The Immune Epitope Database (IEDB): 2018 update

The adaptive immune system in vertebrates has evolved to recognize an immense number of pathogens. B and T cells play important role in the antigen recognition by expressing a substantially diverse repertoire of antigen-specific receptors. The recent advances in the high-throughput immune repertoire sequencing has led to a tremendous increase in the antibody and T-cell receptor information. Identification of specific epitopes recognized by the receptors is vital in analyzing the repertoire sequencing data. Here, we present a tool that examines data from an immune receptor repertoire experiment, and identifies receptors (or highly related variants) that are present in the Immune Epitope Database (IEDB). The IEDB is a free resource which compiles experiments characterizing immune epitopes. IEDB also captures the epitope-specific immune receptor sequences and groups them based on their epitope specificity. IEDB currently has information on over 26,000 receptor groups. Users can provide receptor repertoire sequence data in multiple formats, and specify different CDR sequence identity cutoffs to restrict or broaden the receptor search. We expect that this tool will provide new insights into B and T cell responses captured in the receptor repertoire experiments by identifying specific epitopes, and facilitate development of new strategies for monitoring and treating of infectious, allergic and autoimmune diseases.

Mining Immune Epitopes in Ménière’s Disease and Sudden Sensorineural Hearing Loss

Over the past 20 years, the Immune Epitope Database (IEDB, iedb.org) has established itself as the foremost resource for immune epitope data. The IEDB catalogs published epitopes and their contextual experimental data in a freely searchable public resource. The IEDB team manually curates data from the literature into a structured format and spans infectious, allergic, autoimmune, and transplant diseases. Here, we describe the enhancements made since our 2018 paper, capturing user-directed updates to the search interface, advanced data exports, increases in data quality, and improved interoperability across related resources. As we look forward to the next 20 years, we are confident in our ability to meet the needs of our users and to contribute to the broader field of data standardization.

The adaptive immune system in vertebrates has evolved to recognize non-self antigens, such as proteins expressed by infectious agents and mutated cancer cells. T cells play an important role in antigen recognition by expressing a diverse repertoire of antigen-specific receptors, which bind epitopes to mount targeted immune responses. Recent advances in high-throughput sequencing have enabled the routine generation of T-cell receptor (TCR) repertoire data. Identifying the specific epitopes targeted by different TCRs in these data would be valuable. To accomplish that, we took advantage of the ever-increasing number of TCRs with known epitope specificity curated in the Immune Epitope Database (IEDB) since 2004. We compared seven metrics of sequence similarity to determine their power to predict if two TCRs have the same epitope specificity. We found that a comprehensive k-mer matching approach produced the best results, which we have implemented into TCRMatch, an openly accessible tool (http://tools.iedb.org/tcrmatch/) that takes TCR β-chain CDR3 sequences as an input, identifies TCRs with a match in the IEDB, and reports the specificity of each match. We anticipate that this tool will provide new insights into T cell responses captured in receptor repertoire and single cell sequencing experiments and will facilitate the development of new strategies for monitoring and treatment of infectious, allergic, and autoimmune diseases, as well as cancer.

The recognition of immune epitopes is an important molecular mechanism of the vertebrate immune system to discriminate between self and non-self. Increasing amounts of data on immune epitopes are becoming available due to technological advances in epitope-mapping techniques and the availability of genomic information for pathogens. Organizing this data poses a challenge that is similar to the successful effort that was required to organize genomic data, which needed the establishment of centralized databases that complement the primary literature to make the data readily accessible and searchable by researchers. As described in this Innovation article, the Immune Epitope Database and Analysis Resource aims to achieve the same for the more complex and context-dependent information on immune epitopes, and to integrate this data with existing and emerging knowledge resources.

