Virtual models of the HLA class I antigen processing pathway

How ERAP1 and ERAP2 Shape the Peptidomes of Disease-Associated MHC-I Proteins

Four inflammatory diseases are strongly associated with Major Histocompatibility Complex class I (MHC-I) molecules: birdshot chorioretinopathy (HLA-A^*29:02), ankylosing spondylitis (HLA-B^*27), Behçet's disease (HLA-B^*51), and psoriasis (HLA-C^*06:02). The endoplasmic reticulum aminopeptidases (ERAP) 1 and 2 are also risk factors for these diseases. Since both enzymes are involved in the final processing steps of MHC-I ligands it is reasonable to assume that MHC-I-bound peptides play a significant pathogenetic role. This review will mainly focus on recent studies concerning the effects of ERAP1 and ERAP2 polymorphism and expression on shaping the peptidome of disease-associated MHC-I molecules in live cells. These studies will be discussed in the context of the distinct mechanisms and substrate preferences of both enzymes, their different patterns of genetic association with various diseases, the role of polymorphisms determining changes in enzymatic activity or expression levels, and the distinct peptidomes of disease-associated MHC-I allotypes. ERAP1 and ERAP2 polymorphism and expression induce significant changes in multiple MHC-I-bound peptidomes. These changes are MHC allotype-specific and, without excluding a degree of functional inter-dependence between both enzymes, reflect largely separate roles in their processing of MHC-I ligands. The studies reviewed here provide a molecular basis for the distinct patterns of genetic association of ERAP1 and ERAP2 with disease and for the pathogenetic role of peptides. The allotype-dependent alterations induced on distinct peptidomes may explain that the joint association of both enzymes and unrelated MHC-I alleles influence different pathological outcomes.

Development of Peptide Vaccines in Dengue

Dengue is one of the most important arboviral infections worldwide, infecting up to 390 million people and causing 25,000 deaths annually. Although a licensed dengue vaccine is available, it is not efficacious against dengue serotypes that infect people living in South East Asia, where dengue is an endemic disease. Hence, there is an urgent need to develop an efficient dengue vaccine for this region. Data from different clinical trials indicate that a successful dengue vaccine must elicit both neutralizing antibodies and cell mediated immunity. This can be achieved by designing a multi-epitope peptide vaccine comprising B, CD8+ and CD4+ T cell epitopes. As recognition of T cell epitopes are restricted by human leukocyte antigens (HLA), T cell epitopes which are able to recognize several major HLAs will be preferentially included in the vaccine design. While peptide vaccines are safe, biocompatible and cost-effective, it is poorly immunogenic. Strategies to improve its immunogenicity by the use of long peptides, adjuvants and nanoparticle delivery mechanisms are discussed.

Information technologies for vaccine research

Vaccinology is a combinatorial science which studies the diversity of pathogens and the human immune system, and formulations that can modulate immune responses and prevent or cure disease. Huge amounts of data are produced by genomics and proteomics projects and large-scale screening of pathogen-host and antigen-host interactions. Current developments in computational vaccinology mainly support the analysis of antigen processing and presentation and the characterization of targets of immune response. Future development will also include systemic models of vaccine responses. Immunomics, the large-scale screening of immune processes which includes powerful immunoinformatic tools, offers great promise for future translation of basic immunology research advances into successful vaccines.

EBV BILF1 evolved to downregulate cell surface display of a wide range of HLA class I molecules through their cytoplasmic tail

