Increased diversity of the HLA-B40 ligandome by the presentation of peptides phosphorylated at their main anchor residue

Post-translational modifications reshape the antigenic landscape of the MHC I immunopeptidome in tumors

Post-translational modification (PTM) of antigens provides an additional source of specificities targeted by immune responses to tumors or pathogens, but identifying antigen PTMs and assessing their role in shaping the immunopeptidome is challenging. Here we describe the Protein Modification Integrated Search Engine (PROMISE), an antigen discovery pipeline that enables the analysis of 29 different PTM combinations from multiple clinical cohorts and cell lines. We expanded the antigen landscape, uncovering human leukocyte antigen class I binding motifs defined by specific PTMs with haplotype-specific binding preferences and revealing disease-specific modified targets, including thousands of new cancer-specific antigens that can be shared between patients and across cancer types. Furthermore, we uncovered a subset of modified peptides that are specific to cancer tissue and driven by post-translational changes that occurred in the tumor proteome. Our findings highlight principles of PTM-driven antigenicity, which may have broad implications for T cell-mediated therapies in cancer and beyond.

The HLA Ligandome Comprises a Limited Repertoire of O-GlcNAcylated Antigens Preferentially Associated With HLA-B*07:02

Mass-spectrometry based immunopeptidomics has provided unprecedented insights into antigen presentation, not only charting an enormous ligandome of self-antigens, but also cancer neoantigens and peptide antigens harbouring post-translational modifications. Here we concentrate on the latter, focusing on the small subset of HLA Class I peptides (less than 1%) that has been observed to be post-translationally modified (PTM) by a O-linked N-acetylglucosamine (GlcNAc). Just like neoantigens these modified antigens may have specific immunomodulatory functions. Here we compiled from literature, and a new dataset originating from the JY B cell lymphoblastoid cell line, a concise albeit comprehensive list of O-GlcNAcylated HLA class I peptides. This cumulative list of O-GlcNAcylated HLA peptides were derived from normal and cancerous origin, as well as tissue specimen. Remarkably, the overlap in detected O-GlcNAcylated HLA peptides as well as their source proteins is strikingly high. Most of the O-GlcNAcylated HLA peptides originate from nuclear proteins, notably transcription factors. From this list, we extract that O-GlcNAcylated HLA Class I peptides are preferentially presented by the HLA-B*07:02 allele. This allele loads peptides with a Proline residue anchor at position 2, and features a binding groove that can accommodate well the recently proposed consensus sequence for O-GlcNAcylation, P(V/A/T/S)g(S/T), essentially explaining why HLA-B*07:02 is a favoured binding allele. The observations drawn from the compiled list, may assist in the prediction of novel O-GlcNAcylated HLA antigens, which will be best presented by patients harbouring HLA-B*07:02 or related alleles that use Proline as anchoring residue.

Immunopeptidomics for next-generation bacterial vaccine development

Antimicrobial resistance is an increasing global threat and alternative treatments substituting failing antibiotics are urgently needed. Vaccines are recognized as highly effective tools to mitigate antimicrobial resistance; however, the selection of bacterial antigens as vaccine candidates remains challenging. In recent years, advances in mass spectrometry-based proteomics have led to the development of so-called immunopeptidomics approaches that allow the untargeted discovery of bacterial epitopes that are presented on the surface of infected cells. Especially for intracellular bacterial pathogens, immunopeptidomics holds great promise to uncover antigens that can be encoded in viral vector- or nucleic acid-based vaccines. This review provides an overview of immunopeptidomics studies on intracellular bacterial pathogens and considers future directions and challenges in advancing towards next-generation vaccines.

