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/.

Minimal Information about MHC Multimers (MIAMM).

With the goal of improving the reproducibility and annotatability of MHC multimer reagent data, we present the establishment of a new data standard: Minimal Information about MHC Multimers (https://miamm.lji.org/). Multimers are engineered reagents composed of a ligand and a MHC, which can be represented in a standardized format using ontology terminology. We provide an online Web site to host the details of the standard, as well as a validation tool to assist with the adoption of the standard. We hope that this publication will bring increased awareness of Minimal Information about MHC Multimers and drive acceptance, ultimately improving the quality and documentation of multimer data in the scientific literature.

PEPMatch: a tool to identify short peptide sequence matches in large sets of proteins.

BACKGROUND: Numerous tools exist for biological sequence comparisons and search. One case of particular interest for immunologists is finding matches for linear peptide T cell epitopes, typically between 8 and 15 residues in length, in a large set of protein sequences. Both to find exact matches or matches that account for residue substitutions. The utility of such tools is critical in applications ranging from identifying conservation across viral epitopes, identifying putative epitope targets for allergens, and finding matches for cancer-associated neoepitopes to examine the role of tolerance in tumor recognition. RESULTS: We defined a set of benchmarks that reflect the different practical applications of short peptide sequence matching. We evaluated a suite of existing methods for speed and recall and developed a new tool, PEPMatch. The tool uses a deterministic k-mer mapping algorithm that preprocesses proteomes before searching, achieving a 50-fold increase in speed over methods such as the Basic Local Alignment Search Tool (BLAST) without compromising recall. PEPMatch's code and benchmark datasets are publicly available. CONCLUSIONS: PEPMatch offers significant speed and recall advantages for peptide sequence matching. While it is of immediate utility for immunologists, the developed benchmarking framework also provides a standard against which future tools can be evaluated for improvements. The tool is available at https://nextgen-tools.iedb.org , and the source code can be found at https://github.com/IEDB/PEPMatch .

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.

The Immune Epitope Database (IEDB): 2018 update.

The Immune Epitope Database (IEDB, iedb.org) captures experimental data confined in figures, text and tables of the scientific literature, making it freely available and easily searchable to the public. The scope of the IEDB extends across immune epitope data related to all species studied and includes antibody, T cell, and MHC binding contexts associated with infectious, allergic, autoimmune, and transplant related diseases. Having been publicly accessible for >10 years, the recent focus of the IEDB has been improved query and reporting functionality to meet the needs of our users to access and summarize data that continues to grow in quantity and complexity. Here we present an update on our current efforts and future goals.

A behind-the-scenes tour of the IEDB curation process: an optimized process empirically integrating automation and human curation efforts.

The Immune Epitope Database and Analysis Resource (IEDB) provides the scientific community with open access to epitope data, as well as epitope prediction and analysis tools. The IEDB houses the most extensive collection of experimentally validated B-cell and T-cell epitope data, sourced primarily from published literature by expert curation. The data procurement requires systematic identification, categorization, curation and quality-checking processes. Here, we provide insights into these processes, with particular focus on the dividends they have paid in terms of attaining project milestones, as well as how objective analyses of our processes have identified opportunities for process optimization. These experiences are shared as a case study of the benefits of process implementation and review in biomedical big data, as well as to encourage idea-sharing among players in this ever-growing space.

Reporting and connecting cell type names and gating definitions through ontologies.

BACKGROUND: Human immunology studies often rely on the isolation and quantification of cell populations from an input sample based on flow cytometry and related techniques. Such techniques classify cells into populations based on the detection of a pattern of markers. The description of the cell populations targeted in such experiments typically have two complementary components: the description of the cell type targeted (e.g. 'T cells'), and the description of the marker pattern utilized (e.g. CD14-, CD3+). RESULTS: We here describe our attempts to use ontologies to cross-compare cell types and marker patterns (also referred to as gating definitions). We used a large set of such gating definitions and corresponding cell types submitted by different investigators into ImmPort, a central database for immunology studies, to examine the ability to parse gating definitions using terms from the Protein Ontology (PRO) and cell type descriptions, using the Cell Ontology (CL). We then used logical axioms from CL to detect discrepancies between the two. CONCLUSIONS: We suggest adoption of our proposed format for describing gating and cell type definitions to make comparisons easier. We also suggest a number of new terms to describe gating definitions in flow cytometry that are not based on molecular markers captured in PRO, but on forward- and side-scatter of light during data acquisition, which is more appropriate to capture in the Ontology for Biomedical Investigations (OBI). Finally, our approach results in suggestions on what logical axioms and new cell types could be considered for addition to the Cell Ontology.

