A comparative study of HLA binding affinity and ligand diversity: implications for generating immunodominant CD8+ T cell responses

Gene and protein sequence features augment HLA class I ligand predictions

The sensitivity of malignant tissues to T cell-based immunotherapies depends on the presence of targetable human leukocyte antigen (HLA) class I ligands. Peptide-intrinsic factors, such as HLA class I affinity and proteasomal processing, have been established as determinants of HLA ligand presentation. However, the role of gene and protein sequence features as determinants of epitope presentation has not been systematically evaluated. We perform HLA ligandome mass spectrometry to evaluate the contribution of 7,135 gene and protein sequence features to HLA sampling. This analysis reveals that a number of predicted modifiers of mRNA and protein abundance and turnover, including predicted mRNA methylation and protein ubiquitination sites, inform on the presence of HLA ligands. Importantly, integration of such "hard-coded" sequence features into a machine learning approach augments HLA ligand predictions to a comparable degree as experimental measures of gene expression. Our study highlights the value of gene and protein features for HLA ligand predictions.

Mass Spectrometric Profiling of HLA-B44 Peptidomes Provides Evidence for Tapasin-Mediated Tryptophan Editing

The extreme polymorphisms of HLA class I proteins result in structural variations in their peptide binding sites to achieve diversity in Ag presentation. External factors could independently constrict or alter HLA class I peptide repertoires. Such effects of the assembly factor tapasin were assessed for HLA-B*44:05 (Y116) and a close variant, HLA-B*44:02 (D116), which have low and high tapasin dependence, respectively, for their cell surface expression. Analyses of the HLA-B*44:05 peptidomes in the presence and absence of tapasin reveal that peptides with C-terminal tryptophans and higher predicted affinities are preferentially selected by tapasin, coincident with reduced frequencies of peptides with other C-terminal amino acids, including leucine. Comparisons of the HLA-B*44:05 and HLA-B*44:02 peptidomes indicate the expected structure-based alterations near the peptide C termini, but also C-terminal amino acid frequency and predicted affinity changes among the unique and shared peptide groups for B*44:02 and B*44:05. Overall, these findings indicate that the presence of tapasin and the tapasin dependence of assembly alter HLA class I peptide-binding preferences at the peptide C terminus. The particular C-terminal amino acid preferences that are altered by tapasin are expected to be determined by the intrinsic peptide-binding specificities of HLA class I allotypes. Additionally, the findings suggest that tapasin deficiency and reduced tapasin dependence expand the permissive affinities of HLA class I-bound peptides, consistent with prior findings that HLA class I allotypes with low tapasin dependence have increased breadth of CD8+ T cell epitope presentation and are more protective in HIV infections.

Mass spectrometric profiling of HLA-B44 peptidomes provides evidence for tapasin-mediated tryptophan editing

Activation of CD8 + T cells against pathogens and cancers involves the recognition of antigenic peptides bound to human leukocyte antigen (HLA) class-I proteins. Peptide binding to HLA class I proteins is coordinated by a multi-protein complex called the peptide loading complex (PLC). Tapasin, a key PLC component, facilitates the binding and optimization of HLA class I peptides. However, different HLA class I allotypes have variable requirements for tapasin for their assembly and surface expression. HLA-B*44:02 and HLA-B*44:05, which differ only at residue 116 of their heavy chain sequences, fall at opposite ends of the tapasin-dependency spectrum. HLA-B*44:02 (D116) is highly tapasin-dependent, whereas HLA-B*44:05 (Y116) is highly tapasinindependent. Mass spectrometric comparisons of HLA-B*4405 and HLA-B*44:02 peptidomes were undertaken to better understand the influences of tapasin upon HLA-B44 peptidome compositions. Analyses of the HLA-B*44:05 peptidomes in the presence and absence of tapasin reveal that peptides with the C-terminal tryptophan residues and those with higher predicted binding affinities are selected in the presence of tapasin. Additionally, when tapasin is present, C-terminal tryptophans are also more highly represented among peptides unique to B*44:02 and those shared between B*44:02 and B*44:05, compared with peptides unique to B*44:05. Overall, our findings demonstrate that tapasin influences the C-terminal composition of HLA class I-bound peptides and favors the binding of higher affinity peptides. For the HLA-B44 family, the presence of tapasin or high tapasin-dependence of an allotype results in better binding of peptides with C-terminal tryptophans, consistent with a role for tapasin in stabilizing an open conformation to accommodate bulky C-terminal residues.

Unique pathogen peptidomes facilitate pathogen-specific selection and specialization of MHC alleles

A key component of pathogen-specific adaptive immunity in vertebrates is the presentation of pathogen-derived antigenic peptides by major histocompatibility complex (MHC) molecules. The excessive polymorphism observed at MHC genes is widely presumed to result from the need to recognize diverse pathogens, a process called pathogen-driven balancing selection. This process assumes that pathogens differ in their peptidomes—the pool of short peptides derived from the pathogen’s proteome—so that different pathogens select for different MHC variants with distinct peptide-binding properties. Here, we tested this assumption in a comprehensive data set of 51.9 Mio peptides, derived from the peptidomes of 36 representative human pathogens. Strikingly, we found that 39.7% of the 630 pairwise comparisons among pathogens yielded not a single shared peptide and only 1.8% of pathogen pairs shared more than 1% of their peptides. Indeed, 98.8% of all peptides were unique to a single pathogen species. Using computational binding prediction to characterize the binding specificities of 321 common human MHC class-I variants, we investigated quantitative differences among MHC variants with regard to binding peptides from distinct pathogens. Our analysis showed signatures of specialization toward specific pathogens especially by MHC variants with narrow peptide-binding repertoires. This supports the hypothesis that such fastidious MHC variants might be maintained in the population because they provide an advantage against particular pathogens. Overall, our results establish a key selection factor for the excessive allelic diversity at MHC genes observed in natural populations and illuminate the evolution of variable peptide-binding repertoires among MHC variants.

