key: cord-0936887-jog2e5yu
authors: Leddy, Owen K.; White, Forest M.; Bryson, Bryan D.
title: Leveraging Immunopeptidomics To Study and Combat Infectious Disease
date: 2021-08-03
journal: mSystems
DOI: 10.1128/msystems.00310-21
sha: cc3c23ebcd4cb267f19da9b434ce9506391a9fbc
doc_id: 936887
cord_uid: jog2e5yu

T cells must recognize pathogen-derived peptides bound to major histocompatibility complexes (MHCs) in order to initiate a cell-mediated immune response against an infection, or to support the development of high-affinity antibody responses. Identifying antigens presented on MHCs by infected cells and professional antigen-presenting cells (APCs) during infection may therefore provide a route toward developing new vaccines. Peptides bound to MHCs can be identified at whole-proteome scale using mass spectrometry—a technique referred to as “immunopeptidomics.” This technique has emerged as a powerful tool for identifying potential vaccine targets in the context of many infectious diseases. In this review, we discuss the contributions immunopeptidomic studies have made to understanding antigen presentation and T cell priming in the context of infection and the potential for immunopeptidomics to inform the development of vaccines to address pressing global health problems in infectious disease.

R ecognition of pathogen-specific peptide-major histocompatibility complexes (pMHCs) is required for naive T cells to become activated and proliferate, and for effector T cells to recognize infected cells and carry out effector functions such as cytokine secretion and lysis of infected cells that ultimately lead to control of an infection. Antigen-specific T cell responses are also required for B cells to undergo affinity maturation and produce high-affinity antibodies (1) . Identifying antigens that can be presented on MHCs and recognized by T cells is therefore essential for understanding natural immunity to infection and developing effective vaccines.

The pMHC repertoire is highly complex within any given individual, and highly variable among individuals. MHC class I and class II present peptides for recognition by CD8 1 and CD4 1 T cells, respectively, and are loaded with peptides via distinct pathways of antigen processing that have been reviewed in detail elsewhere (2, 3) . MHC molecules are highly genetically polymorphic, and different alleles preferentially bind different sets of peptide sequences. Three distinct human leukocyte antigen (HLA) loci encode the alpha chain of MHC class I (HLA-A, HLA-B, and HLA-C), for up to six alleles in a given individual. Similarly, three loci each encode an alpha chain and a beta chain of MHC class II (HLA-DR, HLA-DQ, and HLA-DP) (4) . Given the diversity and variability of the immunopeptidome, whole-proteome-scale approaches are needed to comprehensively identify pathogen-derived antigens presented on MHCs.

Mass spectrometry (MS) provides a means of experimentally identifying the repertoire of peptide antigens presented on MHCs by cells infected with a pathogen of interest. In a typical immunopeptidomic workflow, MHC-peptide complexes are immunoprecipitated (IP) from a population of cells of interest using an HLA-allele-specific antibody or pan-HLA antibody. Peptides are released by acid elution, MHCs are separated from peptides by size exclusion or solid-phase extraction, and eluted peptides are analyzed by liquid chromatography coupled to tandem MS (LC-MS/MS) ( Fig. 1) (5, 6) . Mass spectra are then searched against a database of possible peptides that could be derived from the host and/or pathogen proteomes to identify pathogen-derived peptides presented on MHCs.

MS analyses of MHC peptides can be carried out in two distinct modes. In data-dependent mode, peptides are detected and prioritized for tandem mass spectrum acquisition based on their abundance, without prior knowledge of the composition of the sample, enabling discovery of novel vaccine targets. In targeted mode, a predetermined list of targets is selected for acquisition, enabling studies of the mechanism and kinetics of presentation of known antigens by providing reliable detection and quantification across multiple samples.

