Next-generation IEDB tools: a platform for epitope prediction and analysis.

The Next-Generation (NG) IEDB Tools website (https://nextgen-tools.iedb.org) provides users with a redesigned interface to many of the algorithms for epitope prediction and analysis that were originally released on the legacy IEDB Tools website. The initial release focuses on consolidation of all tools related to HLA class I epitopes (MHC binding, elution, immunogenicity, and processing), making all of these predictions accessible from a single application and allowing for their simultaneous execution with minimal user inputs. Additionally, the PEPMatch tool for identifying highly similar epitopes in a set of curated proteomes, as well as a tool for epitope clustering, are available on the site. The NG Tools site allows users to build data pipelines by sending the output of one tool as input for the next. Over the next several years, all pre-existing IEDB Tools, and any newly developed tools, will be integrated into this new site. Here we describe the philosophy behind the redesign and demonstrate the utility and productivity enhancements that are enabled by the new interface.

The Cancer Epitope Database and Analysis Resource (CEDAR).

We established The Cancer Epitope Database and Analysis Resource (CEDAR) to catalog all epitope data in the context of cancer. The specific molecular targets of adaptive T cell and B cell immune responses are referred to as epitopes. Epitopes derived from cancer antigens are of high relevance as they are recognized by anti-cancer immune cells. Detailed knowledge of the molecular characteristic of cancer epitopes and associated metadata is relevant to understanding and planning prophylactic and therapeutic applications and accurately characterizing naturally occurring immune responses and cancer immunopathology. CEDAR provides a freely accessible, comprehensive collection of cancer epitope and receptor data curated from the literature and serves as a companion site to the Immune Epitope Database (IEDB), which is focused on infectious, autoimmune, and allergic diseases. CEDAR is freely accessible at https://cedar.iedb.org/.

Towards the prediction of non-peptidic epitopes.

In-silico methods for the prediction of epitopes can support and improve workflows for vaccine design, antibody production, and disease therapy. So far, the scope of B cell and T cell epitope prediction has been directed exclusively towards peptidic antigens. Nevertheless, various non-peptidic molecular classes can be recognized by immune cells. These compounds have not been systematically studied yet, and prediction approaches are lacking. The ability to predict the epitope activity of non-peptidic compounds could have vast implications; for example, for immunogenic risk assessment of the vast number of drugs and other xenobiotics. Here we present the first general attempt to predict the epitope activity of non-peptidic compounds using the Immune Epitope Database (IEDB) as a source for positive samples. The molecules stored in the Chemical Entities of Biological Interest (ChEBI) database were chosen as background samples. The molecules were clustered into eight homogeneous molecular groups, and classifiers were built for each cluster with the aim of separating the epitopes from the background. Different molecular feature encoding schemes and machine learning models were compared against each other. For those models where a high performance could be achieved based on simple decision rules, the molecular features were then further investigated. Additionally, the findings were used to build a web server that allows for the immunogenic investigation of non-peptidic molecules (http://tools-staging.iedb.org/np_epitope_predictor). The prediction quality was tested with samples from independent evaluation datasets, and the implemented method received noteworthy Receiver Operating Characteristic-Area Under Curve (ROC-AUC) values, ranging from 0.69-0.96 depending on the molecule cluster.

IEDB and CEDAR: Two Sibling Databases to Serve the Global Scientific Community.

Various methodologies have been utilized to analyze epitope-specific responses in the context of non-self-antigens, such as those associated with infectious diseases and allergies, and in the context of self-antigens, such as those associated with transplantation, autoimmunity, and cancer. Further to this, epitope-specific data, and its associated immunological context, are crucial to training and developing predictive algorithms and pipelines for the development of specific vaccines and diagnostics. In this chapter, we describe the methodology utilized to derive two sibling resources, the Immune Epitope Database (IEDB) and Cancer Epitope Database and Analysis Resource (CEDAR), to specifically host this data, and make them freely available to the scientific community.

A meta-analysis of epitopes in prostate-specific antigens identifies opportunities and knowledge gaps.

