Challenges in the Use of in silico Tools for Predicting Peptides Binding to HLA-Class II Molecules of HCV E1, E2, and P7

The application of in silico tools for the development of T-cell vaccines is crucial. Yet, due to myriad of polymorphisms of human T-lymphocytic antigen challenges, such therapeutic opportunities present unique roadblocks. There is an obvious advantage in using immunoinformatics (i.e., significantly decreasing cost related to laboratory expenses). A previous publication looked at random binding and nonbinding peptides in order to test the practicality of using such in silico tools to obtain possible immunogenic peptides. The present in silico study applied the same basic approaches to an applicable problem that was to identify promiscuous peptide vaccine candidates for hepatitis C virus (HCV) infection. The data sets used, included the proteins HCV E1, E2 and P7 as the binders (non-self antigens) and the GAD65 and ICA69, which have an association with diabetes, as non-binders (self-antigens). The in silico tools utilized were ProPred, MHC2PRED, and RANKPEP. The resulting differences were identifiable in each of the statistical parameters examined. Variations in the outcomes were evident by the dissimilarities found among the major indices of evaluation Sensitivity, Specificity, Accuracy, Positive Predictive Value (PPV), Negative Predictive Value (NPV) and Matthews's correlation coefficient (MCC) of the percentages of the predicted promiscuous peptides to HLA-DRB1*0101, *0301, and *0401. The conclusion from this study indicates that more work needs to be done in order to enhance the predictability of programs for the identification of peptide vaccine candidates for HCV. Such programs should not be solely relied upon without in vitro assay verification.     

Bioinformatics tools for identifying class I-restricted epitopes

The lack of simple methods to identify relevant T-cell epitopes, the high mutation rate of many pathogens, and restriction of T-cell response to epitopes due to human lymphocyte antigen (HLA) polymorphism have significantly hindered the development of cytotoxic T-lymphocyte (CTL) epitope-based or "epitope-driven" vaccines. Previously, CTL epitopes were mapped using large arrays of overlapping synthetic peptides. The large number of protein sequences available for mapping is now making this method prohibitively expensive and time-consuming. Bioinformatics tools such as EpiMatrix and Conservatrix, which search for unique or multi-HLA-restricted (promiscuous) T-cell epitopes and identify epitopes that are conserved across variant strains of the same pathogen, accelerate epitope mapping. These tools offer a significant advantage over other methods of epitope selection because high-throughput screening can be performed in silico, followed by confirmatory studies in vitro. CTL epitopes discovered using these tools might be used to develop novel vaccines and therapeutics for the prevention and treatment of infectious diseases such as human immunodeficiency virus, hepatitis C, tuberculosis, and some cancers.     

Evaluation of different generic in silico methods for predicting HLA class I binding peptide vaccine candidates using a reverse approach

Since CD8⁺ T cell response is crucial to combat intracellular infections and cancer, identification of class I HLA binding peptides is of immense clinical value. The experimental identification of such peptides is protracted and laborious. Exploiting in silico tools to discover such peptides is an attractive alternative. However, this approach needs a thorough assessment before its elaborate application. We have adopted a reverse approach to evaluate the reliability of eight different servers (inclusive of 55 predictors) by exploiting experimentally proven data. A comprehensive data set of more than 960 peptides was employed to test the efficacy of the programs. We have validated commonly used strategies to predict peptides that bind to seven most prevalent HLA class I alleles. We conclude that four of the eight servers are more adept in predictions. Although the overall predictions for class I MHC binders were superior to class II MHC binders, individual predictors for different alleles belonging to the same program were highly variable in their efficiencies. We have also addressed whether a consensus approach can yield better prediction efficiency. We observed that combining the results from different in silico programs could not increase the efficiency significantly.     

