Immune epitope database analysis resource

Discovery of protective B-cell epitopes for development of antimicrobial vaccines and antibody therapeutics

Protective antibodies play an essential role in immunity to infection by neutralizing microbes or their toxins and recruiting microbicidal effector functions. Identification of the protective B-cell epitopes, those parts of microbial antigens that contact the variable regions of the protective antibodies, can lead to development of antibody therapeutics, guide vaccine design, enable assessment of protective antibody responses in infected or vaccinated individuals, and uncover or localize pathogenic microbial functions that could be targeted by novel antimicrobials. Monoclonal antibodies are required to link in vivo or in vitro protective effects to specific epitopes and may be obtained from experimental animals or from humans, and their binding can be localized to specific regions of antigens by immunochemical assays. The epitopes are then identified with mapping methods such as X-ray crystallography of antigen-antibody complexes, antibody inhibition of hydrogen-deuterium exchange in the antigen, antibody-induced alteration of the nuclear magnetic resonance spectrum of the antigen, and experimentally validated computational docking of antigen-antibody complexes. The diversity in shape, size and structure of protective B-cell epitopes, and the increasing importance of protective B-cell epitope discovery to development of vaccines and antibody therapeutics are illustrated through examples from different microbe categories, with emphasis on epitopes targeted by broadly neutralizing antibodies to pathogens of high antigenic variation. Examples include the V-shaped Ab52 glycan epitope in the O-antigen of Francisella tularensis, the concave CR6261 peptidic epitope in the haemagglutinin stem of influenza virus H1N1, and the convex/concave PG16 glycopeptidic epitope in the gp120 V1/V2 loop of HIV type 1.

Tumor antigen discovery through translation of the cancer genome

Cancer cells harbor unique mutations that theoretically create corresponding unique tumor-specific antigens. This class of mutated antigens represents an attractive target for cancer immunotherapy, but their identification has been cumbersome. By combining cancer genome sequencing with computational analysis of MHC binding, it is possible to predict and rank all of the possible mutated tumor antigens. This form of antigen screen is being combined with high throughput methods to measure the immune response to each candidate mutated antigen. Using these techniques, it is possible to systematically test each mutated tumor antigens for an associated immune response. Only a small fraction of the putative mutated antigens tested in this manner have been found to elicit an immune response, yet these responses appear to be both robust and durable. It is becoming increasingly clear that these mutated tumor antigens are an important target in the antitumor response. Studies incorporating this approach promise to improve our understanding of the inherent immunogenicity of individual cancers, potentially providing an explanation for the varying clinical responses to novel immunotherapeutic agents.

A Dataset of Experimental HLA-B*2705 Peptide Binding Affinities

T-cell epitopes form the basis of many vaccines, diagnostics, and reagents. Current methods for the in silico identification of T-cell epitopes rely, in the main, on the accurate quantitative prediction of peptide-Major Histocompatibility Complex (pMHC) affinity using data-driven computational approaches. Here, we describe a dataset of experimentally determined pMHC binding affinities for the problematic human class I allele HLA-B*2705. Using an in-house, FACS-based, MHC stabilization assay, we measured binding of 223 peptides. This dataset includes both nonbinding and binding peptides, with measured affinities (expressed as −log10 of the half-maximal binding level) ranging from 1.2 to 7.4. This dataset should provide a useful independent benchmark for new and existing methods for predicting peptide binding to HLA-B*2705.

