Julia V. Ponomarenko

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.

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.

ElliPro: a new structure-based tool for the prediction of antibody epitopes

BackgroundReliable prediction of antibody, or B-cell, epitopes remains challenging yet highly desirable for the design of vaccines and immunodiagnostics. A correlation between antigenicity, solvent accessibility, and flexibility in proteins was demonstrated. Subsequently, Thornton and colleagues proposed a method for identifying continuous epitopes in the protein regions protruding from the proteins globular surface. The aim of this work was to implement that method as a web-tool and evaluate its performance on discontinuous epitopes known from the structures of antibody-protein complexes.ResultsHere we present ElliPro, a web-tool that implements Thorntons method and, together with a residue clustering algorithm, the MODELLER program and the Jmol viewer, allows the prediction and visualization of antibody epitopes in a given protein sequence or structure. ElliPro has been tested on a benchmark dataset of discontinuous epitopes inferred from 3D structures of antibody-protein complexes. In comparison with six other structure-based methods that can be used for epitope prediction, ElliPro performed the best and gave an AUC value of 0.732, when the most significant prediction was considered for each protein. Since the rank of the best prediction was at most in the top three for more than 70% of proteins and never exceeded five, ElliPro is considered a useful research tool for identifying antibody epitopes in protein antigens. ElliPro is available at http://tools.immuneepitope.org/tools/ElliPro.ConclusionThe results from ElliPro suggest that further research on antibody epitopes considering more features that discriminate epitopes from non-epitopes may further improve predictions. As ElliPro is based on the geometrical properties of protein structure and does not require training, it might be more generally applied for predicting different types of protein-protein interactions.

Immune epitope database analysis resource

The immune epitope database analysis resource (IEDB-AR: http://tools.iedb.org) is a collection of tools for prediction and analysis of molecular targets of T- and B-cell immune responses (i.e. epitopes). Since its last publication in the NAR webserver issue in 2008, a new generation of peptide:MHC binding and T-cell epitope predictive tools have been added. As validated by different labs and in the first international competition for predicting peptide:MHC-I binding, their predictive performances have improved considerably. In addition, a new B-cell epitope prediction tool was added, and the homology mapping tool was updated to enable mapping of discontinuous epitopes onto 3D structures. Furthermore, to serve a wider range of users, the number of ways in which IEDB-AR can be accessed has been expanded. Specifically, the predictive tools can be programmatically accessed using a web interface and can also be downloaded as software packages.

Immune epitope database analysis resource (IEDB-AR).

We present a new release of the immune epitope database analysis resource (IEDB-AR, http://tools.immuneepitope.org), a repository of web-based tools for the prediction and analysis of immune epitopes. New functionalities have been added to most of the previously implemented tools, and a total of eight new tools were added, including two B-cell epitope prediction tools, four T-cell epitope prediction tools and two analysis tools.

Antibody-protein interactions: benchmark datasets and prediction tools evaluation

BackgroundThe ability to predict antibody binding sites (aka antigenic determinants or B-cell epitopes) for a given protein is a precursor to new vaccine design and diagnostics. Among the various methods of B-cell epitope identification X-ray crystallography is one of the most reliable methods. Using these experimental data computational methods exist for B-cell epitope prediction. As the number of structures of antibody-protein complexes grows, further interest in prediction methods using 3D structure is anticipated. This work aims to establish a benchmark for 3D structure-based epitope prediction methods.ResultsTwo B-cell epitope benchmark datasets inferred from the 3D structures of antibody-protein complexes were defined. The first is a dataset of 62 representative 3D structures of protein antigens with inferred structural epitopes. The second is a dataset of 82 structures of antibody-protein complexes containing different structural epitopes. Using these datasets, eight web-servers developed for antibody and protein binding sites prediction have been evaluated. In no method did performance exceed a 40% precision and 46% recall. The values of the area under the receiver operating characteristic curve for the evaluated methods were about 0.6 for ConSurf, DiscoTope, and PPI-PRED methods and above 0.65 but not exceeding 0.70 for protein-protein docking methods when the best of the top ten models for the bound docking were considered; the remaining methods performed close to random. The benchmark datasets are included as a supplement to this paper.ConclusionIt may be possible to improve epitope prediction methods through training on datasets which include only immune epitopes and through utilizing more features characterizing epitopes, for example, the evolutionary conservation score. Notwithstanding, overall poor performance may reflect the generality of antigenicity and hence the inability to decipher B-cell epitopes as an intrinsic feature of the protein. It is an open question as to whether ultimately discriminatory features can be found.