Immunoinformatics: Predicting Peptide-MHC Binding

Immunoinformatics is a discipline that applies methods of computer science to study and model the immune system. A fundamental question addressed by immunoinformatics is how to understand the rules of antigen presentation by MHC molecules to T cells, a process that is central to adaptive immune responses to infections and cancer. In the modern era of personalized medicine, the ability to model and predict which antigens can be presented by MHC is key to manipulating the immune system and designing strategies for therapeutic intervention. Since the MHC is both polygenic and extremely polymorphic, each individual possesses a personalized set of MHC molecules with different peptide-binding specificities, and collectively they present a unique individualized peptide imprint of the ongoing protein metabolism. Mapping all MHC allotypes is an enormous undertaking that cannot be achieved without a strong bioinformatics component. Computational tools for the prediction of peptide-MHC binding have thus become essential in most pipelines for T cell epitope discovery and an inescapable component of vaccine and cancer research. Here, we describe the development of several such tools, from pioneering efforts to the current state-of-the-art methods, that have allowed for accurate predictions of peptide binding of all MHC molecules, even including those that have not yet been characterized experimentally.

Variations in HLA-B cell surface expression, half-life and extracellular antigen receptivity

The highly polymorphic human leukocyte antigen (HLA) class I molecules present peptide antigens to CD8+ T cells, inducing immunity against infections and cancers. Quality control mediated by peptide loading complex (PLC) components is expected to ensure the cell surface expression of stable peptide-HLA class I complexes. This is exemplified by HLA-B*08:01 in primary human lymphocytes, with both expression level and half-life at the high end of the measured HLA-B expression and stability hierarchies. Conversely, low expression on lymphocytes is measured for three HLA-B allotypes that bind peptides with proline at position 2, which are disfavored by the transporter associated with antigen processing. Surprisingly, these lymphocyte-specific expression and stability differences become reversed or altered in monocytes, which display larger intracellular pools of HLA class I than lymphocytes. Together, the findings indicate that allele and cell-dependent variations in antigen acquisition pathways influence HLA-B surface expression levels, half-lives and receptivity to exogenous antigens.

Bioinformatics Tools for the Prediction of T-Cell Epitopes

T-cell responses are activated by specific peptides, called epitopes, presented on the cell surface by MHC molecules. Binding of peptides to the MHC is the most selective step in T-cell antigen presentation and therefore an essential factor in the selection of potential epitopes. Several in-vitro methods have been developed for the determination of peptide binding to MHC molecules, but these are all costly and time-consuming. In consequence, significant effort has been dedicated to the development of in-silico methods to model this event. Here, we describe two such tools, NetMHCcons and NetMHCIIpan, for the prediction of peptide binding to MHC class I and class II molecules, respectively, involved in the activation pathways of CD8+ and CD4+ T cells.

A Perspective on the Role of Computational Models in Immunology

This is an exciting time for immunology because the future promises to be replete with exciting new discoveries that can be translated to improve health and treat disease in novel ways. Immunologists are attempting to answer increasingly complex questions concerning phenomena that range from the genetic, molecular, and cellular scales to that of organs, whole animals or humans, and populations of humans and pathogens. An important goal is to understand how the many different components involved interact with each other within and across these scales for immune responses to emerge, and how aberrant regulation of these processes causes disease. To aid this quest, large amounts of data can be collected using high-throughput instrumentation. The nonlinear, cooperative, and stochastic character of the interactions between components of the immune system as well as the overwhelming amounts of data can make it difficult to intuit patterns in the data or a mechanistic understanding of the phenomena being studied. Computational models are increasingly important in confronting and overcoming these challenges. I first describe an iterative paradigm of research that integrates laboratory experiments, clinical data, computational inference, and mechanistic computational models. I then illustrate this paradigm with a few examples from the recent literature that make vivid the power of bringing together diverse types of computational models with experimental and clinical studies to fruitfully interrogate the immune system.

NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets

Background

Binding of peptides to MHC class I molecules (MHC-I) is essential for antigen presentation to cytotoxic T-cells.

Results

Here, we demonstrate how a simple alignment step allowing insertions and deletions in a pan-specific MHC-I binding machine-learning model enables combining information across both multiple MHC molecules and peptide lengths. This pan-allele/pan-length algorithm significantly outperforms state-of-the-art methods, and captures differences in the length profile of binders to different MHC molecules leading to increased accuracy for ligand identification. Using this model, we demonstrate that percentile ranks in contrast to affinity-based thresholds are optimal for ligand identification due to uniform sampling of the MHC space.

Conclusions

We have developed a neural network-based machine-learning algorithm leveraging information across multiple receptor specificities and ligand length scales, and demonstrated how this approach significantly improves the accuracy for prediction of peptide binding and identification of MHC ligands. The method is available at www.cbs.dtu.dk/services/NetMHCpan-3.0.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-016-0288-x) contains supplementary material, which is available to authorized users.

Comprehensive Analysis of the Naturally Processed Peptide Repertoire: Differences between HLA-A and B in the Immunopeptidome

The cytotoxic T cell (CTL) response is determined by the peptide repertoire presented by the HLA class I molecules of an individual. We performed an in-depth analysis of the peptide repertoire presented by a broad panel of common HLA class I molecules on four B lymphoblastoid cell-lines (BLCL). Peptide elution and mass spectrometry analysis were utilised to investigate the number and abundance of self-peptides. Altogether, 7897 unique self-peptides, derived of 4344 proteins, were eluted. After viral infection, the number of unique self-peptides eluted significantly decreased compared to uninfected cells, paralleled by a decrease in the number of source proteins. In the overall dataset, the total number of unique self-peptides eluted from HLA-B molecules was larger than from HLA-A molecules, and they were derived from a larger number of source proteins. These results in B cells suggest that HLA-B molecules possibly present a more diverse repertoire compared to their HLA-A counterparts, which may contribute to their immunodominance. This study provides a unique data set giving new insights into the complex system of antigen presentation for a broad panel of HLA molecules, many of which were never studied this extensively before.

