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.     

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.     

Metadherin peptides containing CD4+ and CD8+ T cell epitopes as a therapeutic vaccine candidate against cancer

The concept of peptide‐based vaccines against cancer has made noteworthy progress. Metadherin (MTDH) overexpression and its role in the development of diverse cancers make it an attractive target for cancer immunotherapy. In the current study, six different T cell epitope prediction tools were run to identify MTDH peptides with multiple immunogenic regions. Further, molecular docking was performed to assess HLA‐peptide binding interactions. Nine and eleven peptides fragments containing multiple CD8 ⁺ and CD4 ⁺ T‐cell epitopes, ranging from 9 to 20 amino acids, respectively, were obtained using a consensus immunoinformatics approach. The three peptides that were finally identified as having overlapping CD4 ⁺ and CD8 ⁺ T‐ cell epitopes are ARLREMLSVGLGFLRTELG, FLLGYGWAAACAGAR, YIDDEWSGLNGLSSADP. These peptides were found to not only have multiple T cell epitopes but also to have binding affinity with wide HLA molecules. A molecular docking study revealed that the predicted immunogenic peptides (with single or multiple T cell epitopes) of MTDH have comparable binding energies with naturally bound peptides for both HLA classes I and II. Thus, these peptides have the potential to induce immune responses that could be considered for developing synthetic peptide vaccines against multiple cancers.     

Stepwise identification of HLA-A*0201-restricted CD8+ T-cell epitope peptides from herpes simplex virus type 1 genome boosted by a StepRank scheme

Identification of immunodominant epitopes is the first step in the rational design of peptide vaccines aimed at T-cell immunity. To date, however, it is yet a great challenge for accurately predicting the potent epitope peptides from a pool of large-scale candidates with an efficient manner. In this study, a method that we named StepRank has been developed for the reliable and rapid prediction of binding capabilities/affinities between proteins and genome-wide peptides. In this procedure, instead of single strategy used in most traditional epitope identification algorithms, four steps with different purposes and thus different computational demands are employed in turn to screen the large-scale peptide candidates that are normally generated from, for example, pathogenic genome. The steps 1 and 2 aim at qualitative exclusion of typical nonbinders by using empirical rule and linear statistical approach, while the steps 3 and 4 focus on quantitative examination and prediction of the interaction energy profile and binding affinity of peptide to target protein via quantitative structure-activity relationship (QSAR) and structure-based free energy analysis. We exemplify this method through its application to binding predictions of the peptide segments derived from the 76 known open-reading frames (ORFs) of herpes simplex virus type 1 (HSV-1) genome with or without affinity to human major histocompatibility complex class I (MHC I) molecule HLA-A*0201, and find that the predictive results are well compatible with the classical anchor residue theory and perfectly match for the extended motif pattern of MHC I-binding peptides. The putative epitopes are further confirmed by comparisons with 11 experimentally measured HLA-A*0201-restrcited peptides from the HSV-1 glycoproteins D and K. We expect that this well-designed scheme can be applied in the computational screening of other viral genomes as well.     

Viral peptide immunogens: current challenges and opportunities.

Synthetic peptide vaccines have potential to control viral infections. Successful experimental models using this approach include the protection of mice against the lethal Sendai virus infection by MHC class I binding CTL peptide epitope. The main benefit of vaccination with peptide epitopes is the ability to minimize the amount and complexity of a well-defined antigen. An appropriate peptide immunogen would also decrease the chance of stimulating a response against self-antigens, thereby providing a safer vaccine by avoiding autoimmunity. In general, the peptide vaccine strategy needs to dissect the specificity of antigen processing, the presence of B-and T-cell epitopes and the MHC restriction of the T-cell responses. This article briefly reviews the implications in the design of peptide vaccines and discusses the various approaches that are applied to improve their immunogenicity.     

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.     

A structure-based approach for prediction of MHC-binding peptides

Identification of immunodominant peptides is the first step in the rational design of peptide vaccines aimed at T-cell immunity. The advances in sequencing techniques and the accumulation of many protein sequences without the purified protein challenge the development of computer algorithms to identify dominant T-cell epitopes based on sequence data alone. Here, we focus on antigenic peptides recognized by cytotoxic T cells. The selection of T-cell epitopes along a protein sequence is influenced by the specificity of each of the processing stages that precede antigen presentation. The most selective of these processing stages is the binding of the peptides to the major histocompatibility complex molecules, and therefore many of the predictive algorithms focus on this stage. Most of these algorithms are based on known binding peptides whose sequences have been used for the characterization of binding motifs or profiles. Here, we describe a structure-based algorithm that does not rely on previous binding data. It is based on observations from crystal structures that many of the bound peptides adopt similar conformations and placements within the MHC groove. The algorithm uses a structural template of the peptide in the MHC groove upon which peptide candidates are threaded and their fit to the MHC groove is evaluated by statistical pairwise potentials. It can rank all possible peptides along a protein sequence or within a suspected group of peptides, directing the experimental efforts towards the most promising peptides. This approach is especially useful when no previous peptide binding data are available.     