IEDB-3D is the 3D structural component of the Immune Epitope Database (IEDB) available via the ‘Browse by 3D Structure’ page at http://www.iedb.org. IEDB-3D catalogs B- and T-cell epitopes and Major Histocompatibility Complex (MHC) ligands for which 3D structures of complexes with antibodies, T-cell receptors or MHC molecules are available in the Protein Data Bank (PDB). Journal articles that are primary citations of PDB structures and that define immune epitopes are curated within IEDB as any other reference along with accompanying functional assays and immunologically relevant information. For each curated structure, IEDB-3D provides calculated data on intermolecular contacts and interface areas and includes an application, EpitopeViewer, to visualize the structures. IEDB-3D is fully embedded within IEDB, thus allowing structural data, both curated and calculated, and all accompanying information to be queried using multiple search interfaces. These include queries for epitopes recognized in different pathogens, eliciting different functional immune responses, and recognized by different components of the immune system. The query results can be downloaded in Microsoft Excel format, or the entire database, together with structural data both curated and calculated, can be downloaded in either XML or MySQL formats.

BackgroundStructural information about epitopes, particularly the three-dimensional (3D) structures of antigens in complex with immune receptors, presents a valuable source of data for immunology. This information is available in the Protein Data Bank (PDB) and provided in curated form by the Immune Epitope Database and Analysis Resource (IEDB). With continued growth in these data and the importance in understanding molecular level interactions of immunological interest there is a need for new specialized molecular visualization and analysis tools.ResultsThe EpitopeViewer is a platform-independent Java application for the visualization of the three-dimensional structure and sequence of epitopes and analyses of their interactions with antigen-specific receptors of the immune system (antibodies, T cell receptors and MHC molecules). The viewer renders both 3D views and two-dimensional plots of intermolecular interactions between the antigen and receptor(s) by reading curated data from the IEDB and/or calculated on-the-fly from atom coordinates from the PDB. The 3D views and associated interactions can be saved for future use and publication. The EpitopeViewer can be accessed from the IEDB Web site through the quick link 'Browse Records by 3D Structure.'ConclusionThe EpitopeViewer is designed and been tested for use by immunologists with little or no training in molecular graphics. The EpitopeViewer can be launched from most popular Web browsers without user intervention. A Java Runtime Environment (RJE) 1.4.2 or higher is required.

A Roadmap for the Immunomics of Category A–C Pathogens

Epitopedia: identifying molecular mimicry between pathogens and known immune epitopes

The Immune Epitope Database (IEDB) is a freely available repository of epitope data from infectious disease agents, allergy, autoimmunity and transplantation. The IEDB captures details of experiments associated with each epitope and thus provides an information rich resource and user-friendly platform for performing broad-based or more detailed queries. Recent analysis performed using the IEDB, including HCV, allergy, myasthenia gravis and diabetes; highlight the breadth of information accessible through the database, as well as tools and unique features provided therein. Each analysis serves to provide an inventory of all related data, thereby revealing interesting trends, experimental bias and/or knowledge gaps. Features like the Immunomebrowser and Disease Finder can be used synergistically to explore comparative immune reactivity across entire genomes or individual antigens for humans and animal models of disease, and tools like homology mapping can enable visualization of 3D discontinuous B cell epitopes. Moreover, these works provide outreach to the individual community of scientists, help elicit feedback to identify areas for improvement and can serve as ‘tutorials’ for those interested in using the database.

After viral infection, CD8+ T-cells can rapidly detect their intracellular presence by recognizing peptide fragments presented by MHC class I molecules on the cell surface that are derived from degraded viral protein. This appears to be in conflict with the observation that viral proteins have relatively long half-lives. To explain this, the DRiPs hypothesis (Yewdell and Nicchitta, 2006) was proposed, which states that these epitopes come from Defective Ribosomal Products (DRiPs), rather than degradation of mature protein products. If epitopes do come from defective ribosomal products, this could lead to a positional bias of epitopes in their corresponding antigens. Specifically, if a major source of defective ribosomal products is pre-mature stop in translation, an overrepresentation of CD8+ T-cell epitopes in the N-terminal portion of antigens would be expected. In contrast, if downstream initiation is a major source of defective ribosomal products, C-terminal portions of antigens would be a dominant source of epitopes. In this study, we systematically assess positional bias of epitopes in viral antigens, taking advantage of the large set of immune epitope data available in the Immune Epitope Database. We demonstrate that there is a small degree of positional skewing detectable in the epitope distribution. Epitopes from both ends of antigens tend to be slightly underrepresented, which suggests that both pre-mature stops in translation and downstream initiation play an equal but minor role in shaping the overall viral epitope repertoire.