The Carboxy Terminus of the Ligand Peptide Determines the Stability of the MHC Class I Molecule H-2Kb: A Combined Molecular Dynamics and Experimental Study

Major histocompatibility complex (MHC) class I molecules (proteins) bind peptides of eight to ten amino acids to present them at the cell surface to cytotoxic T cells. The class I binding groove binds the peptide via hydrogen bonds with the peptide termini and via diverse interactions with the anchor residue side chains of the peptide. To elucidate which of these interactions is most important for the thermodynamic and kinetic stability of the peptide-bound state, we have combined molecular dynamics simulations and experimental approaches in an investigation of the conformational dynamics and binding parameters of a murine class I molecule (H-2K^b) with optimal and truncated natural peptide epitopes. We show that the F pocket region dominates the conformational and thermodynamic properties of the binding groove, and that therefore the binding of the C terminus of the peptide to the F pocket region plays a crucial role in bringing about the peptide-bound state of MHC class I.

Differential structural dynamics and antigenicity of two similar influenza H5N1 virus HA-specific HLA-A*0201-restricted CLT epitopes

The free RI-10 and KI-10 peptides showed bent rather than extended conformations as binding in the cleft of the HLA-A*0201 molecule, and KI-10–HLA-A*0201 processed a much higher flexibility than RI-10–HLA-A*0201.

Immuno-epidemiological modeling of HIV-1 predicts high heritability of the set-point virus load, while selection for CTL escape dominates virulence evolution

It has been suggested that HIV-1 has evolved its set-point virus load to be optimized for transmission. Previous epidemiological models and studies into the heritability of set-point virus load confirm that this mode of adaptation within the human population is feasible. However, during the many cycles of replication between infection of a host and transmission to the next host, HIV-1 is under selection for escape from immune responses, and not transmission. Here we investigate with computational and mathematical models how these two levels of selection, within-host and between-host, are intertwined. We find that when the rate of immune escape is comparable to what has been observed in patients, immune selection within hosts is dominant over selection for transmission. Surprisingly, we do find high values for set-point virus load heritability, and argue that high heritability estimates can be caused by the 'footprints' left by differing hosts' immune systems on the virus.

NetMHCstab - predicting stability of peptide-MHC-I complexes; impacts for cytotoxic T lymphocyte epitope discovery

Major histocompatibility complex class I (MHC-I) molecules play an essential role in the cellular immune response, presenting peptides to cytotoxic T lymphocytes (CTLs) allowing the immune system to scrutinize ongoing intracellular production of proteins. In the early 1990s, immunogenicity and stability of the peptide-MHC-I (pMHC-I) complex were shown to be correlated. At that time, measuring stability was cumbersome and time consuming and only small data sets were analysed. Here, we investigate this fairly unexplored area on a large scale compared with earlier studies. A recent small-scale study demonstrated that pMHC-I complex stability was a better correlate of CTL immunogenicity than peptide-MHC-I affinity. We here extended this study and analysed a total of 5509 distinct peptide stability measurements covering 10 different HLA class I molecules. Artificial neural networks were used to construct stability predictors capable of predicting the half-life of the pMHC-I complex. These predictors were shown to predict T-cell epitopes and MHC ligands from SYFPEITHI and IEDB to form significantly more stable MHC-I complexes compared with affinity-matched non-epitopes. Combining the stability predictions with a state-of-the-art affinity predictions NetMHCcons significantly improved the performance for identification of T-cell epitopes and ligands. For the HLA alleles included in the study, we could identify distinct sub-motifs that differentiate between stable and unstable peptide binders and demonstrate that anchor positions in the N-terminal of the binding motif (primarily P2 and P3) play a critical role for the formation of stable pMHC-I complexes. A webserver implementing the method is available at www.cbs.dtu.dk/services/NetMHCstab.

Dipeptides promote folding and peptide binding of MHC class I molecules

MHC class I molecules bind only those peptides with high affinity that conform to stringent length and sequence requirements. We have now investigated which peptides can aid the in vitro folding of class I molecules, and we find that the dipeptide glycyl-leucine efficiently supports the folding of HLA-A*02:01 and H-2K(b) into a peptide-receptive conformation that rapidly binds high-affinity peptides. Treatment of cells with glycyl-leucine induces accumulation of peptide-receptive H-2K(b) and HLA-A*02:01 at the surface of cells. Other dipeptides with a hydrophobic second amino acid show similar enhancement effects. Our data suggest that the dipeptides bind into the F pocket like the C-terminal amino acids of a high-affinity peptide.

Quorum sensing allows T cells to discriminate between self and nonself

T cells orchestrate pathogen-specific adaptive immune responses by identifying peptides derived from pathogenic proteins that are displayed on the surface of infected cells. Host cells also display peptide fragments from the host's own proteins. Incorrectly identifying peptides derived from the body's own proteome as pathogenic can result in autoimmune disease. To minimize autoreactivity, immature T cells that respond to self-peptides are deleted in the thymus by a process called negative selection. However, negative selection is imperfect, and autoreactive T cells exist in healthy individuals. To understand how autoimmunity is yet avoided, without loss of responsiveness to pathogens, we have developed a model of T-cell training and response. Our model shows that T cells reliably respond to infection and avoid autoimmunity because collective decisions made by the T-cell population, rather than the responses of individual T cells, determine biological outcomes. The theory is qualitatively consistent with experimental data and yields a criterion for thymic selection to be adequate for suppressing autoimmunity.

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.

Simultaneous alignment and clustering of peptide data using a Gibbs sampling approach

MOTIVATION

Proteins recognizing short peptide fragments play a central role in cellular signaling. As a result of high-throughput technologies, peptide-binding protein specificities can be studied using large peptide libraries at dramatically lower cost and time. Interpretation of such large peptide datasets, however, is a complex task, especially when the data contain multiple receptor binding motifs, and/or the motifs are found at different locations within distinct peptides.