TEpredict: Software for T-Cell epitope prediction

The program TEpredict was developed for T-cell epitope prediction. The used models for T-cell epitope prediction were constructed by the partial least squares regression method using the data extracted from the IEDB (Immune Epitope Database), the most complete resource of experimental peptide-MHC binding data. TEpredict is also able to predict proteasomal processing of protein antigens and the ability of produced oligopeptides to bind to the transporters associated with antigen processing, to discard the peptides sharing local similarity with human proteins from the set of predicted epitopes, and to estimate the expected population coverage by the selected peptides using the data on HLA allele genotypic frequencies. The main part of the constructed models demonstrated a high prediction sensitivity (0.50–0.80) in combination with a high specificity (0.75–0.99). Comparative testing demonstrated that TEpredict was competitive with or even superior to the programs ProPred1, SVRMHC, SVMHC, and SYFPEITHI. Thus, TEpredict can become an efficient tool for developing polyepitope vaccines, including the vaccines against various human pathogens, such as HIV and the influenza virus. TEpredict and the source code are available at http://tepredict.source- forge.net.     

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.     

Identification of novel MAGE-A6- and MAGE-A12-derived HLA-A24-restricted cytotoxic T lymphocyte epitopes using an in silico peptide-docking assay

Many cancer-testis antigen genes have been identified; however, few human leukocyte antigen (HLA)-A24-restricted cytotoxic T cell (CTL) epitope peptides are available for clinical immunotherapy. To solve this problem, novel tools increasing the efficacy and accuracy of CTL epitope detection are needed. In the present study, we utilized a highly active dendritic cell (DC)-culture method and an in silico HLA-A24 peptide-docking simulation assay to identify novel CTL epitopes from MAGE-A6 and MAGE-A12 antigens. The highly active DCs, called ??-type-1 DCs, were prepared using a combination of maturation reagents to produce a large amount of interleukin-12. Meanwhile, our HLA-A24 peptide-docking simulation assay was previously demonstrated to have an obvious advantage of accuracy over the conventional prediction tool, bioinformatics and molecular analysis section. For CTL induction assays, peripheral blood mononuclear cells derived from six cases of HLA-A24⁺ melanoma were used. Through CTL induction against melanoma cell lines and peptide-docking simulation assays, two peptides (IFGDPKKLL from MAGE-A6 and IFSKASEYL from MAGE-A12) were identified as novel CTL epitope candidates. Finally, we verified that the combination of the highly active DC-culture method and HLA-A24 peptide-docking simulation assay might be tools for predicting CTL epitopes against cancer antigens.     

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.     

A systematic bioinformatics approach for selection of epitope-based vaccine targets

Epitope-based vaccines provide a new strategy for prophylactic and therapeutic application of pathogen-specific immunity. A critical requirement of this strategy is the identification and selection of T-cell epitopes that act as vaccine targets. This study describes current methodologies for the selection process, with dengue virus as a model system. A combination of publicly available bioinformatics algorithms and computational tools are used to screen and select antigen sequences as potential T-cell epitopes of supertype human leukocyte antigen (HLA) alleles. The selected sequences are tested for biological function by their activation of T-cells of HLA transgenic mice and of pathogen infected subjects. This approach provides an experimental basis for the design of pathogen specific, T-cell epitope-based vaccines that are targeted to majority of the genetic variants of the pathogen, and are effective for a broad range of differences in human leukocyte antigens among the global human population.     

T cell epitope identification for bovine vaccines: an epitope mapping method for BoLA A-11