Anthem: a user customised tool for fast and accurate prediction of binding between peptides and HLA class I molecules

Neopeptide-based immunotherapy has been recognised as a promising approach for the treatment of cancers. For neopeptides to be recognised by CD8+ T cells and induce an immune response, their binding to human leukocyte antigen class I (HLA-I) molecules is a necessary first step. Most epitope prediction tools thus rely on the prediction of such binding. With the use of mass spectrometry, the scale of naturally presented HLA ligands that could be used to develop such predictors has been expanded. However, there are rarely efforts that focus on the integration of these experimental data with computational algorithms to efficiently develop up-to-date predictors. Here, we present Anthem for accurate HLA-I binding prediction. In particular, we have developed a user-friendly framework to support the development of customisable HLA-I binding prediction models to meet challenges associated with the rapidly increasing availability of large amounts of immunopeptidomic data. Our extensive evaluation, using both independent and experimental datasets shows that Anthem achieves an overall similar or higher area under curve value compared with other contemporary tools. It is anticipated that Anthem will provide a unique opportunity for the non-expert user to analyse and interpret their own in-house or publicly deposited datasets.

Conserved epitopes with high HLA-I population coverage are targets of CD8+ T cells associated with high IFN-γ responses against all dengue virus serotypes

Cytotoxic CD8⁺ T cells are key for immune protection against viral infections. The breadth and cross-reactivity of these responses are important against rapidly mutating RNA viruses, such as dengue (DENV), yet how viral diversity affect T cell responses and their cross-reactivity against multiple variants of the virus remains poorly defined. In this study, an integrated analysis was performed to map experimentally validated CD8⁺ T cell epitopes onto the distribution of DENV genome sequences across the 4 serotypes worldwide. Despite the higher viral diversity observed within HLA-I restricted epitopes, mapping of 609 experimentally validated epitopes sequences on 3985 full-length viral genomes revealed 19 highly conserved epitopes across the four serotypes within the immunogenic regions of NS3, NS4B and NS5. These conserved epitopes were associated with a higher magnitude of IFN-γ response when compared to non-conserved epitopes and were restricted to 13 HLA class I genotypes, hence providing high coverage among human populations. Phylogeographic analyses showed that these epitopes are largely conserved in most of the endemic regions of the world, and with only some of these epitopes presenting distinct mutated variants circulating in South America and Asia.This study provides evidence for the existence of highly immunogenic and conserved epitopes across serotypes, which may impact design of new universal T-cell-inducing vaccine candidates that minimise detrimental effects of viral diversification and at the same time induce responses to a broad human population.

Mass Spectrometry Based Immunopeptidomics Leads to Robust Predictions of Phosphorylated HLA Class I Ligands

The presentation of peptides on class I human leukocyte antigen (HLA-I) molecules plays a central role in immune recognition of infected or malignant cells. In cancer, non-self HLA-I ligands can arise from many different alterations, including non-synonymous mutations, gene fusion, cancer-specific alternative mRNA splicing or aberrant post-translational modifications. Identifying HLA-I ligands remains a challenging task that requires either heavy experimental work for in vivo identification or optimized bioinformatics tools for accurate predictions. To date, no HLA-I ligand predictor includes post-translational modifications. To fill this gap, we curated phosphorylated HLA-I ligands from several immunopeptidomics studies (including six newly measured samples) covering 72 HLA-I alleles and retrieved a total of 2,066 unique phosphorylated peptides. We then expanded our motif deconvolution tool to identify precise binding motifs of phosphorylated HLA-I ligands. Our results reveal a clear enrichment of phosphorylated peptides among HLA-C ligands and demonstrate a prevalent role of both HLA-I motifs and kinase motifs on the presentation of phosphorylated peptides. These data further enabled us to develop and validate the first predictor of interactions between HLA-I molecules and phosphorylated peptides.

The Human Immunopeptidome Project: A Roadmap to Predict and Treat Immune Diseases

The science that investigates the ensembles of all peptides associated to human leukocyte antigen (HLA) molecules is termed "immunopeptidomics" and is typically driven by mass spectrometry (MS) technologies. Recent advances in MS technologies, neoantigen discovery and cancer immunotherapy have catalyzed the launch of the Human Immunopeptidome Project (HIPP) with the goal of providing a complete map of the human immunopeptidome and making the technology so robust that it will be available in every clinic. Here, we provide a long-term perspective of the field and we use this framework to explore how we think the completion of the HIPP will truly impact the society in the future. In this context, we introduce the concept of immunopeptidome-wide association studies (IWAS). We highlight the importance of large cohort studies for the future and how applying quantitative immunopeptidomics at population scale may provide a new look at individual predisposition to common immune diseases as well as responsiveness to vaccines and immunotherapies. Through this vision, we aim to provide a fresh view of the field to stimulate new discussions within the community, and present what we see as the key challenges for the future for unlocking the full potential of immunopeptidomics in this era of precision medicine.

