The DRB3 gene of the bovine major histocompatibility complex: discovery, diversity and distribution of alleles in commercial breeds of cattle and applications for development of vaccines.

The bovine Major Histocompatibility Complex (MHC), also known as the Bovine Leucocyte Antigen (BoLA) complex, is the genomic region that encodes the most important molecules for antigen presentation to initiate immune responses. The first evidence of MHC in bovines pointed to a locus containing 2 antigens, one detected by cytotoxic antiserum (MHC class I) and another studied by mixed lymphocyte culture tests (MHC class II). The most studied gene in the BoLA region is the highly polymorphic BoLA-DRB3, which encodes a ß chain with a peptide groove domain involved in antigen presentation for T cells that will develop and co-stimulate cellular and humoral effector responses. BoLA-DRB3 alleles have been associated with outcomes in infectious diseases such as mastitis, trypanosomiasis, and tick loads, and with production traits. To catalog these alleles, 2 nomenclature methods were proposed, and the current use of both systems makes it difficult to list, comprehend and apply these data effectively. In this review we have organized the knowledge available in all of the reports on the frequencies of BoLA-DRB3 alleles. It covers information from studies made in at least 26 countries on more than 30 breeds; studies are lacking in countries that are important producers of cattle livestock. We highlight practical applications of BoLA studies for identification of markers associated with resistance to infectious and parasitic diseases, increased production traits and T cell epitope mapping, in addition to genetic diversity and conservation studies of commercial and creole and locally adapted breeds. Finally, we provide support for the need of studies to discover new BoLA alleles and uncover unknown roles of this locus in production traits.

Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy.

Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.

How to Predict Binding Specificity and Ligands for New MHC-II Alleles with MixMHC2pred.

MHC-II molecules are key mediators of antigen presentation in vertebrate species and bind to their ligands with high specificity. The very high polymorphism of MHC-II genes within species and the fast-evolving nature of these genes across species has resulted in tens of thousands of different alleles, with hundreds of new alleles being discovered yearly through large sequencing projects in different species. Here we describe how to use MixMHC2pred to predict the binding specificity of any MHC-II allele directly from its amino acid sequence. We then show how both MHC-II ligands and CD4+ T cell epitopes can be predicted in different species with our approach. MixMHC2pred is available at http://mixmhc2pred.gfellerlab.org/ .

Surfaceome: a new era in the discovery of immune evasion mechanisms of circulating tumor cells.

Circulating tumor cells (CTCs) are cancer cells that detach from the original site and reach the bloodstream. The most aggressive CTCs survive various immune system attacks and initiate metastasis formation. Importantly, CTCs are not specifically targeted by the current immunotherapies due to the limited knowledge on specific targets. Proteomic profiling can be a powerful tool for understanding some of the immune evasion mechanisms used by cancer cells and particularly CTCs. These mechanisms are generally linked to the expression of specific surface proteins/peptides (i.e. the surfaceome). The study of the peptides that bind to class I molecules of the major histocompatibility complex (MHC-I) and of the various glycoproteins expressed on CTC surface may open a completely new avenue for the discovery of novel mechanisms of immune evasion. In this review, we discuss how immunopeptidomic and glycoproteomic studies of CTCs that interact with immune cells could help to better understand how metastasis-initiator CTCs escape the host immune response. We also describe how immunopeptidomic and glycoproteomic studies are carried out.

FASTMAP-a flexible and scalable immunopeptidomics pipeline for HLA- and antigen-specific T-cell epitope mapping based on artificial antigen-presenting cells.