T cell responses play an important role in immunity to parasites and other microbial agents of infectious diseases, therefore a number of T cell-directed vaccines are in development. Computer-driven algorithms that facilitate the discovery of T cell epitopes from protein and genome sequences are now being used to accelerate preclinical studies of human vaccines. Similar tools are not yet available for predicting T cell epitopes for animal vaccines, but there may be sufficient data available to begin the process of compiling the algorithms. We describe the construction of a novel mathematical 'matrix' that describes the properties of bovine major histocompatibility complex (BoLA) system antigen (BoLA) A-11 peptide ligands, developed for use with EpiMatrix, an existing T cell epitope-mapping algorithm. An alternative means of developing BoLA matrices, using the pocket profile method, is also discussed. Matrices such as the one described here may be used to develop T cell epitope-mapping tools for cattle and other ruminants. Epitope-mapping algorithms offer a significant advantage over other methods of epitope selection, such as the screening of synthetic overlapping peptides, because high throughput screening can be performed in silico, followed by ex vivo confirmatory studies. Furthermore, using epitope-mapping algorithms, putative T cell epitopes can be derived directly from genomic sequences, allowing researchers to circumvent labor-intensive cloning steps in the genome-to-vaccine discovery pathway.     

Direct class I HLA antigen discovery to distinguish virus-infected and cancerous cells

Class I human leukocyte antigen molecules are nature’s proteome-scanning chips, presenting thousands of endogenously loaded peptides on the surface of virtually every cell in the body. Cytotoxic T cells survey the class I human leukocyte antigen peptide cargo presented, recognize peptides unique to unhealthy cells and destroy diseased cells. A precise understanding of how class I molecules distinguish diseased cells is positioned to drive immune-based diagnostics, therapies and vaccines. When identifying epitopes unique to unhealthy cells, the most experimentally direct approach is to examine the class I-presented peptides of infected/cancerous cells. Here we discuss the strategies adapted for protein production, protein/peptide purification, peptide separation and for maintaining experimental reproducibility during the direct characterization of class I human leukocyte antigen peptides.     

Direct class I HLA antigen discovery to distinguish virus-infected and cancerous cells

Class I human leukocyte antigen molecules are nature's proteome-scanning chips, presenting thousands of endogenously loaded peptides on the surface of virtually every cell in the body. Cytotoxic T cells survey the class I human leukocyte antigen peptide cargo presented, recognize peptides unique to unhealthy cells and destroy diseased cells. A precise understanding of how class I molecules distinguish diseased cells is positioned to drive immune-based diagnostics, therapies and vaccines. When identifying epitopes unique to unhealthy cells, the most experimentally direct approach is to examine the class I-presented peptides of infected/cancerous cells. Here we discuss the strategies adapted for protein production, protein/peptide purification, peptide separation and for maintaining experimental reproducibility during the direct characterization of class I human leukocyte antigen peptides.     

Clustering HLA Class I Superfamilies Using Structural Interaction Patterns

Human leukocyte antigen (HLA) class I molecules are critical components of the cell-mediated immune system that bind and present intracellular antigenic peptides to CD8⁺ T cell receptors. To understand the interaction mechanism underlying human leukocyte antigen (HLA) class I specificity in detail, we studied the structural interaction characteristics of 16,393 nonameric peptides binding to 58 HLA-A and -B molecules. Our analysis showed for the first time that HLA-peptide intermolecular bonding patterns vary among different alleles and may be grouped in a superfamily dependent manner. Through the use of these HLA class I ‘fingerprints’, a high resolution HLA class I superfamily classification schema was developed. This classification is capable of separating HLA alleles into well resolved, non-overlapping clusters, which is consistent with known HLA superfamily definitions. Such structural interaction approach serves as an excellent alternative to the traditional methods of HLA superfamily definitions that use peptide binding motifs or receptor information, and will help identify appropriate antigens suitable for broad-based subunit vaccine design.     

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.     

MHC-peptide specificity and T-cell epitope mapping: where immunotherapy starts

The evaluation and characterization of epitope-specific human leukocyte antigen (HLA)-restricted memory T-cell reactivity is an important step for the development of preventive vaccines and peptide-based immunotherapies for viral and tumor diseases. The past decade has witnessed the use of HLA-restricted peptides as tools to activate strong immune responses of na?ve or memory T cells specifically. This has fuelled an active search for methodological approaches focusing on HLA and peptide associations. Here, we outline new perspective on the emerging opportunity of evaluating HLA and peptide restriction by using novel approaches, such as quantitative real-time PCR, that can identify epitope specificities that are potentially useful in clinical settings.     

