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.

Aberrant glycosylation of mucins and other extracellular proteins is an important event in carcinogenesis and the resulting cancer associated glycans have been suggested as targets in cancer immunotherapy. We assessed the role of O-linked GalNAc glycosylation on antigen uptake, processing, and presentation on MHC class I and II molecules. The effect of GalNAc O-glycosylation was monitored with a model system based on ovalbumin (OVA)-MUC1 fusion peptides (+/− glycosylation) loaded onto dendritic cells co-cultured with IL-2 secreting OVA peptide-specific T cell hybridomas. To evaluate the in vivo response to a cancer related tumor antigen, Balb/c or B6.Cg(CB)-Tg(HLA-A/H2-D)2Enge/J (HLA-A2 transgenic) mice were immunized with a non-glycosylated or GalNAc-glycosylated MUC1 derived peptide followed by comparison of T cell proliferation, IFN-γ release, and antibody induction. GalNAc-glycosylation promoted presentation of OVA-MUC1 fusion peptides by MHC class II molecules and the MUC1 antigen elicited specific Ab production and T cell proliferation in both Balb/c and HLA-A2 transgenic mice. In contrast, GalNAc-glycosylation inhibited the presentation of OVA-MUC1 fusion peptides by MHC class I and abolished MUC1 specific CD8+ T cell responses in HLA-A2 transgenic mice. GalNAc glycosylation of MUC1 antigen therefore facilitates uptake, MHC class II presentation, and antibody response but might block the antigen presentation to CD8+ T cells.

The genetic polymorphism that has the greatest impact on immune control of human immunodeficiency virus (HIV) infection is expression of HLA-B*57. Understanding of the mechanism for this strong effect remains incomplete. HLA-B*57 alleles and the closely related HLA-B*5801 are often grouped together because of their similar peptide-binding motifs and HIV disease outcome associations. However, we show here that the apparently small differences between HLA-B*57 alleles, termed HLA-B*57 micropolymorphisms, have a significant impact on immune control of HIV. In a study cohort of >2,000 HIV C-clade-infected subjects from southern Africa, HLA-B*5703 is associated with a lower viral-load set point than HLA-B*5702 and HLA-B*5801 (medians, 5,980, 15,190, and 19,000 HIV copies/ml plasma; P = 0.24 and P = 0.0005). In order to better understand these observed differences in HLA-B*57/5801-mediated immune control of HIV, we undertook, in a study of >1,000 C-clade-infected subjects, a comprehensive analysis of the epitopes presented by these 3 alleles and of the selection pressure imposed on HIV by each response. In contrast to previous studies, we show that each of these three HLA alleles is characterized both by unique CD8+ T-cell specificities and by clear-cut differences in selection pressure imposed on the virus by those responses. These studies comprehensively define for the first time the CD8+ T-cell responses and immune selection pressures for which these protective alleles are responsible. These findings are consistent with HLA class I alleles mediating effective immune control of HIV through the number of p24 Gag-specific CD8+ T-cell responses generated that can drive significant selection pressure on the virus.

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, KD, 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 KD below 100 nM and the peptides with KD 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 KD 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.

Background

Identification of human leukocyte antigen class I (HLA-I) restricted cytotoxic T cell (CTL) epitopes from influenza virus is of importance for the development of new effective peptide-based vaccines.

Methodology/Principal Findings

In the present work, bioinformatics was used to predict 9mer peptides derived from available influenza A viral proteins with binding affinity for at least one of the 12 HLA-I supertypes. The predicted peptides were then selected in a way that ensured maximal coverage of the available influenza A strains. One hundred and thirty one peptides were synthesized and their binding affinities for the HLA-I supertypes were measured in a biochemical assay. Influenza-specific T cell responses towards the peptides were quantified using IFNγ ELISPOT assays with peripheral blood mononuclear cells (PBMC) from adult healthy HLA-I typed donors as responder cells. Of the 131 peptides, 21 were found to induce T cell responses in 19 donors. In the ELISPOT assay, five peptides induced responses that could be totally blocked by the pan-specific anti-HLA-I antibody W6/32, whereas 15 peptides induced responses that could be completely blocked in the presence of the pan-specific anti-HLA class II (HLA-II) antibody IVA12. Blocking of HLA-II subtype reactivity revealed that 8 and 6 peptide responses were blocked by anti-HLA-DR and -DP antibodies, respectively. Peptide reactivity of PBMC depleted of CD4+ or CD8+ T cells prior to the ELISPOT culture revealed that effectors are either CD4+ (the majority of reactivities) or CD8+ T cells, never a mixture of these subsets. Three of the peptides, recognized by CD4+ T cells showed binding to recombinant DRA1*0101/DRB1*0401 or DRA1*0101/DRB5*0101 molecules in a recently developed biochemical assay.

Conclusions/Significance

HLA-I binding 9mer influenza virus-derived peptides induce in many cases CD4+ T cell responses restricted by HLA-II molecules.

Background

Molecules of the class II major histocompability complex (MHC-II) specifically bind and present exogenously derived peptide epitopes to CD4+ T helper cells. The extreme polymorphism of the MHC-II hampers the complete analysis of peptide binding. It is also a significant hurdle in the generation of MHC-II molecules as reagents to study and manipulate specific T helper cell responses. Methods to generate functional MHC-II molecules recombinantly, and measure their interaction with peptides, would be highly desirable; however, no consensus methodology has yet emerged.