Cellular vaccines in listeriosis: role of the Listeria antigen GAPDH

The use of live Listeria-based vaccines carries serious difficulties when administrated to immunocompromised individuals. However, cellular carriers have the advantage of inducing multivalent innate immunity as well as cell-mediated immune responses, constituting novel and secure vaccine strategies in listeriosis. Here, we compare the protective efficacy of dendritic cells (DCs) and macrophages and their safety. We examined the immune response of these vaccine vectors using two Listeria antigens, listeriolysin O (LLO) and glyceraldehyde-3-phosphate-dehydrogenase (GAPDH), and several epitopes such as the LLO peptides, LLO_189−201 and LLO₉₁₋₉₉ and the GAPDH peptide, GAPDH₁₋₂₂. We discarded macrophages as safe vaccine vectors because they show anti-Listeria protection but also high cytotoxicity. DCs loaded with GAPDH₁₋₂₂ peptide conferred higher protection and security against listeriosis than the widely explored LLO₉₁₋₉₉ peptide. Anti-Listeria protection was related to the changes in DC maturation caused by these epitopes, with high production of interleukin-12 as well as significant levels of other Th1 cytokines such as monocyte chemotactic protein-1, tumor necrosis factor-α, and interferon-γ, and with the induction of GAPDH₁₋₂₂-specific CD4⁺ and CD8⁺ immune responses. This is believed to be the first study to explore the use of a novel GAPDH antigen as a potential DC-based vaccine candidate for listeriosis, whose efficiency appears to highlight the relevance of vaccine designs containing multiple CD4⁺ and CD8⁺ epitopes.

Model for vaccine design by prediction of B-epitopes of IEDB given perturbations in peptide sequence, in vivo process, experimental techniques, and source or host organisms

Perturbation methods add variation terms to a known experimental solution of one problem to approach a solution for a related problem without known exact solution. One problem of this type in immunology is the prediction of the possible action of epitope of one peptide after a perturbation or variation in the structure of a known peptide and/or other boundary conditions (host organism, biological process, and experimental assay). However, to the best of our knowledge, there are no reports of general-purpose perturbation models to solve this problem. In a recent work, we introduced a new quantitative structure-property relationship theory for the study of perturbations in complex biomolecular systems. In this work, we developed the first model able to classify more than 200,000 cases of perturbations with accuracy, sensitivity, and specificity >90% both in training and validation series. The perturbations include structural changes in >50000 peptides determined in experimental assays with boundary conditions involving >500 source organisms, >50 host organisms, >10 biological process, and >30 experimental techniques. The model may be useful for the prediction of new epitopes or the optimization of known peptides towards computational vaccine design.

Association of the autoimmune disease scleroderma with an immunologic response to cancer

Autoimmune diseases are thought to be initiated by exposures to foreign antigens that cross-react with endogenous molecules. Scleroderma is an autoimmune connective tissue disease in which patients make antibodies to a limited group of autoantigens, including RPC1, encoded by the POLR3A gene. As patients with scleroderma and antibodies against RPC1 are at increased risk for cancer, we hypothesized that the "foreign" antigens in this autoimmune disease are encoded by somatically mutated genes in the patients' incipient cancers. Studying cancers from scleroderma patients, we found genetic alterations of the POLR3A locus in six of eight patients with antibodies to RPC1 but not in eight patients without antibodies to RPC1. Analyses of peripheral blood lymphocytes and serum suggested that POLR3A mutations triggered cellular immunity and cross-reactive humoral immune responses. These results offer insight into the pathogenesis of scleroderma and provide support for the idea that acquired immunity helps to control naturally occurring cancers.

Hybrid methods for B-cell epitope prediction

Many computational approaches to B-cell epitope prediction have been published, including combinations of previously proposed methods, which complicates the tasks of further developing such computational approaches and of selecting those most appropriate for practical applications (e.g., the design of novel immunodiagnostics and vaccines). These tasks are considered together herein to clarify their close but often overlooked interrelationship, thereby providing a guide to their performance in mutual support of one another, with emphasis on key physicochemical and biological considerations that are relevant from an applications perspective. This aims to assist investigators in performing either or both tasks, with the overall goals of successfully applying computational tools towards practical ends and of generating informative new data towards iterative improvement of the tools, particularly as regards the design of peptide-based immunogens for eliciting the production of antipeptide antibodies that modulate biological activity of protein targets via functionally relevant cross-reactivity in relation to the phenomena of protein folding and protein disorder.