Here, we review the use of MS-based immunopeptidomics to study antigen presentation in infectious disease. These studies reveal basic insights into the biology of antigen presentation, provide data sets for training predictive models of antigen presentation, and guide the rational selection of vaccine targets. We go on to discuss opportunities for further study in this area, technical pitfalls that remain to be overcome, and the potential for immunopeptidomics to inform vaccine development.

Vaccinia virus infection has served as a model system for research on the basic biology of antigen presentation in viral infection. Vaccinia virus is a large, enveloped poxvirus and a relative of variola virus (the etiologic agent of smallpox). It was widely used as a vaccine against smallpox before the eradication of the disease, and some variants such as Modified Vaccinia Ankara (MVA) are being used experimentally as viral vectors for delivery of vaccine antigens (7-9). Although the immunopeptidome of vaccinia virus could provide insight into the mechanism by which it provides protection against FIG 1 Schematic representation of an immunopeptidomic workflow to identify pathogen-derived peptides in infected cells. Infected cells and mock-infected control cells are separately lysed, lysate is subjected to immunoprecipitation with an MHC-specific antibody, and peptides are eluted in acid and separated from MHC proteins using size filtration or solid-phase extraction. Finally, the eluted peptides are analyzed by LC-MS/MS, and mass spectra are searched against a database that includes the proteomes of both the pathogen and the host to identify MHC-associated peptides. Putative pathogen-derived peptides identified in the samples from infected cells can be looked for in the data from the mock-infected control to identify and eliminate some false positives. smallpox (10), its use in basic studies of antigen presentation is due primarily to its attractiveness as a well-characterized, tractable model of viral infection.

An immunopeptidomic study of vaccinia virus-infected cells assessed whether most viral peptides presented by MHCs are immunogenic or if a large proportion are immunologically silent (11) . The authors identified 170 viral peptides bound to MHC class I, over 80% of which could be recognized by T cells isolated from vaccinia virus-infected mice (11) . The authors argue that their data represent evidence that most viral peptides presented on MHCs are immunogenic, but it is unclear to what extent this conclusion can be generalized beyond vaccinia virus.

Vaccinia virus infection has also been used as a model to study the kinetics of antigen presentation during viral infection and the relationship between pMHC abundance and immunogenicity. Croft et al. (12) used a targeted MS approach to monitor presentation of eight known vaccinia virus peptides in infected mouse B cells over time. They found that peptide presentation occurred almost immediately after initiation of viral gene expression, consistent with cotranslational antigen processing (13, 14) . They also found that comparably immunogenic peptides can vary widely in their abundance in the MHC repertoire, suggesting that pMHC abundance may not strongly correlate with immunogenicity. Using targeted MS to monitor antigen presentation over time could be useful in selecting vaccine antigens presented during different stages of infection (15) .

Computational methods for predicting peptides likely to be presented on MHCs were first trained using data from in vitro binding assays (16) (17) (18) (19) , but recently developed models incorporate large MS-based immunopeptidomic data sets as training data, resulting in improvements in performance (20) (21) (22) (23) (24) . The high-throughput nature of in silico predictions enables such analyses to cover a much wider range of HLA alleles. Computational methods can also select sets of epitopes that maximize coverage of a population with a given set of HLA allele frequencies (25) , which will aid in the design of broadly protective vaccines.

Immunopeptidomic data sets have facilitated the development of models that explicitly take into account factors beyond MHC binding affinity alone, such as gene expression level, protein subcellular localization, and predicted protease cleavage sites (22) (23) (24) 26) . These modeling approaches could be applied to learn pathogen-specific rules for antigen presentation, such as whether secreted bacterial proteins are preferentially presented on MHCs, relative to proteins in the pathogen's cytosol (27) . Existing data sets of pathogen-derived MHC peptides are small, but highly data-efficient machine learning methods (28) may help build pathogen-specific models from limited numbers of training examples.