RESULTS

The algorithm presented in this article, based on Gibbs sampling, identifies multiple specificities in peptide data by performing two essential tasks simultaneously: alignment and clustering of peptide data. We apply the method to de-convolute binding motifs in a panel of peptide datasets with different degrees of complexity spanning from the simplest case of pre-aligned fixed-length peptides to cases of unaligned peptide datasets of variable length. Example applications described in this article include mixtures of binders to different MHC class I and class II alleles, distinct classes of ligands for SH3 domains and sub-specificities of the HLA-A*02:01 molecule.

AVAILABILITY

The Gibbs clustering method is available online as a web server at http://www.cbs.dtu.dk/services/GibbsCluster.

The cancer exome generated by alternative mRNA splicing dilutes predicted HLA class I epitope density

Several studies have shown that cancers actively regulate alternative splicing. Altered splicing mechanisms in cancer lead to cancer-specific transcripts different from the pool of transcripts occurring only in healthy tissue. At the same time, altered presentation of HLA class I epitopes is frequently observed in various types of cancer. Down-regulation of genes related to HLA class I antigen processing has been observed in several cancer types, leading to fewer HLA class I antigens on the cell surface. Here, we use a peptidome wide analysis of predicted alternative splice forms, based on a publicly available database, to show that peptides over-represented in cancer splice variants comprise significantly fewer predicted HLA class I epitopes compared to peptides from normal transcripts. Peptides over-represented in cancer transcripts are in the case of the three most common HLA class I supertype representatives consistently found to contain fewer predicted epitopes compared to normal tissue. We observed a significant difference in amino acid composition between protein sequences associated with normal versus cancer tissue, as transcripts found in cancer are enriched with hydrophilic amino acids. This variation contributes to the observed significant lower likelihood of cancer-specific peptides to be predicted epitopes compared to peptides found in normal tissue.

Estimating the fitness cost of escape from HLA presentation in HIV-1 protease and reverse transcriptase

Human immunodeficiency virus (HIV-1) is, like most pathogens, under selective pressure to escape the immune system of its host. In particular, HIV-1 can avoid recognition by cytotoxic T lymphocytes (CTLs) by altering the binding affinity of viral peptides to human leukocyte antigen (HLA) molecules, the role of which is to present those peptides to the immune system. It is generally assumed that HLA escape mutations carry a replicative fitness cost, but these costs have not been quantified. In this study, we assess the replicative cost of mutations which are likely to escape presentation by HLA molecules in the region of HIV-1 protease and reverse transcriptase. Specifically, we combine computational approaches for prediction of in vitro replicative fitness and peptide binding affinity to HLA molecules. We find that mutations which impair binding to HLA-A molecules tend to have lower in vitro replicative fitness than mutations which do not impair binding to HLA-A molecules, suggesting that HLA-A escape mutations carry higher fitness costs than non-escape mutations. We argue that the association between fitness and HLA-A binding impairment is probably due to an intrinsic cost of escape from HLA-A molecules, and these costs are particularly strong for HLA-A alleles associated with efficient virus control. Counter-intuitively, we do not observe a significant effect in the case of HLA-B, but, as discussed, this does not argue against the relevance of HLA-B in virus control. Overall, this article points to the intriguing possibility that HLA-A molecules preferentially target more conserved regions of HIV-1, emphasizing the importance of HLA-A genes in the evolution of HIV-1 and RNA viruses in general.

Our immune system can recognize and kill virus-infected cells by distinguishing between self and virus-derived protein fragments, called peptides, displayed on the surface of each cell. One requirement for a successful recognition is that those peptides bind to the human leukocyte antigen (HLA) class I molecules, which present them to the immune system. As a counter-strategy, human immunodeficiency virus type 1 (HIV-1) can acquire mutations that prevent this binding, thereby helping the virus to escape the surveillance of T-lymphocytes. It is likely that the virus pays a replicative cost for such escape mutations, but the magnitude of this cost has remained elusive. Here, we quantified this fitness cost in HIV-1 protease and reverse transcriptase by combining two computational systems biology approaches: one for prediction of in vitro replicative fitness, and one for the prediction of the efficiency of peptide binding to HLA. We found that in viral proteins targeted by HLA-A molecules, mutations which disrupt binding to those molecules carry a lower replicative fitness than mutations which do not have such an effect. We argue that these results are consistent with the hypothesis that our immune systems might have evolved to target genetic regions of RNA viruses which are costly for the pathogen to alter.

Proteome sampling by the HLA class I antigen processing pathway

The peptide repertoire that is presented by the set of HLA class I molecules of an individual is formed by the different players of the antigen processing pathway and the stringent binding environment of the HLA class I molecules. Peptide elution studies have shown that only a subset of the human proteome is sampled by the antigen processing machinery and represented on the cell surface. In our study, we quantified the role of each factor relevant in shaping the HLA class I peptide repertoire by combining peptide elution data, in silico predictions of antigen processing and presentation, and data on gene expression and protein abundance. Our results indicate that gene expression level, protein abundance, and rate of potential binding peptides per protein have a clear impact on sampling probability. Furthermore, once a protein is available for the antigen processing machinery in sufficient amounts, C-terminal processing efficiency and binding affinity to the HLA class I molecule determine the identity of the presented peptides. Having studied the impact of each of these factors separately, we subsequently combined all factors in a logistic regression model in order to quantify their relative impact. This model demonstrated the superiority of protein abundance over gene expression level in predicting sampling probability. Being able to discriminate between sampled and non-sampled proteins to a significant degree, our approach can potentially be used to predict the sampling probability of self proteins and of pathogen-derived proteins, which is of importance for the identification of autoimmune antigens and vaccination targets.