Control of RelB during dendritic cell activation integrates canonical and noncanonical NF-κB pathways

The NF-κB protein RelB controls dendritic cell (DC) maturation and may be targeted therapeutically to manipulate T cell responses in disease. Here we report that RelB promoted DC activation not as the expected RelB-p52 effector of the noncanonical NF-κB pathway, but as a RelB-p50 dimer regulated by canonical IκBs, IκBα and IκBɛ. IκB control of RelB minimized spontaneous maturation but enabled rapid pathogen-responsive maturation. Computational modeling of the NF-κB signaling module identified control points of this unexpected cell type–specific regulation. Fibroblasts that we engineered accordingly showed DC-like RelB control. Canonical pathway control of RelB regulated pathogen-responsive gene expression programs. This work illustrates the potential utility of systems analyses in guiding the development of combination therapeutics for modulating DC-dependent T cell responses.

The design and implementation of the immune epitope database and analysis resource

Epitopes are defined as parts of antigens interacting with receptors of the immune system. Knowledge about their intrinsic structure and how they affect the immune response is required to continue development of techniques that detect, monitor, and fight diseases. Their scientific importance is reflected in the vast amount of epitope-related information gathered, ranging from interactions between epitopes and major histocompatibility complex molecules determined by X-ray crystallography to clinical studies analyzing correlates of protection for epitope based vaccines. Our goal is to provide a central resource capable of capturing this information, allowing users to access and connect realms of knowledge that are currently separated and difficult to access. Here, we portray a new initiative, “The Immune Epitope Database and Analysis Resource.” We describe how we plan to capture, structure, and store this information, what query interfaces we will make available to the public, and what additional predictive and analytical tools we will provide.

The Specificity of Innate Immune Responses Is Enforced by Repression of Interferon Response Elements by NF-κB p50

Binding of NF-κB p50 homodimers to an interferon response element restricts interferon signaling and antiviral responses to only the appropriate stimuli. NF-κB Interferes Signaling pathways triggered by pattern recognition receptors elicit gene expression programs that culminate in antiviral or proinflammatory responses. Critical to these are members of the nuclear factor κB (NF-κB) and interferon regulatory factor (IRF) families of transcription factors, which bind to κB sites and interferon response elements (IREs), respectively, in their target genes. In addition to transcriptional activators, the NF-κB family of proteins contains p50, which forms homodimers that act as competitive repressors at κB sites. Noting the importance of transcriptional repressors in preventing inappropriate gene expression, Cheng et al. investigated roles for p50 in immune responses. They showed that p50 homodimers bound to and regulated a subset of IREs that were guanine-rich (G-IREs). Homodimers of p50 set the threshold for the activation of genes by competing with IRFs for binding to G-IREs, effectively acting as promoter “gates” to restrict gene expression to specific stimuli. Together, these data provide evidence of cross-regulation between the main sets of transcription factors that coordinate innate immune responses. The specific binding of transcription factors to cognate sequence elements is thought to be critical for the generation of specific gene expression programs. Members of the nuclear factor κB (NF-κB) and interferon (IFN) regulatory factor (IRF) transcription factor families bind to the κB site and the IFN response element (IRE), respectively, of target genes, and they are activated in macrophages after exposure to pathogens. However, how these factors produce pathogen-specific inflammatory and immune responses remains poorly understood. Combining top-down and bottom-up systems biology approaches, we have identified the NF-κB p50 homodimer as a regulator of IRF responses. Unbiased genome-wide expression and biochemical and structural analyses revealed that the p50 homodimer repressed a subset of IFN-inducible genes through a previously uncharacterized subclass of guanine-rich IRE (G-IRE) sequences. Mathematical modeling predicted that the p50 homodimer might enforce the stimulus specificity of composite promoters. Indeed, the production of the antiviral regulator IFN-β was rendered stimulus-specific by the binding of the p50 homodimer to the G-IRE–containing IFNβ enhancer to suppress cytotoxic IFN signaling. Specifically, a deficiency in p50 resulted in the inappropriate production of IFN-β in response to bacterial DNA sensed by Toll-like receptor 9. This role for the NF-κB p50 homodimer in enforcing the specificity of the cellular response to pathogens by binding to a subset of IRE sequences alters our understanding of how the NF-κB and IRF signaling systems cooperate to regulate antimicrobial immunity.

Curation of complex, context-dependent immunological data

BackgroundThe Immune Epitope Database and Analysis Resource (IEDB) is dedicated to capturing, housing and analyzing complex immune epitope related data http://www.immuneepitope.org.DescriptionTo identify and extract relevant data from the scientific literature in an efficient and accurate manner, novel processes were developed for manual and semi-automated annotation.ConclusionFormalized curation strategies enable the processing of a large volume of context-dependent data, which are now available to the scientific community in an accessible and transparent format. The experiences described herein are applicable to other databases housing complex biological data and requiring a high level of curation expertise.