ImmunomeBrowser: a tool to aggregate and visualize complex and heterogeneous epitopes in reference proteins.

Motivation: Datasets that are derived from different studies (e.g. MHC ligand elution, MHC binding, B/T cell epitope screening etc.) often vary in terms of experimental approaches, sizes of peptides tested, including partial and or nested overlapping peptides and in the number of donors tested. Results: We present a customized application of the Immune Epitope Database's ImmunomeBrowser tool, which can be used to effectively aggregate and visualize heterogeneous immunological data. User provided peptide sets and associated response data is mapped to a user-provided protein reference sequence. The output consists of tables and figures representing the aggregated data represented by a Response Frequency score and associated estimated confidence interval. This allows the user to visualizing regions associated with dominant responses and their boundaries. The results are presented both as a user interactive javascript based web interface and a tabular format in a selected reference sequence. Availability and implementation: The 'ImmunomeBrowser' has been a longstanding feature of the IEDB (http://www.iedb.org). The present application extends the use of this tool to work with user-provided datasets, rather than the output of IEDB queries. This new server version of the ImmunomeBrowser is freely accessible at http://tools.iedb.org/immunomebrowser/.

Immunological consequences of intragenus conservation of Mycobacterium tuberculosis T-cell epitopes.

A previous unbiased genome-wide analysis of CD4 Mycobacterium tuberculosis (MTB) recognition using peripheral blood mononuclear cells from individuals with latent MTB infection (LTBI) or nonexposed healthy controls (HCs) revealed that certain MTB sequences were unexpectedly recognized by HCs. In the present study, it was found that, based on their pattern of reactivity, epitopes could be divided into LTBI-specific, mixed reactivity, and HC-specific categories. This pattern corresponded to sequence conservation in nontuberculous mycobacteria (NTMs), suggesting environmental exposure as an underlying cause of differential reactivity. LTBI-specific epitopes were found to be hyperconserved, as previously reported, whereas the opposite was true for NTM conserved epitopes, suggesting that intragenus conservation also influences host pathogen adaptation. The biological relevance of this observation was demonstrated further by several observations. First, the T cells elicited by MTB/NTM cross-reactive epitopes in HCs were found mainly in a CCR6(+)CXCR3(+) memory subset, similar to findings in LTBI individuals. Thus, both MTB and NTM appear to elicit a phenotypically similar T-cell response. Second, T cells reactive to MTB/NTM-conserved epitopes responded to naturally processed epitopes from MTB and NTMs, whereas T cells reactive to MTB-specific epitopes responded only to MTB. Third, cross-reactivity could be translated to antigen recognition. Several MTB candidate vaccine antigens were cross-reactive, but others were MTB-specific. Finally, NTM-specific epitopes that elicit T cells that recognize NTMs but not MTB were identified. These epitopes can be used to characterize T-cell responses to NTMs, eliminating the confounding factor of MTB cross-recognition and providing insights into vaccine design and evaluation.

The immune epitope database (IEDB) 3.0.

The IEDB, www.iedb.org, contains information on immune epitopes--the molecular targets of adaptive immune responses--curated from the published literature and submitted by National Institutes of Health funded epitope discovery efforts. From 2004 to 2012 the IEDB curation of journal articles published since 1960 has caught up to the present day, with >95% of relevant published literature manually curated amounting to more than 15,000 journal articles and more than 704,000 experiments to date. The revised curation target since 2012 has been to make recent research findings quickly available in the IEDB and thereby ensure that it continues to be an up-to-date resource. Having gathered a comprehensive dataset in the IEDB, a complete redesign of the query and reporting interface has been performed in the IEDB 3.0 release to improve how end users can access this information in an intuitive and biologically accurate manner. We here present this most recent release of the IEDB and describe the user testing procedures as well as the use of external ontologies that have enabled it.

Reproducibility and conflicts in immune epitope data.

The Immune Epitope Database is uniquely positioned to assess the body of research related to immune epitopes, we have manually curated all such published data. Thus, we are able to make observations on the state of these fields of research, as well as aggregate the individual data points to present a clearer picture of the immune response to specific antigens in all studied hosts. Additionally, we are able to identify where conflicts in the literature exist and where publications fall short in terms of identifiable methods and in reproducibility. Here we present guidelines to improve the quality of immune epitope data, which will benefit journals and researchers alike.

Immunodominance in allergic T-cell reactivity to Japanese cedar in different geographic cohorts.