Antigen-Antibody Interaction Database (AgAbDb): a compendium of antigen-antibody interactions

Antigen-Antibody Interaction Database (AgAbDb) is an immunoinformatics resource developed at the Bioinformatics Centre, University of Pune, and is available online at http://bioinfo.net.in/AgAbDb.htm. Antigen-antibody interactions are a special class of protein-protein interactions that are characterized by high affinity and strict specificity of antibodies towards their antigens. Several co-crystal structures of antigen-antibody complexes have been solved and are available in the Protein Data Bank (PDB). AgAbDb is a derived knowledgebase developed with an objective to compile, curate, and analyze determinants of interactions between the respective antigen-antibody molecules. AgAbDb lists not only the residues of binding sites of antigens and antibodies, but also interacting residue pairs. It also helps in the identification of interacting residues and buried residues that constitute antibody-binding sites of protein and peptide antigens. The Antigen-Antibody Interaction Finder (AAIF), a program developed in-house, is used to compile the molecular interactions, viz. van der Waals interactions, salt bridges, and hydrogen bonds. A module for curating water-mediated interactions has also been developed. In addition, various residue-level features, viz. accessible surface area, data on epitope segment, and secondary structural state of binding site residues, are also compiled. Apart from the PDB numbering, Wu-Kabat numbering and explicit definitions of complementarity-determining regions are provided for residues of antibodies. The molecular interactions can be visualized using the program Jmol. AgAbDb can be used as a benchmark dataset to validate algorithms for prediction of B-cell epitopes. It can as well be used to improve accuracy of existing algorithms and to design new algorithms. AgAbDb can also be used to design mimotopes representing antigens as well as aid in designing processes leading to humanization of antibodies.

Computational prediction of B cell epitopes from antigen sequences

Computational identification of B-cell epitopes from antigen chains is a difficult and actively pursued research topic. Efforts towards the development of method for the prediction of linear epitopes span over the last three decades, while only recently several predictors of conformational epitopes were released. We review a comprehensive set of 13 recent approaches that predict linear and 4 methods that predict conformational B-cell epitopes from the antigen sequences. We introduce several databases of B-cell epitopes, since the availability of the corresponding data is at the heart of the development and validation of computational predictors. We also offer practical insights concerning the use and availability of these B-cell epitope predictors, and motivate and discuss feature research in this area.

Determining the breadth of the respiratory syncytial virus-specific T cell response

Respiratory syncytial virus (RSV) is the most common cause of viral lower respiratory tract infections in infants and children under the age of 5. Studies examining RSV infection in susceptible BALB/c mice indicate that both CD4 and CD8 T cells not only contribute to viral clearance but also facilitate RSV-induced disease. However, efforts to understand the mechanisms by which RSV-specific T cells mediate disease following acute RSV infection have been hampered by the lack of defined RSV-specific T cell epitopes. Using an overlapping peptide library spanning each of the RSV-derived proteins, intracellular cytokine staining for gamma interferon was utilized to identify novel RSV-specific CD4 and CD8 T cell epitopes. Five novel CD8 T cell epitopes were revealed within the RSV fusion (F) protein and glycoprotein (G). In addition, five previously unidentified CD4 T cell epitopes were discovered, including epitopes in the phosphoprotein (P), polymerase protein (L), M2-1 protein, and nucleoprotein (N). Though the initial CD4 T cell epitopes were 15 amino acids in length, synthesis of longer peptides increased the frequency of responding CD4 T cells. Our results indicate that CD4 T cell epitopes that are 17 amino acids in length result in more optimal CD4 T cell stimulation than the commonly used 15-mer peptides.

IMPORTANCE

Respiratory syncytial virus (RSV) is the leading cause of hospitalization for lower respiratory tract infection in children. T cells play a critical role in clearing an acute RSV infection, as well as contributing to RSV-induced disease. Here we examined the breadth of the RSV-specific T cell response, using for the first time an overlapping peptide library spanning the entire viral genome. We identified 5 new CD4 and 5 new CD8 T cell epitopes, including a CD8 T cell epitope within the G protein that was previously believed not to elicit a CD8 T cell response. Importantly, we also demonstrated that the use of longer, 17-mer peptides elicits a higher frequency of responding CD4 T cells than the more commonly used 15-mer peptides. Our results demonstrate the breadth of the CD4 and CD8 T cell response to RSV and demonstrate the importance of using longer peptides when stimulating CD4 T cell responses.

