Employing proteomics in the study of antigen presentation: an update

APEX-pHLA: A novel method for accurate prediction of the binding between exogenous short peptides and HLA class I molecules

Human leukocyte antigen (HLA) molecules play critically significant role within the realm of immunotherapy due to their capacities to recognize and bind exogenous antigens such as peptides, subsequently delivering them to immune cells. Predicting the binding between peptides and HLA molecules (pHLA) can expedite the screening of immunogenic peptides and facilitate vaccine design. However, traditional experimental methods are time-consuming and inefficient. In this study, an efficient method based on deep learning was developed for predicting peptide-HLA binding, which treated peptide sequences as linguistic entities. It combined the architectures of textCNN and BiLSTM to create a deep neural network model called APEX-pHLA. This model operated without limitations related to HLA class I allele variants and peptide segment lengths, enabling efficient encoding of sequence features for both HLA and peptide segments. On the independent test set, the model achieved Accuracy, ROC_AUC, F1, and MCC is 0.9449, 0.9850, 0.9453, and 0.8899, respectively. Similarly, on an external test set, the results were 0.9803, 0.9574, 0.8835, and 0.7863, respectively. These findings outperformed fifteen methods previously reported in the literature. The accurate prediction capability of the APEX-pHLA model in peptide-HLA binding might provide valuable insights for future HLA vaccine design.

Machine learning-based peptide-spectrum match rescoring opens up the immunopeptidome

Immunopeptidomics is a key technology in the discovery of targets for immunotherapy and vaccine development. However, identifying immunopeptides remains challenging due to their non-tryptic nature, which results in distinct spectral characteristics. Moreover, the absence of strict digestion rules leads to extensive search spaces, further amplified by the incorporation of somatic mutations, pathogen genomes, unannotated open reading frames, and post-translational modifications. This inflation in search space leads to an increase in random high-scoring matches, resulting in fewer identifications at a given false discovery rate. Peptide-spectrum match rescoring has emerged as a machine learning-based solution to address challenges in mass spectrometry-based immunopeptidomics data analysis. It involves post-processing unfiltered spectrum annotations to better distinguish between correct and incorrect peptide-spectrum matches. Recently, features based on predicted peptidoform properties, including fragment ion intensities, retention time, and collisional cross section, have been used to improve the accuracy and sensitivity of immunopeptide identification. In this review, we describe the diverse bioinformatics pipelines that are currently available for peptide-spectrum match rescoring and discuss how they can be used for the analysis of immunopeptidomics data. Finally, we provide insights into current and future machine learning solutions to boost immunopeptide identification.

Thunder-DDA-PASEF enables high-coverage immunopeptidomics and is boosted by MS2Rescore with MS2PIP timsTOF fragmentation prediction model

Human leukocyte antigen (HLA) class I peptide ligands (HLAIps) are key targets for developing vaccines and immunotherapies against infectious pathogens or cancer cells. Identifying HLAIps is challenging due to their high diversity, low abundance, and patient individuality. Here, we develop a highly sensitive method for identifying HLAIps using liquid chromatography-ion mobility-tandem mass spectrometry (LC-IMS-MS/MS). In addition, we train a timsTOF-specific peak intensity MS2PIP model for tryptic and non-tryptic peptides and implement it in MS2Rescore (v3) together with the CCS predictor from ionmob. The optimized method, Thunder-DDA-PASEF, semi-selectively fragments singly and multiply charged HLAIps based on their IMS and m/z. Moreover, the method employs the high sensitivity mode and extended IMS resolution with fewer MS/MS frames (300 ms TIMS ramp, 3 MS/MS frames), doubling the coverage of immunopeptidomics analyses, compared to the proteomics-tailored DDA-PASEF (100 ms TIMS ramp, 10 MS/MS frames). Additionally, rescoring boosts the HLAIps identification by 41.7% to 33%, resulting in 5738 HLAIps from as little as one million JY cell equivalents, and 14,516 HLAIps from 20 million. This enables in-depth profiling of HLAIps from diverse human cell lines and human plasma. Finally, profiling JY and Raji cells transfected to express the SARS-CoV-2 spike protein results in 16 spike HLAIps, thirteen of which have been reported to elicit immune responses in human patients.

The biogenesis of the immunopeptidome

The immunopeptidome is the repertoire of peptides bound and presented by the MHC class I, class II, and non-classical molecules. The peptides are produced by the degradation of most cellular proteins, and in some cases, peptides are produced from extracellular proteins taken up by the cells. This review attempts to first describe some of its known and well-accepted concepts, and next, raise some questions about a few of the established dogmas in this field: The production of novel peptides by splicing is questioned, suggesting here that spliced peptides are extremely rare, if existent at all. The degree of the contribution to the immunopeptidome by degradation of cellular protein by the proteasome is doubted, therefore this review attempts to explain why it is likely that this contribution to the immunopeptidome is possibly overstated. The contribution of defective ribosome products (DRiPs) and non-canonical peptides to the immunopeptidome is noted and methods are suggested to quantify them. In addition, the common misconception that the MHC class II peptidome is mostly derived from extracellular proteins is noted, and corrected. It is stressed that the confirmation of sequence assignments of non-canonical and spliced peptides should rely on targeted mass spectrometry using spiking-in of heavy isotope-labeled peptides. Finally, the new methodologies and modern instrumentation currently available for high throughput kinetics and quantitative immunopeptidomics are described. These advanced methods open up new possibilities for utilizing the big data generated and taking a fresh look at the established dogmas and reevaluating them critically.