Rapid computational predictions can inform efforts to develop vaccines for emerging infectious diseases sooner than experimental data. Multiple computational analyses of predicted T cell epitopes in the SARS-CoV-2 proteome were published early in the pandemic (29, 30) , whereas experimental data identifying MHC peptides derived from SARS-CoV-2 were not published until months later (31, 32) . Obtaining accurate predictions of T cell epitopes could therefore be critical to rapidly developing vaccines against future emerging pathogens.

The direct discovery of vaccine targets is one of the most exciting applications of immunopeptidomics in infectious disease. Whereas T cell responses contribute to protective immunity against many globally important infectious diseases, most rationally designed vaccines licensed to date have been designed to elicit protective immunity primarily through humoral immune responses (33) . Immunopeptidomics can provide a systematic, rational basis on which to select targets for vaccines that elicit protective cell-mediated immunity, which may be essential in order to develop highly effective vaccines against pathogens for which none yet exist.

Developing effective vaccines against intracellular bacteria has historically proven challenging, in part due to a lack of knowledge of protective T cell antigens (34) . Immunopeptidomic analyses have helped to address this problem by identifying protective T cell epitopes of Mycobacterium tuberculosis-the causative agent of tuberculosis (35)-and Chlamydia trachomatis-the causative agent of chlamydia (36) and trachoma (37) . M. tuberculosis-derived peptides have been identified that are presented on MHC class I (38) and on the noncanonical class Ib MHC molecule HLA-E (39) by M. tuberculosis-infected human cells, as well as MHC class I and II peptides presented by human macrophages infected with the bacillus Calmette-Guérin (BCG) vaccine strain (40) . Administering these antigens to mice using the ChAdOx1 viral vector in a primeboost regimen with BCG reduced M. tuberculosis bacterial burden in the lung significantly more than did BCG alone (40) . Similarly, nine antigens identified in an immunopetidomic analysis of C. trachomatis-infected cells (41) were recognized by T cells of infected mice and reduced vaginal shedding of C. trachomatis when delivered as a recombinant protein vaccine (42) . These findings suggest that immunopeptidomics can help advance the development of effective vaccines against intracellular bacterial pathogens, though the clinical utility of these vaccine targets has yet to be tested in humans.

Like intracellular bacteria, eukaryotic parasites have proven difficult to vaccinate against, with only one vaccine currently licensed (43) . Mou et al. (44) used MS to identify peptides presented on MHC class II by murine macrophages infected with Leishmania major, a parasite that causes leishmaniasis (45) . They identified an immunodominant epitope of phosphoenolpyruvate carboxykinase (PEPCK) that reduced the parasite burden in a mouse model of leishmaniasis when administered as a peptide vaccine or DNA vaccine. These results demonstrate the potential for immunopeptidomics to aid in the development of vaccines against intracellular parasites.

Immunopeptidomics may help identify viral T cell epitopes that vaccines can target to confer lasting protection in the face of viral evasion of antibody-mediated immunity. A high frequency of escape mutations (46) has made it difficult to develop vaccines that elicit antibody-mediated protection against human immunodeficiency virus (HIV) -the causative agent of AIDS (47) . However, some individuals naturally control HIV infection in a manner associated with CD8 1 T cell responses, suggesting T cells can contribute to durable immunity against HIV (48) (49) (50) (51) . Several research groups have used immunopeptidomic methods to identify HIV-derived epitopes presented on MHC class I by infected CD4 1 T cells (52) (53) (54) (55) (56) . Some of these studies specifically isolated peptides bound to HLA alleles associated with improved control of HIV infection (53, 55) , thereby identifying antigenic targets that may be associated with durable protection. Many of these epitopes elicited gamma interferon (IFN-g) production in T cells of HIVpositive patients, validating their relevance to T cell responses against HIV. Including additional viral T cell epitopes in future HIV vaccine candidates could help mitigate the effects of viral escape mutations, but this concept has not been tested in animal models or humans.

