The MHC motif viewer: a visualization tool for MHC binding motifs

Connection between MHC class II binding and aggregation propensity: The antigenic peptide 10 of Paracoccidioides brasiliensis as a benchmark study

The aggregation of epitopes that are also able to bind major histocompatibility complex (MHC) alleles raises questions around the potential connection between the formation of epitope aggregates and their affinities to MHC receptors. We first performed a general bioinformatic assessment over a public dataset of MHC class II epitopes, finding that higher experimental binding correlates with higher aggregation-propensity predictors. We then focused on the case of P10, an epitope used as a vaccine candidate against Paracoccidioides brasiliensis that aggregates into amyloid fibrils. We used a computational protocol to design variants of the P10 epitope to study the connection between the binding stabilities towards human MHC class II alleles and their aggregation propensities. The binding of the designed variants was tested experimentally, as well as their aggregation capacity. High-affinity MHC class II binders in vitro were more disposed to aggregate forming amyloid fibrils capable of binding Thioflavin T and congo red, while low affinity MHC class II binders remained soluble or formed rare amorphous aggregates. This study shows a possible connection between the aggregation propensity of an epitope and its affinity for the MHC class II cleft.

Impact of Micropolymorphism Outside the Peptide Binding Groove in the Clinically Relevant Allele HLA-C*14 on T Cell Responses in HIV-1 Infection

There is increasing evidence for the importance of human leukocyte antigen C (HLA-C)-restricted CD8⁺ T cells in HIV-1 control, but these responses are relatively poorly investigated. The number of HLA-C-restricted HIV-1 epitopes identified is much smaller than those of HLA-A-restricted or HLA-B-restricted ones. Here, we utilized a mass spectrometry-based approach to identify HIV-1 peptides presented by HLA-C*14:03 protective and HLA-C*14:02 nonprotective alleles. We identified 25 8- to 11-mer HLA-I-bound HIV-1 peptides from HIV-1-infected HLA-C*14:02⁺/14:03⁺ cells. Analysis of T cell responses to these peptides identified novel 6 T cell epitopes targeted in HIV-1-infected HLA-C*14:02⁺/14:03⁺ subjects. Analyses using HLA stabilization assays demonstrated that all 6 epitope peptides exhibited higher binding to and greater cell surface stabilization of HLA-C*14:02 than HLA-C*14:03. T cell response magnitudes were typically higher in HLA-C*14:02⁺ than HLA-C*14:03⁺ individuals, with responses to the Pol KM9 and Nef epitopes being significantly higher. The results show that HLA-C*14:02 can elicit stronger T cell responses to HIV-1 than HLA-C*14:03 and suggest that the single amino acid difference between these HLA-C14 subtypes at position 21, outside the peptide-binding groove, indirectly influences the stability of peptide-HLA-C*14 complexes and induction/expansion of HIV-specific T cells. Taken together with a previous finding that KIR2DL2⁺ NK cells recognized HLA-C*14:03⁺ HIV-1-infected cells more than HLA-C*14:02⁺ ones, the present study indicates that these HLA-C*14 subtypes differentially impact HIV-1 control by T cells and NK cells.

IMPORTANCE Some human leukocyte antigen (HLA) class I alleles are associated with good clinical outcomes in HIV-1 infection and are called protective HLA alleles. Identification of T cell epitopes restricted by protective HLA alleles can give important insight into virus-immune system interactions and inform design of immune-based prophylactic/therapeutic strategies. Although epitopes restricted by many protective HLA-A/B alleles have been identified, protective HLA-C alleles are relatively understudied. Here, we identified 6 novel T cell epitopes presented by both HLA-C*14:02 (no association with protection) and HLA-C*14:03 (protective) using a mass spectrometry-based immunopeptidome profiling approach. We found that these peptides bound to and stabilized HLA-C*14:02 better than HLA-C*14:03 and observed differences in induction/expansion of epitope-specific T cell responses in HIV-infected HLA-C*14:02⁺ versus HLA-C*14:03⁺ individuals. These results enhance understanding of how the microstructural difference at position 21 between these HLA-C*14 subtypes may influence cellular immune responses involved in viral control in HIV-1 infection.

Association of Protective HLA-A With HLA-B∗27 Positive Ankylosing Spondylitis

Objectives

To further elucidate the role of the MHC in ankylosing spondylitis by typing 17 genes, searching for HLA-B^∗27 independent associations and assessing the impact of sex on this male biased disease.

Methods

High-confidence two-field resolution genotyping was performed on 310 cases and 2196 controls using an n-1 concordance method. Protein-coding variants were called from next-generation sequencing reads using up to four software programs and the consensus result recorded. Logistic regression tests were applied to the dataset as a whole, and also in stratified sets based on sex or HLA-B^∗27 status. The amino acids driving association were also examined.