Identification of noncanonical melanoma-associated T cell epitopes for cancer immunotherapy

The identification of tumor-associated T cell epitopes has contributed significantly to the understanding of the interrelationship of tumor and immune system and is instrumental in the development of therapeutic vaccines for the treatment of cancer. Most of the known epitopes have been identified with prediction algorithms that compute the potential capacity of a peptide to bind to HLA class I molecules. However, naturally expressed T cell epitopes need not necessarily be strong HLA binders. To overcome this limitation of the available prediction algorithms we established a strategy for the identification of T cell epitopes that include suboptimal HLA binders. To this end, an artificial neural network was developed that predicts HLA-binding peptides in protein sequences by taking the entire sequence context into consideration rather than computing the sum of the contribution of the individual amino acids. Using this algorithm, we predicted seven HLA A*0201-restricted potential T cell epitopes from known melanoma-associated Ags that do not conform to the canonical anchor motif for this HLA molecule. All seven epitopes were validated as T cell epitopes and three as naturally processed by melanoma tumor cells. T cells for four of the new epitopes were found at elevated frequencies in the peripheral blood of melanoma patients. Modification of the peptides to the canonical sequence motifs led to improved HLA binding and to improved capacity to stimulate T cells.     

Immunoinformatics and the prediction of immunogenicity

Immunoinformatics is the application of informatics techniques to molecules of the immune system. One of the key goals of immunoinformatics is the development of computer aided vaccine design (CAVD), or computational vaccinology, and its application to the search for new vaccines. Key to solving this challenge is the prediction of immunogenicity, be that at the level of epitope, subunit vaccine or attenuated pathogen. This paper reviews the current state of play in the prediction of immunogenicity and focuses on well developed methods for the prediction of peptide binding affinity to major histocompatibility complexes, which are the necessary preliminary to the in silico identification of T cell epitopes.     

Mapping of B cell epitopes on the zona pellucida 2 protein of a marsupial, the brushtail possum (Trichosurus vulpecula)

Immunocontraceptive vaccines against zona pellucida (ZP) proteins are being developed for brushtail possum (Trichosurus vulpecula) management in New Zealand. Mapping of B cell epitopes on the ZP2 protein of possums was undertaken in this study to define the antigenic regions that may be crucial to sperm-egg binding. The amino acid sequence of the full-length possum ZP2 protein (712 amino acids) was used to synthesize a complete set of 71 (15-mer) biotinylated peptides with an offset of five amino acids. The peptides were used in a modified enzyme-linked immunosorbent assay (ELISA) to identify continuous epitopes recognized by antibodies in the sera of possums immunized with recombinant possum ZP2 (rZP2) constructs. Seventeen continuous epitopes were located on possum ZP2 protein. Comparisons of the peptide binding pattern of antibodies in individual sera with the fertility status of the same immunized possums revealed three significant infertility-relevant peptide epitopes (amino acids 111-125, 301-315, and 431-445). One of these (amino acids 431-445) bound to possum spermatozoa from the caudal epididymis. The implications of these findings for developing immunocontraceptive vaccines for possum control are discussed.     

Computer aided selection of candidate vaccine antigens

Immunoinformatics is an emergent branch of informatics science that long ago pullulated from the tree of knowledge that is bioinformatics. It is a discipline which applies informatic techniques to problems of the immune system. To a great extent, immunoinformatics is typified by epitope prediction methods. It has found disappointingly limited use in the design and discovery of new vaccines, which is an area where proper computational support is generally lacking. Most extant vaccines are not based around isolated epitopes but rather correspond to chemically-treated or attenuated whole pathogens or correspond to individual proteins extract from whole pathogens or correspond to complex carbohydrate. In this chapter we attempt to review what progress there has been in an as-yet-underexplored area of immunoinformatics: the computational discovery of whole protein antigens. The effective development of antigen prediction methods would significantly reduce the laboratory resource required to identify pathogenic proteins as candidate subunit vaccines. We begin our review by placing antigen prediction firmly into context, exploring the role of reverse vaccinology in the design and discovery of vaccines. We also highlight several competing yet ultimately complementary methodological approaches: sub-cellular location prediction, identifying antigens using sequence similarity, and the use of sophisticated statistical approaches for predicting the probability of antigen characteristics. We end by exploring how a systems immunomics approach to the prediction of immunogenicity would prove helpful in the prediction of antigens.     