Some infections are known to be associated with the onset of autoimmune disease, but the mechanisms underlying this relationship are not fully understood (57) . Immunopeptidomics offers a way to systematically identify pathogen-derived MHC peptides that can trigger self-reactive T cell responses, leading to autoimmunity. For example, Alvarez-Navarro et al. (58) used immunopeptidomics to investigate why Chlamydia trachomatis infection is associated with reactive arthritis in individuals carrying HLA-B alleles of the HLA-B*27 group (59). They identified three C. trachomatis peptides presented by HLA-B*27 that had high sequence similarity to endogenous human peptides and were predicted to adopt similar conformations when bound to HLA-B*27. T cells that respond to these C. trachomatis peptides might cross-react with endogenous peptides, leading to autoimmunity. Similarly, Wang et al. (60) used immunopeptidomics to reveal a mechanism that may explain why the HLA-DR*15 haplotype and Epstein-Barr virus (EBV) infection are both associated with increased risk of multiple sclerosis. They identified peptides presented by HLA-DR*15 derived from the neuronal marker RASGRP2 and found that T cells specific for these peptides cross-reacted with peptides derived from EBV. These results suggest that EBV infection may lead to T cell responses that crossreact with neuronal markers in individuals expressing HLA-DR*15, leading to neurodegeneration. Cross-reactive epitopes identified in immunopeptidomic studies could in principle be targeted by therapies designed to induce epitope-specific immune tolerance (61, 62) and mitigate autoimmunity associated with infection.

Immunopeptidomics can elucidate the basic biology and mechanisms of antigen presentation in infectious disease, improve predictive modeling of pathogen-derived T cell epitopes, draw connections between infection and autoimmunity, and identify promising vaccine targets. The discovery of new T cell antigens would aid in the development of vaccines against several globally important pathogens. For example, liverresident CD8 1 T cells are known to be a strong correlate of immunity to malaria (63, 64) , but only a few T cell antigens presented in liver-stage malaria have been identified (65, 66) . T cell responses are also thought to be important for immunity to bacterial pathogens with a rapidly growing incidence of antibiotic resistance, such as Salmonella spp. (67) and Staphylococcus aureus (68), as well as parasites such as Trypanosoma cruzi, which causes Chagas disease (69) . Some of these protective T cell responses could target ligands that standard immunopeptidomic workflows do not detect, such as noncanonical translation products (70) or small molecules presented by the HLA-like molecules CD1 (71) and MR1 (72) , but methods are being developed to analyze these unconventional epitopes by MS as well (70, 73, 74) . The use of immunopeptidomics to comprehensively identify T cell antigens could therefore help combat both long-standing global health problems and emerging crises.

Although immunopeptidomics can provide valuable information about the repertoire of pathogen-derived MHC ligands at a whole-proteome scale, it currently has at least three important limitations, enumerated in Table 1 . Some of these limitations can be overcome by combining MS-based immunopeptidomics with other complementary techniques, and some may be overcome through ongoing method development.

Determining whether a peptide discovered through immunopeptidomics is immunogenic (problem 1) generally requires combining immunopeptidomics with techniques that Targets identified by immunopeptidomics should ideally be further screened for T cell recognition and immunogenicity using other assays.

2

Cell-to-cell heterogeneity in antigen presentation cannot be resolved Some contributions to the MS signal in an immunopeptidomic study may come from uninfected bystander cells, and a mixture of different cell types cannot be deconvoluted to learn which types are important for antigen presentation. Presentation of specific antigens can be probed at a single-cell level using pMHC-specific antibodies or biotin labels transferred between cells. can directly measure a T cell response against a given peptide of interest. Traditionally, this has been done by measuring production of IFN-g by T cells specific for each epitope of interest (40) . T cells specific for a pMHC complex of interest can also be stained using pMHC tetramers (75) . More recently, a suite of methods for high-throughput measurement of T cell receptor (TCR)-pMHC interactions has been developed (76) . Computational models have also been developed to predict the immunogenicity of putative T cell epitopes (77) (78) (79) , which may help prioritize hits identified in an immunopeptidomic experiment for development as potential vaccine targets.