Substantial Influence of ERAP2 on the HLA-B*40:02 Peptidome: Implications for HLA-B*27-Negative Ankylosing Spondylitis

HLA-B*40:02 is one of a few major histocompatibility complex class I (MHC-I) molecules associated with ankylosing spondylitis (AS) independently of HLA-B*27. The endoplasmic reticulum aminopeptidase 2 (ERAP2), an enzyme that process MHC-I ligands and preferentially trims N-terminal basic residues, is also a risk factor for this disease. Like HLA-B*27 and other AS-associated MHC-I molecules, HLA-B*40:02 binds a relatively high percentage of peptides with ERAP2-susceptible residues. In this study, the effects of ERAP2 depletion on the HLA-B*40:02 peptidome were analyzed. ERAP2 protein expression was knocked out by CRISPR in the transfectant cell line C1R-B*40:02, and the differences between the peptidomes from the wild-type and ERAP2-KO cells were determined by label-free quantitative comparisons. The qualitative changes dependent on ERAP2 affected about 5% of the peptidome, but quantitative changes in peptide amounts were much more substantial, reflecting a significant influence of this enzyme on the generation/destruction balance of HLA-B*40:02 ligands. As in HLA-B*27, a major effect was on the frequencies of N-terminal residues. In this position, basic and small residues were increased, and aliphatic/aromatic ones decreased in the ERAP2 knockout. Other peptide positions were also affected. Because most of the non-B*27 MHC-I molecules associated with AS risk bind a relatively high percentage of peptides with N-terminal basic residues, we hypothesize that the non-epistatic association of ERAP2 with AS might be related to the processing of peptides with these residues, thus affecting the peptidomes of AS-associated MHC-I molecules.

Understanding the Influence of a Bifunctional Polyethylene Glycol Derivative in Protein Corona Formation around Iron Oxide Nanoparticles

Superparamagnetic iron oxide nanoparticles are one of the most prominent agents used in theranostic applications, with MRI imaging the main application assessed. The biomolecular interface formed on the surface of a nanoparticle in a biological medium determines its behaviour in vitro and in vivo. In this study, we have compared the formation of the protein corona on highly monodisperse iron oxide nanoparticles with two different coatings, dimercaptosuccinic acid (DMSA), and after conjugation, with a bifunctional polyethylene glycol (PEG)-derived molecule (2000 Da) in the presence of Wistar rat plasma. The protein fingerprints around the nanoparticles were analysed in an extensive proteomic study. The results presented in this work indicate that the composition of the protein corona is very difficult to predict. Proteins from different functional categories—cell components, lipoproteins, complement, coagulation, immunoglobulins, enzymes and transport proteins—were identified in all samples with very small variability. Although both types of nanoparticles have similar amounts of bonded proteins, very slight differences in the composition of the corona might explain the variation observed in the uptake and biotransformation of these nanoparticles in Caco-2 and RAW 264.7 cells. Cytotoxicity was also studied using a standard 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide assay. Controlling nanoparticles’ reactivity to the biological environment by deciding on its surface functionalization may suggest new routes in the control of the biodistribution, biodegradation and clearance of multifunctional nanomedicines.