Antibodies to a single, conserved epitope in Anopheles APN1 inhibit universal transmission of Plasmodium falciparum and Plasmodium vivax malaria

Malaria transmission-blocking vaccines (TBVs) represent a promising approach for the elimination and eradication of this disease. AnAPN1 is a lead TBV candidate that targets a surface antigen on the midgut of the obligate vector of the Plasmodium parasite, the Anopheles mosquito. In this study, we demonstrated that antibodies targeting AnAPN1 block transmission of Plasmodium falciparum and Plasmodium vivax across distantly related anopheline species in countries to which malaria is endemic. Using a biochemical and immunological approach, we determined that the mechanism of action for this phenomenon stems from antibody recognition of a single protective epitope on AnAPN1, which we found to be immunogenic in murine and nonhuman primate models and highly conserved among anophelines. These data indicate that AnAPN1 meets the established target product profile for TBVs and suggest a potential key role for an AnAPN1-based panmalaria TBV in the effort to eradicate malaria.

HLA class I alleles are associated with peptide-binding repertoires of different size, affinity, and immunogenicity

Prediction of HLA binding affinity is widely used to identify candidate T cell epitopes, and an affinity of 500 nM is routinely used as a threshold for peptide selection. However, the fraction (percentage) of peptides predicted to bind with affinities of 500 nM varies by allele. For example, of a large collection of ~30,000 dengue virus-derived peptides only 0.3% were predicted to bind HLA A*0101, whereas nearly 5% were predicted for A*0201. This striking difference could not be ascribed to variation in accuracy of the algorithms used, as predicted values closely correlated with affinity measured in vitro with purified HLA molecules. These data raised the question whether different alleles would also vary in terms of epitope repertoire size, defined as the number of associated epitopes or, alternatively, whether alleles vary drastically in terms of the affinity threshold associated with immunogenicity. To address this issue, strains of HLA transgenic mice with wide (A*0201), intermediate (B*0702), or narrow (A*0101) repertoires were immunized with peptides of varying binding affinity and relative percentile ranking. The results show that absolute binding capacity is a better predictor of immunogenicity, and analysis of epitopes from the Immune Epitope Database revealed that predictive efficacy is increased using allele-specific affinity thresholds. Finally, we investigated the genetic and structural basis of the phenomenon. Although no stringent correlate was defined, on average HLA B alleles are associated with significantly narrower repertoires than are HLA A alleles.

A novel strategy for classifying the output from an in silico vaccine discovery pipeline for eukaryotic pathogens using machine learning algorithms

BACKGROUND

An in silico vaccine discovery pipeline for eukaryotic pathogens typically consists of several computational tools to predict protein characteristics. The aim of the in silico approach to discovering subunit vaccines is to use predicted characteristics to identify proteins which are worthy of laboratory investigation. A major challenge is that these predictions are inherent with hidden inaccuracies and contradictions. This study focuses on how to reduce the number of false candidates using machine learning algorithms rather than relying on expensive laboratory validation. Proteins from Toxoplasma gondii, Plasmodium sp., and Caenorhabditis elegans were used as training and test datasets.

RESULTS

The results show that machine learning algorithms can effectively distinguish expected true from expected false vaccine candidates (with an average sensitivity and specificity of 0.97 and 0.98 respectively), for proteins observed to induce immune responses experimentally.

CONCLUSIONS

Vaccine candidates from an in silico approach can only be truly validated in a laboratory. Given any in silico output and appropriate training data, the number of false candidates allocated for validation can be dramatically reduced using a pool of machine learning algorithms. This will ultimately save time and money in the laboratory.

Single dose of glycoprotein K (gK)-deleted HSV-1 live-attenuated virus protects mice against lethal vaginal challenge with HSV-1 and HSV-2 and induces lasting T cell memory immune responses

Background

Herpes simplex virus type-1(HSV-1) and HSV-2 are important human pathogens that cause significant ocular and urogenital complications, respectively. We have previously shown that HSV-1 virions lacking glycoprotein K (gK) are unable to enter into neurons via synaptic axonal membranes and be transported in either retrograde or anterograde manner. Here, we tested the ability of HSV-1 (F) gK-null to protect against lethal challenge with either highly virulent ocular HSV-1 (McKrae strain), or genital HSV-2 (G strain). The gK-null virus vaccine efficiently protected mice against lethal vaginal infection with either HSV-1(McKrae) or HSV-2 (G).