Identification of peptides from foot‐and‐mouth disease virus structural proteins bound by class I swine leukocyte antigen (SLA) alleles,SLA‐1*0401 and SLA‐2*0401

Characterization of the peptide‐binding specificity of swine leukocyte antigen (SLA) class I and II molecules is critical to the understanding of adaptive immune responses of swine toward infectious pathogens. Here, we describe the complete binding motif of the SLA‐2*0401 molecule based on a positional scanning combinatorial peptide library approach. By combining this binding motif with data achieved by applying the NetMHCpan peptide prediction algorithm to both SLA‐1*0401 and SLA‐2*0401, we identified high‐affinity binding peptides. A total of 727 different 9mer and 726 different 10mer peptides within the structural proteins of foot‐and‐mouth disease virus (FMDV), strain A24 were analyzed as candidate T‐cell epitopes. Peptides predicted by the NetMHCpan were tested in ELISA for binding to the SLA‐1*0401 and SLA‐2*0401 major histocompatibility complex class I proteins. Four of the 10 predicted FMDV peptides bound to SLA‐2*0401, whereas five of the nine predicted FMDV peptides bound to SLA‐1*0401. These methods provide the characterization of T‐cell epitopes in response to pathogens in more detail. The development of such approaches to analyze vaccine performance will contribute to a more accelerated improvement of livestock vaccines by virtue of identifying and focusing analysis on bona fide T‐cell epitopes.     

Prediction of continuous B-cell epitopes in an antigen using recurrent neural network

B-cell epitopes play a vital role in the development of peptide vaccines, in diagnosis of diseases, and also for allergy research. Experimental methods used for characterizing epitopes are time consuming and demand large resources. The availability of epitope prediction method(s) can rapidly aid experimenters in simplifying this problem. The standard feed-forward (FNN) and recurrent neural network (RNN) have been used in this study for predicting B-cell epitopes in an antigenic sequence. The networks have been trained and tested on a clean data set, which consists of 700 non-redundant B-cell epitopes obtained from Bcipep database and equal number of non-epitopes obtained randomly from Swiss-Prot database. The networks have been trained and tested at different input window length and hidden units. Maximum accuracy has been obtained using recurrent neural network (Jordan network) with a single hidden layer of 35 hidden units for window length of 16. The final network yields an overall prediction accuracy of 65.93% when tested by fivefold cross-validation. The corresponding sensitivity, specificity, and positive prediction values are 67.14, 64.71, and 65.61%, respectively. It has been observed that RNN (JE) was more successful than FNN in the prediction of B-cell epitopes. The length of the peptide is also important in the prediction of B-cell epitopes from antigenic sequences. The webserver ABCpred is freely available at www.imtech.res.in/raghava/abcpred/.     

Design of a heterosubtypic epitope-based peptide vaccine fused with hemokinin-1 against influenza viruses

Influenza viruses continue to emerge and re-emerge, posing new threats for public health. Control and treatment of influenza depends mainly on vaccination and chemoprophylaxis with approved antiviral drugs. Identification of specific epitopes derived from influenza viruses has significantly advanced the development of epitope-based vaccines. Here, we explore the idea of using HLA binding data to design an epitope-based vaccine that can elicit heterosubtypic T-cell responses against circulating H7N9, H5N1, and H9N2 subtypes. The hemokinin-1(HK-1) peptide sequence was used to induce immune responses against the influenza viruses. Five conserved high score cytotoxic T lymphocyte(CTL) epitopes restricted to HLA-A*0201-binding peptides within the hemagglutinin(HA) protein of the viruses were chosen, and two HA CTL/HK-1 chimera protein models designed. Using in silico analysis, which involves interferon epitope scanning, protein structure prediction, antigenic epitope determination, and model quality evaluation, chimeric proteins were designed. The applicability of one of these proteins as a heterosubtypic epitopebased vaccine candidate was analyzed.     

Identification of three HLA-A*0201-restricted cytotoxic T cell epitopes in the cytomegalovirus protein pp65 that are conserved between eight strains of the virus.

