechanisms of MHC I molecular transporting are all reviewed. After more than 40 years of research, the specific mechanism of cross-presentation is still unclear. In this paper, we summarize cross-presentation and anticipate the research and development prospects for cross-presentation.

HLA DQ protein changes the cell surface distribution pattern of HLA proteins as monitored by Förster resonance energy transfer and high-resolution electron microscopy

Peptide presentation by MHC class I and MHC class II molecules plays important roles in the regulation of the immune response. One factor in these displays is the density of antigen, which must exceed a critical threshold for the effective activation of T cells. Nonrandom distribution of MHC class I and class II has already been detected at the nanometer level and at higher hierarchical levels. It is not clear how the absence and reappearance of some protein molecules can influence the nonrandom distribution. Therefore, we performed experiments on HLA II-deficient bare lymphocyte syndrome (BLS1) cells: we created a stable transfected cell line, tDQ6-BLS-1, and were able to detect the effect of the appearance of HLA-DQ6 molecules on the homo and heteroassociation of different cell surface molecules by comparing Förster resonance energy transfer (FRET) efficiency on transfected cells to that on nontransfected BLS-1 and JY human B-cell lines. Our FRET results show a decrease in homoassociation FRET between HLA I chains in HLA-DQ6-transfected tDQ6-BLS-1 cells compared with the parent BLS-1 cell line and an increase in heteroassociation FRET between HLA I and HLA II (compared with JY cells), suggesting a similar pattern of antigen presentation by the HLA-DQ6 allele. Transmission electron microscopy (TEM) revealed that both HLA class I and class II molecules formed clusters at higher hierarchical levels on the tDQ6-BLS-1 cells, and the de novo synthesized HLA DQ molecules did not intersperse with HLA class I islands. These observations could be important in understanding the fine tuning of the immune response.

Immune checkpoint inhibitors in bone metastasis: Clinical challenges, toxicities, and mechanisms

Immune checkpoint inhibitors (ICIs) have revolutionized the field of anti-cancer therapy over the last decade; they provide durable clinical responses against tumors by inhibiting immune checkpoint proteins that canonically regulate the T cell-mediated immune response. Despite their success in many primary tumors and soft tissue metastases, ICIs function poorly in patients with bone metastases, and these patients do not have the same survival benefit as patients with the same primary tumor type (e.g., non-small cell lung cancer [NSCLC], urothelial, renal cell carcinoma [RCC], etc.) that has not metastasized to the bone. Additionally, immune-related adverse events including rheumatologic and musculoskeletal toxicities, bone loss, and increased fracture risk develop after treatment with ICIs. There are few preclinical studies that investigate the interplay of the immune system in bone metastases; however, the current literature suggests a role for CD8+ T cells and myeloid cell subsets in bone homeostasis. As such, this review focuses on findings from the clinical and pre-clinical studies that have investigated immune checkpoint blockade in the bone metastatic setting and highlights the need for more comprehensive investigations into the relationship between immune cell subsets, ICIs, and the bone-tumor microenvironment.

Neural network models for sequence-based TCR and HLA association prediction

T cells rely on their T cell receptors (TCRs) to discern foreign antigens presented by human leukocyte antigen (HLA) proteins. The TCRs of an individual contain a record of this individual's past immune activities, such as immune response to infections or vaccines. Mining the TCR data may recover useful information or biomarkers for immune related diseases or conditions. Some TCRs are observed only in the individuals with certain HLA alleles, and thus characterizing TCRs requires a thorough understanding of TCR-HLA associations. The extensive diversity of HLA alleles and the rareness of some HLA alleles present a formidable challenge for this task. Existing methods either treat HLA as a categorical variable or represent an HLA by its alphanumeric name, and have limited ability to generalize to the HLAs that are not seen in the training process. To address this challenge, we propose a neural network-based method named Deep learning Prediction of TCR-HLA association (DePTH) to predict TCR-HLA associations based on their amino acid sequences. We demonstrate that DePTH is capable of making reasonable predictions for TCR-HLA associations, even when neither the HLA nor the TCR have been included in the training dataset. Furthermore, we establish that DePTH can be used to quantify the functional similarities among HLA alleles, and that these HLA similarities are associated with the survival outcomes of cancer patients who received immune checkpoint blockade treatments.

γδ T cells as a potential therapeutic agent for glioblastoma

Although γδ T cells comprise a small population of T cells, they perform important roles in protecting against infection and suppressing tumors. With their distinct tissue-localizing properties, combined with their various target recognition mechanisms, γδ T cells have the potential to become an effective solution for tumors that do not respond to current therapeutic procedures. One such tumor, glioblastoma (GBM), is a malignant brain tumor with the highest World Health Organization grade and therefore the worst prognosis. The immune-suppressive tumor microenvironment (TME) and immune-evasive glioma stem cells are major factors in GBM immunotherapy failure. Currently, encouraged by the strong anti-tumoral function of γδ T cells revealed at the preclinical and clinical levels, several research groups have shown progression of γδ T cell-based GBM treatment. However, several limitations still exist that block effective GBM treatment using γδ T cells. Therefore, understanding the distinct roles of γδ T cells in anti-tumor immune responses and the suppression mechanism of the GBM TME are critical for successful γδ T cell-mediated GBM therapy. In this review, we summarize the effector functions of γδ T cells in tumor immunity and discuss current advances and limitations of γδ T cell-based GBM immunotherapy. Additionally, we suggest future directions to overcome the limitations of γδ T cell-based GBM immunotherapy to achieve successful treatment of GBM.