The Evolving Landscape of Autoantigen Discovery and Characterization in Type 1 Diabetes

Type 1 diabetes (T1D) is an autoimmune disease that is caused, in part, by T cell-mediated destruction of insulin-producing β-cells. High risk for disease, in those with genetic susceptibility, is predicted by the presence of two or more autoantibodies against insulin, the 65-kDa form of glutamic acid decarboxylase (GAD65), insulinoma-associated protein 2 (IA-2), and zinc transporter 8 (ZnT8). Despite this knowledge, we still do not know what leads to the breakdown of tolerance to these autoantigens, and we have an incomplete understanding of T1D etiology and pathophysiology. Several new autoantibodies have recently been discovered using innovative technologies, but neither their potential utility in monitoring disease development and treatment nor their role in the pathophysiology and etiology of T1D has been explored. Moreover, neoantigen generation (through posttranslational modification, the formation of hybrid peptides containing two distinct regions of an antigen or antigens, alternative open reading frame usage, and translation of RNA splicing variants) has been reported, and autoreactive T cells that target these neoantigens have been identified. Collectively, these new studies provide a conceptual framework to understand the breakdown of self-tolerance, if such modifications occur in a tissue- or disease-specific context. A recent workshop sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases brought together investigators who are using new methods and technologies to identify autoantigens and characterize immune responses toward these proteins. Researchers with diverse expertise shared ideas and identified resources to accelerate antigen discovery and the detection of autoimmune responses in T1D. The application of this knowledge will direct strategies for the identification of improved biomarkers for disease progression and treatment response monitoring and, ultimately, will form the foundation for novel antigen-specific therapeutics. This Perspective highlights the key issues that were addressed at the workshop and identifies areas for future investigation.

Predicting peptide presentation by major histocompatibility complex class I: an improved machine learning approach to the immunopeptidome

BACKGROUND

To further our understanding of immunopeptidomics, improved tools are needed to identify peptides presented by major histocompatibility complex class I (MHC-I). Many existing tools are limited by their reliance upon chemical affinity data, which is less biologically relevant than sampling by mass spectrometry, and other tools are limited by incomplete exploration of machine learning approaches. Herein, we assemble publicly available data describing human peptides discovered by sampling the MHC-I immunopeptidome with mass spectrometry and use this database to train random forest classifiers (ForestMHC) to predict presentation by MHC-I.

RESULTS

As measured by precision in the top 1% of predictions, our method outperforms NetMHC and NetMHCpan on test sets, and it outperforms both these methods and MixMHCpred on new data from an ovarian carcinoma cell line. We also find that random forest scores correlate monotonically, but not linearly, with known chemical binding affinities, and an information-based analysis of classifier features shows the importance of anchor positions for our classification. The random-forest approach also outperforms a deep neural network and a convolutional neural network trained on identical data. Finally, we use our large database to confirm that gene expression partially determines peptide presentation.

CONCLUSIONS

ForestMHC is a promising method to identify peptides bound by MHC-I. We have demonstrated the utility of random forest-based approaches in predicting peptide presentation by MHC-I, assembled the largest known database of MS binding data, and mined this database to show the effect of gene expression on peptide presentation. ForestMHC has potential applicability to basic immunology, rational vaccine design, and neoantigen binding prediction for cancer immunotherapy. This method is publicly available for applications and further validation.

PRBAM: a new tool to analyze the MHC class I and HLA-DR anchor motifs

Major histocompatibility complex (MHC) genes are highly polymorphic, which makes each MHC molecule different regarding their peptide repertoire, so they can bind and present to T lymphocytes. The increasing importance of immunopeptidomics and its use in personalized medicine in different fields such as oncology or autoimmunity demand the correct analysis of the peptide repertoires bound to human leukocyte antigen type 1 (HLA-I) and HLA-II molecules. Purification of the peptide pool by affinity chromatography and individual peptide sequencing using mass spectrometry techniques is the standard protocol to define the binding motifs of the different MHC-I and MHC-II molecules. The identification of MHC-I binding motifs is relatively simple, but it is more complicated for MHC-II. There are some programs that identify the anchor motifs of MHC-II molecules. However, these programs do not identify the anchor motif correctly for some HLA-II molecules and some anchor motifs have been deduced using subjective interpretation of the data. Here, we present a new software, called PRBAM (Peptide Repertoire-Based Anchor Motif) that uses a new algorithm based on the peptide-MHC interactions and, using peptide lists obtained by mass spectrometry sequencing, identifies the binding motif of MHC-I and HLA-DR molecules. PRBAM has an easy-to-use interface, and the results are presented in graphics, tables and peptide lists. Finally, the fact that PRBAM uses a new algorithm makes it complementary to other existing programs.