Results

We generated α and β MHC-II chain constructs, where the membrane-spanning regions were replaced by dimerization motifs, and the C-terminal of the β chains was fused to a biotinylation signal peptide (BSP) allowing for in vivo biotinylation. These chains were produced separately as inclusion bodies in E. coli , extracted into urea, and purified under denaturing and non-reducing conditions using conventional column chromatography. Subsequently, diluting the two chains into a folding reaction with appropriate peptide resulted in efficient peptide-MHC-II complex formation. Several different formats of peptide-binding assay were developed including a homogeneous, non-radioactive, high-throughput (HTS) binding assay. Binding isotherms were generated allowing the affinities of interaction to be determined. The affinities of the best binders were found to be in the low nanomolar range. Recombinant MHC-II molecules and accompanying HTS peptide-binding assay were successfully developed for nine different MHC-II molecules including the DPA1*0103/DPB1*0401 (DP401) and DQA1*0501/DQB1*0201, where both α and β chains are polymorphic, illustrating the advantages of producing the two chains separately.

Conclusion

We have successfully developed versatile MHC-II resources, which may assist in the generation of MHC class II -wide reagents, data, and tools.

NetMHC-3.0 is trained on a large number of quantitative peptide data using both affinity data from the Immune Epitope Database and Analysis Resource (IEDB) and elution data from SYFPEITHI. The method generates high-accuracy predictions of major histocompatibility complex (MHC): peptide binding. The predictions are based on artificial neural networks trained on data from 55 MHC alleles (43 Human and 12 non-human), and position-specific scoring matrices (PSSMs) for additional 67 HLA alleles. As only the MHC class I prediction server is available, predictions are possible for peptides of length 8–11 for all 122 alleles. artificial neural network predictions are given as actual IC50 values whereas PSSM predictions are given as a log-odds likelihood scores. The output is optionally available as download for easy post-processing. The training method underlying the server is the best available, and has been used to predict possible MHC-binding peptides in a series of pathogen viral proteomes including SARS, Influenza and HIV, resulting in an average of 75–80% confirmed MHC binders. Here, the performance is further validated and benchmarked using a large set of newly published affinity data, non-redundant to the training set. The server is free of use and available at: http://www.cbs.dtu.dk/services/NetMHC.

Background

Binding of peptides to Major Histocompatibility Complex (MHC) molecules is the single most selective step in the recognition of pathogens by the cellular immune system. The human MHC class I system (HLA-I) is extremely polymorphic. The number of registered HLA-I molecules has now surpassed 1500. Characterizing the specificity of each separately would be a major undertaking.

Principal Findings

Here, we have drawn on a large database of known peptide-HLA-I interactions to develop a bioinformatics method, which takes both peptide and HLA sequence information into account, and generates quantitative predictions of the affinity of any peptide-HLA-I interaction. Prospective experimental validation of peptides predicted to bind to previously untested HLA-I molecules, cross-validation, and retrospective prediction of known HIV immune epitopes and endogenous presented peptides, all successfully validate this method. We further demonstrate that the method can be applied to perform a clustering analysis of MHC specificities and suggest using this clustering to select particularly informative novel MHC molecules for future biochemical and functional analysis.

Conclusions

Encompassing all HLA molecules, this high-throughput computational method lends itself to epitope searches that are not only genome- and pathogen-wide, but also HLA-wide. Thus, it offers a truly global analysis of immune responses supporting rational development of vaccines and immunotherapy. It also promises to provide new basic insights into HLA structure-function relationships. The method is available at http://www.cbs.dtu.dk/services/NetMHCpan.

Recognition of peptides bound to major histocompatibility complex (MHC) class I molecules by T lymphocytes is an essential part of immune surveillance. Each MHC allele has a characteristic peptide binding preference, which can be captured in prediction algorithms, allowing for the rapid scan of entire pathogen proteomes for peptide likely to bind MHC. Here we make public a large set of 48,828 quantitative peptide-binding affinity measurements relating to 48 different mouse, human, macaque, and chimpanzee MHC class I alleles. We use this data to establish a set of benchmark predictions with one neural network method and two matrix-based prediction methods extensively utilized in our groups. In general, the neural network outperforms the matrix-based predictions mainly due to its ability to generalize even on a small amount of data. We also retrieved predictions from tools publicly available on the internet. While differences in the data used to generate these predictions hamper direct comparisons, we do conclude that tools based on combinatorial peptide libraries perform remarkably well. The transparent prediction evaluation on this dataset provides tool developers with a benchmark for comparison of newly developed prediction methods. In addition, to generate and evaluate our own prediction methods, we have established an easily extensible web-based prediction framework that allows automated side-by-side comparisons of prediction methods implemented by experts. This is an advance over the current practice of tool developers having to generate reference predictions themselves, which can lead to underestimating the performance of prediction methods they are not as familiar with as their own. The overall goal of this effort is to provide a transparent prediction evaluation allowing bioinformaticians to identify promising features of prediction methods and providing guidance to immunologists regarding the reliability of prediction tools.

Synopsis

In higher organisms, major histocompatibility complex (MHC) class I molecules are present on nearly all cell surfaces, where they present peptides to T lymphocytes of the immune system. The peptides are derived from proteins expressed inside the cell, and thereby allow the immune system to “peek inside” cells to detect infections or cancerous cells. Different MHC molecules exist, each with a distinct peptide binding specificity. Many algorithms have been developed that can predict which peptides bind to a given MHC molecule. These algorithms are used by immunologists to, for example, scan the proteome of a given virus for peptides likely to be presented on infected cells. In this paper, the authors provide a large-scale experimental dataset of quantitative MHC–peptide binding data. Using this dataset, they compare how well different approaches are able to identify binding peptides. This comparison identifies an artificial neural network as the most successful approach to peptide binding prediction currently available. This comparison serves as a benchmark for future tool development, allowing bioinformaticians to document advances in tool development as well as guiding immunologists to choose good prediction algorithm.