Thermostability profiling of MHC-bound peptides: a new dimension in immunopeptidomics and aid for immunotherapy design

Leveraging mRNA technology for antigen based immuno-oncology therapies

The application of messenger RNA (mRNA) technology in antigen-based immuno-oncology therapies represents a significant advancement in cancer treatment. Cancer vaccines are an effective combinatorial partner to sensitize the host immune system to the tumor and boost the efficacy of immune therapies. Selecting suitable tumor antigens is the key step to devising effective vaccinations and amplifying the immune response. Tumor neoantigens are de novo epitopes derived from somatic mutations, avoiding T-cell central tolerance of self-epitopes and inducing immune responses to tumors. The identification and prioritization of patient-specific tumor neoantigens are based on advanced computational algorithms taking advantage of the profiling with next-generation sequencing considering factors involved in human leukocyte antigen (HLA)-peptide-T-cell receptor (TCR) complex formation, including peptide presentation, HLA-peptide affinity, and TCR recognition. This review discusses the development and clinical application of mRNA vaccines in oncology, with a particular focus on recent clinical trials and the computational workflows and methodologies for identifying both shared and individual antigens. While this review centers on therapeutic mRNA vaccines targeting existing tumors, it does not cover preventative vaccines. Preclinical experimental validations are crucial in cancer vaccine development, but we emphasize the computational approaches that facilitate neoantigen selection and design, highlighting their role in advancing mRNA vaccine development. The versatility and rapid development potential of mRNA make it an ideal platform for personalized neoantigen immunotherapy. We explore various strategies for antigen target identification, including tumor-associated and tumor-specific antigens and the computational tools used to predict epitopes capable of eliciting strong immune responses. We address key design considerations for enhancing the immunogenicity and stability of mRNA vaccines, as well as emerging trends and challenges in the field. This comprehensive overview highlights the therapeutic potential of mRNA-based cancer vaccines and underscores ongoing research efforts aimed at optimizing these therapies for improved clinical outcomes.

ImmuneApp for HLA-I epitope prediction and immunopeptidome analysis

Advances in mass spectrometry accelerates the characterization of HLA ligandome, necessitating the development of efficient methods for immunopeptidomics analysis and (neo)antigen prediction. We develop ImmuneApp, an interpretable deep learning framework trained on extensive HLA ligand datasets, which improves the prediction of HLA-I epitopes, prioritizes neoepitopes, and enhances immunopeptidomics deconvolution. ImmuneApp extracts informative embeddings and identifies key residues for pHLA binding. We also present a more accurate model-based deconvolution approach and systematically analyzed 216 multi-allelic immunopeptidomics samples, identifying 835,551 ligands restricted to over 100 HLA-I alleles. Our investigation reveals the effectiveness of the composite model, denoted as ImmuneApp-MA, which integrates mono- and multi-allelic data to enhance predictive performance. Leveraging ImmuneApp-MA as a pre-trained model, we built ImmuneApp-Neo, an immunogenicity predictor that outperforms existing methods for prioritizing immunogenic neoepitope. ImmuneApp demonstrates its utility across various immunopeptidomics datasets, which will promote the discovery of novel neoantigens and the development of new immunotherapies.

Deciphering the HLA-E immunopeptidome with mass spectrometry: an opportunity for universal mRNA vaccines and T-cell-directed immunotherapies

Advances in immunotherapy rely on targeting novel cell surface antigens, including therapeutically relevant peptide fragments presented by HLA molecules, collectively known as the actionable immunopeptidome. Although the immunopeptidome of classical HLA molecules is extensively studied, exploration of the peptide repertoire presented by non-classical HLA-E remains limited. Growing evidence suggests that HLA-E molecules present pathogen-derived and tumor-associated peptides to CD8+ T cells, positioning them as promising targets for universal immunotherapies due to their minimal polymorphism. This mini-review highlights recent developments in mass spectrometry (MS) technologies for profiling the HLA-E immunopeptidome in various diseases. We discuss the unique features of HLA-E, its expression patterns, stability, and the potential for identifying new therapeutic targets. Understanding the broad repertoire of actionable peptides presented by HLA-E can lead to innovative treatments for viral and pathogen infections and cancer, leveraging its monomorphic nature for broad therapeutic efficacy.