BACKGROUND: Japanese cedar (JC) pollen is a common trigger for allergic rhinitis in Japan. Pollen proteins targeted by IgE, including Cry j 1 and Cry j 2, and isoflavone reductase (IFR) have been identified. OBJECTIVE: To compare antigen-specific IgE titers and T-cell responses to JC pollen-derived extract and peptides in cohorts with high and low pollen exposure. METHODS: Peripheral blood mononuclear cells from JC pollen allergic or nonallergic patients who have lived in Japan for at least 1 year and JC pollen allergic patients who have never been to Japan were tested for T-cell responses against JC pollen extract and peptide pools derived from Cry j 1, Cry j 2, or IFR. T-cell reactivity was assessed by interleukin 5 and interferon γ production by ELISPOT. RESULTS: JC pollen-specific T-cell reactivity and IgE titers were significantly higher in the allergic compared with the nonallergic Japanese cohort, which was also associated with different patterns of polysensitization. Interestingly, a significant overlap was observed in the hierarchy of the T-cell epitopes in the allergic Japanese cohort compared with the allergic non-Japanese cohort. In all 3 cohorts, T-cell reactivity was dominantly directed against peptides from the major allergens Cry j 1 and 2, with few T-cell responses detected against IFR. CONCLUSION: Our studies identify common denominators of T-cell reactivity in patient populations with different sensitization patterns, suggesting that generally applicable immunotherapeutic approaches might be developed irrespective of exposure modality.

Antibody informatics for drug discovery.

More and more antibody therapeutics are being approved every year, mainly due to their high efficacy and antigen selectivity. However, it is still difficult to identify the antigen, and thereby the function, of an antibody if no other information is available. There are obstacles inherent to the antibody science in every project in antibody drug discovery. Recent experimental technologies allow for the rapid generation of large-scale data on antibody sequences, affinity, potency, structures, and biological functions; this should accelerate drug discovery research. Therefore, a robust bioinformatic infrastructure for these large data sets has become necessary. In this article, we first identify and discuss the typical obstacles faced during the antibody drug discovery process. We then summarize the current status of three sub-fields of antibody informatics as follows: (i) recent progress in technologies for antibody rational design using computational approaches to affinity and stability improvement, as well as ab-initio and homology-based antibody modeling; (ii) resources for antibody sequences, structures, and immune epitopes and open drug discovery resources for development of antibody drugs; and (iii) antibody numbering and IMGT. Here, we review "antibody informatics," which may integrate the above three fields so that bridging the gaps between industrial needs and academic solutions can be accelerated. This article is part of a Special Issue entitled: Recent advances in molecular engineering of antibody.

T cell responses to known allergen proteins are differently polarized and account for a variable fraction of total response to allergen extracts.

A panel of 133 allergens derived from 28 different sources, including fungi, trees, grasses, weeds, and indoor allergens, was surveyed utilizing prediction of HLA class II-binding peptides and ELISPOT assays with PBMC from allergic donors, resulting in the identification of 257 T cell epitopes. More than 90% of the epitopes were novel, and for 14 allergen sources were the first ever identified to our knowledge. The epitopes identified in the different allergen sources summed up to a variable fraction of the total extract response. In cases of allergens in which the identified T cell epitopes accounted for a minor fraction of the extract response, fewer known protein sequences were available, suggesting that for low epitope coverage allergen sources, additional allergen proteins remain to be identified. IL-5 and IFN-γ responses were measured as prototype Th2 and Th1 responses, respectively. Whereas in some cases (e.g., orchard grass, Alternaria, cypress, and Russian thistle) IL-5 production greatly exceeded IFN-γ, in others (e.g., Aspergillus, Penicillum, and alder) the production of IFN-γ exceeded IL-5. Thus, different allergen sources are associated with variable polarization of the responding T cells. The present study represents the most comprehensive survey to date of human allergen-derived T cell epitopes. These epitopes might be used to characterize T cell phenotype/T cell plasticity as a function of seasonality, or as a result of specific immunotherapy treatment or varying disease severity (asthma or rhinitis).

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.

Coronavirus Immunotherapeutic Consortium Database.

The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has seen multiple anti-SARS-CoV-2 antibodies being generated globally. It is difficult, however, to assemble a useful compendium of these biological properties if they are derived from experimental measurements performed at different sites under different experimental conditions. The Coronavirus Immunotherapeutic Consortium (COVIC) circumvents these issues by experimentally testing blinded antibodies side by side for several functional activities. To collect these data in a consistent fashion and make it publicly available, we established the COVIC database (COVIC-DB, https://covicdb.lji.org/). This database enables systematic analysis and interpretation of this large-scale dataset by providing a comprehensive view of various features such as affinity, neutralization, in vivo protection and effector functions for each antibody. Interactive graphs enable direct comparisons of antibodies based on select functional properties. We demonstrate how the COVIC-DB can be utilized to examine relationships among antibody features, thereby guiding the design of therapeutic antibody cocktails. Database URL  https://covicdb.lji.org/.