Elucidation of binding mechanism, affinity, and complex structure between mWT1 tumor-associated antigen peptide and HLA-A*24:02

We have applied our advanced computational and experimental methodologies to investigate the complex structure and binding mechanism of a modified Wilms' Tumor 1 (mWT1) protein epitope to the understudied Asian-dominant allele HLA-A*24:02 (HLA-A24) in aqueous solution. We have applied our developed multicanonical molecular dynamics (McMD)-based dynamic docking method to analyze the binding pathway and mechanism, which we verified by comparing the highest probability structures from simulation with our experimentally solved x-ray crystal structure. Subsequent path sampling MD simulations elucidated the atomic details of the binding process and indicated that first an encounter complex is formed between the N-terminal's positive charge of the 9-residue mWT1 fragment peptide and a cluster of negative residues on the surface of HLA-A24, with the major histocompatibility complex (MHC) molecule preferring a predominantly closed conformation. The peptide first binds to this closed MHC conformation, forming an encounter complex, after which the binding site opens due to increased entropy of the binding site, allowing the peptide to bind to form the native complex structure. Further sequence and structure analyses also suggest that although the peptide loading complex would help with stabilizing the MHC molecule, the binding depends in a large part on the intrinsic affinity between the MHC molecule and the antigen peptide. Finally, our computational tools and analyses can be of great benefit to study the binding mechanism of different MHC types to their antigens, where it could also be useful in the development of higher affinity variant peptides and for personalized medicine.

Massively-multiplexed epitope mapping techniques for viral antigen discovery

Following viral infection, viral antigens bind specifically to receptors on the surface of lymphocytes thereby activating adaptive immunity in the host. An epitope, the smallest structural and functional unit of an antigen, binds specifically to an antibody or antigen receptor, to serve as key sites for the activation of adaptive immunity. The complexity and diverse range of epitopes are essential to study and map for the diagnosis of disease, the design of vaccines and for immunotherapy. Mapping the location of these specific epitopes has become a hot topic in immunology and immune therapy. Recently, epitope mapping techniques have evolved to become multiplexed, with the advent of high-throughput sequencing and techniques such as bacteriophage-display libraries and deep mutational scanning. Here, we briefly introduce the principles, advantages, and disadvantages of the latest epitope mapping techniques with examples for viral antigen discovery.

Learning from the microbes: exploiting the microbiome to enforce T cell immunotherapy

The opportunities genetic engineering has created in the field of adoptive cellular therapy for cancer are accelerating the development of novel treatment strategies using chimeric antigen receptor (CAR) and T cell receptor (TCR) T cells. The great success in the context of hematologic malignancies has made especially CAR T cell therapy a promising approach capable of achieving long-lasting remission. However, the causalities involved in mediating resistance to treatment or relapse are still barely investigated. Research on T cell exhaustion and dysfunction has drawn attention to host-derived factors that define both the immune and tumor microenvironment (TME) crucially influencing efficacy and toxicity of cellular immunotherapy. The microbiome, as one of the most complex host factors, has become a central topic of investigations due to its ability to impact on health and disease. Recent findings support the hypothesis that commensal bacteria and particularly microbiota-derived metabolites educate and modulate host immunity and TME, thereby contributing to the response to cancer immunotherapy. Hence, the composition of microbial strains as well as their soluble messengers are considered to have predictive value regarding CAR T cell efficacy and toxicity. The diversity of mechanisms underlying both beneficial and detrimental effects of microbiota comprise various epigenetic, metabolic and signaling-related pathways that have the potential to be exploited for the improvement of CAR T cell function. In this review, we will discuss the recent findings in the field of microbiome-cancer interaction, especially with respect to new trajectories that commensal factors can offer to advance cellular immunotherapy.

Antibody Cross-Reactivity in Auto-Immune Diseases

Autoimmunity is defined by the presence of antibodies and/or T cells directed against self-components. Although of unknown etiology, autoimmunity commonly is associated with environmental factors such as infections, which have been reported to increase the risk of developing autoimmune diseases. Occasionally, similarities between infectious non-self and self-tissue antigens may contribute to immunological cross-reactivity in autoimmune diseases. These reactions may be interpreted as molecular mimicry, which describes cross-reactivity between foreign pathogens and self-antigens that have been reported to cause tissue damage and to contribute to the development of autoimmunity. By focusing on the nature of antibodies, cross-reactivity in general, and antibody-antigen interactions, this review aims to characterize the nature of potential cross-reactive immune reactions between infectious non-self and self-tissue antigens which may be associated with autoimmunity but may not actually be the cause of disease onset.