Employing proteomics in the study of antigen presentation: an update

INTRODUCTION

Our immune system discriminates self from non-self by examining the peptide cargo of human leukocyte antigen (HLA) molecules displayed on the cell surface. Successful recognition of HLA-bound non-self peptides can induce T cell responses leading to, for example, the destruction of infected cells. Today, largely due to advances in technology, we have an unprecedented capability to identify the nature of these presented peptides and unravel the true complexity of antigen presentation. Areas covered: In addition to conventional linear peptides, HLA molecules also present post-translationally modified sequences comprising a wealth of chemical and structural modifications, including a novel class of noncontiguous spliced peptides. This review focuses on these emerging themes in antigen presentation and how mass spectrometry in particular has contributed to a new view of the antigenic landscape that is presented to the immune system. Expert Commentary: Advances in the sensitivity of mass spectrometers and use of hybrid fragmentation technologies will provide more information-rich spectra of HLA bound peptides leading to more definitive identification of T cell epitopes. Coupled with improvements in sample preparation and new informatics workflows, studies will access novel classes of peptide antigen and allow interrogation of rare and clinically relevant samples.

The Role of Mass Spectrometry and Proteogenomics in the Advancement of HLA Epitope Prediction

A challenge in developing personalized cancer immunotherapies is the prediction of putative cancer-specific antigens. Currently, predictive algorithms are used to infer binding of peptides to human leukocyte antigen (HLA) heterodimers to aid in the selection of putative epitope targets. One drawback of current epitope prediction algorithms is that they are trained on datasets containing biochemical HLA-peptide binding data that may not completely capture the rules associated with endogenous processing and presentation. The field of MS has made great improvements in instrumentation speed and sensitivity, chromatographic resolution, and proteogenomic database search strategies to facilitate the identification of HLA-ligands from a variety of cell types and tumor tissues. As such, these advances have enabled MS profiling of HLA-binding peptides to be a tractable, orthogonal approach to lower throughput biochemical assays for generating comprehensive datasets to train epitope prediction algorithms. In this review, we will highlight the progress made in the field of HLA-ligand profiling enabled by MS and its impact on current and future epitope prediction strategies.

Gaining Insight Into Posttranslationally Modified HIV Antigens and Their Underlying Characteristics

Mass spectrometry (MS)-based immunopeptidomics has developed as one of the leading methodologies for comprehensive characterization of in vivo presented human leukocyte antigen (HLA)-bound peptides. Unveiling the identity of HLA-bound peptides derived from diseased cells is crucial to gain knowledge on the constitution of efficient disease-specific T cell responses. The HLA-presented peptidome reflects the status of the cellular proteome, hence disease-related aberrations of posttranslational modifications (PTMs) might lead to presentation of peptides harboring PTMs. Therefore, characterization of HLA-bound PTM peptides could shed light on their relevance in immune and disease processes. In this issue, Ramarathinam et al. investigate the presentation of HIV envelope (HIVenv) peptides bound to the HLA-B*57:01 allele. Among these peptides, the authors specifically focused on a kynurenine-modified peptide. To this end, they characterize the possible origin of the kynurenine modification, its effect on HLA binding affinity, stability, conformation within the complex, and its immunogenicity compared to the native counterpart.

Identification of Immunogenic Epitopes by MS/MS

The interrogation of cell surface-presented immunogenic epitopes is of great importance to differentiate diseased cells in consequence to malignant transformation or viral infections. On the basis of this knowledge, next-generation immunotherapies against cancers, autoimmunity, or infectious diseases can be developed. The identification of altered peptide repertoires of transformed cells renders mass spectrometry-based analysis indispensable. This is evident considering the low correlation of gene or protein expression alterations, respectively, with changes in the peptide repertoire rendering those analyses less informative. Nevertheless, immunogenicity of peptides appearing to be exclusively found on diseased cells has to be finally proven in T cell-based assays. This review highlights the capabilities and limitations of mass spectrometry in the identification of entire immunopeptidomes, as well as individual potential immunogenic epitopes with a strong focus on cancer. Furthermore, an overview of state-of-the-art immunogenicity screens is presented.