HLA class I molecules are expressed on the cell surface of almost all cells of the human body in complex with short fragments (peptides) of cytosolic proteins, thereby providing a snapshot of the intracellular state of a cell to circulating CD8+ T cells. Several processes are involved in shaping the peptide ligand repertoire of an HLA class I molecule, which generally represents only a small fraction of the proteins available in the cytosol. In our work we addressed protein sampling by HLA class I molecules to answer two questions: 1) Which proteins are sampled by the antigen processing pathway and why, and 2) which peptides of a given protein are picked to represent the source protein on the cell surface? To this end we quantified the contribution of each process involved in peptide processing and presentation individually and combined them into a logistic regression model. This simple model enabled us to predict the sampling probability of self proteins and may aid in the identification of autoimmune antigens.

Protective human leucocyte antigen haplotype, HLA-DRB1*01-B*14, against chronic Chagas disease in Bolivia

BACKGROUND

Chagas disease, caused by the flagellate parasite Trypanosoma cruzi affects 8-10 million people in Latin America. The mechanisms that underlie the development of complications of chronic Chagas disease, characterized primarily by pathology of the heart and digestive system, are not currently understood. To identify possible host genetic factors that may influence the clinical course of Chagas disease, Human Leucocyte Antigen (HLA) regional gene polymorphism was analyzed in patients presenting with differing clinical symptoms.

METHODOLOGY

Two hundred and twenty nine chronic Chagas disease patients in Santa Cruz, Bolivia, were examined by serological tests, electrocardiogram (ECG), and Barium enema colon X-ray. 31.4% of the examinees showed ECG alterations, 15.7% megacolon and 58.1% showed neither of them. A further 62 seropositive megacolon patients who had undergone colonectomy due to acute abdomen were recruited. We analyzed their HLA genetic polymorphisms (HLA-A, HLA-B, MICA, MICB, DRB1 and TNF-alpha promoter region) mainly through Sequence based and LABType SSO typing test using LUMINEX Technology.

PRINCIPAL FINDINGS

The frequencies of HLA-DRB1*01 and HLA-B*14:02 were significantly lower in patients suffering from megacolon as well as in those with ECG alteration and/or megacolon compared with a group of patients with indeterminate symptoms. The DRB1*0102, B*1402 and MICA*011 alleles were in strong Linkage Disequilibrium (LD), and the HLA-DRB1*01-B*14-MICA*011 haplotype was associated with resistance against chronic Chagas disease.

CONCLUSIONS

This is the first report of HLA haplotype association with resistance to chronic Chagas disease.

The efficiency of the human CD8+ T cell response: how should we quantify it, what determines it, and does it matter?

Multidisciplinary techniques, in particular the combination of theoretical and experimental immunology, can address questions about human immunity that cannot be answered by other means. From the turnover of virus-infected cells in vivo, to rates of thymic production and HLA class I epitope prediction, theoretical techniques provide a unique insight to supplement experimental approaches. Here we present our opinion, with examples, of some of the ways in which mathematics has contributed in our field of interest: the efficiency of the human CD8+ T cell response to persistent viruses.

NNAlign: a web-based prediction method allowing non-expert end-user discovery of sequence motifs in quantitative peptide data

Recent advances in high-throughput technologies have made it possible to generate both gene and protein sequence data at an unprecedented rate and scale thereby enabling entirely new “omics”-based approaches towards the analysis of complex biological processes. However, the amount and complexity of data that even a single experiment can produce seriously challenges researchers with limited bioinformatics expertise, who need to handle, analyze and interpret the data before it can be understood in a biological context. Thus, there is an unmet need for tools allowing non-bioinformatics users to interpret large data sets. We have recently developed a method, NNAlign, which is generally applicable to any biological problem where quantitative peptide data is available. This method efficiently identifies underlying sequence patterns by simultaneously aligning peptide sequences and identifying motifs associated with quantitative readouts. Here, we provide a web-based implementation of NNAlign allowing non-expert end-users to submit their data (optionally adjusting method parameters), and in return receive a trained method (including a visual representation of the identified motif) that subsequently can be used as prediction method and applied to unknown proteins/peptides. We have successfully applied this method to several different data sets including peptide microarray-derived sets containing more than 100,000 data points.

NNAlign is available online at http://www.cbs.dtu.dk/services/NNAlign.

Human leukocyte antigen (HLA) class I restricted epitope discovery in yellow fewer and dengue viruses: importance of HLA binding strength

Epitopes from all available full-length sequences of yellow fever virus (YFV) and dengue fever virus (DENV) restricted by Human Leukocyte Antigen class I (HLA-I) alleles covering 12 HLA-I supertypes were predicted using the NetCTL algorithm. A subset of 179 predicted YFV and 158 predicted DENV epitopes were selected using the EpiSelect algorithm to allow for optimal coverage of viral strains. The selected predicted epitopes were synthesized and approximately 75% were found to bind the predicted restricting HLA molecule with an affinity, K_D, stronger than 500 nM. The immunogenicity of 25 HLA-A*02:01, 28 HLA-A*24:02 and 28 HLA-B*07:02 binding peptides was tested in three HLA-transgenic mice models and led to the identification of 17 HLA-A*02:01, 4 HLA-A*2402 and 4 HLA-B*07:02 immunogenic peptides. The immunogenic peptides bound HLA significantly stronger than the non-immunogenic peptides. All except one of the immunogenic peptides had K_D below 100 nM and the peptides with K_D below 5 nM were more likely to be immunogenic. In addition, all the immunogenic peptides that were identified as having a high functional avidity had K_D below 20 nM. A*02:01 transgenic mice were also inoculated twice with the 17DD YFV vaccine strain. Three of the YFV A*02:01 restricted peptides activated T-cells from the infected mice in vitro. All three peptides that elicited responses had an HLA binding affinity of 2 nM or less. The results indicate the importance of the strength of HLA binding in shaping the immune response.