Amino acid insertion in Bat MHC-I enhances complex stability and augments peptide presentation

Bats serve as reservoirs for numerous zoonotic viruses, yet they typically remain asymptomatic owing to their unique immune system. Of particular significance is the MHC-I in bats, which plays crucial role in anti-viral response and exhibits polymorphic amino acid (AA) insertions. This study demonstrated that both 5AA and 3AA insertions enhance the thermal stability of the bat MHC-I complex and enrich the diversity of bound peptides in terms of quantity and length distribution, by stabilizing the 310 helix, a region prone to conformational changes during peptide loading. However, the mismatched insertion could diminish the stability of bat pMHC-I. We proposed that a suitable insertion may help bat MHC-I adapt to high body temperatures during flight while enhancing antiviral responses. Moreover, this site-specific insertions may represent a strategy of evolutionary adaptation of MHC-I molecules to fluctuations in body temperature, as similar insertions have been found in other lower vertebrates.

Transfer learning improves pMHC kinetic stability and immunogenicity predictions

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

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.

BMX-A and BMX-S: Accessible cell-free methods to estimate peptide-MHC-I affinity and stability

The affinity and stability of peptide binding to Major Histocompatibility Complex Class I (MHC-I) molecules are fundamental parameters that underpin the specificity and magnitude of CD8+ T cell responses. These parameters can be estimated in some cases by computational tools, but experimental validation remains valuable, especially for stability. Methods to measure peptide binding can be broadly categorised into either cell-based assays using TAP-deficient cell lines such as RMA/S, or cell-free strategies, such as peptide competition-binding assays and surface plasmon resonance. Cell-based assays are subject to confounding biological activity, including peptide trimming by peptidases and dilution of peptide-loaded MHC-I on the surface of cells through cell division. Current cell-free methods require in-house production and purification of MHC-I. In this study, we present the development of new cell-free assays to estimate the relative affinity and dissociation kinetics of peptide binding to MHC-I. These assays, which we have called BMX-A (relative affinity) and BMX-S (kinetic stability), are reliable, scalable and accessible, in that they use off-the-shelf commercial reagents and standard flow cytometry techniques.

Impact of peptide:HLA complex stability for the identification of SARS-CoV-2-specific CD8+T cells

Induction of a lasting protective immune response is dependent on presentation of epitopes to patrolling T cells through the HLA complex. While peptide:HLA (pHLA) complex affinity alone is widely exploited for epitope selection, we demonstrate that including the pHLA complex stability as a selection parameter can significantly reduce the high false discovery rate observed with predicted affinity. In this study, pHLA complex stability was measured on three common class I alleles and 1286 overlapping 9-mer peptides derived from the SARS-CoV-2 Spike protein. Peptides were pooled based on measured stability and predicted affinity. Strikingly, stability of the pHLA complex was shown to strongly select for immunogenic epitopes able to activate functional CD8⁺T cells. This result was observed across the three studied alleles and in both vaccinated and convalescent COVID-19 donors. Deconvolution of peptide pools showed that specific CD8⁺T cells recognized one or two dominant epitopes. Moreover, SARS-CoV-2 specific CD8⁺T cells were detected by tetramer-staining across multiple donors. In conclusion, we show that stability analysis of pHLA is a key factor for identifying immunogenic epitopes.

Workflow enabling deepscale immunopeptidome, proteome, ubiquitylome, phosphoproteome, and acetylome analyses of sample-limited tissues

Serial multi-omic analysis of proteome, phosphoproteome, and acetylome provides insights into changes in protein expression, cell signaling, cross-talk and epigenetic pathways involved in disease pathology and treatment. However, ubiquitylome and HLA peptidome data collection used to understand protein degradation and antigen presentation have not together been serialized, and instead require separate samples for parallel processing using distinct protocols. Here we present MONTE, a highly sensitive multi-omic native tissue enrichment workflow, that enables serial, deep-scale analysis of HLA-I and HLA-II immunopeptidome, ubiquitylome, proteome, phosphoproteome, and acetylome from the same tissue sample. We demonstrate that the depth of coverage and quantitative precision of each 'ome is not compromised by serialization, and the addition of HLA immunopeptidomics enables the identification of peptides derived from cancer/testis antigens and patient specific neoantigens. We evaluate the technical feasibility of the MONTE workflow using a small cohort of patient lung adenocarcinoma tumors.

