The use of bioinformatics for identifying class II-restricted T-cell epitopes

An important step in the design of subunit vaccines is the identification of promiscuous T helper cell epitopes in sets of disease-specific gene products. Most of the epitope prediction models are based on HLA-II peptide binding, which constitutes a major bottleneck in the natural selection of epitopes. Here we describe a computer model, TEPITOPE, that enables the systematic prediction of promiscuous peptide ligands for a broad range of HLA binding specificity. We show how to apply the TEPITOPE prediction model to identify T-cell epitopes, and provide examples of its successful application in the context of oncology, allergy, and infectious and autoimmune diseases.     

Cutting edge: identification of novel T cell epitopes in Lol p5a by computational prediction.

Although atopic allergy affects 相似文献   

In silico prediction of peptides binding to multiple HLA-DR molecules accurately identifies immunodominant epitopes from gp43 of Paracoccidioides brasiliensis frequently recognized in primary peripheral blood mononuclear cell responses from sensitized individuals

One of the major drawbacks limiting the use of synthetic peptide vaccines in genetically distinct populations is the fact that different epitopes are recognized by T cells from individuals displaying distinct major histocompatibility complex molecules. Immunization of mice with peptide (181-195) from the immunodominant 43 kDa glycoprotein of Paracoccidioides brasiliensis (gp43), the causative agent of Paracoccidioidomycosis (PCM), conferred protection against infectious challenge by the fungus. To identify immunodominant and potentially protective human T-cell epitopes in gp43, we used the TEPITOPE algorithm to select peptide sequences that would most likely bind multiple HLA-DR molecules and tested their recognition by T cells from sensitized individuals. The 5 most promiscuous peptides were selected from the gp43 sequence and the actual promiscuity of HLA binding was assessed by direct binding assays to 9 prevalent HLA-DR molecules. Synthetic peptides were tested in proliferation assays with peripheral blood mononuclear cells (PBMC) from PCM patients after chemotherapy and healthy controls. PBMC from 14 of 19 patients recognized at least one of the promiscuous peptides, whereas none of the healthy controls recognized the gp43 promiscuous peptides. Peptide gp43(180-194) was recognized by 53% of patients, whereas the other promiscuous gp43 peptides were recognized by 32% to 47% of patients. The frequency of peptide binding and peptide recognition correlated with the promiscuity of HLA-DR binding, as determined by TEPITOPE analysis. In silico prediction of promiscuous epitopes led to the identification of naturally immunodominant epitopes recognized by PBMC from a significant proportion of a genetically heterogeneous patient population exposed to P. brasiliensis. The combination of several such epitopes may increase the frequency of positive responses and allow the immunization of genetically distinct populations.     

An effective and effecient peptide binding prediction approach for a broad set of HLA-DR molecules based on ordered weighted averaging of binding pocket profiles

Background

The immune system must detect a wide variety of microbial pathogens, such as viruses, bacteria, fungi and parasitic worms, to protect the host against disease. Antigenic peptides displayed by MHC II (class II Major Histocompatibility Complex) molecules is a pivotal process to activate CD4+ T_H cells (Helper T cells). The activated T_H cells can differentiate into effector cells which assist various cells in activating against pathogen invasion. Each MHC locus encodes a great number of allele variants. Yet this limited number of MHC molecules are required to display enormous number of antigenic peptides. Since the peptide binding measurements of MHC molecules by biochemical experiments are expensive, only a few of the MHC molecules have suffecient measured peptides. To perform accurate binding prediction for those MHC alleles without suffecient measured peptides, a number of computational algorithms were proposed in the last decades.

Results

Here, we propose a new MHC II binding prediction approach, OWA-PSSM, which is a significantly extended version of a well known method called TEPITOPE. The TEPITOPE method is able to perform prediction for only 50 MHC alleles, while OWA-PSSM is able to perform prediction for much more, up to 879 HLA-DR molecules. We evaluate the method on five benchmark datasets. The method is demonstrated to be the best one in identifying binding cores compared with several other popular state-of-the-art approaches. Meanwhile, the method performs comparably to the TEPITOPE and NetMHCIIpan2.0 approaches in identifying HLA-DR epitopes and ligands, and it performs significantly better than TEPITOPEpan in the identification of HLA-DR ligands and MultiRTA in identifying HLA-DR T cell epitopes.