The study of peptide repertoires presented by major histocompatibility complex (MHC) molecules and the identification of potential T-cell epitopes contribute to a multitude of immunopeptidome-based treatment approaches. Epitope mapping is essential for the development of promising epitope-based approaches in vaccination as well as for innovative therapeutics for autoimmune diseases, infectious diseases, and cancer. It also plays a critical role in the immunogenicity assessment of protein therapeutics with regard to safety and efficacy concerns. The main challenge emerges from the highly polymorphic nature of the human leukocyte antigen (HLA) molecules leading to the requirement of a peptide mapping strategy for a single HLA allele. As many autoimmune diseases are linked to at least one specific antigen, we established FASTMAP, an innovative strategy to transiently co-transfect a single HLA allele combined with a disease-specific antigen into a human cell line. This approach allows the specific identification of HLA-bound peptides using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Using FASTMAP, we found a comparable spectrum of endogenous peptides presented by the most frequently expressed HLA alleles in the world's population compared to what has been described in literature. To ensure a reliable peptide mapping workflow, we combined the HLA alleles with well-known human model antigens like coagulation factor VIII, acetylcholine receptor subunit alpha, protein structures of the SARS-CoV-2 virus, and myelin basic protein. Using these model antigens, we have been able to identify a broad range of peptides that are in line with already published and in silico predicted T-cell epitopes of the specific HLA/model antigen combination. The transient co-expression of a single affinity-tagged MHC molecule combined with a disease-specific antigen in a human cell line in our FASTMAP pipeline provides the opportunity to identify potential T-cell epitopes/endogenously processed MHC-bound peptides in a very cost-effective, fast, and customizable system with high-throughput potential.

Reanalysis of Immunopeptidomics Datasets Provides Mechanistic Insight into TAPBPR-Mediated Peptide Editing on HLA-A, -B and -C Molecules.

Background: Major histocompatibility class I (MHC-I, human leukocyte antigen [HLA]-I in humans) molecules present small fragments of the proteome on the cell surface for immunosurveillance, which is pivotal to control infected and malignant cells. Immunogenic peptides are generated and selected in the MHC-I antigen processing and presentation pathway. In this pathway, two homologous molecules, tapasin and TAPBPR, optimise the MHC-I peptide repertoire that is ultimately presented at the plasma membrane. Peptide exchange on HLA-I by human TAPBPR involves the flexible loop region K22-D35, with the leucine at position 30 (L30) involved in mediating peptide dissociation. However, our understanding of the exact molecular mechanisms governing TAPBPR-mediated peptide exchange on HLA-I allotypes remains incomplete. Methods: Here, in-depth re-analyses of published immunopeptidomics datasets was used to further examine TAPBPR peptide editing activity and mechanism of action on HLA-I. The role of the TAPBPR editing loop in opening the HLA-I peptide binding groove was assessed using a molecular dynamics simulation. Results: We show that TAPBPR shapes the peptide repertoire on HLA-A, -B and -C allotypes. The TAPBPR editing loop was not essential to allow HLA-I to adopt an open state. L30 in the TAPBPR editing loop was typically sufficient to mediate peptide repertoire restriction on the three HLA-I allotypes expressed by HeLa cells. TAPBPR was also able to load peptides onto HLA-I in a loop-dependent manner. Conclusions: These results unify the previously hypothesised scoop loop and peptide trap mechanisms of TAPBPR-mediated peptide exchange, with the former involved in peptide filtering and the latter in peptide loading.

Major histocompatibility complex (MHC) class I molecules play an essential role in alerting the immune system to infection and cellular changes. They do this by displaying small fragments of proteins (peptides) from pathogen-infected cells and tumours on the cell surface to immune cells. When activated, immune cells can then destroy the target cell. In 2015, we discovered that a novel accessory protein, called TAPBPR, assists in the selection of peptides displayed on MHC class I molecules for immune surveillance. A specific region in the TAPBPR protein ­ the editing loop ­ is known to be involved in removing peptides from MHC class I. However, our understanding of the process of peptide selection on MHC class I molecules remains incomplete. Here, we show that TAPBPR is not only involved in removing peptides from MHC class I molecules but also assists in peptide loading. Additionally, we demonstrate that the TAPBPR editing loop is involved in both removing and loading of peptides. Our results suggest that TAPBPR fine-tunes the peptide repertoire displayed on three different types of MHC class I molecules. Developing our understanding of the mechanisms of peptide selection on MHC class I molecules has important implications in disease and the development of new therapies.

Identifying major histocompatibility complex class II-DR molecules in bovine and swine peripheral blood monocyte-derived macrophages using mAb-L243.