Results

Female mice were immunized via a single intramuscular injection with 10⁶ PFU of the gK-null virus. Immunized mice were treated with Depo-Provera fourteen days after vaccination and were challenged via the vaginal route one week later. Ninety percent of mice vaccinated with the gK-null virus survived HSV-1 (McKrae) challenge, while 70% of these mice survived after HSV-2 (G) challenge. Moreover, all vaccinated mice exhibited substantially reduced disease symptoms irrespective of HSV-1 or HSV-2 challenge as compared to the mock vaccinated challenge group. T-cell memory immune responses to specific glycoprotein B (gB) and glycoprotein D (gD) peptide epitopes were detectable at 7 months post vaccination.

Conclusions

These results suggest that the highly attenuated, non-neurotropic gK-null virus may be used as an effective vaccine to protect against both virulent HSV-1 and HSV-2 genital infections and induce lasting immune responses.

Evaluating the immunogenicity of protein drugs by applying in vitro MHC binding data and the immune epitope database and analysis resource

The immune system has evolved to become highly specialized in recognizing and responding to pathogens and foreign molecules. Specifically, the function of HLA class II is to ensure that a sufficient sample of peptides derived from foreign molecules is presented to T cells. This leads to an important concern in human drug development as the possible immunogenicity of biopharmaceuticals, especially those intended for chronic administration, can lead to reduced efficacy and an undesired safety profile for biological therapeutics. As part of this review, we will highlight the molecular basis of antigen presentation as a key step in the induction of T cell responses, emphasizing the events associated with peptide binding to polymorphic and polygenic HLA class II molecules. We will further review methodologies that predict HLA class II binding peptides and candidate epitopes. We will focus on tools provided by the Immune Epitope Database and Analysis Resource, discussing the basic features of different prediction methods, the objective evaluation of prediction quality, and general guidelines for practical use of these tools. Finally the use, advantages, and limitations of the methodology will be demonstrated in a review of two previous studies investigating the immunogenicity of erythropoietin and timothy grass pollen.

Metadata-driven comparative analysis tool for sequences (meta-CATS): an automated process for identifying significant sequence variations that correlate with virus attributes

The Virus Pathogen Resource (ViPR; www.viprbrc.org) and Influenza Research Database (IRD; www.fludb.org) have developed a metadata-driven Comparative Analysis Tool for Sequences (meta-CATS), which performs statistical comparative analyses of nucleotide and amino acid sequence data to identify correlations between sequence variations and virus attributes (metadata). Meta-CATS guides users through: selecting a set of nucleotide or protein sequences; dividing them into multiple groups based on any associated metadata attribute (e.g. isolation location, host species); performing a statistical test at each aligned position; and identifying all residues that significantly differ between the groups. As proofs of concept, we have used meta-CATS to identify sequence biomarkers associated with dengue viruses isolated from different hemispheres, and to identify variations in the NS1 protein that are unique to each of the 4 dengue serotypes. Meta-CATS is made freely available to virology researchers to identify genotype-phenotype correlations for development of improved vaccines, diagnostics, and therapeutics.

Proline substitution independently enhances H-2D(b) complex stabilization and TCR recognition of melanoma-associated peptides

The immunogenicity of H-2D(b) (D(b)) restricted epitopes can be significantly increased by substituting peptide position 3 to a proline (p3P). The p3P modification enhances MHC stability without altering the conformation of the modified epitope allowing for T-cell cross-reactivity with the native peptide. The present study reveals how specific interactions between p3P and the highly conserved MHC heavy chain residue Y159 increase the stability of D(b) in complex with an optimized version of the melanoma-associated epitope gp10025-33 . Furthermore, the p3P modification directly increased the affinity of the D(b)/gp10025-33 -specific T-cell receptor (TCR) pMel. Surprisingly, the enhanced TCR binding was independent from the observed increased stability of the optimized D(b)/gp10025-33 complex and from the interactions formed between p3P and Y159, indicating a direct effect of the p3P modification on TCR recognition.