Benchmarking bioinformatics pipelines in data-independent acquisition mass spectrometry for immunopeptidomics

Immunopeptidomes are the peptide repertoires bound by the molecules encoded by the major histocompatibility complex (MHC) (human leukocyte antigen (HLA) in humans). These HLA-peptide complexes are presented on the cell surface for immune T-cell recognition. Immunopeptidomics denotes the utilization of tandem mass spectrometry (MS/MS) to identify and quantify peptides bound to HLA molecules. Data-independent acquisition (DIA) has emerged as a powerful strategy for quantitative proteomics and deep proteome-wide identification; however, DIA application to immunopeptidomics analyses has so far seen limited use. Further, of the many DIA data processing tools currently available, there is no consensus in the immunopeptidomics community on the most appropriate pipeline(s) for in-depth and accurate HLA peptide identification. Herein, we benchmarked four commonly used spectral library-based DIA pipelines developed for proteomics applications (Skyline, Spectronaut, DIA-NN, and PEAKS) for their ability to perform immunopeptidome quantification. We validated and assessed the capability of each tool to identify and quantify HLA-bound peptides. Generally, DIA-NN and PEAKS provided higher immunopeptidome coverage with more reproducible results. Skyline and Spectronaut conferred more accurate peptide identification with lower experimental false-positive rates. All tools demonstrated reasonable correlations in quantifying precursors of HLA-bound peptides. Our benchmarking study suggests a combined strategy of applying at least two complementary DIA software tools to achieve the greatest degree of confidence and in-depth coverage of immunopeptidome data.

Molecular basis of MHC I quality control in the peptide loading complex

Major histocompatibility complex class I (MHC I) molecules are central to adaptive immunity. Their assembly, epitope selection, and antigen presentation are controlled by the MHC I glycan through a sophisticated network of chaperones and modifying enzymes. However, the mechanistic integration of the corresponding processes remains poorly understood. Here, we determine the multi-chaperone-client interaction network of the peptide loading complex (PLC) and report the PLC editing module structure by cryogenic electron microscopy at 3.7 Å resolution. Combined with epitope-proofreading studies of the PLC in near-native lipid environment, these data show that peptide-receptive MHC I molecules are stabilized by multivalent chaperone interactions including the calreticulin-engulfed mono-glucosylated MHC I glycan, which only becomes accessible for processing by α-glucosidase II upon loading of optimal epitopes. Our work reveals allosteric coupling between peptide-MHC I assembly and glycan processing. This inter-process communication defines the onset of an adaptive immune response and provides a prototypical example of the tightly coordinated events in endoplasmic reticulum quality control.

The immune system monitors the health status of cells by surveilling fragments of foreign molecules from invaders presented on MHC I complexes at the cell surface. Here, the authors investigate the sequence of events of MHC I assembly and quality control cycle.

Practical Implementation of Genetics: New Concepts in Immunogenomics to Predict, Prevent, and Diagnose Drug Hypersensitivity

Delayed drug hypersensitivities are CD8⁺ T cell-mediated reactions associated with up to 50% mortality. Human leukocyte antigen (HLA) alleles are known to predispose disease and are specific to drug, reaction, and patient ethnicity. Pretreatment screening is recommended for a handful of the strongest associations to identify and prevent drug use in high-risk patients. However, an incomplete predictive value implicates other HLA-imposed risk factors, and low carriage of many identified HLA-risk alleles combined with the high cost of sequence-based typing has limited economic viability for similar recommendation of screening across drugs and health care systems. For mitigation, an expanding armory of low-cost polymerase chain reaction-based screens is being developed, and HLA-imposed risk factors are being discovered. These include (1) polymorphic variants of metabolic and endoplasmic reticulum aminopeptidase enzymes toward multiallelic screening with increased predictivity; (2) regulation by immune checkpoint inhibitors, enabling detolerized animal models of human disease; and (3) immunodominant T cell receptors (TCR) on clonally expanded CD8⁺ T cells. For the latter, HLA risk-restricted TCR provides immunogenomic strategies and samples from a single patient to identify novel HLA-risk associations in underserved minority populations, tissue-relevant effector biomarkers toward earlier diagnosis and treatment, and HLA-TCR-presented immunogenic structures to aid future drug development.

PACTS-Assisted Thermal Proteome Profiling for Use in Identifying Peptide-Interacting Proteins

Bioactive peptides play important roles in various biological processes. However, the traditional methods for profiling the peptide-interacting proteins require modifications to the peptide molecules, often leading to false identifications. We found that the interaction between peptide ligands and protein receptors induced significant changes in the abundance of the interacting proteins, which is a signature indicating the interaction and providing complementary information for use in the classical thermal proteome profiling (TPP) technique. Herein, we developed a novel Peptide-ligand-induced Abundance Change of proTeinS (PACTS)-assisted TPP strategy for the identification of peptide-interacting proteins based on the peptide-ligand-induced change in protein abundance. The utility and efficacy of this approach were demonstrated by the identification of the interaction of the protein 3-phosphoinositide-dependent protein kinase 1 (PDPK1) and PDPK1-interacting fragment (PIF) pair and by large-scale profiling of the interacting proteins of PIF. The PACTS-assisted TPP approach was applied to describe the interactome of amyloid beta (Aβ) 1-42 in THP-1 cells and resulted in the identification of 103 interacting proteins. Validation experiments indicated that Aβ1-42 interacted directly with fatty acid synthase and inhibited its enzymatic activity, providing insights into fatty acid metabolic disorders in Alzheimer's disease (AD). Overall, PACTS-assisted TPP is an efficient approach, and the newly identified Aβ-interacting proteins provide rich resources for the research on AD.