Major histocompatibility complex class II (MHC-II) molecules are involved in immune responses against pathogens and vaccine candidates' immunogenicity. Immunopeptidomics for identifying cancer and infection-related antigens and epitopes have benefited from advances in immunopurification methods and mass spectrometry analysis. The mouse anti-MHC-II-DR monoclonal antibody L243 (mAb-L243) has been effective in recognising MHC-II-DR in both human and non-human primates. It has also been shown to cross-react with other animal species, although it has not been tested in livestock. This study used mAb-L243 to identify Staphylococcus aureus and Salmonella enterica serovar Typhimurium peptides binding to cattle and swine macrophage MHC-II-DR molecules using flow cytometry, mass spectrometry and two immunopurification techniques. Antibody cross-reactivity led to identifying expressed MHC-II-DR molecules, together with 10 Staphylococcus aureus peptides in cattle and 13 S. enterica serovar Typhimurium peptides in swine. Such data demonstrates that MHC-II-DR expression and immunocapture approaches using L243 mAb represents a viable strategy for flow cytometry and immunopeptidomics analysis of bovine and swine antigen-presenting cells.

Untranslated regions (UTRs) are a potential novel source of neoantigens for personalised immunotherapy.

Background: Neoantigens, mutated tumour-specific antigens, are key targets of anti-tumour immunity during checkpoint inhibitor (CPI) treatment. Their identification is fundamental to designing neoantigen-directed therapy. Non-canonical neoantigens arising from the untranslated regions (UTR) of the genome are an overlooked source of immunogenic neoantigens. Here, we describe the landscape of UTR-derived neoantigens and release a computational tool, PrimeCUTR, to predict UTR neoantigens generated by start-gain and stop-loss mutations. Methods: We applied PrimeCUTR to a whole genome sequencing dataset of pre-treatment tumour samples from CPI-treated patients (n = 341). Cancer immunopeptidomic datasets were interrogated to identify MHC class I presentation of UTR neoantigens. Results: Start-gain neoantigens were predicted in 72.7% of patients, while stop-loss mutations were found in 19.3% of patients. While UTR neoantigens only accounted 2.6% of total predicted neoantigen burden, they contributed 12.4% of neoantigens with high dissimilarity to self-proteome. More start-gain neoantigens were found in CPI responders, but this relationship was not significant when correcting for tumour mutational burden. While most UTR neoantigens are private, we identified two recurrent start-gain mutations in melanoma. Using immunopeptidomic datasets, we identify two distinct MHC class I-presented UTR neoantigens: one from a recurrent start-gain mutation in melanoma, and one private to Jurkat cells. Conclusion: PrimeCUTR is a novel tool which complements existing neoantigen discovery approaches and has potential to increase the detection yield of neoantigens in personalised therapeutics, particularly for neoantigens with high dissimilarity to self. Further studies are warranted to confirm the expression and immunogenicity of UTR neoantigens.

Birinapant Reshapes the Tumor Immunopeptidome and Enhances Antigen Presentation.

Birinapant, an antagonist of the inhibitor of apoptosis proteins, upregulates MHCs in tumor cells and displays a better tumoricidal effect when used in combination with immune checkpoint inhibitors, indicating that Birinapant may affect the antigen presentation pathway; however, the mechanism remains elusive. Based on high-resolution mass spectrometry and in vitro and in vivo models, we adopted integrated genomics, proteomics, and immunopeptidomics strategies to study the mechanism underlying the regulation of tumor immunity by Birinapant from the perspective of antigen presentation. Firstly, in HT29 and MCF7 cells, Birinapant increased the number and abundance of immunopeptides and source proteins. Secondly, a greater number of cancer/testis antigen peptides with increased abundance and more neoantigens were identified following Birinapant treatment. Moreover, we demonstrate the existence and immunogenicity of a neoantigen derived from insertion/deletion mutation. Thirdly, in HT29 cell-derived xenograft models, Birinapant administration also reshaped the immunopeptidome, and the tumor exhibited better immunogenicity. These data suggest that Birinapant can reshape the tumor immunopeptidome with respect to quality and quantity, which improves the presentation of CTA peptides and neoantigens, thus enhancing the immunogenicity of tumor cells. Such changes may be vital to the effectiveness of combination therapy, which can be further transferred to the clinic or aid in the development of new immunotherapeutic strategies to improve the anti-tumor immune response.

Mass Spectrometry-Based Immunopeptidomics of Peptides Presented on Human Leukocyte Antigen Proteins.