Beyond αβ T cells: NK, iNKT, and γδT cell biology in leukemic patients and potential for off-the-shelf adoptive cell therapies for AML

Acute myeloid leukemia (AML) remains an elusive disease to treat, let alone cure, even after highly intensive therapies such as stem cell transplants. Adoptive cell therapeutic strategies based on conventional alpha beta (αβ)T cells are an active area of research in myeloid neoplasms given their remarkable success in other hematologic malignancies, particularly B-cell-derived acute lymphoid leukemia, myeloma, and lymphomas. Several limitations have hindered clinical application of adoptive cell therapies in AML including lack of leukemia-specific antigens, on-target-off-leukemic toxicity, immunosuppressive microenvironments, and leukemic stem cell populations elusive to immune recognition and destruction. While there are promising T cell-based therapies including chimeric antigen receptor (CAR)-T designs under development, other cytotoxic lymphocyte cell subsets have unique phenotypes and capabilities that might be of additional benefit in AML treatment. Of particular interest are the natural killer (NK) and unconventional T cells known as invariant natural killer T (iNKT) and gamma delta (γδ) T cells. NK, iNKT, and γδT cells exhibit intrinsic anti-malignant properties, potential for alloreactivity, and human leukocyte-antigen (HLA)-independent function. Here we review the biology of each of these unconventional cytotoxic lymphocyte cell types and compare and contrast their strengths and limitations as the basis for adoptive cell therapies for AML.

Defining and Studying B Cell Receptor and TCR Interactions

BCRs (Abs) and TCRs (or adaptive immune receptors [AIRs]) are the means by which the adaptive immune system recognizes foreign and self-antigens, playing an integral part in host defense, as well as the emergence of autoimmunity. Importantly, the interaction between AIRs and their cognate Ags defies a simple key-in-lock paradigm and is instead a complex many-to-many mapping between an individual's massively diverse AIR repertoire, and a similarly diverse antigenic space. Understanding how adaptive immunity balances specificity with epitopic coverage is a key challenge for the field, and terms such as broad specificity, cross-reactivity, and polyreactivity remain ill-defined and are used inconsistently. In this Immunology Notes and Resources article, a group of experimental, structural, and computational immunologists define commonly used terms associated with AIR binding, describe methodologies to study these binding modes, as well as highlight the implications of these different binding modes for therapeutic design.

Molecular mechanism of phosphopeptide neoantigen immunogenicity

Altered protein phosphorylation in cancer cells often leads to surface presentation of phosphopeptide neoantigens. However, their role in cancer immunogenicity remains unclear. Here we describe a mechanism by which an HLA-B*0702-specific acute myeloid leukemia phosphoneoantigen, pMLL747-755 (EPR(pS)PSHSM), is recognized by a cognate T cell receptor named TCR27, a candidate for cancer immunotherapy. We show that the replacement of phosphoserine P4 with serine or phosphomimetics does not affect pMHC conformation or peptide-MHC affinity but abrogates TCR27-dependent T cell activation and weakens binding between TCR27 and pMHC. Here we describe the crystal structures for TCR27 and cognate pMHC, map of the interface produced by nuclear magnetic resonance, and a ternary complex generated using information-driven protein docking. Our data show that non-covalent interactions between the epitope phosphate group and TCR27 are crucial for TCR specificity. This study supports development of new treatment options for cancer patients through target expansion and TCR optimization.

Neural Network Models for Sequence-Based TCR and HLA Association Prediction

T cells rely on their T cell receptors (TCRs) to recognize foreign antigens presented by human leukocyte antigen (HLA) proteins. TCRs contain a record of an individual's past immune activities, and some TCRs are observed only in individuals with certain HLA alleles. As a result, characterising TCRs requires a thorough understanding of TCR-HLA associations. To this end, we propose a neural network method named Deep learning Prediction of TCR-HLA association (DePTH) to predict TCR-HLA associations based on their amino acid sequences. We show that the DePTH can be used to quantify the functional similarities of HLA alleles, and that these HLA similarities are associated with the survival outcomes of cancer patients who received immune checkpoint blockade treatment.

iTCep: a deep learning framework for identification of T cell epitopes by harnessing fusion features

Neoantigens recognized by cytotoxic T cells are effective targets for tumor-specific immune responses for personalized cancer immunotherapy. Quite a few neoantigen identification pipelines and computational strategies have been developed to improve the accuracy of the peptide selection process. However, these methods mainly consider the neoantigen end and ignore the interaction between peptide-TCR and the preference of each residue in TCRs, resulting in the filtered peptides often fail to truly elicit an immune response. Here, we propose a novel encoding approach for peptide-TCR representation. Subsequently, a deep learning framework, namely iTCep, was developed to predict the interactions between peptides and TCRs using fusion features derived from a feature-level fusion strategy. The iTCep achieved high predictive performance with AUC up to 0.96 on the testing dataset and above 0.86 on independent datasets, presenting better prediction performance compared with other predictors. Our results provided strong evidence that model iTCep can be a reliable and robust method for predicting TCR binding specificities of given antigen peptides. One can access the iTCep through a user-friendly web server at http://biostatistics.online/iTCep/, which supports prediction modes of peptide-TCR pairs and peptide-only. A stand-alone software program for T cell epitope prediction is also available for convenient installing at https://github.com/kbvstmd/iTCep/.