The Ag specificity of the CTL response against CMV is directed almost entirely to a single CMV tegument protein, the phosphoprotein pp65. We report the identification of three peptides derived from the protein pp65 that displayed a high or intermediate binding to HLA-A*0201 molecules, which were also able to induce an in vitro CTL response in peripheral blood lymphocytes from CMV seropositive individuals. The peptide-specific CTLs generated were capable of recognizing the naturally processed pp65 either presented by CMV-infected cells or by cells infected with an adenovirus construct expressing pp65 in an HLA-A*0201-restricted manner. Thus, we were able to demonstrate responses to subdominant CTL epitopes in CMV-pp65 that were not detected in polyclonal cultures obtained by conventional stimulations. We also found that the amino acid sequences of the three peptides identified as HLA-A*0201-restricted CTL epitopes were conserved among different wild-type strains of CMV obtained from renal transplant patients, an AIDS patient, and a congenitally infected infant, as well as three laboratory strains of the virus (AD169, Towne and Davis). These observations suggest that these pp65 CTL peptide epitopes could potentially be used as synthetic peptide vaccines or for other therapeutic strategies aimed at HLA-A*0201-positive individuals, who represent approximately 40% of the European Caucasoid population. However, strain variation must be taken in consideration when the search for CTL epitopes is extended to other HLA class I alleles, because these mutations may span potential CTL epitopes for other HLA molecules, as it is described in this study.     

In vitro and in vivo identification of a novel cytotoxic T lymphocyte epitope from Rv3425 of Mycobacterium tuberculosis

The identification of novel cytotoxic T lymphocyte (CTL) epitopes is important to analysis of the involvement of CD8(+) T cells in Mycobacterium tuberculosis infection as well as to the development of peptide vaccines. In this study, a novel CTL epitope from region of difference 11 encoded antigen Rv3425 was identified. Epitopes were predicted by the reversal immunology approach. Rv3425-p118 (LIASNVAGV) was identified as having relatively strong binding affinity and stability towards the HLA-A*0201 molecule. Peripheral blood mononuclear cells pulsed by this peptide were able to release interferon-γ in healthy donors (HLA-A*02(+) purified protein derivative(+)). In cytotoxicity assays in vitro and in vivo, Rv3425-p118 induced CTLs to specifically lyse the target cells. Therefore, this epitope could provide a subunit component for designing vaccines against Mycobacterium tuberculosis.     

Covalent linkage to beta2-microglobulin enhances the MHC stability and antigenicity of suboptimal CTL epitopes

Many CTL epitopes of clinical importance, particularly those derived from tumor Ags, display relatively poor MHC binding affinity and stability. Because in vivo immunogenicity, and thus the efficacy of peptide-based vaccines, is thought to be determined by MHC/peptide complex stability, there is a need to develop a simple strategy for enhancing the binding of suboptimal epitopes. Toward this goal, the ability to enhance suboptimal peptides through covalent linkage to beta2-microglobulin (beta2m) was explored. Two suboptimal variants of a high-affinity Db-restricted influenza nucleoprotein peptide were covalently linked, via a polypeptide spacer, to the amino terminus of human beta2m and the recombinant fusion proteins expressed in Escherichia coli. When compared with their uncoupled counterparts, the beta2m-linked epitopes display enhanced MHC stabilization and antigenicity. Thus, tethering epitopes to beta2m provides a simple method for augmenting the biological activity of suboptimal peptides and could be useful in the design of peptide-based vaccines or immunotherapeutics.     

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.     

A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach

The identification of MHC class II restricted peptide epitopes is an important goal in immunological research. A number of computational tools have been developed for this purpose, but there is a lack of large-scale systematic evaluation of their performance. Herein, we used a comprehensive dataset consisting of more than 10,000 previously unpublished MHC-peptide binding affinities, 29 peptide/MHC crystal structures, and 664 peptides experimentally tested for CD4+ T cell responses to systematically evaluate the performances of publicly available MHC class II binding prediction tools. While in selected instances the best tools were associated with AUC values up to 0.86, in general, class II predictions did not perform as well as historically noted for class I predictions. It appears that the ability of MHC class II molecules to bind variable length peptides, which requires the correct assignment of peptide binding cores, is a critical factor limiting the performance of existing prediction tools. To improve performance, we implemented a consensus prediction approach that combines methods with top performances. We show that this consensus approach achieved best overall performance. Finally, we make the large datasets used publicly available as a benchmark to facilitate further development of MHC class II binding peptide prediction methods.     

Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles

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.