Universal peptide vaccines - optimal peptide vaccine design based on viral sequence conservation

Rapidly mutating viruses such as the hepatitis C virus (HCV), the human immunodeficiency virus (HIV), or influenza viruses (Flu) call for highly effective universal peptide vaccines, i.e. vaccines that do not only yield broad population coverage but also broad coverage of various viral strains. The efficacy of such vaccines is determined by multiple properties of the epitopes they comprise. Beyond the specific properties of each epitope, properties of the corresponding source antigens are of great importance. If a response is mounted against viral proteins with a low copy number within the cell or against proteins expressed very late, this response may fail to induce lysis of the infected cells before budding can take place. We here propose a novel methodology to optimize the epitope composition and assembly in order to induce maximum protection. In order for a peptide vaccine to yield the best possible universal protection, several conditions should be met: (a) an optimal choice of target antigens, (b) an optimal choice of highly conserved epitopes, (c) maximum coverage of the target population, and (d) the proper ordering of the epitopes in the final vaccine to ensure favorable cleavage. We propose a mathematical formalism for epitope selection and ordering that balances the constraints imposed by these different conditions. Focusing on HCV, HIV, and Flu, we show that not all of the conditions can be satisfied for all viruses. Depending on the virus, different constraints are harder to fulfill: for Flu, the conservation constraint is violated first, while for HIV, it is difficult to focus the response at the optimal target antigens. The proposed methodology can be applied to any virus to assess the feasibility of optimally combining the above-mentioned constraints.

Large-scale characterization of peptide-MHC binding landscapes with structural simulations

Class I major histocompatibility complex proteins play a critical role in the adaptive immune system by binding to peptides derived from cytosolic proteins and presenting them on the cell surface for surveillance by T cells. The varied peptide binding specificity of these highly polymorphic molecules has important consequences for vaccine design, transplantation, autoimmunity, and cancer development. Here, we describe a molecular modeling study of MHC-peptide interactions that integrates sampling techniques from protein-protein docking, loop modeling, de novo structure prediction, and protein design in order to construct atomically detailed peptide binding landscapes for a diverse set of MHC proteins. Specificity profiles derived from these landscapes recover key features of experimental binding profiles and can be used to predict peptide binding with reasonable accuracy. Family wide comparison of the predicted binding landscapes recapitulates previously reported patterns of specificity divergence and peptide-repertoire diversity while providing a structural basis for observed specificity patterns. The size and sequence diversity of these structure-based binding landscapes enable us to identify subtle patterns of covariation between peptide sequence positions; analysis of the associated structural models suggests physical interactions that may mediate these sequence correlations.

Human leukocyte antigens A*3001 and A*3002 show distinct peptide-binding patterns of the Mycobacterium tuberculosis protein TB10.4: consequences for immune recognition

High-tuberculosis (TB)-burden countries are located in sub-Saharan Africa. We examined the frequency of human leukocyte antigen (HLA) alleles, followed by recombinant expression of the most frequent HLA-A alleles, i.e., HLA-A*3001 and HLA-A*3002, to study differences in mycobacterial peptide presentation and CD8(+) T-cell recognition. We screened a peptide library (9-mer peptides with an 8-amino-acid overlap) for binding, affinity, and off-rate of the Mycobacterium tuberculosis-associated antigen TB10.4 and identified only three TB10.4 peptides with considerable binding to HLA-A*3001. In contrast, 22 peptides bound to HLA-A*3002. This reflects a marked difference in the binding preference between the two alleles, with A*3002 tolerating a more promiscuous peptide-binding pattern and A*3001 accommodating only a very selective peptide repertoire. Subsequent analysis of the affinity and off-rate of the binding peptides revealed a strong affinity (8 nM to 7 μM) and moderate off-rate (20 min to 3 h) for both alleles. Construction of HLA-A*3001 and HLA-A*3002 tetramers containing selected binding peptides from TB10.4, including a peptide which was shared among both alleles, QIMYNYPAM (TB10.4(3-11)), allowed us to enumerate epitope-specific T cells in HLA-A*3001- and HLA-A*3002-typed patients with active TB. HLA-A*3001 and HLA-A*3002 major histocompatibility complex-peptide complexes were recognized in individuals with active TB, irrespective of their homozygous HLA-A*3001 or HLA-A*3002 genetic background. The antigen-specific T cells exhibited the CD45RA(+) CCR7(+) precursor phenotype and the interleukin-7 receptor (CD127), which were different from the phenotype and receptor exhibited by the parental CD8(+) T-cell population.

Structural properties of MHC class II ligands, implications for the prediction of MHC class II epitopes

Major Histocompatibility 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. Prediction of MHC class II ligands is complicated by the open binding cleft of the MHC class II molecule, allowing binding of peptides extending out of the binding groove. Furthermore, only a few HLA-DR alleles have been characterized with a sufficient number of peptides (100–200 peptides per allele) to derive accurate description of their binding motif. Little work has been performed characterizing structural properties of MHC class II ligands. Here, we perform one such large-scale analysis. A large set of SYFPEITHI MHC class II ligands covering more than 20 different HLA-DR molecules was analyzed in terms of their secondary structure and surface exposure characteristics in the context of the native structure of the corresponding source protein. We demonstrated that MHC class II ligands are significantly more exposed and have significantly more coil content than other peptides in the same protein with similar predicted binding affinity. We next exploited this observation to derive an improved prediction method for MHC class II ligands by integrating prediction of MHC- peptide binding with prediction of surface exposure and protein secondary structure. This combined prediction method was shown to significantly outperform the state-of-the-art MHC class II peptide binding prediction method when used to identify MHC class II ligands. We also tried to integrate N- and O-glycosylation in our prediction methods but this additional information was found not to improve prediction performance. In summary, these findings strongly suggest that local structural properties influence antigen processing and/or the accessibility of peptides to the MHC class II molecule.