T cell effects and mechanisms in immunotherapy of head and neck tumors

Head and neck tumors (HNCs) are a common tumor in otorhinolaryngology head and neck surgery, accounting for 5% of all malignant tumors in the body and are the sixth most common malignant tumor worldwide. In the body, immune cells can recognize, kill, and remove HNCs. T cell-mediated antitumor immune activity is the most important antitumor response in the body. T cells have different effects on tumor cells, among which cytotoxic T cells and helper T cells play a major killing and regulating role. T cells recognize tumor cells, activate themselves, differentiate into effector cells, and activate other mechanisms to induce antitumor effects. In this review, the immune effects and antitumor mechanisms mediated by T cells are systematically described from the perspective of immunology, and the application of new immunotherapy methods related to T cells are discussed, with the objective of providing a theoretical basis for exploring and forming new antitumor treatment strategies. Video Abstract.

Artificial intelligence in cancer immunotherapy: Applications in neoantigen recognition, antibody design and immunotherapy response prediction

Cancer immunotherapy is a method of controlling and eliminating tumors by reactivating the body's cancer-immunity cycle and restoring its antitumor immune response. The increased availability of data, combined with advancements in high-performance computing and innovative artificial intelligence (AI) technology, has resulted in a rise in the use of AI in oncology research. State-of-the-art AI models for functional classification and prediction in immunotherapy research are increasingly used to support laboratory-based experiments. This review offers a glimpse of the current AI applications in immunotherapy, including neoantigen recognition, antibody design, and prediction of immunotherapy response. Advancing in this direction will result in more robust predictive models for developing better targets, drugs, and treatments, and these advancements will eventually make their way into the clinical setting, pushing AI forward in the field of precision oncology.

Engagement with the TCR induces plasticity in antigenic ligands bound to MHC class I and CD1 molecules

Complementarity-determining regions (CDRs) of αβ T-cell receptors (TCRs) sense peptide-bound MHC (pMHC) complexes via chemical interactions, thereby mediating antigen specificity and MHC restriction. Flexible finger-like movement of CDR loops contributes to the establishment of optimal interactions with pMHCs. In contrast, peptide ligands captured in MHC molecules are considered more static because of the rigid hydrogen-bond network that stabilizes peptide ligands in the antigen-binding groove of MHC molecules. An array of crystal structures delineating pMHC complexes in TCR-docked and TCR-undocked forms is now available, which enables us to assess TCR engagement-induced conformational changes in peptide ligands. In this short review, we overview conformational changes in MHC class I-bound peptide ligands upon TCR docking, followed by those for CD1-bound glycolipid ligands. Finally, we analyze the co-crystal structure of the TCR:lipopeptide-bound MHC class I complex that we recently reported. We argue that TCR engagement-induced conformational changes markedly occur in lipopeptide ligands, which are essential for exposure of a primary T-cell epitope to TCRs. These conformational changes are affected by amino acid residues, such as glycine, that do not interact directly with TCRs. Thus, ligand recognition by specific TCRs involves not only T-cell epitopes but also non-epitopic amino acid residues. In light of their critical function, we propose to refer to these residues as non-epitopic residues affecting ligand plasticity and antigenicity (NR-PA).

Prediction and validation of murine MHC class I epitopes of the recombinant virus VSV-GP

Oncolytic viruses are currently tested as a novel platform for cancer therapy. These viruses preferentially replicate in and kill malignant cells. Due to their microbial origin, treatment with oncolytic viruses naturally results in anti-viral responses and general immune activation. Consequently, the oncolytic virus treatment also induces anti-viral T cells. Since these can constitute the dominant activated T cell pool, monitoring of the anti-viral T cell response may aid in better understanding of the immune responses post oncolytic virotherapy. This study aimed to identify the anti-viral T cells raised by VSV-GP virotherapy in C57BL/6J mice, one of the most widely used models for preclinical studies. VSV-GP is a novel oncolytic agent that recently entered a clinical phase I study. To identify the VSV-GP epitopes to which mouse anti-viral T cells react, we used a multilevel adapted bioinformatics viral epitope prediction approach based on the tools netMHCpan, MHCflurry and netMHCstabPan, which are commonly used in neoepitope identification. Predicted viral epitopes were ranked based on consensus binding strength categories, predicted stability, and dissimilarity to the mouse proteome. The top ranked epitopes were selected and included in the peptide candidate matrix in order to use a matrix deconvolution approach. Using ELISpot, we showed which viral epitopes presented on C57BL/6J mouse MHC-I alleles H2-Db and H2-Kb trigger IFN-γ secretion due to T cell activation. Furthermore, we validated these findings using an intracellular cytokine staining. Collectively, identification of the VSV-GP T cell epitopes enables monitoring of the full range of anti-viral T cell responses upon VSV-GP virotherapy in future studies with preclinical mouse models to more comprehensively delineate anti-viral from anti-tumor T cell responses. These findings also support the development of novel VSV-GP variants expressing immunomodulatory transgenes and can improve the assessment of anti-viral immunity in preclinical models.

