Discovery of promiscuous HLA-II-restricted T cell epitopes with TEPITOPE

TEPITOPE is a prediction model that has been successfully applied to the in silico identification of T cell epitopes in the context of oncology, allergy, infectious diseases, and autoimmune diseases. Like most epitope prediction models, TEPITOPE's underlying algorithm is based on the prediction of HLA-II peptide binding, which constitutes a major bottleneck in the natural selection of epitopes. An important step in the design of subunit vaccines is the identification of promiscuous HLA-II ligands in sets of disease-specific gene products. TEPITOPE's user interface 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 both a road map and examples of its successful application.     

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.     

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

Although atopic allergy affects 相似文献   

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.     

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.

     

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.

     

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.     

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.     

Identification of novel immunodominant CD4+ Th1-type T-cell peptide epitopes from herpes simplex virus glycoprotein D that confer protective immunity

The molecular characterization of the epitope repertoire on herpes simplex virus (HSV) antigens would greatly expand our knowledge of HSV immunity and improve immune interventions against herpesvirus infections. HSV glycoprotein D (gD) is an immunodominant viral coat protein and is considered an excellent vaccine candidate antigen. By using the TEPITOPE prediction algorithm, we have identified and characterized a total of 12 regions within the HSV type 1 (HSV-1) gD bearing potential CD4(+) T-cell epitopes, each 27 to 34 amino acids in length. Immunogenicity studies of the corresponding medium-sized peptides confirmed all previously known gD epitopes and additionally revealed four new immunodominant regions (gD(49-82), gD(146-179), gD(228-257), and gD(332-358)), each containing naturally processed epitopes. These epitopes elicited potent T-cell responses in mice of diverse major histocompatibility complex backgrounds. Each of the four new immunodominant peptide epitopes generated strong CD4(+) Th1 T cells that were biologically active against HSV-1-infected bone marrow-derived dendritic cells. Importantly, immunization of H-2(d) mice with the four newly identified CD4(+) Th1 peptide epitopes but not with four CD4(+) Th2 peptide epitopes induced a robust protective immunity against lethal ocular HSV-1 challenge. These peptide epitopes may prove to be important components of an effective immunoprophylactic strategy against herpes.     

TEPITOPEpan: extending TEPITOPE for peptide binding prediction covering over 700 HLA-DR molecules

Motivation

Accurate identification of peptides binding to specific Major Histocompatibility Complex Class II (MHC-II) molecules is of great importance for elucidating the underlying mechanism of immune recognition, as well as for developing effective epitope-based vaccines and promising immunotherapies for many severe diseases. Due to extreme polymorphism of MHC-II alleles and the high cost of biochemical experiments, the development of computational methods for accurate prediction of binding peptides of MHC-II molecules, particularly for the ones with few or no experimental data, has become a topic of increasing interest. TEPITOPE is a well-used computational approach because of its good interpretability and relatively high performance. However, TEPITOPE can be applied to only 51 out of over 700 known HLA DR molecules.

Method

We have developed a new method, called TEPITOPEpan, by extrapolating from the binding specificities of HLA DR molecules characterized by TEPITOPE to those uncharacterized. First, each HLA-DR binding pocket is represented by amino acid residues that have close contact with the corresponding peptide binding core residues. Then the pocket similarity between two HLA-DR molecules is calculated as the sequence similarity of the residues. Finally, for an uncharacterized HLA-DR molecule, the binding specificity of each pocket is computed as a weighted average in pocket binding specificities over HLA-DR molecules characterized by TEPITOPE.

Result

The performance of TEPITOPEpan has been extensively evaluated using various data sets from different viewpoints: predicting MHC binding peptides, identifying HLA ligands and T-cell epitopes and recognizing binding cores. Among the four state-of-the-art competing pan-specific methods, for predicting binding specificities of unknown HLA-DR molecules, TEPITOPEpan was roughly the second best method next to NETMHCIIpan-2.0. Additionally, TEPITOPEpan achieved the best performance in recognizing binding cores. We further analyzed the motifs detected by TEPITOPEpan, examining the corresponding literature of immunology. Its online server and PSSMs therein are available at http://www.biokdd.fudan.edu.cn/Service/TEPITOPEpan/.     

