Search

Celiac disease is caused by an abnormal intestinal T cell response to cereal gluten proteins. The disease has a strong human leukocyte antigen (HLA) association, and CD4+ T cells recognizing gluten epitopes presented by disease-associated HLA-DQ allotypes are considered to be drivers of the disease. This paper provides an update of the currently known HLA-DQ restricted gluten T cell epitopes with their names and sequences. [ABSTRACT FROM AUTHOR]

Celiac disease is a chronic inflammatory condition of the small intestine caused by aberrant adaptive immune response to gluten protein from wheat and related cereal plants. Over 90% of celiac disease patients carry the HLA-DQ2.5 allotype and HLA-DQ2.5 presents gluten peptides to gluten-reactive CD4+ T cells in celiac disease patients. A large number of HLA-DQ2.5-restricted gluten T cell epitopes have been identified over the years. These epitopes are in general proline-rich and contain at least one glutamic acid residue that is generated from glutamine in the native gluten protein by deamidation. The deamidation is mediated by the enzyme transglutaminase 2 (TG2). It has been shown that the same T cell could recognize several different HLA-DQ2.5-restricted gluten T cell epitopes due to sequence similarities. In this paper, we demonstrate that three T cell clones derived from duodenal biopsies of different celiac disease patients are able to respond to at least five different gluten T cell epitopes within the DQ2.5-glia-γ4 epitope family, including two novel epitopes. [ABSTRACT FROM AUTHOR]

Vaccination clearly decreases coronavirus disease 2019 (COVID-19) mortality; however, they also impose selection pressure on the virus, which promotes the evolution of immune escape variants. For example, despite the high vaccination level in especially Western countries, the Omicron variant caused millions of breakthrough infections, suggesting that the highly mutated spike protein in the Omicron variant can escape antibody immunity much more efficiently than the other variants of concern (VOCs). In this study, we investigated the resistance/susceptibility of T helper cell responses that are necessary for generating efficient long-lasting antibody immunity, in several VOCs. By predicting T helper cell epitopes on the spike protein for most common HLA-DRB1 alleles worldwide, we found that although most of high frequency HLA-DRB1 alleles have several potential T helper cell epitopes, few alleles like HLA-DRB1 13:01 and 11:01 are not predicted to have any significant T helper cell responses after vaccination. Using these predictions, a population based on realistic human leukocyte antigen-II (HLA-II) frequencies were simulated to visualize the T helper cell immunity on the population level. While a small fraction of this population had alarmingly little predicted CD4 T cell epitopes, the majority had several epitopes that should be enough to generate efficient B cell responses. Moreover, we show that VOC spike mutations hardly affect T helper epitopes and mainly occur in other residues of the spike protein. These results suggest that lack of long-lasting antibody responses is not likely due to loss of T helper cell epitopes in new VOCs. [ABSTRACT FROM AUTHOR]

The main objective of antigen processing is to orchestrate the selection of immunodominant epitopes for recognition by CD4 T cells. To achieve this, MHC class II molecules have evolved with a flexible peptide-binding groove in need of a bound peptide. Newly synthesized MHC-II molecules bind a class II invariant chain (Ii) upon synthesis and are shuttled to a specialized compartment, where they encounter exogenous antigens. Ii serves multiple functions, one of which is to maintain the shape of the MHC-II groove so that it can readily bind exogenous antigens upon dissociation of the Ii peptide in MHC- II compartment. MIIC contains processing enzymes, one or both accessory molecules, HLA-DM/H2-M (DM) and HLA-DO/H2-O (DO), and optimal denaturing conditions. In a process known as "editing," DM facilitates the dissociation of the invariant chain peptide, CLIP, for exchange with exogenous antigens. Despite the availability of mechanistic insights into DM functions, understanding how DO contributes to epitope selection has proven to be more challenging. The current dogma assumes that DO inhibits DM, whereas an opposing model suggests that DO fine-tunes the epitope selection process. Understanding which of these, or potentially other models of DO function is important, as DO variants have been linked to autoimmunity, cancer, and the generation of broadly neutralizing antibodies to viruses. This review therefore attempts to evaluate experimental evidence in support of these hypotheses, with an emphasis on the less discussed model, and to explore intriguing questions about the importance of DO in biology. [ABSTRACT FROM AUTHOR]