Computationally profiling peptide:MHC recognition by T-cell receptors and T-cell receptor-mimetic antibodies

T-cell receptor-mimetic antibodies (TCRms) targeting disease-associated peptides presented by Major Histocompatibility Complexes (pMHCs) are set to become a major new drug modality. However, we lack a general understanding of how TCRms engage pMHC targets, which is crucial for predicting their specificity and safety. Several new structures of TCRm:pMHC complexes have become available in the past year, providing sufficient initial data for a holistic analysis of TCRms as a class of pMHC binding agents. Here, we profile the complete set of TCRm:pMHC complexes against representative TCR:pMHC complexes to quantify the TCR-likeness of their pMHC engagement. We find that intrinsic molecular differences between antibodies and TCRs lead to fundamentally different roles for their heavy/light chains and Complementarity-Determining Region loops during antigen recognition. The idiotypic properties of antibodies may increase the likelihood of TCRms engaging pMHCs with less peptide selectivity than TCRs. However, the pMHC recognition features of some TCRms, including the two TCRms currently in clinical trials, can be remarkably TCR-like. The insights gained from this study will aid in the rational design and optimisation of next-generation TCRms.

Antigen receptor structure and signaling

The key to mounting an immune response is that the host cells must be coordinated to generate an appropriate immune response against the pathogenic invaders. Antigen receptors recognize specific molecular structures and recruit adaptors through their effector domains, triggering trans-membrane transduction signaling pathway to exert immune response. The T cell antigen receptor (TCR) and B cell antigen receptor (BCR) are the primary determinant of immune responses to antigens. Their structure determines the mode of signaling and signal transduction determines cell fate, leading to changes at the molecular and cellular level. Studies of antigen receptor structure and signaling revealed the basis of immune response triggering, providing clues to antigen receptor priming and a foundation for the rational design of immunotherapies. In recent years, the increased research on the structure of antigen receptors has greatly contributed to the understanding of immune response, different immune-related diseases and even tumors. In this review, we describe in detail the current view and advances of the antigen structure and signaling.

The Intracellular Proteome as a Source for Novel Targets in CAR-T and T-Cell Engagers-Based Immunotherapy

The impressive clinical success of cancer immunotherapy has motivated the continued search for new targets that may serve to guide potent effector functions in an attempt to efficiently kill malignant cells. The intracellular proteome is an interesting source for such new targets, such as neo-antigens and others, with growing interest in their application for cell-based immunotherapies. These intracellular-derived targets are peptides presented by MHC class I molecules on the cell surface of malignant cells. These disease-specific class I HLA-peptide complexes can be targeted by specific TCRs or by antibodies that mimic TCR-specificity, termed TCR-like (TCRL) antibodies. Adoptive cell transfer of TCR engineered T cells and T-cell-receptor-like based CAR-T cells, targeted against a peptide-MHC of interest, are currently tested as cancer therapeutic agents in pre-clinical and clinical trials, along with soluble TCR- and TCRL-based agents, such as immunotoxins and bi-specific T cell engagers. Targeting the intracellular proteome using TCRL- and TCR-based molecules shows promising results in cancer immunotherapy, as exemplified by the success of the anti-gp100/HLA-A2 TCR-based T cell engager, recently approved by the FDA for the treatment of unresectable or metastatic uveal melanoma. This review is focused on the selection and isolation processes of TCR- and TCRL-based targeting moieties, with a spotlight on pre-clinical and clinical studies, examining peptide-MHC targeting agents in cancer immunotherapy.

Machine learning methods for protein-protein binding affinity prediction in protein design

Protein-protein interactions govern a wide range of biological activity. A proper estimation of the protein-protein binding affinity is vital to design proteins with high specificity and binding affinity toward a target protein, which has a variety of applications including antibody design in immunotherapy, enzyme engineering for reaction optimization, and construction of biosensors. However, experimental and theoretical modelling methods are time-consuming, hinder the exploration of the entire protein space, and deter the identification of optimal proteins that meet the requirements of practical applications. In recent years, the rapid development in machine learning methods for protein-protein binding affinity prediction has revealed the potential of a paradigm shift in protein design. Here, we review the prediction methods and associated datasets and discuss the requirements and construction methods of binding affinity prediction models for protein design.