Epitope-Based Peptides Prediction from Proteome of Nipah Virus

The identification of MHC class II epitope-based peptides are urgently needed for appropriate vaccination against Nipah virus (NiV) because there are currently no approved vaccines for human NiV infection. In the present study, prediction and modeling of T cell epitopes of NiV antigenic proteins nucleocapsid, phosphoprotein, matrix, fusion, glycoprotein, L protein, W protein, V protein and C protein followed by the binding simulation studies of predicted highest binding scores with their corresponding MHC class II alleles were done. Immunoinformatic tool ProPred was used to predict the promiscuous MHC class II epitopes of viral antigenic proteins. PEPstr server did the 3D structure models of the epitopes and Modeller 9.10 did alleles. We docked epitope with allele structure using the AutoDock 4.2 Tool. The docked peptide–allele complex structure was optimized using molecular dynamics simulation for 5 ps with the CHARMM-22 force field using NAnoscale Molecular Dynamics program incorporated in visual molecular dynamics (VMD 1.9.2) and then evaluating the stability of complex structure by calculating RMSD values. Epitope MKLQFSLGS of Matrix protein has considerable binding energy and score with DRBI*0421 MHC class II allele. This predicted peptide has potential to induce T cell-mediated immune response and is expected to useful in designing epitope-based vaccines against NiV after further testing by wet lab studies.     

Prediction of MHC class II binding peptides based on an iterative learning model

Background

Prediction of the binding ability of antigen peptides to major histocompatibility complex (MHC) class II molecules is important in vaccine development. The variable length of each binding peptide complicates this prediction. Motivated by a text mining model designed for building a classifier from labeled and unlabeled examples, we have developed an iterative supervised learning model for the prediction of MHC class II binding peptides.

Results

A linear programming (LP) model was employed for the learning task at each iteration, since it is fast and can re-optimize the previous classifier when the training sets are altered. The performance of the new model has been evaluated with benchmark datasets. The outcome demonstrates that the model achieves an accuracy of prediction that is competitive compared to the advanced predictors (the Gibbs sampler and TEPITOPE). The average areas under the ROC curve obtained from one variant of our model are 0.753 and 0.715 for the original and homology reduced benchmark sets, respectively. The corresponding values are respectively 0.744 and 0.673 for the Gibbs sampler and 0.702 and 0.667 for TEPITOPE.

Conclusion

The iterative learning procedure appears to be effective in prediction of MHC class II binders. It offers an alternative approach to this important predictionproblem.     

Sequence and Structure Based Binding Prediction Study of HLA Class I and cTAP Binding Peptides for Japanese Encephalitis Vaccine Development

Japanese encephalitis is a major threat in developing countries, even the availability of several conventional vaccines, which demand development of more effective vaccines. The present study used propred I and Immune Epitope Database Artificial Neural Network (ANN) algorithm (IEDB-ANN) to identify the conserve and promiscuous T cell epitopes from JEV proteome followed by structure based analysis of potential epitopes. Among all identified 102 epitopes, ten epitope were promiscuous but two epitopes of glycoprotein viz. ⁵⁵LVTVNPFVA⁶³ and ³⁸IPIVSVASL⁴⁶ were found most promiscuous, highly conserved and high population coverage in comparison of known antigenic positive control peptides. The B cell epitopes of glycoprotein also share these two T cell epitopes revealed by BCPred algorithm which can be a basis to confer the protection by neutralizing antibody combined with an effective cell-mediated response. Further, Autodock 4.2 and NAMD–VMD molecular dynamics simulation were used for docking and molecular dynamics simulation respectively, to validate epitope and allele complex binding stability. The 3D structure models were generated for epitopes and corresponding HLA allele by Pepstr and Modeller 9.10 respectively. Epitope LVTVNPFVA–B5101 allele complex showed best energy minimization and stability over the time window during simulation. Here we also present the binding sequel of epitope LVTVNPFVA and its eventual transport through cTAP1 (PDB ID: 1JJ7) revealed by Autodock 4.2, which is an essential path for HLA class I binding epitopes to elicit immune response. The docking experiment of epitope LVTVNPFVA and cTAP1 very well show a 2 H-bond with a binding energy of ?1.88 kcal/mol and other binding state of epitope forming no H-bond with a binding energy of ?1.13 kcal/mol in the lower area of cTAP1 cavity. These results show a smooth pass through of the epitope across the channel of cTAP1. Overall, identified peptides have potential application in the design and development of short peptide based vaccines and diagnostic agents for Japanese encephalitis.     

ProPred1: prediction of promiscuous MHC Class-I binding sites

SUMMARY: ProPred1 is an on-line web tool for the prediction of peptide binding to MHC class-I alleles. This is a matrix-based method that allows the prediction of MHC binding sites in an antigenic sequence for 47 MHC class-I alleles. The server represents MHC binding regions within an antigenic sequence in user-friendly formats. These formats assist user in the identification of promiscuous MHC binders in an antigen sequence that can bind to large number of alleles. ProPred1 also allows the prediction of the standard proteasome and immunoproteasome cleavage sites in an antigenic sequence. This server allows identification of MHC binders, who have the cleavage site at the C terminus. The simultaneous prediction of MHC binders and proteasome cleavage sites in an antigenic sequence leads to the identification of potential T-cell epitopes. AVAILABILITY: Server is available at http://www.imtech.res.in/raghava/propred1/. Mirror site of this server is available at http://bioinformatics.uams.edu/mirror/propred1/ Supplementary information: Matrices and document on server are available at http://www.imtech.res.in/raghava/propred1/page2.html     