High-sensitivity HLA class I peptidome analysis enables a precise definition of peptide motifs and the identification of peptides from cell lines and patients' sera

The characterization of peptides bound to human leukocyte antigen (HLA) class I is of fundamental importance for understanding CD8+ T cell-driven immunological processes and for the development of immunomodulatory therapeutic strategies. However, until now, the mass spectrometric analysis of HLA-bound peptides has typically required billions of cells, still resulting in relatively few high-confidence peptide identifications. Capitalizing on the recent developments in mass spectrometry and bioinformatics, we have implemented a methodology for the efficient recovery of acid-eluted HLA peptides after purification with the pan-reactive antibody W6/32 and have identified a total of 27 862 unique peptides with high confidence (1% false discovery rate) from five human cancer cell lines. More than 93% of the identified peptides were eight to 11 amino acids in length and contained signatures that were in excellent agreement with published HLA binding motifs. Furthermore, by purifying soluble HLA class I complexes (sHLA) from sera of melanoma patients, up to 972 high-confidence peptides could be identified, including melanoma-associated antigens already described in the literature. Knowledge of the HLA class I peptidome should facilitate multiplex tetramer technology-based characterization of T cells, and allow the development of patient selection, stratification and immunomodulatory therapeutic strategies.

A Molecular Basis for the Presentation of Phosphorylated Peptides by HLA-B Antigens

As aberrant protein phosphorylation is a hallmark of tumor cells, the display of tumor-specific phosphopeptides by Human Leukocyte Antigen (HLA) class I molecules can be exploited in the treatment of cancer by T-cell-based immunotherapy. Yet, the characterization and prediction of HLA-I phospholigands is challenging as the molecular determinants of the presentation of such post-translationally modified peptides are not fully understood. Here, we employed a peptidomic workflow to identify 256 unique phosphorylated ligands associated with HLA-B*40, -B*27, -B*39, or -B*07. Remarkably, these phosphopeptides showed similar molecular features. Besides the specific anchor motifs imposed by the binding groove of each allotype, the predominance of phosphorylation at peptide position 4 (P4) became strikingly evident, as was the enrichment of basic residues at P1. To determine the structural basis of this observation, we carried out a series of peptide binding assays and solved the crystal structures of HLA-B*40 in complex with a phosphorylated ligand or its nonphosphorylated counterpart. Overall, our data provide a clear explanation to the common motif found in the phosphopeptidomes associated to different HLA-B molecules. The high prevalence of phosphorylation at P4 is dictated by the presence of the conserved residue Arg62 in the heavy chain, a structural feature shared by most HLA-B alleles. In contrast, the preference for basic residues at P1 is allotype-dependent and might be linked to the structure of the A pocket. This molecular understanding of the presentation of phosphopeptides by HLA-B molecules provides a base for the improved prediction and identification of phosphorylated neo-antigens, as potentially used for cancer immunotherapy.

Analysis of Major Histocompatibility Complex (MHC) Immunopeptidomes Using Mass Spectrometry

The myriad of peptides presented at the cell surface by class I and class II major histocompatibility complex (MHC) molecules are referred to as the immunopeptidome and are of great importance for basic and translational science. For basic science, the immunopeptidome is a critical component for understanding the immune system; for translational science, exact knowledge of the immunopeptidome can directly fuel and guide the development of next-generation vaccines and immunotherapies against autoimmunity, infectious diseases, and cancers. In this mini-review, we summarize established isolation techniques as well as emerging mass spectrometry-based platforms (i.e. SWATH-MS) to identify and quantify MHC-associated peptides. We also highlight selected biological applications and discuss important current technical limitations that need to be solved to accelerate the development of this field.