A class-mismatched TCR bypasses MHC restriction via an unorthodox but fully functional binding geometry

MHC restriction, which describes the binding of TCRs from CD4⁺ T cells to class II MHC proteins and TCRs from CD8⁺ T cells to class I MHC proteins, is a hallmark of immunology. Seemingly rare TCRs that break this paradigm exist, but mechanistic insight into their behavior is lacking. TIL1383I is a prototypical class-mismatched TCR, cloned from a CD4⁺ T cell but recognizing the tyrosinase tumor antigen presented by the class I MHC HLA-A2 in a fully functional manner. Here we find that TIL1383I binds this class I target with a highly atypical geometry. Despite unorthodox binding, TCR signaling, antigen specificity, and the ability to use CD8 are maintained. Structurally, a key feature of TIL1383I is an exceptionally long CDR3β loop that mediates functions that are traditionally performed separately by hypervariable and germline loops in canonical TCR structures. Our findings thus expand the range of known TCR binding geometries compatible with normal function and specificity, provide insight into the determinants of MHC restriction, and may help guide TCR selection and engineering for immunotherapy.

Molecular Basis for the Recognition of HIV Nef138-8 Epitope by a Pair of Human Public T Cell Receptors

Cross-recognized public TCRs against HIV epitopes have been proposed to be important for the control of AIDS disease progression and HIV variants. The overlapping Nef138-8 and Nef138-10 peptides from the HIV Nef protein are HLA-A24-restricted immunodominant T cell epitopes, and an HIV mutant strain with a Y139F substitution in Nef protein can result in immune escape and is widespread in Japan. Here, we identified a pair of public TCRs specific to the HLA-A24-restricted Nef-138-8 epitope using PBMCs from White and Japanese patients, respectively, namely TD08 and H25-11. The gene use of the variable domain for TD08 and H25-11 is TRAV8-3, TRAJ10 for the α-chain and TRBV7-9, TRBD1*01, TRBJ2-5 for the β-chain. Both TCRs can recognize wild-type and Y2F-mutated Nef138-8 epitopes. We further determined three complex structures, including TD08/HLA-A24-Nef138-8, H25-11/HLA-A24-Nef138-8, and TD08/HLA-A24-Nef138-8 (2F). Then, we revealed the molecular basis of the public TCR binding to the peptide HLA, which mostly relies on the interaction between the TCR and HLA and can tolerate the mutation in the Nef138-8 peptide. These findings promote the molecular understanding of T cell immunity against HIV epitopes and provide an important basis for the engineering of TCRs to develop T cell-based immunotherapy against HIV infection.

Ablation of CD8+ T cell recognition of an immunodominant epitope in SARS-CoV-2 Omicron variants BA.1, BA.2 and BA.3

The emergence of the SARS-CoV-2 Omicron variant has raised concerns of escape from vaccine-induced immunity. A number of studies have demonstrated a reduction in antibody-mediated neutralization of the Omicron variant in vaccinated individuals. Preliminary observations have suggested that T cells are less likely to be affected by changes in Omicron. However, the complexity of human leukocyte antigen genetics and its impact upon immunodominant T cell epitope selection suggests that the maintenance of T cell immunity may not be universal. In this study, we describe the impact that changes in Omicron BA.1, BA.2 and BA.3 have on recognition by spike-specific T cells. These T cells constitute the immunodominant CD8⁺ T cell response in HLA-A*29:02⁺ COVID-19 convalescent and vaccinated individuals; however, they fail to recognize the Omicron-encoded sequence. These observations demonstrate that in addition to evasion of antibody-mediated immunity, changes in Omicron variants can also lead to evasion of recognition by immunodominant T cell responses.

Homologous peptides derived from influenza A, B and C viruses induce variable CD8+ T cell responses with cross-reactive potential

Objective

Influenza A, B and C viruses (IAV, IBV and ICV, respectively) circulate globally, infecting humans and causing widespread morbidity and mortality. Here, we investigate the T cell response towards an immunodominant IAV epitope, NP_265‐273, and its IBV and ICV homologues, presented by HLA‐A*03:01 molecule expressed in ~ 4% of the global population (~ 300 million people).

Methods

We assessed the magnitude (tetramer staining) and quality of the CD8⁺ T cell response (intracellular cytokine staining) towards NP_265‐IAV and described the T cell receptor (TCR) repertoire used to recognise this immunodominant epitope. We next assessed the immunogenicity of NP_265‐IAV homologue peptides from IBV and ICV and the ability of CD8⁺ T cells to cross‐react towards these homologous peptides. Furthermore, we determined the structures of NP_265‐IAV and NP_323‐IBV peptides in complex with HLA‐A*03:01 by X‐ray crystallography.

Results

Our study provides a detailed characterisation of the CD8⁺ T cell response towards NP_265‐IAV and its IBV and ICV homologues. The data revealed a diverse repertoire for NP_265‐IAV that is associated with superior anti‐viral protection. Evidence of cross‐reactivity between the three different influenza virus strain‐derived epitopes was observed, indicating the discovery of a potential vaccination target that is broad enough to cover all three influenza strains.

Conclusion

We show that while there is a potential to cross‐protect against distinct influenza virus lineages, the T cell response was stronger against the IAV peptide than IBV or ICV, which is an important consideration when choosing targets for future vaccine design.