Identification of immunogenic consensus T-cell epitopes in globally distributed influenza-A H1N1 neuraminidase

Antigenic drift is the ability of the swine influenza virus to undergo continuous and progressive changes in response to the host immune system. These changes dictate influenza vaccine updates annually to ensure inclusion of antigens of the most current strains. The identification of those peptides that stimulate T-cell responses, termed T-cell epitopes, is essential for the development of successful vaccines. In this study, the highly conserved and specific epitopes from neuraminidase of globally distributed H1N1 strains were predicted so that these potential vaccine candidates may escape with antigenic drift. A total of nine novel CD8(+) T-cell epitopes for MHC class-I and eight novel CD4(+) T-cell epitopes for MHC class-II alleles were proposed as novel epitope based vaccine candidates. Additionally, the epitope FSYKYGNGV was identified as a highly conserved, immunogenic and potential vaccine candidate, capable for generating both CD8(+) and CD4(+) responses.

HLArestrictor--a tool for patient-specific predictions of HLA restriction elements and optimal epitopes within peptides

Traditionally, T cell epitope discovery requires considerable amounts of tedious, slow, and costly experimental work. During the last decade, prediction tools have emerged as essential tools allowing researchers to select a manageable list of epitope candidates to test from a larger peptide, protein, or even proteome. However, no current tools address the complexity caused by the highly polymorphic nature of the restricting HLA molecules, which effectively individualizes T cell responses. To fill this gap, we here present an easy-to-use prediction tool named HLArestrictor ( http://www.cbs.dtu.dk/services/HLArestrictor ), which is based on the highly versatile and accurate NetMHCpan predictor, which here has been optimized for the identification of both the MHC restriction element and the corresponding minimal epitope of a T cell response in a given individual. As input, it requires high-resolution (i.e., 4-digit) HLA typing of the individual. HLArestrictor then predicts all 8-11mer peptide binders within one or more larger peptides and provides an overview of the predicted HLA restrictions and minimal epitopes. The method was tested on a large dataset of HIV IFNγ ELIspot peptide responses and was shown to identify HLA restrictions and minimal epitopes for about 90% of the positive peptide/patient pairs while rejecting more than 95% of the negative peptide-HLA pairs. Furthermore, for 18 peptide/HLA tetramer validated responses, HLArestrictor in all cases predicted both the HLA restriction element and minimal epitope. Thus, HLArestrictor should be a valuable tool in any T cell epitope discovery process aimed at identifying new epitopes from infectious diseases and other disease models.

Identification of CD8+ T cell epitopes in the West Nile virus polyprotein by reverse-immunology using NetCTL

Background

West Nile virus (WNV) is a growing threat to public health and a greater understanding of the immune response raised against WNV is important for the development of prophylactic and therapeutic strategies.

Methodology/Principal Findings

In a reverse-immunology approach, we used bioinformatics methods to predict WNV-specific CD8⁺ T cell epitopes and selected a set of peptides that constitutes maximum coverage of 20 fully-sequenced WNV strains. We then tested these putative epitopes for cellular reactivity in a cohort of WNV-infected patients. We identified 26 new CD8⁺ T cell epitopes, which we propose are restricted by 11 different HLA class I alleles. Aiming for optimal coverage of human populations, we suggest that 11 of these new WNV epitopes would be sufficient to cover from 48% to 93% of ethnic populations in various areas of the World.

Conclusions/Significance

The 26 identified CD8⁺ T cell epitopes contribute to our knowledge of the immune response against WNV infection and greatly extend the list of known WNV CD8⁺ T cell epitopes. A polytope incorporating these and other epitopes could possibly serve as the basis for a WNV vaccine.

Generation of robust CD8+ T-cell responses against subdominant epitopes in conserved regions of HIV-1 by repertoire mining with mimotopes

HLA-A 0201-restricted virus-specific CD8(+) CTL do not appear to control HIV effectively in vivo. To enhance the immunogenicity of a highly conserved subdominant epitope, TV9 (TLNAWVKVV, p24 Gag(19-27)), mimotopes were designed by screening a large combinatorial nonapeptide library with TV9-specific CTL primed in vitro from healthy donors. A mimic peptide with a low binding affinity to HLA-A 0201, TV9p6 (KINAWIKVV), was studied further. Parallel cultures of in vitro-primed CTL showed that TV9p6 consistently activated cross-reactive and equally functional CTL as measured by cytotoxicity, cytokine production and suppression of HIV replication in vitro. Comparison of TCRB gene usage between CTL primed from the same donors with TV9 or TV9p6 revealed a degree of clonal overlap in some cases and an example of a conserved TCRB sequence encoded distinctly at the nucleotide level between individuals (a "public" TCR); however, in the main, distinct clonotypes were recruited by each peptide antigen. These findings indicate that mimotopes can mobilize functional cross-reactive clonotypes that are less readily recruited from the naïve T-cell pool by the corresponding WT epitope. Mimotope-induced repertoire diversification could potentially override subdominance under certain circumstances and enhance vaccine-induced responses to conserved but poorly immunogenic determinants within the HIV proteome.

Statistical mechanical concepts in immunology

Higher organisms, such as humans, have an adaptive immune system that usually enables them to successfully combat diverse (and evolving) microbial pathogens. The adaptive immune system is not preprogrammed to respond to prescribed pathogens. Yet it mounts pathogen-specific responses against diverse microbes and establishes memory of past infections (the basis of vaccination). Although major advances have been made in understanding pertinent molecular and cellular phenomena, the mechanistic principles that govern many aspects of an immune response are not known. We illustrate how complementary approaches from the physical and life sciences can help confront this challenge. Specifically, we describe work that brings together statistical mechanics and cell biology to shed light on how key molecular/cellular components of the adaptive immune system are selected to enable pathogen-specific responses. We hope these examples encourage physical chemists to work at this crossroad of disciplines where fundamental discoveries with implications for human health might be made.