Coevolution of herpesviruses and their hosts has driven the development of both host antiviral mechanisms to detect and eliminate infected cells and viral ploys to escape immune surveillance. Among the immune-evasion strategies used by the lymphocryptovirus (γ(1)-herpesvirus) EBV is the downregulation of surface HLA class I expression by the virally encoded G protein-coupled receptor BILF1, thereby impeding presentation of viral Ags and cytotoxic T cell recognition of the infected cell. In this study, we show EBV BILF1 to be expressed early in the viral lytic cycle. BILF1 targets a broad range of HLA class I molecules, including multiple HLA-A and -B types and HLA-E. In contrast, HLA-C was only marginally affected. We advance the mechanistic understanding of the process by showing that the cytoplasmic C-terminal tail of EBV BILF1 is required for reducing surface HLA class I expression. Susceptibility to BILF1-mediated downregulation, in turn, is conferred by specific residues in the intracellular tail of the HLA class I H chain. Finally, we explore the evolution of BILF1 within the lymphocryptovirus genus. Although the homolog of BILF1 encoded by the lymphocryptovirus infecting Old World rhesus primates shares the ability of EBV to downregulate cell surface HLA class I expression, this function is not possessed by New World marmoset lymphocryptovirus BILF1. Therefore, this study furthers our knowledge of the evolution of immunoevasive functions by the lymphocryptovirus genus of herpesviruses.

Dana-Farber repository for machine learning in immunology

The immune system is characterized by high combinatorial complexity that necessitates the use of specialized computational tools for analysis of immunological data. Machine learning (ML) algorithms are used in combination with classical experimentation for the selection of vaccine targets and in computational simulations that reduce the number of necessary experiments. The development of ML algorithms requires standardized data sets, consistent measurement methods, and uniform scales. To bridge the gap between the immunology community and the ML community, we designed a repository for machine learning in immunology named Dana-Farber Repository for Machine Learning in Immunology (DFRMLI). This repository provides standardized data sets of HLA-binding peptides with all binding affinities mapped onto a common scale. It also provides a list of experimentally validated naturally processed T cell epitopes derived from tumor or virus antigens. The DFRMLI data were preprocessed and ensure consistency, comparability, detailed descriptions, and statistically meaningful sample sizes for peptides that bind to various HLA molecules. The repository is accessible at http://bio.dfci.harvard.edu/DFRMLI/.

High throughput T epitope mapping and vaccine development

Mapping of antigenic peptide sequences from proteins of relevant pathogens recognized by T helper (Th) and by cytolytic T lymphocytes (CTL) is crucial for vaccine development. In fact, mapping of T-cell epitopes provides useful information for the design of peptide-based vaccines and of peptide libraries to monitor specific cellular immunity in protected individuals, patients and vaccinees. Nevertheless, epitope mapping is a challenging task. In fact, large panels of overlapping peptides need to be tested with lymphocytes to identify the sequences that induce a T-cell response. Since numerous peptide panels from antigenic proteins are to be screened, lymphocytes available from human subjects are a limiting factor. To overcome this limitation, high throughput (HTP) approaches based on miniaturization and automation of T-cell assays are needed. Here we consider the most recent applications of the HTP approach to T epitope mapping. The alternative or complementary use of in silico prediction and experimental epitope definition is discussed in the context of the recent literature. The currently used methods are described with special reference to the possibility of applying the HTP concept to make epitope mapping an easier procedure in terms of time, workload, reagents, cells and overall cost.

Improved secretion of the cancer-testis antigen SSX2 in Pichia pastoris by deletion of its nuclear localization signal

The cancer-testis (CT) antigen synovial sarcoma X break point 2 (SSX2) was expressed in Pichia pastoris as a means to produce a delayed-type hypersensitivity skin test reagent for monitoring SSX2-specific anti-cancer immune responses. SSX2 was detected intracellularly in P. pastoris despite the addition of the Saccharomyces cerevisiae alpha-mating factor secretion signal. Increasing the SSX2 gene copy number did not improve its secretion but did enhance intracellular SSX2 levels. SSX2 with its C-terminal nuclear localization signal (NLS) deleted (SSX2NORD), however, was secreted. Indirect immunofluorescence indicated that SSX2 containing the NLS did not translocate to the nucleus but accumulated in the endoplasmic reticulum (ER). Experimental results further suggested that SSX2 containing the NLS was misfolded in the ER, while deletion of the NLS facilitated correct folding of SSX2 inside the ER and improved its secretion. Production of SSX2NORD was scaled-up to a 2-L fermentor using a fed-batch protocol to maintain methanol at a concentration of 1 g L(-1). Decreasing the cultivation temperature from 25 degrees C to 16 degrees C improved protein stability in the culture supernatant. In this process, after 120 h cultivation, the wet cell weight of P. pastoris reached 280 mg mL(-1), and the yield of SSX2NORD was 21.6 mg L(-1).