Human leukocyte antigen (HLA) proteins are a group of glycoproteins that are expressed at the cell surface, where they present peptides to T cells through physical interactions with T-cell receptors (TCRs). Hence, characterizing the set of peptides presented by HLA proteins, referred to hereafter as the immunopeptidome, is fundamental for neoantigen identification, immunotherapy, and vaccine development. As a result, different methods have been used over the years to identify peptides presented by HLA proteins, including competition assays, peptide microarrays, and yeast display systems. Nonetheless, over the last decade, mass spectrometry-based immunopeptidomics (MS-immunopeptidomics) has emerged as the gold-standard method for identifying peptides presented by HLA proteins. MS-immunopeptidomics enables the direct identification of the immunopeptidome in different tissues and cell types in different physiological and pathological states, for example, solid tumors or virally infected cells. Despite its advantages, it is still an experimentally and computationally challenging technique with different aspects that need to be considered before planning an MS-immunopeptidomics experiment, while conducting the experiment and with analyzing and interpreting the results. Hence, we aim in this chapter to provide an overview of this method and discuss different practical considerations at different stages starting from sample collection until data analysis. These points should aid different groups aiming at utilizing MS-immunopeptidomics, as well as, identifying future research directions to improve the method.

Peptidomics Strategies to Evaluate Cancer Diagnosis, Prognosis, and Treatment.

Peptides have potential bioactive functions, and the peptidomics landscape has been broadly investigated for various diseases, including cancer. In this chapter, we reviewed the past four years of literature available and selected 16 peer-reviewed publications exploring peptidomics in diagnosis, prognosis, and treatment in cancer research. We highlighted their main aims, mass spectrometry-based peptidomics, multi-omics, data-driven and in silico strategies, functional assays, and clinical applications. Moreover, we underscored several levels of difficulties in translating the peptidomics findings to clinical practice, aiming to learn with the accumulated knowledge and guide upcoming studies. Finally, this review reinforces the peptidomics robustness in discovering potential candidates for monitoring the several stages of cancer disease and therapeutic treatment, leveraging the management of cancer patients in the future.

Deep Learning-Assisted Analysis of Immunopeptidomics Data.

Liquid chromatography-coupled mass spectrometry (LC-MS/MS) is the primary method to obtain direct evidence for the presentation of disease- or patient-specific human leukocyte antigen (HLA). However, compared to the analysis of tryptic peptides in proteomics, the analysis of HLA peptides still poses computational and statistical challenges. Recently, fragment ion intensity-based matching scores assessing the similarity between predicted and observed spectra were shown to substantially increase the number of confidently identified peptides, particularly in use cases where non-tryptic peptides are analyzed. In this chapter, we describe in detail three procedures on how to benefit from state-of-the-art deep learning models to analyze and validate single spectra, single measurements, and multiple measurements in mass spectrometry-based immunopeptidomics. For this, we explain how to use the Universal Spectrum Explorer (USE), online Oktoberfest, and offline Oktoberfest. For intensity-based scoring, Oktoberfest uses fragment ion intensity and retention time predictions from the deep learning framework Prosit, a deep neural network trained on a very large number of synthetic peptides and tandem mass spectra generated within the ProteomeTools project. The examples shown highlight how deep learning-assisted analysis can increase the number of identified HLA peptides, facilitate the discovery of confidently identified neo-epitopes, or provide assistance in the assessment of the presence of cryptic peptides, such as spliced peptides.

MHCpLogics: an interactive machine learning-based tool for unsupervised data visualization and cluster analysis of immunopeptidomes.