NetCTLpan: pan-specific MHC class I pathway epitope predictions

Reliable predictions of immunogenic peptides are essential in rational vaccine design and can minimize the experimental effort needed to identify epitopes. In this work, we describe a pan-specific major histocompatibility complex (MHC) class I epitope predictor, NetCTLpan. The method integrates predictions of proteasomal cleavage, transporter associated with antigen processing (TAP) transport efficiency, and MHC class I binding affinity into a MHC class I pathway likelihood score and is an improved and extended version of NetCTL. The NetCTLpan method performs predictions for all MHC class I molecules with known protein sequence and allows predictions for 8-, 9-, 10-, and 11-mer peptides. In order to meet the need for a low false positive rate, the method is optimized to achieve high specificity. The method was trained and validated on large datasets of experimentally identified MHC class I ligands and cytotoxic T lymphocyte (CTL) epitopes. It has been reported that MHC molecules are differentially dependent on TAP transport and proteasomal cleavage. Here, we did not find any consistent signs of such MHC dependencies, and the NetCTLpan method is implemented with fixed weights for proteasomal cleavage and TAP transport for all MHC molecules. The predictive performance of the NetCTLpan method was shown to outperform other state-of-the-art CTL epitope prediction methods. Our results further confirm the importance of using full-type human leukocyte antigen restriction information when identifying MHC class I epitopes. Using the NetCTLpan method, the experimental effort to identify 90% of new epitopes can be reduced by 15% and 40%, respectively, when compared to the NetMHCpan and NetCTL methods. The method and benchmark datasets are available at http://www.cbs.dtu.dk/services/NetCTLpan/.

Effects of thymic selection of the T-cell repertoire on HLA class I-associated control of HIV infection

Without therapy, most people infected with human immunodeficiency virus (HIV) ultimately progress to AIDS. Rare individuals ('elite controllers') maintain very low levels of HIV RNA without therapy, thereby making disease progression and transmission unlikely. Certain HLA class I alleles are markedly enriched in elite controllers, with the highest association observed for HLA-B57 (ref. 1). Because HLA molecules present viral peptides that activate CD8(+) T cells, an immune-mediated mechanism is probably responsible for superior control of HIV. Here we describe how the peptide-binding characteristics of HLA-B57 molecules affect thymic development such that, compared to other HLA-restricted T cells, a larger fraction of the naive repertoire of B57-restricted clones recognizes a viral epitope, and these T cells are more cross-reactive to mutants of targeted epitopes. Our calculations predict that such a T-cell repertoire imposes strong immune pressure on immunodominant HIV epitopes and emergent mutants, thereby promoting efficient control of the virus. Supporting these predictions, in a large cohort of HLA-typed individuals, our experiments show that the relative ability of HLA-B alleles to control HIV correlates with their peptide-binding characteristics that affect thymic development. Our results provide a conceptual framework that unifies diverse empirical observations, and have implications for vaccination strategies.

Integrating in silico and in vitro analysis of peptide binding affinity to HLA-Cw*0102: a bioinformatic approach to the prediction of new epitopes

Background

Predictive models of peptide-Major Histocompatibility Complex (MHC) binding affinity are important components of modern computational immunovaccinology. Here, we describe the development and deployment of a reliable peptide-binding prediction method for a previously poorly-characterized human MHC class I allele, HLA-Cw*0102.

Methodology/Findings

Using an in-house, flow cytometry-based MHC stabilization assay we generated novel peptide binding data, from which we derived a precise two-dimensional quantitative structure-activity relationship (2D-QSAR) binding model. This allowed us to explore the peptide specificity of HLA-Cw*0102 molecule in detail. We used this model to design peptides optimized for HLA-Cw*0102-binding. Experimental analysis showed these peptides to have high binding affinities for the HLA-Cw*0102 molecule. As a functional validation of our approach, we also predicted HLA-Cw*0102-binding peptides within the HIV-1 genome, identifying a set of potent binding peptides. The most affine of these binding peptides was subsequently determined to be an epitope recognized in a subset of HLA-Cw*0102-positive individuals chronically infected with HIV-1.

Conclusions/Significance

A functionally-validated in silico-in vitro approach to the reliable and efficient prediction of peptide binding to a previously uncharacterized human MHC allele HLA-Cw*0102 was developed. This technique is generally applicable to all T cell epitope identification problems in immunology and vaccinology.

Extensive major histocompatibility complex class I binding promiscuity for Mycobacterium tuberculosis TB10.4 peptides and immune dominance of human leucocyte antigen (HLA)-B*0702 and HLA-B*0801 alleles in TB10.4 CD8 T-cell responses

The molecular definition of major histocompatibility complex (MHC) class I-presented CD8(+) T-cell epitopes from clinically relevant Mycobacterium tuberculosis (Mtb) target proteins will aid in the rational design of T-cell-based diagnostics of tuberculosis (TB) and the measurement of TB vaccine-take. We used an epitope discovery system, based on recombinant MHC class I molecules that cover the most frequent Caucasian alleles [human leucocyte antigen (HLA)-A*0101, A*0201, A*0301, A*1101, A*2402, B*0702, B*0801 and B*1501], to identify MHC class I-binding peptides from overlapping 9-mer peptides representing the Mtb protein TB10.4. A total of 33 MHC class I-binding epitopes were identified, spread across the entire amino acid sequence, with some clustering at the N- and C-termini of the protein. Binding of individual peptides or closely related peptide species to different MHC class I alleles was frequently observed. For instance, the common motif of xIMYNYPAMx bound to six of eight alleles. Affinity (50% effective dose) and off-rate (half life) analysis of candidate Mtb peptides will help to define the conditions for CD8(+) T-cell interaction with their nominal MHC class I-peptide ligands. Subsequent construction of tetramers allowed us to confirm the recognition of some of the epitopes by CD8(+) T cells from patients with active pulmonary TB. HLA-B alleles served as the dominant MHC class I restricting molecules for anti-Mtb TB10.4-specific CD8(+) T-cell responses measured in CD8(+) T cells from patients with pulmonary TB.