Here we describe a detailed quantitative peptide-binding motif for the common equine leukocyte antigen (ELA) class I allele Eqca-1*00101, present in roughly 25 % of Thoroughbred horses. We determined a preliminary binding motif by sequencing endogenously bound ligands. Subsequently, a positional scanning combinatorial library (PSCL) was used to further characterize binding specificity and derive a quantitative motif involving aspartic acid in position 2 and hydrophobic residues at the C-terminus. Using this motif, we selected and tested 9- and 10-mer peptides derived from the equine herpesvirus type 1 (EHV-1) proteome for their capacity to bind Eqca-1*00101. PSCL predictions were very efficient, with an receiver operating characteristic (ROC) curve performance of 0.877, and 87 peptides derived from 40 different EHV-1 proteins were identified with affinities of 500 nM or higher. Quantitative analysis revealed that Eqca-1*00101 has a narrow peptide-binding repertoire, in comparison to those of most human, non-human primate, and mouse class I alleles. Peripheral blood mononuclear cells from six EHV-1-infected, or vaccinated but uninfected, Eqca-1*00101-positive horses were used in IFN-γ enzyme-linked immunospot (ELISPOT) assays. When we screened the 87 Eqca-1*00101-binding peptides for T cell reactivity, only one Eqca-1*00101 epitope, derived from the intermediate-early protein ICP4, was identified. Thus, despite its common occurrence in several horse breeds, Eqca-1*00101 is associated with a narrow binding repertoire and a similarly narrow T cell response to an important equine viral pathogen. Intriguingly, these features are shared with other human and macaque major histocompatibility complex (MHC) molecules with a similar specificity for D in position 2 or 3 in their main anchor motif. [ABSTRACT FROM AUTHOR]

Viral epitopes have a distinct codon usage that reflects their dual role in infection and immunity. On the one hand, epitopes are part of proteins important to viral function; on the other hand, they are targets of the immune response. Studies of selection are most commonly based on changes of amino acid and seen through the accumulation of non-synonymous mutations. An independent measure of selection is the codon usage and underlying changeability of the nucleotide sequences. We here use multiple tools and a large-scale analysis of viral genomes to demonstrate that viral epitopes have a distinct codon usage and that this codon usage reflects distinct short- and long-term types of selection during viral evolution. We show that CD8 T cell epitopes are encoded by codons more distant from stop codons and more changeable than codons outside epitopes. This biased codon usage reflects the viral population toggling back and forth from a wild-type sequence to an escape mode, which enable them to avoid immune detection when needed, and go back to the functionally favorable form when the threat is removed (i.e., in a new host). [ABSTRACT FROM AUTHOR]

Multiple factors determine the ability of a peptide to elicit a cytotoxic T cell lymphocyte response. Binding to a major histocompatibility complex class I (MHC-I) molecule is one of the most essential factors, as no peptide can become a T cell epitope unless presented on the cell surface in complex with an MHC-I molecule. As such, peptide-MHC (pMHC) binding affinity predictors are currently the premier methods for T cell epitope prediction, and these prediction methods have been shown to have high predictive performances in multiple studies. However, not all MHC-I binders are T cell epitopes, and multiple studies have investigated what additional factors are important for determining the immunogenicity of a peptide. A recent study suggested that pMHC stability plays an important role in determining if a peptide can become a T cell epitope. Likewise, a T cell propensity model has been proposed for identifying MHC binding peptides with amino acid compositions favoring T cell receptor interactions. In this study, we investigate if improved accuracy for T cell epitope discovery can be achieved by integrating predictions for pMHC binding affinity, pMHC stability, and T cell propensity. We show that a weighted sum approach allows pMHC stability and T cell propensity predictions to enrich pMHC binding affinity predictions. The integrated model leads to a consistent and significant increase in predictive performance and we demonstrate how this can be utilized to decrease the experimental workload of epitope screens. The final method, NetTepi, is publically available at . [ABSTRACT FROM AUTHOR]