Personalized peptide vaccination: a new approach for advanced cancer as therapeutic cancer vaccine

Since both tumor cells and host immune cell repertoires are diverse and heterogeneous, immune responses against tumor-associated antigens should differ substantially among individual cancer patients. Selection of suitable peptide vaccines for individual patients based on the preexisting host immunity before vaccination could induce potent anti-tumor responses that provide clinical benefit to cancer patients. We have developed a novel immunotherapeutic approach of personalized peptide vaccination (PPV) in which a maximum of four human leukocyte antigen (HLA) class IA-matched peptides are selected for vaccination among pooled peptides on the basis of both HLA class IA type and the preexisting host immunity before vaccination. In this review, we discuss our recent results of preclinical and clinical studies of PPV for various types of advanced cancer.     

Overview of marker vaccine and differential diagnostic test technology.

Recent advances in molecular biology, immunology, microbiology, genetics and microbial pathogenesis have lead to the development of a wide variety of new approaches for developing safer and more effective vaccines based on designs such as subunit vaccines, gene deleted vaccines, live vectored vaccines, and DNA mediated vaccines. Technology tools can be as basic as identifying naturally occurring strains with deletions that support differentiating infected from vaccinated animal (DIVA) needs or be based on higher technology developments such as improved protein expression and purification methods, transgenic plant- and plant virus-based antigen production, and novel adjuvants that target specific immune responses. These new approaches, when applied to the development of marker vaccines and companion diagnostic test kits hold tremendous potential for developing improved tools for eradication and control programs. Marker vaccines and companion diagnostic test kits must meet the established licensing requirements for purity, potency, safety and efficacy. Efficacy claims are based on evaluation of the level of protection demonstrated in host animal trials and may range from "prevents infection with (a specific agent)", to "for use as an aid in the reduction of disease due to (a specific agent)." The differences in claims and recommendations are a function of the variation in protection elicited by various vaccines. For designing effective eradication programs, vaccine efficacy characteristics such as for reducing susceptibility to infections and spread of infections must be well defined; similarly, diagnostic test performance characteristics (efficacy) must be determined. In addition to data to support efficacy claims, it is imperative that safety of production and use of vaccines be evaluated. During the design of marker vaccines and diagnostic tests, it is important to consider the application of appropriate technologies to improve the safety of these products. Use of recombinant technologies for production of vaccines and/or diagnostic test antigens can reduce the biosafety concerns during production and during use, including human exposure to zoonotic pathogens during production and use, and potential spread of foreign animal disease agents due to loss of biocontainment. In addition, vaccines may induce adverse reactions. It is important to determine the frequency of adverse events and to reduce the likelihood of induction of adverse reactions through proper design.     

A quantitative view on Mycobacterium leprae antigens by proteomics

Leprosy is an ancient disease and the focus of the researchers' scrutiny for more than a century. However, many of the molecular aspects related to transmission, virulence, antigens and immune responses are far from known. Initially, the implementation of recombinant DNA library screens raised interesting antigen candidates. Finally, the availability of Mycobacterium leprae genomic information showed an intriguing genome reduction which is now largely used in comparative genomics. While predictive in silico tools are commonly used to identify possible antigens, proteomic approaches have not yet been explored fully to study M. leprae biology. Quantitative information obtained at the protein level, and its analysis as part of a complex system, would be a key feature to be used to help researchers to validate and understand many of such in silico predictions. Through a re-analysis of data from a previous publication of our group, we could easily tackle many questions regarding antigen prediction and pseudogene expression. Several well known antigens are among the quantitatively dominant proteins, while several major proteins have not been explored as antigens. We argue that combining proteomic approaches together with bioinformatic workflows is a required step in the characterization of important pathogens.