Conclusions

The proposed approach OWA-PSSM is fast and robust in identifying ligands, epitopes and binding cores for up to 879 MHC II molecules.

     

In vitro and in vivo induction of a Th cell response toward peptides of the melanoma-associated glycoprotein 100 protein selected by the TEPITOPE program

The melanoma-associated Ag glycoprotein 100 was analyzed by the T cell epitope prediction software TEPITOPE. Seven HLA-DR promiscuous peptides predicted with a stringent threshold were used to load dendritic cells (DC), and induction of a proliferative response was monitored. PBMC of all nine donors including two patients with malignant melanoma responded to at least one of the peptides. The proliferative response was defined as a Th response by the selective expansion of CD4(+) cells, up-regulation of CD25 and CD40L, and IL-2 and IFN-gamma expression. Peptide-loaded DC also initiated a T helper response in vivo (i.e., tumor growth in the SCID mouse was significantly retarded by the transfer of PBMC together with peptide-loaded DC). Because the use of the TEPITOPE program allows for a prediction of T cell epitopes; because the predicted peptides can be rapidly confirmed by inducing a Th response in the individual patient; and because application of peptide-loaded DC suffices for the in vivo activation of helper cells, vaccination with MHC class II-binding peptides of tumor-associated Ags becomes a feasible and likely powerful tool in the immunotherapy of cancer.     

MultiRTA: A simple yet reliable method for predicting peptide binding affinities for multiple class II MHC allotypes

Background

The binding of peptide fragments of antigens to class II MHC is a crucial step in initiating a helper T cell immune response. The identification of such peptide epitopes has potential applications in vaccine design and in better understanding autoimmune diseases and allergies. However, comprehensive experimental determination of peptide-MHC binding affinities is infeasible due to MHC diversity and the large number of possible peptide sequences. Computational methods trained on the limited experimental binding data can address this challenge. We present the MultiRTA method, an extension of our previous single-type RTA prediction method, which allows the prediction of peptide binding affinities for multiple MHC allotypes not used to train the model. Thus predictions can be made for many MHC allotypes for which experimental binding data is unavailable.

Results

We fit MultiRTA models for both HLA-DR and HLA-DP using large experimental binding data sets. The performance in predicting binding affinities for novel MHC allotypes, not in the training set, was tested in two different ways. First, we performed leave-one-allele-out cross-validation, in which predictions are made for one allotype using a model fit to binding data for the remaining MHC allotypes. Comparison of the HLA-DR results with those of two other prediction methods applied to the same data sets showed that MultiRTA achieved performance comparable to NetMHCIIpan and better than the earlier TEPITOPE method. We also directly tested model transferability by making leave-one-allele-out predictions for additional experimentally characterized sets of overlapping peptide epitopes binding to multiple MHC allotypes. In addition, we determined the applicability of prediction methods like MultiRTA to other MHC allotypes by examining the degree of MHC variation accounted for in the training set. An examination of predictions for the promiscuous binding CLIP peptide revealed variations in binding affinity among alleles as well as potentially distinct binding registers for HLA-DR and HLA-DP. Finally, we analyzed the optimal MultiRTA parameters to discover the most important peptide residues for promiscuous and allele-specific binding to HLA-DR and HLA-DP allotypes.

Conclusions

The MultiRTA method yields competitive performance but with a significantly simpler and physically interpretable model compared with previous prediction methods. A MultiRTA prediction webserver is available at http://bordnerlab.org/MultiRTA.