Results

Twenty-five HLA protein-coding variants were significantly associated to disease in the population. Three novel protective associations were found in a HLA-B^∗27 positive population, HLA-A^∗24:02 (OR = 0.4, CI = 0.2–0.7), and HLA-A amino acids Leu95 and Gln156. We identified a key set of seven loci that were common to both sexes, and robust to change in sample size. Stratifying by sex uncovered three novel risk variants restricted to the female population (HLA-DQA1^∗04.01, -DQB1^∗04:02, -DRB1^∗08:01; OR = 2.4–3.1). We also uncovered a set of neutral variants in the female population, which in turn conferred strong effects in the male set, highlighting how population composition can lead to the masking of true associations.

Conclusion

Population stratification allowed for a nuanced investigation into the tightly linked MHC region, revealing novel HLA-B^∗27 signals as well as replicating previous HLA-B^∗27 dependent results. This dissection of signals may help to elucidate sex biased disease predisposition and clinical progression.

Deep generative selection models of T and B cell receptor repertoires with soNNia

Subclasses of lymphocytes carry different functional roles to work together and produce an immune response and lasting immunity. Additionally to these functional roles, T and B cell lymphocytes rely on the diversity of their receptor chains to recognize different pathogens. The lymphocyte subclasses emerge from common ancestors generated with the same diversity of receptors during selection processes. Here, we leverage biophysical models of receptor generation with machine learning models of selection to identify specific sequence features characteristic of functional lymphocyte repertoires and subrepertoires. Specifically, using only repertoire-level sequence information, we classify CD4+ and CD8+ T cells, find correlations between receptor chains arising during selection, and identify T cell subsets that are targets of pathogenic epitopes. We also show examples of when simple linear classifiers do as well as more complex machine learning methods.

Total predicted MHC-I epitope load is inversely associated with population mortality from SARS-CoV-2

Polymorphisms in MHC-I protein sequences across human populations significantly affect viral peptide binding capacity, and thus alter T cell immunity to infection. In the present study, we assess the relationship between observed SARS-CoV-2 population mortality and the predicted viral binding capacities of 52 common MHC-I alleles. Potential SARS-CoV-2 MHC-I peptides are identified using a consensus MHC-I binding and presentation prediction algorithm called EnsembleMHC. Starting with nearly 3.5 million candidates, we resolve a few hundred highly probable MHC-I peptides. By weighing individual MHC allele-specific SARS-CoV-2 binding capacity with population frequency in 23 countries, we discover a strong inverse correlation between predicted population SARS-CoV-2 peptide binding capacity and mortality rate. Our computations reveal that peptides derived from the structural proteins of the virus produce a stronger association with observed mortality rate, highlighting the importance of S, N, M, and E proteins in driving productive immune responses.

Logomaker: beautiful sequence logos in Python

Summary

Sequence logos are visually compelling ways of illustrating the biological properties of DNA, RNA and protein sequences, yet it is currently difficult to generate and customize such logos within the Python programming environment. Here we introduce Logomaker, a Python API for creating publication-quality sequence logos. Logomaker can produce both standard and highly customized logos from either a matrix-like array of numbers or a multiple-sequence alignment. Logos are rendered as native matplotlib objects that are easy to stylize and incorporate into multi-panel figures.

Availability and implementation

Logomaker can be installed using the pip package manager and is compatible with both Python 2.7 and Python 3.6. Documentation is provided at http://logomaker.readthedocs.io; source code is available at http://github.com/jbkinney/logomaker.

Impact of Cysteine Residues on MHC Binding Predictions and Recognition by Tumor-Reactive T Cells

The availability of MHC-binding prediction tools has been useful in guiding studies aimed at identifying candidate target Ags to generate reactive T cells and to characterize viral and tumor-reactive T cells. Nevertheless, prediction algorithms appear to function poorly for epitopes containing cysteine (Cys) residues, which can oxidize and form disulfide bonds with other Cys residues under oxidizing conditions, thus potentially interfering with their ability to bind to MHC molecules. Analysis of the results of HLA-A*02:01 class I binding assays carried out in the presence and absence of the reducing agent 2-ME indicated that the predicted affinity for 25% of Cys-containing epitopes was underestimated by a factor of 3 or more. Additional analyses were undertaken to evaluate the responses of human CD8+ tumor-reactive T cells against 10 Cys-containing HLA class I-restricted minimal determinants containing substitutions of α-aminobutyric acid (AABA), a cysteine analogue containing a methyl group in place of the sulfhydryl group present in Cys, for the native Cys residues. Substitutions of AABA for Cys at putative MHC anchor positions often significantly enhanced T cell recognition, whereas substitutions at non-MHC anchor positions were neutral, except for one epitope where this modification abolished T cell recognition. These findings demonstrate the need to evaluate MHC binding and T cell recognition of Cys-containing peptides under conditions that prevent Cys oxidation, and to adjust current prediction binding algorithms for HLA-A*02:01 and potentially additional class I alleles to more accurately rank peptides containing Cys anchor residues.