The major histocompatibility complex (MHC) encodes a range of immune response genes, including the human leukocyte antigens (HLAs) in humans. These molecules bind peptide antigens and present them on the cell surface for T cell recognition. The repertoires of peptides presented by HLA molecules are termed immunopeptidomes. The highly polymorphic nature of the genres that encode the HLA molecules confers allotype-specific differences in the sequences of bound ligands. Allotype-specific ligand preferences are often defined by peptide-binding motifs. Individuals express up to six classical class I HLA allotypes, which likely present peptides displaying different binding motifs. Such complex datasets make the deconvolution of immunopeptidomic data into allotype-specific contributions and further dissection of binding-specificities challenging. Herein, we developed MHCpLogics as an interactive machine learning-based tool for mining peptide-binding sequence motifs and visualization of immunopeptidome data across complex datasets. We showcase the functionalities of MHCpLogics by analyzing both in-house and published mono- and multi-allelic immunopeptidomics data. The visualization modalities of MHCpLogics allow users to inspect clustered sequences down to individual peptide components and to examine broader sequence patterns within multiple immunopeptidome datasets. MHCpLogics can deconvolute large immunopeptidome datasets enabling the interrogation of clusters for the segregation of allotype-specific peptide sequence motifs, identification of sub-peptidome motifs, and the exportation of clustered peptide sequence lists. The tool facilitates rapid inspection of immunopeptidomes as a resource for the immunology and vaccine communities. MHCpLogics is a standalone application available via an executable installation at: https://github.com/PurcellLab/MHCpLogics.

pXg: Comprehensive Identification of Noncanonical MHC-I-Associated Peptides From De Novo Peptide Sequencing Using RNA-Seq Reads.

Discovering noncanonical peptides has been a common application of proteogenomics. Recent studies suggest that certain noncanonical peptides, known as noncanonical major histocompatibility complex-I (MHC-I)-associated peptides (ncMAPs), that bind to MHC-I may make good immunotherapeutic targets. De novo peptide sequencing is a great way to find ncMAPs since it can detect peptide sequences from their tandem mass spectra without using any sequence databases. However, this strategy has not been widely applied for ncMAP identification because there is not a good way to estimate its false-positive rates. In order to completely and accurately identify immunopeptides using de novo peptide sequencing, we describe a unique pipeline called proteomics X genomics. In contrast to current pipelines, it makes use of genomic data, RNA-Seq abundance and sequencing quality, in addition to proteomic features to increase the sensitivity and specificity of peptide identification. We show that the peptide-spectrum match quality and genetic traits have a clear relationship, showing that they can be utilized to evaluate peptide-spectrum matches. From 10 samples, we found 24,449 canonical MHC-I-associated peptides and 956 ncMAPs by using a target-decoy competition. Three hundred eighty-seven ncMAPs and 1611 canonical MHC-I-associated peptides were new identifications that had not yet been published. We discovered 11 ncMAPs produced from a squirrel monkey retrovirus in human cell lines in addition to the two ncMAPs originating from a complementarity determining region 3 in an antibody thanks to the unrestricted search space assumed by de novo sequencing. These entirely new identifications show that proteomics X genomics can make the most of de novo peptide sequencing's advantages and its potential use in the search for new immunotherapeutic targets.

Relevance of mutation-derived neoantigens and non-classical antigens for anticancer therapies.

Efficacy of cancer immunotherapies relies on correct recognition of tumor antigens by lymphocytes, eliciting thus functional responses capable of eliminating tumor cells. Therefore, important efforts have been carried out in antigen identification, with the aim of understanding mechanisms of response to immunotherapy and to design safer and more efficient strategies. In addition to classical tumor-associated antigens identified during the last decades, implementation of next-generation sequencing methodologies is enabling the identification of neoantigens (neoAgs) arising from mutations, leading to the development of new neoAg-directed therapies. Moreover, there are numerous non-classical tumor antigens originated from other sources and identified by new methodologies. Here, we review the relevance of neoAgs in different immunotherapies and the results obtained by applying neoAg-based strategies. In addition, the different types of non-classical tumor antigens and the best approaches for their identification are described. This will help to increase the spectrum of targetable molecules useful in cancer immunotherapies.

Enhancing Mass spectrometry-based tumor immunopeptide identification: machine learning filter leveraging HLA binding affinity, aliphatic index and retention time deviation.