BACKGROUND: The Cancer Epitope Database and Analysis Resource (CEDAR) is a newly developed repository of cancer epitope data from peer-reviewed publications, which includes epitope-specific T cell, antibody, and MHC ligand assays. Here we focus on prostate cancer as our first cancer category to demonstrate the capabilities of CEDAR, and to shed light on the advances of epitope-related prostate cancer research. RESULTS: The meta-analysis focused on a subset of data describing epitopes from 8 prostate-specific (PS) antigens. A total of 460 epitopes were associated with these proteins, 187 T cell, 109B cell, and 271 MHC ligand epitopes. The number of epitopes was not correlated with the length of the protein; however, we found a significant positive correlation between the number of references per specific PS antigen and the number of reported epitopes. Forty-four different class I and 27 class II restrictions were found, with the most epitopes described for HLA-A*02:01 and HLA-DRB1*01:01. Cytokine assays were mostly limited to IFNg assays and a very limited number of tetramer assays were performed. Monoclonal and polyclonal B cell responses were balanced, with the highest number of epitopes studied in ELISA/Western blot assays. Additionally, epitopes were generically described as associated with prostate cancer, with little granularity specifying diseases state. We found that in vivo and tumor recognition assays were sparse, and the number of epitopes with annotated B/T cell receptor information were limited. Potential immunodominant regions were identified by the use of the ImmunomeBrowser tool. CONCLUSION: CEDAR provides a comprehensive repository of epitopes related to prostate-specific antigens. This inventory of epitope data with its wealth of searchable T cell, B cell and MHC ligand information provides a useful tool for the scientific community. At the same time, we identify significant knowledge gaps that could be addressed by experimental analysis.

IEDB-3D 2.0: Structural data analysis within the Immune Epitope Database.

The Immune Epitope Database (IEDB) catalogs T cell, B cell, and major histocompatibility complex ligand information in the context of infectious disease, allergy, autoimmunity, and transplantation. An important component of this information is three-dimensional structural data on T cell receptors, antibodies, and pairwise residue interactions between immune receptors and antigens, which we refer to as IEDB-3D. Such data is highly valuable for mechanically understanding receptor:ligand interactions. Here, we present IEDB-3D 2.0, which comprises a complete overhaul of how we obtain and present 3D structural data. A new 3D viewer experience that utilizes iCn3D has been implemented to replace outdated java-based technology. In addition, we have designed a new epitope mapping system that matches each epitope available in the IEDB with its antigen structural data. Finally, immunogenicity data retrieved from the IEDB's ImmunomeBrowser can now be used to highlight immunogenic regions of an antigen directly in iCn3D. Overall, the IEDB-3D 2.0 provides an updated tool platform to visualize epitope data cataloged in the IEDB.

An immunologically friendly classification of non-peptidic ligands.

The Immune Epitope Database (IEDB) freely provides experimental data regarding immune epitopes to the scientific public. The main users of the IEDB are immunologists who can easily use our web interface to search for peptidic epitopes via their simple single-letter codes. For example, 'A' stands for 'alanine'. Similarly, users can easily navigate the IEDB's simplified NCBI taxonomy hierarchy to locate proteins from specific organisms. However, some epitopes are non-peptidic, such as carbohydrates, lipids, chemicals and drugs, and it is more challenging to consistently name them and search upon, making access to their data more problematic for immunologists. Therefore, we set out to improve access to non-peptidic epitope data in the IEDB through the simplification of the non-peptidic hierarchy used in our search interfaces. Here, we present these efforts and their outcomes. Database URL:  http://www.iedb.org/.

The Cancer Epitope Database and Analysis Resource: A Blueprint for the Establishment of a New Bioinformatics Resource for Use by the Cancer Immunology Community.

Recent years have witnessed a dramatic rise in interest towards cancer epitopes in general and particularly neoepitopes, antigens that are encoded by somatic mutations that arise as a consequence of tumorigenesis. There is also an interest in the specific T cell and B cell receptors recognizing these epitopes, as they have therapeutic applications. They can also aid in basic studies to infer the specificity of T cells or B cells characterized in bulk and single-cell sequencing data. The resurgence of interest in T cell and B cell epitopes emphasizes the need to catalog all cancer epitope-related data linked to the biological, immunological, and clinical contexts, and most importantly, making this information freely available to the scientific community in a user-friendly format. In parallel, there is also a need to develop resources for epitope prediction and analysis tools that provide researchers access to predictive strategies and provide objective evaluations of their performance. For example, such tools should enable researchers to identify epitopes that can be effectively used for immunotherapy or in defining biomarkers to predict the outcome of checkpoint blockade therapies. We present here a detailed vision, blueprint, and work plan for the development of a new resource, the Cancer Epitope Database and Analysis Resource (CEDAR). CEDAR will provide a freely accessible, comprehensive collection of cancer epitope and receptor data curated from the literature and provide easily accessible epitope and T cell/B cell target prediction and analysis tools. The curated cancer epitope data will provide a transparent benchmark dataset that can be used to assess how well prediction tools perform and to develop new prediction tools relevant to the cancer research community.