Neural models for predicting viral vaccine targets

We applied artificial neural networks (ANN) for the prediction of targets of immune responses that are useful for study of vaccine formulations against viral infections. Using a novel data representation, we developed a system termed MULTIPRED that can predict peptide binding to multiple related human leukocyte antigens (HLA). This implementation showed high accuracy in the prediction of the promiscuous peptides that bind to five HLA-A2 allelic variants. MULTIPRED is useful for the identification of peptides that bind multiple HLA-A2 variants as a group. By implementing ANN as a classification engine, we enabled both the prediction of peptides binding to multiple individual HLA-A2 molecules and the prediction of promiscuous binders using a single model. The ANN MULTIPRED predicts peptide binding to HLA-A*0205 with excellent accuracy (area under the receiver operating characteristic curve--AROC>0.90), and to HLA-A*0201, HLA-A*0204 and HLA-A*0206 with high accuracy (AROC>0.85). Antigenic regions with high density of binders ("antigenic hot-spots") represent best targets for vaccine design. MULTIPRED not only predicts individual 9-mer binders but also predicts antigenic hot spots. Two HLA-A2 hot-spots in Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) membrane protein were predicted by using MULTIPRED.     

Peptide vaccine models using statistical data mining

Design and synthesis of peptide vaccines is of significant pharmaceutical importance. A knowledge based statistical model is fitted here for prediction of binding of an antigenic site of a protein or a B-cell epitope on a CDR (complementarity determining region) of an immunoglobulin. Linear analogues of the 3D structure of the epitopes are computed using this model. Extension for prediction of peptide epitopes from the protein sequence alone is also presented. Validation results show promising potential of this approach in computer-aided peptide vaccine production. The computed probabilities of binding also provide a pioneering approach for ab-initio prediction of 'potency' of protein or peptide vaccines modeled by this method.     

Class II MHC self-antigen presentation in human B and T lymphocytes

Human CD4(+) T cells process and present functional class II MHC-peptide complexes, but the endogenous peptide repertoire of these non-classical antigen presenting cells remains unknown. We eluted and sequenced HLA-DR-bound self-peptides presented by CD4(+) T cells in order to compare the T cell-derived peptide repertoire to sequences derived from genetically identical B cells. We identified several novel epitopes derived from the T cell-specific proteome, including fragments of CD4 and IL-2. While these data confirm that T cells can present peptides derived from the T-cell specific proteome, the vast majority of peptides sequenced after elution from MHC were derived from the common proteome. From this pool, we identified several identical peptide epitopes in the T and B cell repertoire derived from common endogenous proteins as well as novel endogenous epitopes with promiscuous binding. These findings indicate that the endogenous HLA-DR-bound peptide repertoire, regardless of APC type and across MHC isotype, is largely derived from the same pool of self-protein.     

Towards universal structure-based prediction of class II MHC epitopes for diverse allotypes

The binding of peptide fragments of antigens to class II MHC proteins is a crucial step in initiating a helper T cell immune response. The discovery of these peptide epitopes is important for understanding the normal immune response and its misregulation in autoimmunity and allergies and also for vaccine design. In spite of their biomedical importance, the high diversity of class II MHC proteins combined with the large number of possible peptide sequences make comprehensive experimental determination of epitopes for all MHC allotypes infeasible. Computational methods can address this need by predicting epitopes for a particular MHC allotype. We present a structure-based method for predicting class II epitopes that combines molecular mechanics docking of a fully flexible peptide into the MHC binding cleft followed by binding affinity prediction using a machine learning classifier trained on interaction energy components calculated from the docking solution. Although the primary advantage of structure-based prediction methods over the commonly employed sequence-based methods is their applicability to essentially any MHC allotype, this has not yet been convincingly demonstrated. In order to test the transferability of the prediction method to different MHC proteins, we trained the scoring method on binding data for DRB1*0101 and used it to make predictions for multiple MHC allotypes with distinct peptide binding specificities including representatives from the other human class II MHC loci, HLA-DP and HLA-DQ, as well as for two murine allotypes. The results showed that the prediction method was able to achieve significant discrimination between epitope and non-epitope peptides for all MHC allotypes examined, based on AUC values in the range 0.632-0.821. We also discuss how accounting for peptide binding in multiple registers to class II MHC largely explains the systematically worse performance of prediction methods for class II MHC compared with those for class I MHC based on quantitative prediction performance estimates for peptide binding to class II MHC in a fixed register.