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.

Ionizing Radiation Drives Key Regulators of Antigen Presentation and a Global Expansion of the Immunopeptidome

Little is known about the pathways regulating MHC antigen presentation and the identity of treatment-specific T cell antigens induced by ionizing radiation. For this reason, we investigated the radiation-specific changes in the colorectal tumor cell proteome. We found an increase in DDX58 and ZBP1 protein expression, two nucleic acid sensing molecules likely involved in induction of the dominant interferon response signature observed after genotoxic insult. We further observed treatment-induced changes in key regulators and effector proteins of the antigen processing and presentation machinery. Differential regulation of MHC allele expression was further driving the presentation of a significantly broader MHC-associated peptidome postirradiation, defining a radiation-specific peptide repertoire. Interestingly, treatment-induced peptides originated predominantly from proteins involved in catecholamine synthesis and metabolic pathways. A nuanced relationship between protein expression and antigen presentation was observed where radiation-induced changes in proteins do not correlate with increased presentation of associated peptides. Finally, we detected an increase in the presentation of a tumor-specific neoantigen derived from Mtch1. This study provides new insights into how radiation enhances antigen processing and presentation that could be suitable for the development of combinatorial therapies. Data are available via ProteomeXchange with identifier PXD032003.

Neoepitopes in Type 1 Diabetes: Etiological Insights, Biomarkers and Therapeutic Targets

The mechanisms underlying type 1 diabetes (T1D) pathogenesis remain largely unknown. While autoantibodies to pancreatic beta-cell antigens are often the first biological response and thereby a useful biomarker for identifying individuals in early stages of T1D, their role in T1D pathogenesis is not well understood. Recognition of these antigenic targets by autoreactive T-cells plays a pathological role in T1D development. Recently, several beta-cell neoantigens have been described, indicating that both neoantigens and known T1D antigens escape central or peripheral tolerance. Several questions regarding the mechanisms by which tolerance is broken in T1D remain unanswered. Further delineating the timing and nature of antigenic responses could allow their use as biomarkers to improve staging, as targets for therapeutic intervention, and lead to a better understanding of the mechanisms leading to loss of tolerance. Multiple factors that contribute to cellular stress may result in the generation of beta-cell derived neoepitopes and contribute to autoimmunity. Understanding the cellular mechanisms that induce beta-cells to produce neoantigens has direct implications on development of therapies to intercept T1D disease progression. In this perspective, we will discuss evidence for the role of neoantigens in the pathogenesis of T1D, including antigenic responses and cellular mechanisms. We will additionally discuss the pathways leading to neoepitope formation and the cross talk between the immune system and the beta-cells in this regard. Ultimately, delineating the timing of neoepitope generation in T1D pathogenesis will determine their role as biomarkers as well as therapeutic targets.

IDENTIFICATION OF MHC PEPTIDES USING MASS SPECTROMETRY FOR NEOANTIGEN DISCOVERY AND CANCER VACCINE DEVELOPMENT

Immunotherapy with neoantigens presented by major histocompatibility complex (MHC) is one of the most promising approaches in cancer treatment. Using this approach, cancer vaccines can be designed to target tumor-specific mutations that are not found in normal tissues. Clinical trials have demonstrated an increased immune response and eradication of tumors after injecting synthetic peptides selected from the immunopeptidome. Although the sequence of MHC binding peptides can be predicted from genome sequencing and prediction algorithms, this approach results in large numbers of predicted peptides, requiring the confirmation by mass spectrometry (MS) analysis. Identification of MHC peptides by direct MS analysis of immunopeptidome is accurate and sensitive, with tens of thousands of unique peptides potentially identified from either cancer cell line or tumor tissue. Peptides with mutations can also be identified with patient-specific protein databases constructed from genome or transcriptome sequencing data. MS analysis also enables the characterization of the post-translational modifications (PTMs) of those antigens that cannot be predicted. Moreover, PTMs were found to be more efficient in triggering an immune response. In addition to reviewing recent advances in the identification of neoantigens using MS, the techniques for cancer vaccine candidate selection and formulation, vaccine delivery systems, and the potential for use in combination with other therapeutics are also discussed. It is anticipated that MS-based techniques will play an important role in future cancer vaccine development. © 2019 John Wiley & Sons Ltd. Mass Spec Rev 40:110-125, 2021.

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.