Genome-scale search of tumor-specific antigens by collective analysis of mutations, expressions and T-cell recognition

BACKGROUND

Tumor-specific antigens (TSAs) are potential sources of cancer vaccines, some of which are derived from T-cell epitopes of over-expressed mutant proteins to elicit immunogenicity and overcome tolerance and evasion. The lack of effective vaccines for many cancers has prompted strong interest in improved TSA search methods. Recent progresses in profiling somatic mutations and expressions of human cancer genomes, and in predicting T-cell epitopes enable genome-scale TSA search by collectively analyzing these profiles. Such a collective approach has not been explored in spite of the availability and usage of individual methods.

METHODOLOGY

Genome-scale TSA search was conducted by genome-scale search of tumor-specific mutations in differentially over-expressed genes of specific cancers based on tumor-specific somatic mutation and microarray gene expression data, followed by T-cell recognition analysis of the identified mutant and over-expressed peptides to determine if they are substrates of proteasomal cleavage, TAP mediated transport and MHC-I alleles capable of eliciting immune response. The performance of our method was tested against 12 and 4 known T-cell defined melanoma and lung cancer TSAs in the Cancer Immunity database.

CONCLUSIONS

Our approach identified 50% and 75% of the 12 and 4 known TSAs and predicted from the human cancer genomes additional 8-250 and 14-359 putative TSAs of 5 and 3 HLA alleles respectively. The known TSA hit rates (1.9% and 0.8%) are enriched by 29-fold and 35-fold over those of mutation analysis. The numbers of predicted TSAs are within the testing range of typical screening campaigns. Noises in expression data of small sample sizes appear to be a major factor for misidentification of known TSAs. With improved data quality and analysis methods, the collective approach is potentially useful for facilitating genome-scale TSA search.

Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes

BACKGROUND

T-cell epitopes that promiscuously bind to multiple alleles of a human leukocyte antigen (HLA) supertype are prime targets for development of vaccines and immunotherapies because they are relevant to a large proportion of the human population. The presence of clusters of promiscuous T-cell epitopes, immunological hotspots, has been observed in several antigens. These clusters may be exploited to facilitate the development of epitope-based vaccines by selecting a small number of hotspots that can elicit all of the required T-cell activation functions. Given the large size of pathogen proteomes, including of variant strains, computational tools are necessary for automated screening and selection of immunological hotspots.

RESULTS

Hotspot Hunter is a web-based computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes through analysis of antigenic diversity. It allows screening and selection of hotspots specific to four common HLA supertypes, namely HLA class I A2, A3, B7 and class II DR. The system uses Artificial Neural Network and Support Vector Machine methods as predictive engines. Soft computing principles were employed to integrate the prediction results produced by both methods for robust prediction performance. Experimental validation of the predictions showed that Hotspot Hunter can successfully identify majority of the real hotspots. Users can predict hotspots from a single protein sequence, or from a set of aligned protein sequences representing pathogen proteome. The latter feature provides a global view of the localizations of the hotspots in the proteome set, enabling analysis of antigenic diversity and shift of hotspots across protein variants. The system also allows the integration of prediction results of the four supertypes for identification of hotspots common across multiple supertypes. The target selection feature of the system shortlists candidate peptide hotspots for the formulation of an epitope-based vaccine that could be effective against multiple variants of the pathogen and applicable to a large proportion of the human population.