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 , and allows the user in an automatic manner to obtain the most accurate predictions for any given MHC molecule. [ABSTRACT FROM AUTHOR]

The presentation of identical peptides by different major histocompatibility complex class I (MHC-I) molecules, termed promiscuity, is a controversial feature of T cell-mediated immunity to pathogens. The astounding diversity of MHC-I molecules in human populations, presumably to enable binding of equally diverse peptides, implies promiscuity would be a rare phenomenon. However, if it occurs, it would have important implications for immunity. We screened 77 animals for responses to peptides known to bind MHC-I molecules that were not expressed by these animals. Some cases of supposed promiscuity were determined to be the result of either non-identical optimal peptides or were simply not mapped to the correct MHC-I molecule in previous studies. Cases of promiscuity, however, were associated with alterations of immunodominance hierarchies, either in terms of the repertoire of peptides presented by the different MHC-I molecules or in the magnitude of the responses directed against the epitopes themselves. Specifically, we found that the Mamu-B*017:01-restricted peptides Vif HW8 and cRW9 were also presented by Mamu-A2*05:26 and targeted by an animal expressing that allele. We also found that the normally subdominant Mamu-A1*001:01 presented peptide Gag QI9 was also presented by Mamu-B*056:01. Both A2*05:26 and B*056:01 are molecules typically or exclusively expressed by animals of Chinese origin. These data clearly demonstrate that MHC-I epitope promiscuity, though rare, might have important implications for immunodominance and for the transmission of escape mutations, depending on the relative frequencies of the given alleles in a population. [ABSTRACT FROM AUTHOR]

Previous studies have attempted to define human leukocyte antigen (HLA) class II supertypes, analogous to the case for class I, on the basis of shared peptide-binding motifs or structure. In the present study, we determined the binding capacity of a large panel of non-redundant peptides for a set of 27 common HLA DR, DQ, and DP molecules. The measured binding data were then used to define class II supertypes on the basis of shared binding repertoires. Seven different supertypes (main DR, DR4, DRB3, main DQ, DQ7, main DP, and DP2) were defined. The molecules associated with the respective supertypes fell largely along lines defined by MHC locus and reflect, in broad terms, commonalities in reported peptide-binding motifs. Repertoire overlaps between molecules within the same class II supertype were found to be similar in magnitude to what has been observed for HLA class I supertypes. Surprisingly, however, the degree to which repertoires between molecules in the different class II supertypes also overlapped was found to be five to tenfold higher than repertoire overlaps noted between molecules in different class I supertypes. These results highlight a high degree of repertoire overlap amongst all HLA class II molecules, perhaps reflecting binding in multiple registers, and more pronounced dependence on backbone interactions rather than peptide anchor residues. This fundamental difference between HLA class I and class II would not have been predicted on the basis of analysis of either binding motifs or the sequence/predicted structures of the HLA molecules. [ABSTRACT FROM AUTHOR]

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 (), 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. [ABSTRACT FROM AUTHOR]

Prediction of which peptides can bind major histocompatibility complex (MHC) molecules is commonly used to assist in the identification of T cell epitopes. However, because of the large numbers of different MHC molecules of interest, each associated with different predictive tools, tool generation and evaluation can be a very resource intensive task. A methodology commonly used to predict MHC binding affinity is the matrix or linear coefficients method. Herein, we described Average Relative Binding (ARB) matrix methods that directly predict IC50 values allowing combination of searches involving different peptide sizes and alleles into a single global prediction. A computer program was developed to automate the generation and evaluation of ARB predictive tools. Using an in-house MHC binding database, we generated a total of 85 and 13 MHC class I and class II matrices, respectively. Results from the automated evaluation of tool efficiency are presented. We anticipate that this automation framework will be generally applicable to the generation and evaluation of large numbers of MHC predictive methods and tools, and will be of value to centralize and rationalize the process of evaluation of MHC predictions. MHC binding predictions based on ARB matrices were made available at web server. [ABSTRACT FROM AUTHOR]