Identification of HLA-E Binding Mycobacterium tuberculosis-Derived Epitopes through Improved Prediction Models

Tuberculosis (TB) remains one of the deadliest infectious diseases worldwide, posing great social and economic burden to affected countries. Novel vaccine approaches are needed to increase protective immunity against the causative agent Mycobacterium tuberculosis (Mtb) and to reduce the development of active TB disease in latently infected individuals. Donor-unrestricted T cell responses represent such novel potential vaccine targets. HLA-E-restricted T cell responses have been shown to play an important role in protection against TB and other infections, and recent studies have demonstrated that these cells can be primed in vitro. However, the identification of novel pathogen-derived HLA-E binding peptides presented by infected target cells has been limited by the lack of accurate prediction algorithms for HLA-E binding. In this study, we developed an improved HLA-E binding peptide prediction algorithm and implemented it to identify (to our knowledge) novel Mtb-derived peptides with capacity to induce CD8+ T cell activation and that were recognized by specific HLA-E-restricted T cells in Mycobacterium-exposed humans. Altogether, we present a novel algorithm for the identification of pathogen- or self-derived HLA-E-presented peptides.

Protective HLA-B57: T cell and natural killer cell recognition in HIV infection

Understanding the basis of the immune determinants controlling disease outcome is critical to provide better care to patients and could be exploited for therapeutics and vaccine design. The discovery of the human immunodeficiency virus (HIV) virus as the causing agent of acquired immunodeficiency syndrome (AIDS) decades ago, led to a tremendous amount of research. Among the findings, it was discovered that some rare HIV+ individuals, called HIV controllers (HICs), had the ability to control the virus and keep a low viral load without the need of treatment. This ability allows HICs to delay or avoid progression to AIDS. HIV control is strongly associated with the expression of human leukocyte antigen (HLA) alleles in HICs. From the HIV protective HLAs described, HLA-B57 is the most frequent in HIC patients. HLA-B57 can present a large range of highly conserved Gag-derived HIV peptides to CD8+ T cells and natural killer (NK) cells, both the focus of this review. So far there are limited differences in the immune response strength, magnitude, or receptor repertoire towards HIV epitopes that could explain viral control in HICs. Interestingly, some studies revealed that during early infection the large breadth of the immune response towards HIV mutants in HLA-B57+ HIC patients, might in turn influence the disease outcome.

Covalent TCR-peptide-MHC interactions induce T cell activation and redirect T cell fate in the thymus

Interactions between a T cell receptor (TCR) and a peptide-major histocompatibility complex (pMHC) ligand are typically mediated by noncovalent bonds. By studying T cells expressing natural or engineered TCRs, here we describe covalent TCR-pMHC interactions that involve a cysteine-cysteine disulfide bond between the TCR and the peptide. By introducing cysteines into a known TCR-pMHC combination, we demonstrate that disulfide bond formation does not require structural rearrangement of the TCR or the peptide. We further show these disulfide bonds still form even when the initial affinity of the TCR-pMHC interaction is low. Accordingly, TCR-peptide disulfide bonds facilitate T cell activation by pMHC ligands with a wide spectrum of affinities for the TCR. Physiologically, this mechanism induces strong Zap70-dependent TCR signaling, which triggers T cell deletion or agonist selection in the thymus cortex. Covalent TCR-pMHC interactions may thus underlie a physiological T cell activation mechanism that has applications in basic immunology and potentially in immunotherapy.

Differentiation and activation of T cells are normally modulated by non-covalent interactions between T cell receptor (TCR) and antigenic peptides. Here the authors use step-wise mutations, biochemical characterization and structural insights to describe the contributions of natural covalent bonds between TCR and antigenic peptides during these processes.

AttnTAP: A Dual-input Framework Incorporating the Attention Mechanism for Accurately Predicting TCR-peptide Binding

T-cell receptors (TCRs) are formed by random recombination of genomic precursor elements, some of which mediate the recognition of cancer-associated antigens. Due to the complicated process of T-cell immune response and limited biological empirical evidence, the practical strategy for identifying TCRs and their recognized peptides is the computational prediction from population and/or individual TCR repertoires. In recent years, several machine/deep learning-based approaches have been proposed for TCR-peptide binding prediction. However, the predictive performances of these methods can be further improved by overcoming several significant flaws in neural network design. The interrelationship between amino acids in TCRs is critical for TCR antigen recognition, which was not properly considered by the existing methods. They also did not pay more attention to the amino acids that play a significant role in antigen-binding specificity. Moreover, complex networks tended to increase the risk of overfitting and computational costs. In this study, we developed a dual-input deep learning framework, named AttnTAP, to improve the TCR-peptide binding prediction. It used the bi-directional long short-term memory model for robust feature extraction of TCR sequences, which considered the interrelationships between amino acids and their precursors and postcursors. We also introduced the attention mechanism to give amino acids different weights and pay more attention to the contributing ones. In addition, we used the multilayer perceptron model instead of complex networks to extract peptide features to reduce overfitting and computational costs. AttnTAP achieved high areas under the curves (AUCs) in TCR-peptide binding prediction on both balanced and unbalanced datasets (higher than 0.838 on McPAS-TCR and 0.908 on VDJdb). Furthermore, it had the highest average AUCs in TPP-I and TPP-II tasks compared with the other five popular models (TPP-I: 0.84 on McPAS-TCR and 0.894 on VDJdb; TPP-II: 0.837 on McPAS-TCR and 0.893 on VDJdb). In conclusion, AttnTAP is a reasonable and practical framework for predicting TCR-peptide binding, which can accelerate identifying neoantigens and activated T cells for immunotherapy to meet urgent clinical needs.