Computational investigation of peptide binding stabilities of HLA-B*27 and HLA-B*44 alleles

Major Histocompatibility Complex (MHC) is a cell surface glycoprotein that binds to foreign antigens and presents them to T lymphocyte cells on the surface of Antigen Presenting Cells (APCs) for appropriate immune recognition. Recently, studies focusing on peptide-based vaccine design have allowed a better understanding of peptide immunogenicity mechanisms, which is defined as the ability of a peptide to stimulate CTL-mediated immune response. Peptide immunogenicity is also known to be related to the stability of peptide-loaded MHC (pMHC) complex. In this study, ENCoM server was used for structure-based estimation of the impact of single point mutations on pMHC complex stabilities. For this purpose, two human MHC molecules from the HLA-B*27 group (HLA-B*27:05 and HLA-B*27:09) in complex with four different peptides (GRFAAAIAK, RRKWRRWHL, RRRWRRLTV and IRAAPPPLF) and three HLA-B*44 molecules (HLA-B*44:02, HLA-B*44:03 and HLA-B*44:05) in complex with two different peptides (EEYLQAFTY and EEYLKAWTF) were analyzed. We found that the stability of pMHC complexes is dependent on both peptide sequence and MHC allele. Furthermore, we demonstrate that allele-specific peptide-binding preferences can be accurately revealed using structure-based computational methods predicting the effect of mutations on protein stability.

Identification of Immunodominant HIV-1 Epitopes Presented by HLA-C*12:02, a Protective Allele, Using an Immunopeptidomics Approach

Despite the fact that the cell surface expression level of HLA-C on both uninfected and HIV-infected cells is lower than those of HLA-A and -B, increasing evidence suggests an important role for HLA-C and HLA-C-restricted CD8⁺ T cell responses in determining the efficiency of viral control in HIV-1-infected individuals. Nonetheless, HLA-C-restricted T cell responses are much less well studied than HLA-A/B-restricted ones, and relatively few optimal HIV-1 CD8⁺ T cell epitopes restricted by HLA-C alleles have been defined. Recent improvements in the sensitivity of mass spectrometry (MS)-based approaches for profiling the immunopeptidome present an opportunity for epitope discovery on a large scale. Here, we employed an MS-based immunopeptidomic strategy to characterize HIV-1 peptides presented by a protective allele, HLA-C*12:02. We identified a total of 10,799 unique 8- to 12-mer peptides, including 15 HIV-1 peptides. The latter included 2 previously reported immunodominant HIV-1 epitopes, and analysis of T cell responses to the other HIV-1 peptides detected revealed an additional immunodominant epitope. These findings illustrate the utility of MS-based approaches for epitope definition and emphasize the capacity of HLA-C to present immunodominant T cell epitopes in HIV-infected individuals, indicating the importance of further evaluation of HLA-C-restricted responses to identify novel targets for HIV-1 prophylactic and therapeutic strategies.IMPORTANCE Mass spectrometry (MS)-based approaches are increasingly being employed for large-scale identification of HLA-bound peptides derived from pathogens, but only very limited profiling of the HIV-1 immunopeptidome has been conducted to date. Notably, a growing body of evidence has recently begun to indicate a protective role for HLA-C in HIV-1 infection, which may suggest that despite the fact that levels of HLA-C expression on both uninfected and HIV-1-infected cells are lower than those of HLA-A/B, HLA-C still presents epitopes to CD8⁺ T cells effectively. To explore this, we analyzed HLA-C*12:02-restricted HIV-1 peptides presented on HIV-1-infected cells expressing only HLA-C*12:02 (a protective allele) using liquid chromatography-tandem MS (LC-MS/MS). We identified a number of novel HLA-C*12:02-bound HIV-1 peptides and showed that although the majority of them did not elicit T cell responses during natural infection in a Japanese cohort, they included three immunodominant epitopes, emphasizing the contribution of HLA-C to epitope presentation on HIV-infected cells.

Multi-level Proteomics Identifies CT45 as a Chemosensitivity Mediator and Immunotherapy Target in Ovarian Cancer

Most high-grade serous ovarian cancer (HGSOC) patients develop resistance to platinum-based chemotherapy and recur, but 15% remain disease free over a decade. To discover drivers of long-term survival, we quantitatively analyzed the proteomes of platinum-resistant and -sensitive HGSOC patients from minute amounts of formalin-fixed, paraffin-embedded tumors. This revealed cancer/testis antigen 45 (CT45) as an independent prognostic factor associated with a doubling of disease-free survival in advanced-stage HGSOC. Phospho- and interaction proteomics tied CT45 to DNA damage pathways through direct interaction with the PP4 phosphatase complex. In vitro, CT45 regulated PP4 activity, and its high expression led to increased DNA damage and platinum sensitivity. CT45-derived HLA class I peptides, identified by immunopeptidomics, activate patient-derived cytotoxic T cells and promote tumor cell killing. This study highlights the power of clinical cancer proteomics to identify targets for chemo- and immunotherapy and illuminate their biological roles.