Cell-to-cell heterogeneity in antigen presentation (problem 2) may be consequential for immunity to some infections. For example, certain antigens of Mycobacterium tuberculosis can be presented by both infected cells and uninfected bystander cells (80, 81) , while other antigens may be specific to infected cells. An antigen presented by uninfected bystander cells could potentially cause off-target lysis of bystander cells by cytotoxic T cells if targeted by a vaccine and/or might be presented at a lower level on infected cells than immunopeptidomic data would suggest, leading to less effective targeting of infected cells. Antibodies that recognize specific peptide-MHC complexes (82, 83) can enable measurements of antigen presentation at the single-cell level using flow cytometry or microscopy. Interactions between T cells and antigen-presenting cells (APCs) can also be tracked using a transferable biotin label (84, 85) , providing a single-cell readout of pMHC-TCR interactions. These methods can complement immunopeptidomics by probing antigen presentation in a heterogeneous population of cells.

Whereas recent immunopeptidomic studies in cancer immunology have used sample input on the order of 10 6 to 10 7 cells (6), sample input on the order of 10 8 to 10 9 cells (problem 3) is often required to detect pathogen-derived peptides. The use of microfluidic devices or other low-volume sample handling techniques (86) could help reduce input requirements dramatically while retaining high sensitivity. Serial immunoprecipitation has already been successfully used to isolate both MHC class I and class II peptides from the same sample, further conserving input material (40) . Lower sample input requirements could enable immunopeptidomic studies on infected primary cells or ex vivo samples from animals infected with a pathogen of interest or studies of pathogens that are impossible to culture in large quantities, such as the liver stage of malaria parasites (Plasmodium spp.).

Combining computational modeling with targeted mass spectrometry approaches could provide another avenue toward reducing sample input requirements (problem 3). Targeted MS analyses can detect MHC-associated peptides with greater sensitivity than data-dependent analyses, potentially reducing sample input requirements and increasing the likelihood of detecting peptides that would not be detectable in a datadependent analysis. Accurate predictive models of peptide presentation on MHCs could identify promising candidates to experimentally validate using highly sensitive targeted MS workflows, and these experimental results could in turn be used for further model refinement.

Future improvements in immunopeptidomic methods may also focus on overcoming technical pitfalls that can result in inaccurate identification of MHC peptides by MS. In one instance, for example, HIV-derived peptides previously identified as MHC class I ligands (52) were shown to nonspecifically copurify with host membrane proteins (87) . This result highlights the importance of proper controls in immunopeptidomic studies. Ambiguities in peptide identification may also be a source of uncertainty. It has recently been proposed that posttranslationally spliced peptides (88) (89) (90) (91) and peptides derived from noncanonical translation products (92, 93) contribute substantially to the immunopeptidome. However, matching mass spectra against an expanded search space that includes spliced peptides and noncanonical translation products may increase the risk of false positives (70, 94) . The false-discovery rate associated with these identifications must be carefully estimated to obtain accurate results (70) .

Immunopeptidomics has already been used to design vaccines that improve control of infection in mouse models of chlamydia (42), leishmaniasis (44) , and tuberculosis (40) . As the field continues to advance, it is likely that immunopeptidomics and/or computational models trained on immunopeptidomic data will provide a rapid, sensitive, and systematic means of identifying vaccine targets in many human pathogens. Combining immunopeptidomics with other next-generation immunoassays and existing preclinical and clinical vaccine development platforms has the potential to have a transformative impact on global health.