Mass spectrometry-based antigen discovery for cancer immunotherapy

The antigenic landscape of tumors is distinct from healthy cells and has been the rationale behind a variety of vaccination trials. Typically the target tumor-associated antigens have been of self origin and have rarely induced effective anti-tumor responses. Recent data show that activation of the immune system by immune checkpoint blocking therapies leads to tumor rejection and that recognition of mutated antigens, known as 'neo-antigens' plays a key role. Discovery of neo-antigens relies mainly on prediction-based interrogation of the 'mutanome' using genomic information as input, followed by T-cell screening. Recent breakthroughs in mass spectrometry (MS) based immunopeptidomics have allowed the discovery of very large pools of naturally presented peptides, among them neo-epitopes. This review highlights the current progress related to neo-antigens discovery with emphasis on prediction algorithms and MS as well as the synergy of the two methodologies and how they can be exploited to develop effective personalized immunotherapy.

Comparative Analysis of the Endogenous Peptidomes Displayed by HLA-B*27 and Mamu-B*08: Two MHC Class I Alleles Associated with Elite Control of HIV/SIV Infection

Indian rhesus macaques are arguably the most reliable animal models in AIDS research. In this species the MHC class I allele Mamu-B*08, among others, is associated with elite control of SIV replication. A similar scenario is observed in humans where the expression of HLA-B*27 or HLA-B*57 has been linked to slow or no progression to AIDS after HIV infection. Despite having large differences in their primary structure, it has been reported that HLA-B*27 and Mamu-B*08 display peptides with sequence similarity. To fine-map the Mamu-B*08 binding motif and assess its similarities with that of HLA-B*27, we affinity purified the peptidomes bound to these MHC class I molecules and analyzed them by LC-MS, identifying several thousands of endogenous ligands. Sequence analysis of both sets of peptides revealed a degree of similarity in their binding motifs, especially at peptide position 2 (P2), where arginine was present in the vast majority of ligands of both allotypes. In addition, several differences emerged from this analysis: (i) ligands displayed by Mamu-B*08 tended to be shorter and to have lower molecular weight, (ii) Mamu-B*08 showed a higher preference for glutamine at P2 as a suboptimal binding motif, and (iii) the second major anchor position, found at PΩ, was much more restrictive in Mamu-B*08. In this regard, HLA-B*27 bound efficiently peptides with aliphatic, aromatic (including tyrosine), and basic C-terminal residues while Mamu-B*08 preferred peptides with leucine and phenylalanine in this position. Finally, in silico estimations of binding efficiency and competitive binding assays to Mamu-B*08 of several selected peptides revealed a good correlation between the characterized anchor motif and binding affinity. These results deepen our understanding of the molecular basis of the presentation of peptides by Mamu-B*08 and can contribute to the detection of novel SIV epitopes restricted by this allotype.

The Length Distribution of Class I-Restricted T Cell Epitopes Is Determined by Both Peptide Supply and MHC Allele-Specific Binding Preference

HLA class I-binding predictions are widely used to identify candidate peptide targets of human CD8(+) T cell responses. Many such approaches focus exclusively on a limited range of peptide lengths, typically 9 aa and sometimes 9-10 aa, despite multiple examples of dominant epitopes of other lengths. In this study, we examined whether epitope predictions can be improved by incorporating the natural length distribution of HLA class I ligands. We found that, although different HLA alleles have diverse length-binding preferences, the length profiles of ligands that are naturally presented by these alleles are much more homogeneous. We hypothesized that this is due to a defined length profile of peptides available for HLA binding in the endoplasmic reticulum. Based on this, we created a model of HLA allele-specific ligand length profiles and demonstrate how this model, in combination with HLA-binding predictions, greatly improves comprehensive identification of CD8(+) T cell epitopes.

An open-source computational and data resource to analyze digital maps of immunopeptidomes

We present a novel mass spectrometry-based high-throughput workflow and an open-source computational and data resource to reproducibly identify and quantify HLA-associated peptides. Collectively, the resources support the generation of HLA allele-specific peptide assay libraries consisting of consensus fragment ion spectra, and the analysis of quantitative digital maps of HLA peptidomes generated from a range of biological sources by SWATH mass spectrometry (MS). This study represents the first community-based effort to develop a robust platform for the reproducible and quantitative measurement of the entire repertoire of peptides presented by HLA molecules, an essential step towards the design of efficient immunotherapies.