Immunotherapeutic Strategies for Head and Neck Squamous Cell Carcinoma (HNSCC): Current Perspectives and Future Prospects

Neoantigens are abnormal proteins produced by genetic mutations in somatic cells. Because tumour neoantigens are expressed only in tumour cells and have immunogenicity, they may represent specific targets for precision immunotherapy. With the reduction in sequencing cost, continuous advances in artificial intelligence technology and an increased understanding of tumour immunity, neoantigen vaccines and adoptive cell therapy (ACT) targeting neoantigens have become research hotspots. Approximately 900,000 patients worldwide are diagnosed with head and neck squamous cell carcinoma (HNSCC) each year. Due to its high mutagenicity and abundant lymphocyte infiltration, HNSCC naturally generates a variety of potential new antigen targets that may be used for HNSCC immunotherapies. Currently, the main immunotherapy for HNSCC is use of immune checkpoint inhibitors(ICIs). Neoantigen vaccines and adoptive cell therapy targeting neoantigens are extensions of immunotherapy for HNSCC, and a large number of early clinical trials are underway in combination with immune checkpoint inhibitors for the treatment of recurrent or metastatic head and neck squamous cell carcinoma (R/M HNSCC). In this paper, we review recent neoantigen vaccine trials related to the treatment of HNSCC, introduce adoptive cell therapy targeting neoantigens, and propose a potential treatment for HNSCC. The clinical application of immune checkpoint inhibitor therapy and its combination with neoantigen vaccines in the treatment of HNSCC are summarized, and the prospect of using neoantigen to treat HNSCC is discussed and proposed.

Peptide-dependent tuning of major histocompatibility complex motional properties and the consequences for cellular immunity

T cell receptors (TCRs) and other receptors of the immune system recognize peptides presented by class I or class II major histocompatibility complex (MHC) proteins. Although we generally distinguish between the MHC protein and its peptide, at an atomic level the two form a structural composite, which allows peptides to influence MHC properties and vice versa. One consequence is the peptide-dependent tuning of MHC structural dynamics, which contributes to protein structural adaptability and influences how receptors identify and bind targets. Peptide-dependent tuning of MHC protein dynamics can impact processes such as antigenicity, TCR cross-reactivity, and T cell repertoire selection. Motional tuning extends beyond the binding groove, influencing peptide selection and exchange, as well as interactions with other immune receptors. Here, we review recent findings showing how peptides can affect the dynamic and adaptable nature of MHC proteins. We highlight consequences for immunity and demonstrate how MHC proteins have evolved to be highly sensitive dynamic reporters, with broad immunological consequences.

Public T-Cell Receptors (TCRs) Revisited by Analysis of the Magnitude of Identical and Highly-Similar TCRs in Virus-Specific T-Cell Repertoires of Healthy Individuals

Since multiple different T-cell receptor (TCR) sequences can bind to the same peptide-MHC combination and the number of TCR-sequences that can theoretically be generated even exceeds the number of T cells in a human body, the likelihood that many public identical (PUB-I) TCR-sequences frequently contribute to immune responses has been estimated to be low. Here, we quantitatively analyzed the TCR-repertoires of 190 purified virus-specific memory T-cell populations, directed against 21 epitopes of Cytomegalovirus, Epstein-Barr virus and Adenovirus isolated from 29 healthy individuals, and determined the magnitude, defined as prevalence within the population and frequencies within individuals, of PUB-I TCR and of TCR-sequences that are highly-similar (PUB-HS) to these PUB-I TCR-sequences. We found that almost one third of all TCR nucleotide-sequences represented PUB-I TCR amino-acid (AA) sequences and found an additional 12% of PUB-HS TCRs differing by maximally 3 AAs. We illustrate that these PUB-I and PUB-HS TCRs were structurally related and contained shared core-sequences in their TCR-sequences. We found a prevalence of PUB-I and PUB-HS TCRs of up to 50% among individuals and showed frequencies of virus-specific PUB-I and PUB-HS TCRs making up more than 10% of each virus-specific T-cell population. These findings were confirmed by using an independent TCR-database of virus-specific TCRs. We therefore conclude that the magnitude of the contribution of PUB-I and PUB-HS TCRs to these virus-specific T-cell responses is high. Because the T cells from these virus-specific memory TCR-repertoires were the result of successful control of the virus in these healthy individuals, these PUB-HS TCRs and PUB-I TCRs may be attractive candidates for immunotherapy in immunocompromised patients that lack virus-specific T cells to control viral reactivation.