Antigenic Peptide Prediction From E6 and E7 Oncoproteins of HPV Types 16 and 18 for Therapeutic Vaccine Design Using Immunoinformatics and MD Simulation Analysis

Human papillomavirus (HPV) induced cervical cancer is the second most common cause of death, after breast cancer, in females. Three prophylactic vaccines by Merck Sharp & Dohme (MSD) and GlaxoSmithKline (GSK) have been confirmed to prevent high-risk HPV strains but these vaccines have been shown to be effective only in girls who have not been exposed to HPV previously. The constitutively expressed HPV oncoproteins E6 and E7 are usually used as target antigens for HPV therapeutic vaccines. These early (E) proteins are involved, for example, in maintaining the malignant phenotype of the cells. In this study, we predicted antigenic peptides of HPV types 16 and 18, encoded by E6 and E7 genes, using an immunoinformatics approach. To further evaluate the immunogenic potential of the predicted peptides, we studied their ability to bind to class I major histocompatibility complex (MHC-I) molecules in a computational docking study that was supported by molecular dynamics (MD) simulations and estimation of the free energies of binding of the peptides at the MHC-I binding cleft. Some of the predicted peptides exhibited comparable binding free energies and/or pattern of binding to experimentally verified MHC-I-binding epitopes that we used as references in MD simulations. Such peptides with good predicted affinity may serve as candidate epitopes for the development of therapeutic HPV peptide vaccines.

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.

Purification of soluble HLA class I complexes from human serum or plasma deliver high quality immuno peptidomes required for biomarker discovery

Soluble human leukocyte antigen class I (sHLA)-peptide complexes have been suggested to play a role in the modulation of immune responses and in immune evasion of cancer cells. The set of peptides eluted from sHLA molecules could serve as biomarker for the monitoring of patients with cancer or other conditions. Here, we describe an improved sHLA peptidomics methodology resulting in the identification of 1816 to 2761 unique peptide sequences from triplicate analyses of serum or plasma taken from three healthy donors. More than 90% of the identified peptides were 8-11mers and 74% of these sequences were predicted to bind to cognate HLA alleles, confirming the quality of the resulting immunopeptidomes. In comparison to the HLA peptidome of cultured cells, the plasma-derived peptides were predicted to have a higher stability in complex with the cognate HLA molecules and mainly derived from proteins of the plasma membrane or from the extracellular space. The sHLA peptidomes can efficiently be characterized by using the new methodology, thus serving as potential source of biomarkers in various pathological conditions.

The increased ability to present citrullinated peptides is not unique to HLA-SE molecules: arginine-to-citrulline conversion also enhances peptide affinity for HLA-DQ molecules

Background

Presentation of citrullinated neo-epitopes by HLA-DRB1 molecules that carry the shared epitope (SE) sequence was proposed to explain the association between HLA and seropositive RA. Although it is shown that several HLA-DRB1-SE molecules display enhanced binding affinities for citrullinated ligands, the ability of other HLA molecules to present citrullinated epitopes has not been investigated in a systematic manner. To better understand the HLA-RA connection, we aimed to investigate if the enhanced capacity to present arginine-to-citrulline-converted peptides is unique for HLA-SE alleles.

Methods

We selected four HLA molecules (one HLA-DR and three HLA-DQ molecules) that could potentially prefer citrulline over arginine residues in specific pockets and in addition two HLA-SE alleles as a method validation control. The affinity of peptides containing arginine/citrulline residues at positions interacting with the various peptide-binding pockets was compared by HLA class II peptide affinity assays.

Results

Pocket 4 of HLA-DRB1*04:04 and -DRB1*04:05 displayed a preference for citrulline over arginine, a property found in other pockets as well. HLA-DRB1*03:01 did not display an enhanced affinity for peptides containing a citrulline. In contrast, several peptide-binding pockets of the analyzed HLA-DQ molecules showed enhanced affinities for citrulline compared to arginine residues: i.e., pockets 4, 6, 7, and 9 of HLA-DQ2 and pockets 1, 6, and 9 of HLA-DQ7 and HLA-DQ8.

Conclusions

Arginine-to-citrulline conversion of peptides can also enhance the binding affinity for non-HLA-SE molecules. Hence the capacity to present citrullinated neo-epitopes is not confined to HLA-SE molecules, opening the possibility that also other HLA molecules could potentiate a possible breach of T cell tolerance toward citrullinated antigens.

The Value of Online Algorithms to Predict T-Cell Ligands Created by Genetic Variants