H7N3 live attenuated influenza vaccine has a potential to protect against new H7N9 avian influenza virus

After recent emergence of new avian influenza A(H7N9) viruses in humans many people and Governments are asking about H7 influenza vaccine which could provide cross-protection against new viruses, until H7N9 vaccine is prepared from a relevant strain. Here we scientifically justify that available H7N3 live attenuated influenza vaccine (LAIV) can be protective against H7N9 viruses due to the presence of conserved immune epitopes in its hemagglutinin. We used Immune Epitope Database analysis resource to predict B-cell and CTL epitopes distributed across H7N3 HA molecule and assessed their identity with new H7N9 viruses at near 70% and 60% of the epitopes, respectively. In addition, we tested serum samples of volunteers participated in phase I clinical trial of H7N3 LAIV for the presence of anti-H7N9 hemagglutination-inhibition and neutralizing antibodies and found seroconversions in 44.8% of vaccinated persons, which suggests the potential of H7N3 LAIV to protect against new H7N9 avian influenza viruses.

Computational Resources for MHC Ligand Identification

Advances in the high-throughput determination of functional modulators of major histocompatibility complex (MHC) and improved computational predictions of MHC ligands have rendered the rational design of immunomodulatory peptides feasible. Proteome-derived peptides and 'reverse vaccinology' by computational means will play a driving role in future vaccine design. Here we review the molecular mechanisms of the MHC mediated immune response, present the computational approaches that have emerged in this area of biotechnology, and provide an overview of publicly available computational resources for predicting and designing new peptidic MHC ligands.

Comprehensive analysis of dengue virus-specific responses supports an HLA-linked protective role for CD8+ T cells

The role of CD8(+) T cells in dengue virus infection and subsequent disease manifestations is not fully understood. According to the original antigenic sin theory, skewing of T-cell responses induced by primary infection with one serotype causes less effective response upon secondary infection with a different serotype, predisposing individuals to severe disease. A comprehensive analysis of CD8(+) responses in the general population from the Sri Lankan hyperendemic area, involving the measurement of ex vivo IFNγ responses associated with more than 400 epitopes, challenges the original antigenic sin theory. Although skewing of responses toward primary infecting viruses was detected, this was not associated with impairment of responses either qualitatively or quantitatively. Furthermore, we demonstrate higher magnitude and more polyfunctional responses for HLA alleles associated with decreased susceptibility to severe disease, suggesting that a vigorous response by multifunctional CD8(+) T cells is associated with protection from dengue virus disease.

Reverse vaccinology in the 21st century: improvements over the original design

Reverse vaccinology (RV), the first application of genomic technologies in vaccine research, represented a major revolution in the process of discovering novel vaccines. By determining their entire antigenic repertoire, researchers could identify protective targets and design efficacious vaccines for pathogens where conventional approaches had failed. Bexsero, the first vaccine developed using RV, has recently received positive opinion from the European Medicines Agency. The use of RV initiated a cascade of changes that affected the entire vaccine development process, shifting the focus from the identification of a list of vaccine candidates to the definition of a set of high throughput screens to reduce the need for costly and labor intensive tests in animal models. It is now clear that a deep understanding of the epidemiology of vaccine candidates, and their regulation and role in host-pathogen interactions, must become an integral component of the screening workflow. Far from being outdated by technological advancements, RV still represents a paradigm of how high-throughput technologies and scientific insight can be integrated into biotechnology research.