Matrix Thermal Shift Assay for Fast Construction of Multidimensional Ligand-Target Space

Existing thermal shift-based mass spectrometry approaches are able to identify target proteins without chemical modification of the ligand, but they are suffering from complicated workflows with limited throughput. Herein, we present a new thermal shift-based method, termed matrix thermal shift assay (mTSA), for fast deconvolution of ligand-binding targets and binding affinities at the proteome level. In mTSA, a sample matrix, treated horizontally with five different compound concentrations and vertically with five technical replicates of each condition, was denatured at a single temperature to induce protein precipitation, and then, data-independent acquisition was employed for quick protein quantification. Compared with previous thermal shift assays, the analysis throughput of mTSA was significantly improved, but the costs as well as efforts were reduced. More importantly, the matrix experiment design allowed simultaneous computation of the statistical significance and fitting of the dose-response profiles, which can be combined to enable a more accurate identification of target proteins, as well as reporting binding affinities between the ligand and individual targets. Using a pan-specific kinase inhibitor, staurosporine, we demonstrated a 36% improvement in screening sensitivity over the traditional thermal proteome profiling (TPP) and a comparable sensitivity with a latest two-dimensional TPP. Finally, mTSA was successfully applied to delineate the target landscape of perfluorooctanesulfonic acid (PFOS), a persistent organic pollutant that is hard to perform modification on, and revealed several potential targets that might account for the toxicities of PFOS.

The interdependence of machine learning and LC-MS approaches for an unbiased understanding of the cellular immunopeptidome

INTRODUCTION

The comprehensive collection of peptides presented by Major Histocompatibility Complex (MHC) molecules on the cell surface is collectively known as the immunopeptidome. The analysis and interpretation of such data sets holds great promise for furthering our understanding of basic immunology and adaptive immune activation and regulation, and for direct rational discovery of T cell antigens and the design of T-cell based therapeutics and vaccines. These applications are however challenged by the complex nature of immunopeptidome data.

AREAS COVERED

Here, we describe the benefits and shortcomings of applying liquid chromatography-tandem mass spectrometry (MS) to obtain large scale immunopeptidome data sets and illustrate how the accurate analysis and optimal interpretation of such data is reliant on the availability of refined and highly optimized machine learning approaches.

EXPERT OPINION

Further we demonstrate how the accuracy of immunoinformatics prediction methods within the field of MHC antigen presentation has benefited greatly from the availability of MS-immunopeptidomics data, and exemplify how optimal antigen discovery is best performed in a synergistic combination of MS experiments and such in silico models trained on large scale immunopeptidomics data.

Progress Identifying and Analyzing the Human Proteome: 2021 Metrics from the HUPO Human Proteome Project

The 2021 Metrics of the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 357 (92.8%) of the 19 778 predicted proteins coded in the human genome, a gain of 483 since 2020 from reports throughout the world reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 478 to 1421. This represents remarkable progress on the proteome parts list. The utilization of proteomics in a broad array of biological and clinical studies likewise continues to expand with many important findings and effective integration with other omics platforms. We present highlights from the Immunopeptidomics, Glycoproteomics, Infectious Disease, Cardiovascular, Musculo-Skeletal, Liver, and Cancers B/D-HPP teams and from the Knowledgebase, Mass Spectrometry, Antibody Profiling, and Pathology resource pillars, as well as ethical considerations important to the clinical utilization of proteomics and protein biomarkers.

Targeting public neoantigens for cancer immunotherapy

Several current immunotherapy approaches target private neoantigens derived from mutations that are unique to individual patients' tumors. However, immunotherapeutic agents can also be developed against public neoantigens derived from recurrent mutations in cancer driver genes. The latter approaches target proteins that are indispensable for tumor growth, and each therapeutic agent can be applied to numerous patients. Here we review the opportunities and challenges involved in the identification of suitable public neoantigen targets and the development of therapeutic agents targeting them.