CONCLUSION

Hotspot Hunter is publicly accessible at http://antigen.i2r.a-star.edu.sg/hh/. It is a new generation computational tool aiding in epitope-based vaccine design.

Cancer immunomics and application of 'omics' for cancer management

Several 'omics' sciences have been launched. The outstanding diversity of immune system components, together with the complexity of the regulatory pathways and network-type interactions, makes immunology a combinatorial science. Cancer immonomics is a branch of immunomics. The present cancer immunomics project aims at diagnostics and therapy. Comparative and structural genomics can be applied for understanding the underlying tumorogenesis process. For diagnostic purposes, in order to develop a new marker, gene expression analysis technologies, such as DNA microarray, differential display, cDNA subtraction, serial analysis of gene expression and serological proteome analysis, which enable investigators to obtain comprehensive data with respect to gene expression profiles, are progressing rapidly. For therapeutic purposes, vaccine candidate finding, gene therapy and drug-tumor interaction modeling can be clarified by the advance immunomics techniques. In this article, the author will present the basic concepts on cancer immunomics and application for basic study of the pathogenesis, diagnosis and treatment of cancer.

Strength in numbers: achieving greater accuracy in MHC-I binding prediction by combining the results from multiple prediction tools

Background

Peptides derived from endogenous antigens can bind to MHC class I molecules. Those which bind with high affinity can invoke a CD8⁺ immune response, resulting in the destruction of infected cells. Much work in immunoinformatics has involved the algorithmic prediction of peptide binding affinity to various MHC-I alleles. A number of tools for MHC-I binding prediction have been developed, many of which are available on the web.

Results

We hypothesize that peptides predicted by a number of tools are more likely to bind than those predicted by just one tool, and that the likelihood of a particular peptide being a binder is related to the number of tools that predict it, as well as the accuracy of those tools. To this end, we have built and tested a heuristic-based method of making MHC-binding predictions by combining the results from multiple tools. The predictive performance of each individual tool is first ascertained. These performance data are used to derive weights such that the predictions of tools with better accuracy are given greater credence. The combined tool was evaluated using ten-fold cross-validation and was found to signicantly outperform the individual tools when a high specificity threshold is used. It performs comparably well to the best-performing individual tools at lower specificity thresholds. Finally, it also outperforms the combination of the tools resulting from linear discriminant analysis.

Conclusion

A heuristic-based method of combining the results of the individual tools better facilitates the scanning of large proteomes for potential epitopes, yielding more actual high-affinity binders while reporting very few false positives.

Toward a multiscale model of antigen presentation in immunity

A functioning immune system and the process of antigen presentation in particular encompass events that occur at multiple length and time scales. Despite a wealth of information in the biological literature regarding each of these scales, no single representation synthesizing this information into a model of the overall immune response as it depends on antigen presentation is available. In this article, we outline an approach for integrating information over relevant biological and temporal scales to generate such a representation for major histocompatibility complex class II-mediated antigen presentation. In addition, we begin to address how such models can be used to answer questions about mechanisms of infection and new strategies for treatment and vaccines.

MHC-BPS: MHC-binder prediction server for identifying peptides of flexible lengths from sequence-derived physicochemical properties

Major histocompatibility complex (MHC)-binding peptides are essential for antigen recognition by T-cell receptors and are being explored for vaccine design. Computational methods have been developed for predicting MHC-binding peptides of fixed lengths, based on the training of relatively few non-binders. It is desirable to introduce methods applicable for peptides of flexible lengths and trained by using more diverse sets of non-binders. MHC-BPS is a web-based MHC-binder prediction server that uses support vector machines for predicting peptide binders of flexible lengths for 18 MHC class I and 12 class II alleles from sequence-derived physicochemical properties, which were trained by using 4,208 approximately 3,252 binders and 234,333 approximately 168,793 non-binders, and evaluated by an independent set of 545 approximately 476 binders and 110,564 approximately 84,430 non-binders. The binder prediction accuracies are 86 approximately 99% for 25 and 70 approximately 80% for five alleles, and the non-binder accuracies are 96 approximately 99% for 30 alleles. A screening of HIV-1 genome identifies 0.01 approximately 5% and 5 approximately 8% of the constituent peptides as binders for 24 and 6 alleles, respectively, including 75 approximately 100% of the known epitopes. This method correctly predicts 73.3% of the 15 newly published epitopes in the last 4 months of 2005. MHC-BPS is available at http://bidd.cz3.nus.edu.sg/mhc/ .