Allogeneic stem cell transplantation can be a curative treatment for hematological malignancies. After HLA-matched allogeneic stem cell transplantation, beneficial anti-tumor immunity as well as detrimental side-effects can develop due to donor-derived T-cells recognizing polymorphic peptides that are presented by HLA on patient cells. Polymorphic peptides on patient cells that are recognized by specific T-cells are called minor histocompatibility antigens (MiHA), while the respective peptides in donor cells are allelic variants. MiHA can be identified by reverse strategies in which large sets of peptides are screened for T-cell recognition. In these strategies, selection of peptides by prediction algorithms may be relevant to increase the efficiency of MiHA discovery. We investigated the value of online prediction algorithms for MiHA discovery and determined the in silico characteristics of 68 autosomal HLA class I-restricted MiHA that have been identified as natural ligands by forward strategies in which T-cells from in vivo immune responses after allogeneic stem cell transplantation are used to identify the antigen. Our analysis showed that HLA class I binding was accurately predicted for 87% of MiHA of which a relatively large proportion of peptides had strong binding affinity (56%). Weak binding affinity was also predicted for a considerable number of antigens (31%) and the remaining 13% of MiHA were not predicted as HLA class I binding peptides. Besides prediction for HLA class I binding, none of the other online algorithms significantly contributed to MiHA characterization. Furthermore, we demonstrated that the majority of MiHA do not differ from their allelic variants in in silico characteristics, suggesting that allelic variants can potentially be processed and presented on the cell surface. In conclusion, our analyses revealed the in silico characteristics of 68 HLA class I-restricted MiHA and explored the value of online algorithms to predict T-cell ligands that are created by genetic variants.

Approach for Identifying Human Leukocyte Antigen (HLA)-DR Bound Peptides from Scarce Clinical Samples

Immune-mediated diseases strongly associating with human leukocyte antigen (HLA) alleles are likely linked to specific antigens. These antigens are presented to T cells in the form of peptides bound to HLA molecules on antigen presenting cells, e.g. dendritic cells, macrophages or B cells. The identification of HLA-DR-bound peptides presents a valuable tool to investigate the human immunopeptidome. The lung is likely a key player in the activation of potentially auto-aggressive T cells prior to entering target tissues and inducing autoimmune disease. This makes the lung of exceptional interest and presents an ideal paradigm to study the human immunopeptidome and to identify antigenic peptides.Our previous investigation of HLA-DR peptide presentation in the lung required high numbers of cells (800 × 10(6) bronchoalveolar lavage (BAL) cells). Because BAL from healthy nonsmokers typically contains 10-15 × 10(6) cells, there is a need for a highly sensitive approach to study immunopeptides in the lungs of individual patients and controls.In this work, we analyzed the HLA-DR immunopeptidome in the lung by an optimized methodology to identify HLA-DR-bound peptides from low cell numbers. We used an Epstein-Barr Virus (EBV) immortalized B cell line and bronchoalveolar lavage (BAL) cells obtained from patients with sarcoidosis, an inflammatory T cell driven disease mainly occurring in the lung. Specifically, membrane complexes were isolated prior to immunoprecipitation, eluted peptides were identified by nanoLC-MS/MS and processed using the in-house developed ClusterMHCII software. With the optimized procedure we were able to identify peptides from 10 × 10(6) cells, which on average correspond to 10.9 peptides/million cells in EBV-B cells and 9.4 peptides/million cells in BAL cells. This work presents an optimized approach designed to identify HLA-DR-bound peptides from low numbers of cells, enabling the investigation of the BAL immunopeptidome from individual patients and healthy controls in order to identify disease-associated peptides.

HLA class I molecular variation and peptide-binding properties suggest a model of joint divergent asymmetric selection

The main function of HLA class I molecules is to present pathogen-derived peptides to cytotoxic T lymphocytes. This function is assumed to drive the maintenance of an extraordinary amount of polymorphism at each HLA locus, providing an immune advantage to heterozygote individuals capable to present larger repertories of peptides than homozygotes. This seems contradictory, however, with a reduced diversity at individual HLA loci exhibited by some isolated populations. This study shows that the level of functional diversity predicted for the two HLA-A and HLA-B genes considered simultaneously is similar (almost invariant) between 46 human populations, even when a reduced diversity exists at each locus. We thus propose that HLA-A and HLA-B evolved through a model of joint divergent asymmetric selection conferring all populations an equivalent immune potential. The distinct pattern observed for HLA-C is explained by its functional evolution towards killer cell immunoglobulin-like receptor (KIR) activity regulation rather than peptide presentation.

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

Recognition and eradication of infected cells by cytotoxic T lymphocytes is a key defense mechanism against intracellular pathogens. High‐throughput definition of HLA class I‐associated immunopeptidomes by mass spectrometry is an increasingly important analytical tool to advance our understanding of the induction of T‐cell responses against pathogens such as HIV‐1. We utilized a liquid chromatography tandem mass spectrometry workflow including de novo‐assisted database searching to define the HLA class I‐associated immunopeptidome of HIV‐1‐infected human cells. We here report for the first time the identification of 75 HIV‐1‐derived peptides bound to HLA class I complexes that were purified directly from HIV‐1‐infected human primary CD4⁺ T cells and the C8166 human T‐cell line. Importantly, one‐third of eluted HIV‐1 peptides had not been previously known to be presented by HLA class I. Over 82% of the identified sequences originated from viral protein regions for which T‐cell responses have previously been reported but for which the precise HLA class I‐binding sequences have not yet been defined. These results validate and expand the current knowledge of virus‐specific antigenic peptide presentation during HIV‐1 infection and provide novel targets for T‐cell vaccine development.