The immune epitope database 2.0.

The Immune Epitope Database (IEDB, www.iedb.org) provides a catalog of experimentally characterized B and T cell epitopes, as well as data on Major Histocompatibility Complex (MHC) binding and MHC ligand elution experiments. The database represents the molecular structures recognized by adaptive immune receptors and the experimental contexts in which these molecules were determined to be immune epitopes. Epitopes recognized in humans, nonhuman primates, rodents, pigs, cats and all other tested species are included. Both positive and negative experimental results are captured. Over the course of 4 years, the data from 180,978 experiments were curated manually from the literature, which covers approximately 99% of all publicly available information on peptide epitopes mapped in infectious agents (excluding HIV) and 93% of those mapped in allergens. In addition, data that would otherwise be unavailable to the public from 129,186 experiments were submitted directly by investigators. The curation of epitopes related to autoimmunity is expected to be completed by the end of 2010. The database can be queried by epitope structure, source organism, MHC restriction, assay type or host organism, among other criteria. The database structure, as well as its querying, browsing and reporting interfaces, was completely redesigned for the IEDB 2.0 release, which became publicly available in early 2009.

Pre-existing immunity against swine-origin H1N1 influenza viruses in the general human population.

A major concern about the ongoing swine-origin H1N1 influenza virus (S-OIV) outbreak is that the virus may be so different from seasonal H1N1 that little immune protection exists in the human population. In this study, we examined the molecular basis for pre-existing immunity against S-OIV, namely the recognition of viral immune epitopes by T cells or B cells/antibodies that have been previously primed by circulating influenza strains. Using data from the Immune Epitope Database, we found that only 31% (8/26) of B-cell epitopes present in recently circulating H1N1 strains are conserved in the S-OIV, with only 17% (1/6) conserved in the hemagglutinin (HA) and neuraminidase (NA) surface proteins. In contrast, 69% (54/78) of the epitopes recognized by CD8(+) T cells are completely invariant. We further demonstrate experimentally that some memory T-cell immunity against S-OIV is present in the adult population and that such memory is of similar magnitude as the pre-existing memory against seasonal H1N1 influenza. Because protection from infection is antibody mediated, a new vaccine based on the specific S-OIV HA and NA proteins is likely to be required to prevent infection. However, T cells are known to blunt disease severity. Therefore, the conservation of a large fraction of T-cell epitopes suggests that the severity of an S-OIV infection, as far as it is determined by susceptibility of the virus to immune attack, would not differ much from that of seasonal flu. These results are consistent with reports about disease incidence, severity, and mortality rates associated with human S-OIV.

A curated collection of human vaccination response signatures.

Recent advances in high-throughput experiments and systems biology approaches have resulted in hundreds of publications identifying "immune signatures". Unfortunately, these are often described within text, figures, or tables in a format not amenable to computational processing, thus severely hampering our ability to fully exploit this information. Here we present a data model to represent immune signatures, along with the Human Immunology Project Consortium (HIPC) Dashboard ( www.hipc-dashboard.org ), a web-enabled application to facilitate signature access and querying. The data model captures the biological response components (e.g., genes, proteins, cell types or metabolites) and metadata describing the context under which the signature was identified using standardized terms from established resources (e.g., HGNC, Protein Ontology, Cell Ontology). We have manually curated a collection of >600 immune signatures from >60 published studies profiling human vaccination responses for the current release. The system will aid in building a broader understanding of the human immune response to stimuli by enabling researchers to easily access and interrogate published immune signatures.

SARS-CoV-2 human T cell epitopes: Adaptive immune response against COVID-19.

Over the past year, numerous studies in the peer reviewed and preprint literature have reported on the virological, epidemiological and clinical characteristics of the coronavirus, SARS-CoV-2. To date, 25 studies have investigated and identified SARS-CoV-2-derived T cell epitopes in humans. Here, we review these recent studies, how they were performed, and their findings. We review how epitopes identified throughout the SARS-CoV2 proteome reveal significant correlation between number of epitopes defined and size of the antigen provenance. We also report additional analysis of SARS-CoV-2 human CD4 and CD8 T cell epitope data compiled from these studies, identifying 1,400 different reported SARS-CoV-2 epitopes and revealing discrete immunodominant regions of the virus and epitopes that are more prevalently recognized. This remarkable breadth of epitope repertoire has implications for vaccine design, cross-reactivity, and immune escape by SARS-CoV-2 variants.