     

Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan

CD4 positive T helper cells control many aspects of specific immunity. These cells are specific for peptides derived from protein antigens and presented by molecules of the extremely polymorphic major histocompatibility complex (MHC) class II system. The identification of peptides that bind to MHC class II molecules is therefore of pivotal importance for rational discovery of immune epitopes. HLA-DR is a prominent example of a human MHC class II. Here, we present a method, NetMHCIIpan, that allows for pan-specific predictions of peptide binding to any HLA-DR molecule of known sequence. The method is derived from a large compilation of quantitative HLA-DR binding events covering 14 of the more than 500 known HLA-DR alleles. Taking both peptide and HLA sequence information into account, the method can generalize and predict peptide binding also for HLA-DR molecules where experimental data is absent. Validation of the method includes identification of endogenously derived HLA class II ligands, cross-validation, leave-one-molecule-out, and binding motif identification for hitherto uncharacterized HLA-DR molecules. The validation shows that the method can successfully predict binding for HLA-DR molecules-even in the absence of specific data for the particular molecule in question. Moreover, when compared to TEPITOPE, currently the only other publicly available prediction method aiming at providing broad HLA-DR allelic coverage, NetMHCIIpan performs equivalently for alleles included in the training of TEPITOPE while outperforming TEPITOPE on novel alleles. We propose that the method can be used to identify those hitherto uncharacterized alleles, which should be addressed experimentally in future updates of the method to cover the polymorphism of HLA-DR most efficiently. We thus conclude that the presented method meets the challenge of keeping up with the MHC polymorphism discovery rate and that it can be used to sample the MHC "space," enabling a highly efficient iterative process for improving MHC class II binding predictions.     

Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices.

Most pockets in the human leukocyte antigen-group DR (HLA-DR) groove are shaped by clusters of polymorphic residues and, thus, have distinct chemical and size characteristics in different HLA-DR alleles. Each HLA-DR pocket can be characterized by "pocket profiles," a quantitative representation of the interaction of all natural amino acid residues with a given pocket. In this report we demonstrate that pocket profiles are nearly independent of the remaining HLA-DR cleft. A small database of profiles was sufficient to generate a large number of HLA-DR matrices, representing the majority of human HLA-DR peptide-binding specificity. These virtual matrices were incorporated in software (TEPITOPE) capable of predicting promiscuous HLA class II ligands. This software, in combination with DNA microarray technology, has provided a new tool for the generation of comprehensive databases of candidate promiscuous T-cell epitopes in human disease tissues. First, DNA microarrays are used to reveal genes that are specifically expressed or upregulated in disease tissues. Second, the prediction software enables the scanning of these genes for promiscuous HLA-DR binding sites. In an example, we demonstrate that starting from nearly 20,000 genes, a database of candidate colon cancer-specific and promiscuous T-cell epitopes could be fully populated within a matter of days. Our approach has implications for the development of epitope-based vaccines.     

Immuno-informatics based approaches to identify CD8+ T cell epitopes within the Leishmania donovani 3-ectonucleotidase in cured visceral leishmaniasis subjects

Leishmaniases are vector-borne diseases for which no vaccine exists. These diseases are caused by the Leishmania species complex. Activation of the CD8⁺ T cell is crucial for protection against intracellular pathogens, and peptide antigens are attractive strategies for the precise activation of CD8⁺ T in vaccine development against intracellular infections. The traditional approach to mine the epitopes is an arduous task. However, with the advent of immunoinformatics, in silico epitope prediction tools are available to expedite epitope identification. In this study, we employ different immunoinformatics tools to predict CD8⁺ T cell specific 9 mer epitopes presented by HLA-A*02 and HLA-B40 within the highly conserved 3′-ectonucleotidase of Leishmania donovani. We identify five promiscuous epitopes, which have no homologs in humans, theoretically cover 85% of the world's population and are highly conserved (100%) among Leishmania species. Presentation of selected peptides was confirmed by T2 cell line based HLA-stabilization assay, and three of them were found to be strong binders. The in vitro peptide stimulation of peripheral blood mononuclear cells (PBMC) from cured HLA-A02⁺ visceral leishmaniasis (VL) subjects produced significantly higher IFN-γ, IL-2 and IL-12 compared to no peptide control healthy subjects. Further, CD8⁺ cells from treated VL subjects produced significantly higher intracellular IFN-γ, lymphocyte proliferation and cytotoxic activity against selected peptides from the PBMCs of treated HLA-A02⁺ VL subjects. Thus, the CD8⁺ T cell specific epitopes shown in this study will speed up the development of polytope vaccines for leishmaniasis.