Gapped sequence alignment using artificial neural networks: application to the MHC class I system

MOTIVATION

Many biological processes are guided by receptor interactions with linear ligands of variable length. One such receptor is the MHC class I molecule. The length preferences vary depending on the MHC allele, but are generally limited to peptides of length 8-11 amino acids. On this relatively simple system, we developed a sequence alignment method based on artificial neural networks that allows insertions and deletions in the alignment.

RESULTS

We show that prediction methods based on alignments that include insertions and deletions have significantly higher performance than methods trained on peptides of single lengths. Also, we illustrate how the location of deletions can aid the interpretation of the modes of binding of the peptide-MHC, as in the case of long peptides bulging out of the MHC groove or protruding at either terminus. Finally, we demonstrate that the method can learn the length profile of different MHC molecules, and quantified the reduction of the experimental effort required to identify potential epitopes using our prediction algorithm.

AVAILABILITY AND IMPLEMENTATION

The NetMHC-4.0 method for the prediction of peptide-MHC class I binding affinity using gapped sequence alignment is publicly available at: http://www.cbs.dtu.dk/services/NetMHC-4.0.

Mass spectrometry of human leukocyte antigen class I peptidomes reveals strong effects of protein abundance and turnover on antigen presentation

HLA class I molecules reflect the health state of cells to cytotoxic T cells by presenting a repertoire of endogenously derived peptides. However, the extent to which the proteome shapes the peptidome is still largely unknown. Here we present a high-throughput mass-spectrometry-based workflow that allows stringent and accurate identification of thousands of such peptides and direct determination of binding motifs. Applying the workflow to seven cancer cell lines and primary cells, yielded more than 22,000 unique HLA peptides across different allelic binding specificities. By computing a score representing the HLA-I sampling density, we show a strong link between protein abundance and HLA-presentation (p < 0.0001). When analyzing overpresented proteins - those with at least fivefold higher density score than expected for their abundance - we noticed that they are degraded almost 3 h faster than similar but nonpresented proteins (top 20% abundance class; median half-life 20.8h versus 23.6h, p < 0.0001). This validates protein degradation as an important factor for HLA presentation. Ribosomal, mitochondrial respiratory chain, and nucleosomal proteins are particularly well presented. Taking a set of proteins associated with cancer, we compared the predicted immunogenicity of previously validated T-cell epitopes with other peptides from these proteins in our data set. The validated epitopes indeed tend to have higher immunogenic scores than the other detected HLA peptides. Remarkably, we identified five mutated peptides from a human colon cancer cell line, which have very recently been predicted to be HLA-I binders. Altogether, we demonstrate the usefulness of combining MS-analysis with immunogenesis prediction for identifying, ranking, and selecting peptides for therapeutic use.

Genome-wide association study identifies five susceptibility loci for follicular lymphoma outside the HLA region

Genome-wide association studies (GWASs) of follicular lymphoma (FL) have previously identified human leukocyte antigen (HLA) gene variants. To identify additional FL susceptibility loci, we conducted a large-scale two-stage GWAS in 4,523 case subjects and 13,344 control subjects of European ancestry. Five non-HLA loci were associated with FL risk: 11q23.3 (rs4938573, p = 5.79 × 10(-20)) near CXCR5; 11q24.3 (rs4937362, p = 6.76 × 10(-11)) near ETS1; 3q28 (rs6444305, p = 1.10 × 10(-10)) in LPP; 18q21.33 (rs17749561, p = 8.28 × 10(-10)) near BCL2; and 8q24.21 (rs13254990, p = 1.06 × 10(-8)) near PVT1. In an analysis of the HLA region, we identified four linked HLA-DRβ1 multiallelic amino acids at positions 11, 13, 28, and 30 that were associated with FL risk (pomnibus = 4.20 × 10(-67) to 2.67 × 10(-70)). Additional independent signals included rs17203612 in HLA class II (odds ratio [OR(per-allele)] = 1.44; p = 4.59 × 10(-16)) and rs3130437 in HLA class I (OR(per-allele) = 1.23; p = 8.23 × 10(-9)). Our findings further expand the number of loci associated with FL and provide evidence that multiple common variants outside the HLA region make a significant contribution to FL risk.

Production of NY-ESO-1 peptide/DRB1*08:03 tetramers and ex vivo detection of CD4 T-cell responses in vaccinated cancer patients

We established CD4 T-cell clones, Mz-1B7, and Ue-21, which recognized the NY-ESO-1 121-138 peptide from peripheral blood mononuclear cells (PBMCs) of an esophageal cancer patient, E-2, immunized with an NY-ESO-1 protein and determined the NY-ESO-1 minimal epitopes. Minimal peptides recognized by Mz-1B7 and Ue-21 were NY-ESO-1 125-134 and 124-134, respectively, both in restriction to DRB1*08:03. Using a longer peptide, 122-135, and five other related peptides, including either of the minimal epitopes recognized by the CD4 T-cell clones, we investigated the free peptide/DR recognition on autologous EBV-B cells as APC and peptide/DR tetramer binding. The results showed a discrepancy between them. The tetramers with several peptides recognized by either Mz-1B7 or the Ue-21 CD4 T-cell clone did not bind to the respective clone. On the other hand, unexpected binding of the tetramer with the peptide not recognized by CD4 T-cells was observed. The clone Mz-1B7 did not recognize the free peptide 122-135 on APC, but the peptide 122-135/DRB1*08:03 tetramer bound to the TCR on those cells. The failure of tetramer production and the unexpected tetramer binding could be due to a subtly modified structure of the peptide/DR tetramer from the structure of the free peptide/DR molecule. We also demonstrated that the NY-ESO-1 123-135/DRB1*08:03 tetramer detected ex vivo CD4 T-cell responses in PBMCs from patients after NY-ESO-1 vaccination in immunomonitoring.