DOI:http://dx.doi.org/10.7554/eLife.07661.001

The cells of the immune system protect us by recognizing telltale molecules produced by damaged and diseased cells, or by infection-causing microorganisms (which are also called pathogens). To help with this process, the cells in our bodies display small fragments of proteins (called peptides) on their surface that are then checked by the immune cells. Collectively, these peptides are referred to as the ‘immunopeptidome’, and deciphering the complexity of the human immunopeptidome is important for both basic research and medical science. Such an achievement would help to guide the development of next-generation vaccines and therapies against autoimmune disorders, infectious diseases and cancers.

In the past, immune peptides were mostly identified using a technique that is commonly called ‘shotgun’ mass spectrometry. However, this approach doesn't always provide reproducible results. In 2012, researchers reported the development of a new approach—which they called ‘SWATH’ mass spectrometry—that could yield more reproducible data.

Now, Caron et al.—including many of the researchers involved in the 2012 study—have developed a large collection of standardized tests that use SWATH mass spectrometry to analyze the human immunopeptidome. The workflow and the computational and data resources developed as part of this international effort are the first steps toward highly reproducible and measurable analyses of the immunopeptidome across many samples. Moreover, the large repository of assays generated by the project has been made public and will serve a large community of researchers, which should enable better collaborations.

In the future, SWATH mass spectrometry could be used as a robust technology for the reproducible detection and measurement of pathogen-specific or cancer-specific immune peptides. This could greatly help in the design of personalized immune-based therapies.

DOI:http://dx.doi.org/10.7554/eLife.07661.002

Recent advances in enrichment and separation strategies for mass spectrometry-based phosphoproteomics

Due to the significance of protein phosphorylation in various biological processes and signaling events, new analytical techniques for enhanced phosphoproteomics have been rapidly introduced in the recent years. The combinatorial use of the phospho-specific enrichment techniques and prefractionation methods prior to MS analysis enable comprehensive profiling of the phosphoproteome and facilitate deciphering the critical roles that phosphorylation plays in signaling pathways in various biological systems. This review places special emphasis on the recent five-year (2009-2013) advances for enrichment and separation techniques that have been utilized for phosphopeptides prior to MS analysis.

Peptide handling by HLA-B27 subtypes influences their biological behavior, association with ankylosing spondylitis and susceptibility to endoplasmic reticulum aminopeptidase 1 (ERAP1)

HLA-B27 is strongly associated with ankylosing spondylitis (AS). We analyzed the relationship between structure, peptide specificity, folding, and stability of the seven major HLA-B27 subtypes to determine the role of their constitutive peptidomes in the pathogenicity of this molecule. Identification of large numbers of ligands allowed us to define the differences among subtype-bound peptidomes and to elucidate the peptide features associated with AS and molecular stability. The peptides identified only in AS-associated or high thermostability subtypes with identical A and B pockets were longer and had bulkier and more diverse C-terminal residues than those found only among non-AS-associated/lower-thermostability subtypes. Peptides sequenced from all AS-associated subtypes and not from non-AS-associated ones, thus strictly correlating with disease, were very rare. Residue 116 was critical in determining peptide binding, thermodynamic properties, and folding, thus emerging as a key feature that unified HLA-B27 biology. HLA-B27 ligands were better suited to TAP transport than their N-terminal precursors, and AS-associated subtype ligands were better than those from non-AS-associated subtypes, suggesting a particular capacity of AS-associated subtypes to bind epitopes directly produced in the cytosol. Peptides identified only from AS-associated/high-thermostability subtypes showed a higher frequency of ERAP1-resistant N-terminal residues than ligands found only in non-AS-associated/low-thermostability subtypes, reflecting a more pronounced effect of ERAP1 on the former group. Our results reveal the basis for the relationship between peptide specificity and other features of HLA-B27, provide a unified view of HLA-B27 biology and pathogenicity, and suggest a larger influence of ERAP1 polymorphism on AS-associated than non-AS-associated subtypes.