Accurately identifying neoantigens is crucial for developing effective cancer vaccines and improving tumor immunotherapy. Mass spectrometry-based immunopeptidomics has emerged as a promising approach to identifying human leukocyte antigen (HLA) peptides presented on the surface of cancer cells, but false-positive identifications remain a significant challenge. In this study, liquid chromatography-tandem mass spectrometry-based proteomics and next-generation sequencing were utilized to identify HLA-presenting neoantigenic peptides resulting from non-synonymous single nucleotide variations in tumor tissues from 18 patients with renal cell carcinoma or pancreatic cancer. Machine learning was utilized to evaluate Mascot identifications through the prediction of MS/MS spectral consistency, and four descriptors for each candidate sequence: the max Mascot ion score, predicted HLA binding affinity, aliphatic index and retention time deviation, were selected as important features in filtering out identifications with inadequate fragmentation consistency. This suggests that incorporating rescoring filters based on peptide physicochemical characteristics could enhance the identification rate of MS-based immunopeptidomics compared to the traditional Mascot approach predominantly used for proteomics, indicating the potential for optimizing neoantigen identification pipelines as well as clinical applications.

Emerging potential of immunopeptidomics by mass spectrometry in cancer immunotherapy.

With significant advances in analytical technologies, research in the field of cancer immunotherapy, such as adoptive T cell therapy, cancer vaccine, and immune checkpoint blockade (ICB), is currently gaining tremendous momentum. Since the efficacy of cancer immunotherapy is recognized only by a minority of patients, more potent tumor-specific antigens (TSAs, also known as neoantigens) and predictive markers for treatment response are of great interest. In cancer immunity, immunopeptides, presented by human leukocyte antigen (HLA) class I, play a role as initiating mediators of immunogenicity. The latest advancement in the interdisciplinary multiomics approach has rapidly enlightened us about the identity of the "dark matter" of cancer and the associated immunopeptides. In this field, mass spectrometry (MS) is a viable option to select because of the naturally processed and actually presented TSA candidates in order to grasp the whole picture of the immunopeptidome. In the past few years the search space has been enlarged by the multiomics approach, the sensitivity of mass spectrometers has been improved, and deep/machine-learning-supported peptide search algorithms have taken immunopeptidomics to the next level. In this review, along with the introduction of key technical advancements in immunopeptidomics, the potential and further directions of immunopeptidomics will be reviewed from the perspective of cancer immunotherapy.

Identification and relative abundance of naturally presented and cross-reactive influenza A virus MHC class I-restricted T cell epitopes.

Cytotoxic T lymphocytes are key for controlling viral infection. Unravelling CD8+ T cell-mediated immunity to distinct influenza virus strains and subtypes across prominent HLA types is relevant for combating seasonal infections and emerging new variants. Using an immunopeptidomics approach, naturally presented influenza A virus-derived ligands restricted to HLA-A*24:02, HLA-A*68:01, HLA-B*07:02, and HLA-B*51:01 molecules were identified. Functional characterization revealed multifunctional memory CD8+ T cell responses for nine out of sixteen peptides. Peptide presentation kinetics was optimal around 12 h post infection and presentation of immunodominant epitopes shortly after infection was not always persistent. Assessment of immunogenic epitopes revealed that they are highly conserved across the major zoonotic reservoirs and may contain a single substitution in the vicinity of the anchor residues. These findings demonstrate how the identified epitopes promote T cell pools, possibly cross-protective in individuals and can be potential targets for vaccination.

IMBAS-MS Discovers Organ-Specific HLA Peptide Patterns in Plasma.

Distinction of non-self from self is the major task of the immune system. Immunopeptidomics studies the peptide repertoire presented by the human leukocyte antigen (HLA) protein, usually on tissues. However, HLA peptides are also bound to plasma soluble HLA (sHLA), but little is known about their origin and potential for biomarker discovery in this readily available biofluid. Currently, immunopeptidomics is hampered by complex workflows and limited sensitivity, typically requiring several mL of plasma. Here, we take advantage of recent improvements in the throughput and sensitivity of mass spectrometry (MS)-based proteomics to develop a highly sensitive, automated, and economical workflow for HLA peptide analysis, termed Immunopeptidomics by Biotinylated Antibodies and Streptavidin (IMBAS). IMBAS-MS quantifies more than 5000 HLA class I peptides from only 200 µl of plasma, in just 30 min. Our technology revealed that the plasma immunopeptidome of healthy donors is remarkably stable throughout the year and strongly correlated between individuals with overlapping HLA types. Immunopeptides originating from diverse tissues, including the brain, are proportionately represented. We conclude that sHLAs are a promising avenue for immunology and potentially for precision oncology.

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.