MHCcluster, a method for functional clustering of MHC molecules

The identification of peptides binding to major histocompatibility complexes (MHC) is a critical step in the understanding of T cell immune responses. The human MHC genomic region (HLA) is extremely polymorphic comprising several thousand alleles, many encoding a distinct molecule. The potentially unique specificities remain experimentally uncharacterized for the vast majority of HLA molecules. Likewise, for nonhuman species, only a minor fraction of the known MHC molecules have been characterized. Here, we describe a tool, MHCcluster, to functionally cluster MHC molecules based on their predicted binding specificity. The method has a flexible web interface that allows the user to include any MHC of interest in the analysis. The output consists of a static heat map and graphical tree-based visualizations of the functional relationship between MHC variants and a dynamic TreeViewer interface where both the functional relationship and the individual binding specificities of MHC molecules are visualized. We demonstrate that conventional sequence-based clustering will fail to identify the functional relationship between molecules, when applied to MHC system, and only through the use of the predicted binding specificity can a correct clustering be found. Clustering of prevalent HLA-A and HLA-B alleles using MHCcluster confirms the presence of 12 major specificity groups (supertypes) some however with highly divergent specificities. Importantly, some HLA molecules are shown not to fit any supertype classification. Also, we use MHCcluster to show that chimpanzee MHC class I molecules have a reduced functional diversity compared to that of HLA class I molecules. MHCcluster is available at www.cbs.dtu.dk/services/MHCcluster-2.0.

PREDIVAC: CD4+ T-cell epitope prediction for vaccine design that covers 95% of HLA class II DR protein diversity

Background

CD4+ T-cell epitopes play a crucial role in eliciting vigorous protective immune responses during peptide (epitope)-based vaccination. The prediction of these epitopes focuses on the peptide binding process by MHC class II proteins. The ability to account for MHC class II polymorphism is critical for epitope-based vaccine design tools, as different allelic variants can have different peptide repertoires. In addition, the specificity of CD4+ T-cells is often directed to a very limited set of immunodominant peptides in pathogen proteins. The ability to predict what epitopes are most likely to dominate an immune response remains a challenge.

Results

We developed the computational tool Predivac to predict CD4+ T-cell epitopes. Predivac can make predictions for 95% of all MHC class II protein variants (allotypes), a substantial advance over other available methods. Predivac bases its prediction on the concept of specificity-determining residues. The performance of the method was assessed both for high-affinity HLA class II peptide binding and CD4+ T-cell epitope prediction. In terms of epitope prediction, Predivac outperformed three available pan-specific approaches (delivering the highest specificity). A central finding was the high accuracy delivered by the method in the identification of immunodominant and promiscuous CD4+ T-cell epitopes, which play an essential role in epitope-based vaccine design.

Conclusions

The comprehensive HLA class II allele coverage along with the high specificity in identifying immunodominant CD4+ T-cell epitopes makes Predivac a valuable tool to aid epitope-based vaccine design in the context of a genetically heterogeneous human population.The tool is available at: http://predivac.biosci.uq.edu.au/.

The identification of potentially pathogenic and therapeutic epitopes from common human allergens

OBJECTIVES

To outline the processes involved in large-scale T-cell epitope identification from common allergens and illustrate their relevance to development of allergy specific immunotherapy.

DATA SOURCES

A set of studies recently published by our laboratory illustrating high-throughput identification of allergen specific T-cell epitopes.

STUDY SELECTION

T-cell responses contribute both directly and indirectly to allergy-related disease. However, the molecular targets (epitopes) recognized by allergen-specific T cells are largely undefined. We review several different studies in the last 2 years that identified novel T-cell epitopes from a panel of 32 different allergen sources.

RESULTS

Allergen-specific T-cell responses are highly heterogeneous. Epitopes prevalently recognized in allergic patients are often capable of binding to multiple HLA class II molecules. This feature can be used to predict these promiscuous epitopes by bioinformatic predictions. This approach was validated in the Timothy grass system and then applied to a panel of 31 other allergen sources.

CONCLUSION

T-cell epitopes for common allergens have been identified, and a general method to identify epitopes from additional allergens has been validated. Characterization of epitopes for common allergens might enable new diagnostics and immunotherapy regimens. These data will also allow the study of T-cell responses in different patient populations and throughout disease progression.