T-cell-B-cell cooperation

Pathways of antigen processing

The biology and underlying mechanisms of cross-presentation of exogenous antigens on MHC-I molecules

HLA variation and disease

Characterization of peptides bound to the class I MHC molecule HLA-A2.1 by mass spectrometry

Multiplexed relative and absolute quantitative immunopeptidomics reveals MHC I repertoire alterations induced by CDK4/6 inhibition

Construction of live vaccines by using genetically engineered poxviruses: biological activity of recombinant vaccinia virus expressing influenza virus hemagglutinin

Safety and efficacy of MVA85A, a new tuberculosis vaccine, in infants previously vaccinated with BCG: a randomised, placebo-controlled phase 2b trial

Early kinetics of the HLA class I-associated peptidome of MVA.HIVconsvinfected cells

Discovery of naturally processed and HLA-presented class I peptides from vaccinia virus infection using mass spectrometry for vaccine development

Most viral peptides displayed by class I MHC on infected cells are immunogenic

Kinetics of antigen expression and epitope presentation during virus infection

Defective ribosomal products (DRiPs): a major source of antigenic peptides for MHC class I molecules?

Role for ribosome-associated quality control in sampling proteins for MHC class I-mediated antigen presentation

Prevention of tuberculosis in rhesus macaques by a cytomegalovirusbased vaccine

Establishment of a quantitative ELISA capable of determining peptide-MHC class I interaction

A community resource benchmarking predictions of peptide binding to MHC-I molecules

Measurement of MHC/peptide interactions by gel filtration

NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence

NetMHCpan-4.0: improved peptide-MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data

NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data

Mass spectrometry profiling of HLA-associated peptidomes in mono-allelic cells enables more accurate epitope prediction

Defining HLA-II ligand processing and binding rules with mass spectrometry enhances cancer epitope prediction

A large peptidome dataset improves HLA class I epitope prediction across most of the human population

Computationally optimized SARS-CoV-2 MHC class I and II vaccine formulations predicted to target human haplotype distributions

MHCflurry 2.0: improved pan-allele prediction of MHC class I-presented peptides by incorporating antigen processing

Bacterial protein secretion is required for priming of CD81 T cells specific for the Mycobacterium tuberculosis antigen CFP10

Leveraging uncertainty in machine learning accelerates biological discovery and design

A sequence homology and bioinformatic approach can predict candidate targets for immune responses to SARS-CoV-2

Bioinformatic prediction of potential T cell epitopes for SARS-Cov-2

The human leukocyte antigen class II immunopeptidome of the SARS-CoV-2 spike glycoprotein

Profiling SARS-CoV-2 HLA-I peptidome reveals T cell epitopes from out-of-frame ORFs

T cell vaccines for microbial infections

Vaccines against intracellular bacterial pathogens

World Health Organization. 2020. Global tuberculosis report 2020. World Health Organization

Report on global sexually transmitted infection surveillance

Progress and projections in the program to eliminate trachoma

Identification by mass spectrometry of CD81-T-cell Mycobacterium tuberculosis epitopes within the Rv0341 gene product

T cell recognition of Mycobacterium tuberculosis peptides presented by HLA-E derived from infected human cells

Identification of antigens presented by MHC for vaccines against tuberculosis

Immunoproteomic discovery of novel T cell antigens from the obligate intracellular pathogen Chlamydia

Using MHC molecules to define a Chlamydia T cell vaccine

A phase 3 trial of RTS,S/AS01 malaria vaccine in African infants

Identification of broadly conserved cross-species protective Leishmania antigen and its responding CD41 T cells

Leishmaniasis: complexity at the host-pathogen interface

The neutralizing antibody response to HIV-1: viral evasion and escape from humoral immunity

Progress report on HIV, viral hepatitis and sexually transmitted infections

HIV-specific CD81 T cell proliferation is coupled to perforin expression and is maintained in nonprogressors

Vaccineinduced CD8 1 central memory T cells in protection from simian AIDS

Cross-reactive cytotoxic T lymphocytes against a HIV-1 p24 epitope in slow progressors with B*57

CD81 T cells in HIV control, cure and prevention

Direct interrogation of viral peptides presented by the class I HLA of HIV-infected T cells

Identification of immunodominant HIV-1 epitopes presented by HLA-C*12:02, a protective allele, using an immunopeptidomics approach

Defining the HLA class I-associated viral antigen repertoire from HIV-1-infected human cells

Identification of native and posttranslationally modified HLA-B*57:01-restricted HIV envelope derived epitopes using immunoproteomics

Analysis of major histocompatibility complex-bound HIV peptides identified from various cell types reveals common nested peptides and novel T cell responses

Antiviral immune responses: triggers of or triggered by autoimmunity?