Exoproteome and secretome derived broad spectrum novel drug and vaccine candidates in Vibrio cholerae targeted by Piper betel derived compounds

Vibrio cholerae is the causal organism of the cholera epidemic, which is mostly prevalent in developing and underdeveloped countries. However, incidences of cholera in developed countries are also alarming. Because of the emergence of new drug-resistant strains, even though several generic drugs and vaccines have been developed over time, Vibrio infections remain a global health problem that appeals for the development of novel drugs and vaccines against the pathogen. Here, applying comparative proteomic and reverse vaccinology approaches to the exoproteome and secretome of the pathogen, we have identified three candidate targets (ompU, uppP and yajC) for most of the pathogenic Vibrio strains. Two targets (uppP and yajC) are novel to Vibrio, and two targets (uppP and ompU) can be used to develop both drugs and vaccines (dual targets) against broad spectrum Vibrio serotypes. Using our novel computational approach, we have identified three peptide vaccine candidates that have high potential to induce both B- and T-cell-mediated immune responses from our identified two dual targets. These two targets were modeled and subjected to virtual screening against natural compounds derived from Piper betel. Seven compounds were identified first time from Piper betel to be highly effective to render the function of these targets to identify them as emerging potential drugs against Vibrio. Our preliminary validation suggests that these identified peptide vaccines and betel compounds are highly effective against Vibrio cholerae. Currently we are exhaustively validating these targets, candidate peptide vaccines, and betel derived lead compounds against a number of Vibrio species.

Structure-based prediction of Major Histocompatibility Complex (MHC) epitopes

Because of the enormous diversity of both MHC proteins and peptide epitopes, computational epitope prediction methods are needed in order to supplement limited experimental data. These prediction methods are useful for guiding experiments and have many potential biomedical applications. Unlike popular sequence-based methods, structure-based epitope prediction methods can predict epitopes for multiple MHC types with highly distinct peptide binding propensities. In this chapter, we describe in detail our previously developed structure-based epitope prediction methods for both class I and class II MHC proteins. We also discuss the relative advantages and disadvantages of sequence-based versus structure-based methods and how to evaluate prediction performance.

Navigating diabetes-related immune epitope data: resources and tools provided by the Immune Epitope Database (IEDB)

Background

The Immune Epitope Database (IEDB), originally focused on infectious diseases, was recently expanded to allergy, transplantation and autoimmunity diseases. Here we focus on diabetes, chosen as a prototype autoimmune disease. We utilize a combined tutorial and meta-analysis format, which demonstrates how common questions, related to diabetes epitopes can be answered.

Results

A total of 409 references are captured in the IEDB describing >2,500 epitopes from diabetes associated antigens. The vast majority of data were derived from GAD, insulin, IA-2/PTPRN, IGRP, ZnT8, HSP, and ICA-1, and the experiments related to T cell epitopes and MHC binding far outnumbers B cell assays. We illustrate how to search by specific antigens, epitopes or host. Other examples include searching for tetramers or epitopes restricted by specific alleles or assays of interest, or searching based on the clinical status of the host.

Conclusions

The inventory of all published diabetes epitope data facilitates its access for the scientific community. While the global collection of primary data from the literature reflects potential investigational biases present in the literature, the flexible search approach allows users to perform queries tailored to their preferences, including or excluding data as appropriate. Moreover, the analysis highlights knowledge gaps and identifies areas for future investigation.

Virus pathogen database and analysis resource (ViPR): a comprehensive bioinformatics database and analysis resource for the coronavirus research community

Several viruses within the Coronaviridae family have been categorized as either emerging or re-emerging human pathogens, with Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) being the most well known. The NIAID-sponsored Virus Pathogen Database and Analysis Resource (ViPR, www.viprbrc.org) supports bioinformatics workflows for a broad range of human virus pathogens and other related viruses, including the entire Coronaviridae family. ViPR provides access to sequence records, gene and protein annotations, immune epitopes, 3D structures, host factor data, and other data types through an intuitive web-based search interface. Records returned from these queries can then be subjected to web-based analyses including: multiple sequence alignment, phylogenetic inference, sequence variation determination, BLAST comparison, and metadata-driven comparative genomics statistical analysis. Additional tools exist to display multiple sequence alignments, view phylogenetic trees, visualize 3D protein structures, transfer existing reference genome annotations to new genomes, and store or share results from any search or analysis within personal private ‘Workbench’ spaces for future access. All of the data and integrated analysis and visualization tools in ViPR are made available without charge as a service to the Coronaviridae research community to facilitate the research and development of diagnostics, prophylactics, vaccines and therapeutics against these human pathogens.