NetMHCcons: a consensus method for the major histocompatibility complex class I predictions

A key role in cell-mediated immunity is dedicated to the major histocompatibility complex (MHC) molecules that bind peptides for presentation on the cell surface. Several in silico methods capable of predicting peptide binding to MHC class I have been developed. The accuracy of these methods depends on the data available characterizing the binding specificity of the MHC molecules. It has, moreover, been demonstrated that consensus methods defined as combinations of two or more different methods led to improved prediction accuracy. This plethora of methods makes it very difficult for the non-expert user to choose the most suitable method for predicting binding to a given MHC molecule. In this study, we have therefore made an in-depth analysis of combinations of three state-of-the-art MHC-peptide binding prediction methods (NetMHC, NetMHCpan and PickPocket). We demonstrate that a simple combination of NetMHC and NetMHCpan gives the highest performance when the allele in question is included in the training and is characterized by at least 50 data points with at least ten binders. Otherwise, NetMHCpan is the best predictor. When an allele has not been characterized, the performance depends on the distance to the training data. NetMHCpan has the highest performance when close neighbours are present in the training set, while the combination of NetMHCpan and PickPocket outperforms either of the two methods for alleles with more remote neighbours. The final method, NetMHCcons, is publicly available at www.cbs.dtu.dk/services/NetMHCcons , and allows the user in an automatic manner to obtain the most accurate predictions for any given MHC molecule.

Porcine major histocompatibility complex (MHC) class I molecules and analysis of their peptide-binding specificities

In all vertebrate animals, CD8⁺ cytotoxic T lymphocytes (CTLs) are controlled by major histocompatibility complex class I (MHC-I) molecules. These are highly polymorphic peptide receptors selecting and presenting endogenously derived epitopes to circulating CTLs. The polymorphism of the MHC effectively individualizes the immune response of each member of the species. We have recently developed efficient methods to generate recombinant human MHC-I (also known as human leukocyte antigen class I, HLA-I) molecules, accompanying peptide-binding assays and predictors, and HLA tetramers for specific CTL staining and manipulation. This has enabled a complete mapping of all HLA-I specificities (“the Human MHC Project”). Here, we demonstrate that these approaches can be applied to other species. We systematically transferred domains of the frequently expressed swine MHC-I molecule, SLA-1*0401, onto a HLA-I molecule (HLA-A*11:01), thereby generating recombinant human/swine chimeric MHC-I molecules as well as the intact SLA-1*0401 molecule. Biochemical peptide-binding assays and positional scanning combinatorial peptide libraries were used to analyze the peptide-binding motifs of these molecules. A pan-specific predictor of peptide–MHC-I binding, NetMHCpan, which was originally developed to cover the binding specificities of all known HLA-I molecules, was successfully used to predict the specificities of the SLA-1*0401 molecule as well as the porcine/human chimeric MHC-I molecules. These data indicate that it is possible to extend the biochemical and bioinformatics tools of the Human MHC Project to other vertebrate species.

Major histocompatibility complex class I binding predictions as a tool in epitope discovery

SUMMARY

Over the last decade, in silico models of the major histocompatibility complex (MHC) class I pathway have developed significantly. Before, peptide binding could only be reliably modelled for a few major human or mouse histocompatibility molecules; now, high-accuracy predictions are available for any human leucocyte antigen (HLA) -A or -B molecule with known protein sequence. Furthermore, peptide binding to MHC molecules from several non-human primates, mouse strains and other mammals can now be predicted. In this review, a number of different prediction methods are briefly explained, highlighting the most useful and historically important. Selected case stories, where these 'reverse immunology' systems have been used in actual epitope discovery, are briefly reviewed. We conclude that this new generation of epitope discovery systems has become a highly efficient tool for epitope discovery, and recommend that the less accurate prediction systems of the past be abandoned, as these are obsolete.

MHC class II epitope predictive algorithms

SUMMARY

Major histocompatibility complex class II (MHC-II) molecules sample peptides from the extracellular space, allowing the immune system to detect the presence of foreign microbes from this compartment. To be able to predict the immune response to given pathogens, a number of methods have been developed to predict peptide-MHC binding. However, few methods other than the pioneering TEPITOPE/ProPred method have been developed for MHC-II. Despite recent progress in method development, the predictive performance for MHC-II remains significantly lower than what can be obtained for MHC-I. One reason for this is that the MHC-II molecule is open at both ends allowing binding of peptides extending out of the groove. The binding core of MHC-II-bound peptides is therefore not known a priori and the binding motif is hence not readily discernible. Recent progress has been obtained by including the flanking residues in the predictions. All attempts to make ab initio predictions based on protein structure have failed to reach predictive performances similar to those that can be obtained by data-driven methods. Thousands of different MHC-II alleles exist in humans. Recently developed pan-specific methods have been able to make reasonably accurate predictions for alleles that were not included in the training data. These methods can be used to define supertypes (clusters) of MHC-II alleles where alleles within each supertype have similar binding specificities. Furthermore, the pan-specific methods have been used to make a graphical atlas such as the MHCMotifviewer, which allows for visual comparison of specificities of different alleles.