Cytotoxic T cells (CTLs) perceive the world through small peptides that are eight to ten amino acids long. These peptides (epitopes) are initially generated by the proteasome, a multi-subunit protease that is responsible for the majority of intra-cellular protein degradation. The proteasome generates the exact C-terminal of CTL epitopes, and the N-terminal with a possible extension. CTL responses may diminish if the epitopes are destroyed by the proteasomes. Therefore, the prediction of the proteasome cleavage sites is important to identify potential immunogenic regions in the proteomes of pathogenic microorganisms (or humans). We have recently shown that NetChop, a neural network-based prediction method, is the best method available at the moment to do such predictions; however, its performance is still lower than desired. Here, we use novel sequence encoding methods and show that the new version of NetChop predicts approximately 10% more of the cleavage sites correctly while lowering the number of false positives with close to 15%. With this more reliable prediction tool, we study two important questions concerning the function of the proteasome. First, we estimate the N-terminal extension of epitopes after proteasomal cleavage and find that the average extension is relatively short. However, more than 30% of the peptides have N-terminal extensions of three amino acids or more, and thus, N-terminal trimming might play an important role in the presentation of a substantial fraction of the epitopes. Second, we show that good TAP ligands have an increased chance of being cleaved by the proteasome, i.e., the specificity of TAP has evolved to fit the specificity of the proteasome. This evolutionary relationship allows for a more efficient antigen presentation. [ABSTRACT FROM AUTHOR]

We introduced previously an on-line resource, RANKPEP that uses position specific scoring matrices (PSSMs) or profiles for the prediction of peptide-MHC class I (MHCI) binding as a basis for CD8 T-cell epitope identification. Here, using PSSMs that are structurally consistent with the binding mode of MHC class II (MHCII) ligands, we have extended RANKPEP to prediction of peptide-MHCII binding and anticipation of CD4 T-cell epitopes. Currently, 88 and 50 different MHCI and MHCII molecules, respectively, can be targeted for peptide binding predictions in RANKPEP. Because appropriate processing of antigenic peptides must occur prior to major histocompatibility complex (MHC) binding, cleavage site prediction methods are important adjuncts for T-cell epitope discovery. Given that the C-terminus of most MHCI-restricted epitopes results from proteasomal cleavage, we have modeled the cleavage site from known MHCI-restricted epitopes using statistical language models. The RANKPEP server now determines whether the C-terminus of any predicted MHCI ligand may result from such proteasomal cleavage. Also implemented is a variability masking function. This feature focuses prediction on conserved rather than highly variable protein segments encoded by infectious genomes, thereby offering identification of invariant T-cell epitopes to thwart mutation as an immune evasion mechanism. [ABSTRACT FROM AUTHOR]

The first version of PAProC (Prediction Algorithm for Proteasomal Cleavages) is now available to the general public. PAProC is a prediction tool for cleavages by human and yeast proteasomes, based on experimental cleavage data. It will be particularly useful for immunologists working on antigen processing and the prediction of major histocompatibility complex class I molecule (MHC I) ligands and cytotoxic T-lymphocyte (CTL) epitopes. Likewise, in cases in which proteasomal protein degradation has been indicated in disease, PAProC can be used to assess the general cleavability of disease-linked proteins. On its web site (http://www.paproc.de), background information and hyperlinks are provided for the user (e.g., to SYFPEITHI, the database for the prediction of MHC I ligands). [ABSTRACT FROM AUTHOR]