PRED(TAP): a system for prediction of peptide binding to the human transporter associated with antigen processing

Background

The transporter associated with antigen processing (TAP) is a critical component of the major histocompatibility complex (MHC) class I antigen processing and presentation pathway. TAP transports antigenic peptides into the endoplasmic reticulum where it loads them into the binding groove of MHC class I molecules. Because peptides must first be transported by TAP in order to be presented on MHC class I, TAP binding preferences should impact significantly on T-cell epitope selection.

Description

PRED^TAP is a computational system that predicts peptide binding to human TAP. It uses artificial neural networks and hidden Markov models as predictive engines. Extensive testing was performed to valid the prediction models. The results showed that PRED^TAP was both sensitive and specific and had good predictive ability (area under the receiver operating characteristic curve Aroc>0.85).

Conclusion

PRED^TAP can be integrated with prediction systems for MHC class I binding peptides for improved performance of in silico prediction of T-cell epitopes. PRED^TAP is available for public use at [1].

Class I T-cell epitope prediction: improvements using a combination of proteasome cleavage, TAP affinity, and MHC binding

Cleavage by the proteasome is responsible for generating the C terminus of T-cell epitopes. Modeling the process of proteasome cleavage as part of a multi-step algorithm for T-cell epitope prediction will reduce the number of non-binders and increase the overall accuracy of the predictive algorithm. Quantitative matrix-based models for prediction of the proteasome cleavage sites in a protein were developed using a training set of 489 naturally processed T-cell epitopes (nonamer peptides) associated with HLA-A and HLA-B molecules. The models were validated using an external test set of 227 T-cell epitopes. The performance of the models was good, identifying 76% of the C-termini correctly. The best model of proteasome cleavage was incorporated as the first step in a three-step algorithm for T-cell epitope prediction, where subsequent steps predicted TAP affinity and MHC binding using previously derived models.

Integrated modeling of the major events in the MHC class I antigen processing pathway

Rational design of epitope-driven vaccines is a key goal of immunoinformatics. Typically, candidate selection relies on the prediction of MHC-peptide binding only, as this is known to be the most selective step in the MHC class I antigen processing pathway. However, proteasomal cleavage and transport by the transporter associated with antigen processing (TAP) are essential steps in antigen processing as well. While prediction methods exist for the individual steps, no method has yet offered an integrated prediction of all three major processing events. Here we present WAPP, a method combining prediction of proteasomal cleavage, TAP transport, and MHC binding into a single prediction system. The proteasomal cleavage site prediction employs a new matrix-based method that is based on experimentally verified proteasomal cleavage sites. Support vector regression is used for predicting peptides transported by TAP. MHC binding is the last step in the antigen processing pathway and was predicted using a support vector machine method, SVMHC. The individual methods are combined in a filtering approach mimicking the natural processing pathway. WAPP thus predicts peptides that are cleaved by the proteasome at the C terminus, transported by TAP, and show significant affinity to MHC class I molecules. This results in a decrease in false positive rates compared to MHC binding prediction alone. Compared to prediction of MHC binding only, we report an increased overall accuracy and a lower rate of false positive predictions for the HLA-A*0201, HLA-B*2705, HLA-A*01, and HLA-A*03 alleles using WAPP. The method is available online through our prediction server at http://www-bs.informatik.uni-tuebingen.de/WAPP