Novel HLA-B27-restricted epitopes from Chlamydia trachomatis generated upon endogenous processing of bacterial proteins suggest a role of molecular mimicry in reactive arthritis

HLA-B27-associated reactive arthritis: pathogenetic and clinical considerations

HLA-DR15 molecules jointly shape an autoreactive T cell repertoire in multiple sclerosis

Polymeric synthetic nanoparticles for the induction of antigen-specific immunological tolerance

A biodegradable nanoparticle platform for the induction of antigen-specific immune tolerance for treatment of autoimmune disease

CD8 1 T cell responses to plasmodium and intracellular parasites

Protective immunity to liver-stage malaria

Human T-cell recognition of the circumsporozoite protein of Plasmodium falciparum: immunodominant Tcell domains map to the polymorphic regions of the molecule

A natural peptide antigen within the plasmodium ribosomal protein RPL6 confers liver TRM cell-mediated immunity against malaria in mice

Antigen-specific B cells reactivate an effective cytotoxic T cell response against phagocytosed Salmonella through cross-presentation

Targeting skin-resident memory T cells via vaccination to combat Staphylococcus aureus infections

Incomplete recruitment of protective T cells facilitates Trypanosoma cruzi persistence in the mouse colon

Integrated proteogenomic deep sequencing and analytics accurately identify non-canonical peptides in tumor immunopeptidomes

CD1 antigen presentation: how it works

MR1 presents microbial vitamin B metabolites to MAIT cells

Natural lipid ligands associated with human CD1d targeted to different subcellular compartments

MR1 displays the microbial metabolome driving selective MR1-restricted T cell receptor usage

Large-scale detection of antigen-specific T cells using peptide-MHC-I multimers labeled with DNA barcodes

2020. T cell antigen discovery

Properties of MHC class I presented peptides that enhance immunogenicity

Structure based prediction of neoantigen immunogenicity

DeepHLApan: a deep learning approach for neoantigen prediction considering both HLA-peptide binding and immunogenicity

Antigen export reduces antigen presentation and limits T cell control of M. tuberculosis

Cell-to-cell transfer of M. tuberculosis antigens optimizes CD4 T cell priming

Suppression of autophagy and antigen presentation by Mycobacterium tuberculosis PE-PGRS47

Selection of a single domain antibody, specific for an HLA-bound epitope of the mycobacterial Ag85B antigen

T cell antigen discovery via trogocytosis

Detecting tumor antigen-specific T cells via interaction-dependent fucosyl-biotinylation

Miniaturised interaction proteomics on a microfluidic platform with ultra-low input requirements

Discrimination between human leukocyte antigen class Ibound and co-purified HIV-derived peptides in immunopeptidomics workflows

Post-translational peptide splicing and T cell responses

A large fraction of HLA class I ligands are proteasome-generated spliced peptides

A subset of HLA-I peptides are not genomically templated: evidence for cis-and trans-spliced peptide ligands

CD81 T cells of Listeria monocytogenesinfected mice recognize both linear and spliced proteasome products

Noncoding regions are the main source of targetable tumor-specific antigens

Most non-canonical proteins uniquely populate the proteome or immunopeptidome

Comment on "A subset of HLA-I peptides are not genomically templated: evidence for cis-and trans-spliced peptide ligands

We acknowledge helpful, informative conversations with Lauren Stopfer and Cameron