Unbiased Characterization of Peptide-HLA Class II Interactions Based on Large-Scale Peptide Microarrays; Assessment of the Impact on HLA Class II Ligand and Epitope Prediction

Human Leukocyte Antigen class II (HLA-II) molecules present peptides to T lymphocytes and play an important role in adaptive immune responses. Characterizing the binding specificity of single HLA-II molecules has profound impacts for understanding cellular immunity, identifying the cause of autoimmune diseases, for immunotherapeutics, and vaccine development. Here, novel high-density peptide microarray technology combined with machine learning techniques were used to address this task at an unprecedented level of high-throughput. Microarrays with over 200,000 defined peptides were assayed with four exemplary HLA-II molecules. Machine learning was applied to mine the signals. The comparison of identified binding motifs, and power for predicting eluted ligands and CD4+ epitope datasets to that obtained using NetMHCIIpan-3.2, confirmed a high quality of the chip readout. These results suggest that the proposed microarray technology offers a novel and unique platform for large-scale unbiased interrogation of peptide binding preferences of HLA-II molecules.

In-depth mining of the immunopeptidome of an acute myeloid leukemia cell line using complementary ligand enrichment and data acquisition strategies

The identification of T cell epitopes derived from tumour specific antigens remains a significant challenge for the development of peptide-based vaccines and immunotherapies. The use of mass spectrometry-based approaches (immunopeptidomics) can provide powerful new avenues for the identification of such epitopes. In this study we report the use of complementary peptide antigen enrichment methods and a comprehensive mass spectrometric acquisition strategy to provide in-depth immunopeptidome data for the THP-1 cell line, a cell line used widely as a model of human leukaemia. To accomplish this, we combined robust experimental workflows that incorporated ultrafiltration or off-line reversed phase chromatography to enrich peptide ligand as well as a multifaceted data acquisition strategy using an Orbitrap Fusion LC-MS instrument. Using the combined datasets from the two ligand enrichment methods we gained significant depth in immunopeptidome coverage by identifying a total of 41,816 HLA class I peptides from THP-1 cells, including a significant number of peptides derived from different oncogenes or over expressed proteins associated with cancer. The physicochemical properties of the HLA-bound peptides dictated their recovery using the two ligand enrichment approaches and their distribution across the different precursor charge states considered in the data acquisition strategy. The data highlight the complementarity of the two enrichment procedures, and in cases where sample is not limiting, suggest that the combination of both approaches will yield the most comprehensive immunopeptidome information.

A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction

Human leukocyte antigen class I (HLA-I) molecules are encoded by major histocompatibility complex (MHC) class I loci in humans. The binding and interaction between HLA-I molecules and intracellular peptides derived from a variety of proteolytic mechanisms play a crucial role in subsequent T-cell recognition of target cells and the specificity of the immune response. In this context, tools that predict the likelihood for a peptide to bind to specific HLA class I allotypes are important for selecting the most promising antigenic targets for immunotherapy. In this article, we comprehensively review a variety of currently available tools for predicting the binding of peptides to a selection of HLA-I allomorphs. Specifically, we compare their calculation methods for the prediction score, employed algorithms, evaluation strategies and software functionalities. In addition, we have evaluated the prediction performance of the reviewed tools based on an independent validation data set, containing 21 101 experimentally verified ligands across 19 HLA-I allotypes. The benchmarking results show that MixMHCpred 2.0.1 achieves the best performance for predicting peptides binding to most of the HLA-I allomorphs studied, while NetMHCpan 4.0 and NetMHCcons 1.1 outperform the other machine learning-based and consensus-based tools, respectively. Importantly, it should be noted that a peptide predicted with a higher binding score for a specific HLA allotype does not necessarily imply it will be immunogenic. That said, peptide-binding predictors are still very useful in that they can help to significantly reduce the large number of epitope candidates that need to be experimentally verified. Several other factors, including susceptibility to proteasome cleavage, peptide transport into the endoplasmic reticulum and T-cell receptor repertoire, also contribute to the immunogenicity of peptide antigens, and some of them can be considered by some predictors. Therefore, integrating features derived from these additional factors together with HLA-binding properties by using machine-learning algorithms may increase the prediction accuracy of immunogenic peptides. As such, we anticipate that this review and benchmarking survey will assist researchers in selecting appropriate prediction tools that best suit their purposes and provide useful guidelines for the development of improved antigen predictors in the future.

Peptide Presentation to T Cells: Solving the Immunogenic Puzzle: Systems Immunology Profiling of Antigen Presentation for Prediction of CD8+ T Cell Immunogenicity

The vertebrate immune system uses an impressive arsenal of mechanisms to combat harmful cellular states such as infection. One way is via cells delivering real-time snapshots of their protein content to the cell surface in the form of short peptides. Specialized immune cells (T cells) sample these peptides and assess whether they are foreign, warranting an action such as destruction of the infected cell. The delivery of peptides to the cell surface is termed antigen processing and presentation, and decades of research have provided unprecedented understanding of this process. However, predicting the capacity for a given peptide to be immunogenic-to elicit a T cell response-has remained both enigmatic and a long sought-after goal. In the era of big data, a point is being approached where the steps of antigen processing and presentation can be quantified and assessed against peptide immunogenicity in order to build predictive models. This review presents new findings in this area and contemplates challenges ahead.

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.

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.