Background: Molecular mimicry contributes to the development of unwanted responses to self-antigens. Autoimmune phenomena have been observed in diseases caused by Aedes aegypti-transmitted arboviruses, but the occurrence of mimicry between salivary and human proteins has been unexplored. Methods: We used bioinformatic tools to determine if peptides from Aedes aegypti salivary proteins were present in the human proteome. We further characterized the potential of shared sequences to induce immunity by analyzing their predicted binding to MHC molecules and their occurrence in peptides from the Immune Epitope Database (IEDB). Results: We analyzed 9513 octapeptides from 29 Aedes aegypti salivary proteins against the human proteome and found 47 peptides identical to sequences from 52 human proteins, ranging in length from 8 to 18 amino acids. We found 302 matches of peptides predicted to bind with high affinity to MHC-I and MHC-II alleles associated with autoimmune diseases, and 14 human peptides containing shared sequences with Aedes aegypti salivary proteins validated as immunogenic in the IEDB. Conclusions: These results support the existence of molecular mimicry between Aedes aegypti salivary proteins and human antigens and provide a framework for studies to determine its contribution to responses directed to self-antigens in the context of arboviral infections.

Ontologies categorize entities, express relationships between them, and provide standardized definitions. Thus, they can be used to present and enforce the specific relationships between database components. The Immune Epitope Database (IEDB, http://www.iedb.org) utilizes the Ontology for Biomedical Investigations (OBI) and several additional ontologies to represent immune epitope mapping experiments. Here, we describe our experiences utilizing this representation in order to provide enhanced database search functionality. We applied a simple approach to incorporate the benefits of the information captured in a formal ontology directly into the user web interface, resulting in an improved user experience with minimal changes to the database itself. The integration is easy to maintain, provides standardized terms and definitions, and allows for subsumption queries. In addition to these immediate benefits, our long-term goal is to enable true semantic integration of data and knowledge in the biomedical domain. We describe our progress towards that goal and what we perceive as the main obstacles.

Immunodeficiency disorders provide substantial problems to the healthcare system since they frequently necessitate a prompt and precise diagnosis for proper management and treatment. Automating the classification of these illnesses with machine learning (ML) approaches has become a strong tool that can improve clinical decision-making and diagnostic accuracy. This work looks into classifying immunodeficiency disorders using MLP, SVM, and ANN, along with LLE preprocessing and feature extraction from KD trees. Combining MLP with LLE and KD-trees is suggested in a novel way that produces better accuracy, precision, and recall results. The Immune Epitope Database (IEDB) dataset, a well-known resource with extensive data on immune epitopes, is used to test the suggested model thoroughly.

BackgroundThe Immune Epitope Database (IEDB) project manually curates information from published journal articles that describe immune epitopes derived from a wide variety of organisms and associated with different diseases. In the past, abstracts of scientific articles were retrieved by broad keyword queries of PubMed, and were classified as relevant (curatable) or irrelevant (not curatable) to the scope of the database by a Naïve Bayes classifier. The curatable abstracts were subsequently manually classified into categories corresponding to different disease domains. Over the past four years, we have examined how to further improve this approach in order to enhance classification performance and to reduce the need for manual intervention.ResultsUtilizing 89,884 abstracts classified by a domain expert as curatable or uncuratable, we found that a SVM classifier outperformed the previously used Naïve Bayes classifier for curatability predictions with an AUC of 0.899 and 0.854, respectively. Next, using a non-hierarchical and a hierarchical application of SVM classifiers trained on 22,833 curatable abstracts manually classified into three levels of disease specific categories we demonstrated that a hierarchical application of SVM classifiers outperformed non-hierarchical SVM classifiers for categorization. Finally, to optimize the hierarchical SVM classifiers' error profile for the curation process, cost sensitivity functions were developed to avoid serious misclassifications. We tested our design on a benchmark dataset of 1,388 references and achieved an overall category prediction accuracy of 94.4%, 93.9%, and 82.1% at the three levels of categorization, respectively.ConclusionsA hierarchical application of SVM algorithms with cost sensitive output weighting enabled high quality reference classification with few serious misclassifications. This enabled us to significantly reduce the manual component of abstract categorization. Our findings are relevant to other databases that are developing their own document classifier schema and the datasets we make available provide large scale real-life benchmark sets for method developers.