Huynh-Hoa Bui

A consensus epitope prediction approach identifies the breadth of murine T CD8+ -cell responses to vaccinia virus

The value of predictive algorithms for identifying CD8+ T (TCD8+)-cell epitopes has not been adequately tested experimentally. Here we demonstrate that conventional bioinformatic methods predict the vast majority of TCD8+-cell epitopes derived from vaccinia virus WR strain (VACV-WR) in the H-2b mouse model. This approach reveals the breadth of T-cell responses to vaccinia, a widely studied murine viral infection model, and may provide a tool for developing comprehensive antigenic maps of any complex pathogen.

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.

A Community Resource Benchmarking Predictions of Peptide Binding to MHC-I Molecules

Recognition of peptides bound to major histocompatibility complex (MHC) class I molecules by T lymphocytes is an essential part of immune surveillance. Each MHC allele has a characteristic peptide binding preference, which can be captured in prediction algorithms, allowing for the rapid scan of entire pathogen proteomes for peptide likely to bind MHC. Here we make public a large set of 48,828 quantitative peptide-binding affinity measurements relating to 48 different mouse, human, macaque, and chimpanzee MHC class I alleles. We use this data to establish a set of benchmark predictions with one neural network method and two matrix-based prediction methods extensively utilized in our groups. In general, the neural network outperforms the matrix-based predictions mainly due to its ability to generalize even on a small amount of data. We also retrieved predictions from tools publicly available on the internet. While differences in the data used to generate these predictions hamper direct comparisons, we do conclude that tools based on combinatorial peptide libraries perform remarkably well. The transparent prediction evaluation on this dataset provides tool developers with a benchmark for comparison of newly developed prediction methods. In addition, to generate and evaluate our own prediction methods, we have established an easily extensible web-based prediction framework that allows automated side-by-side comparisons of prediction methods implemented by experts. This is an advance over the current practice of tool developers having to generate reference predictions themselves, which can lead to underestimating the performance of prediction methods they are not as familiar with as their own. The overall goal of this effort is to provide a transparent prediction evaluation allowing bioinformaticians to identify promising features of prediction methods and providing guidance to immunologists regarding the reliability of prediction tools.

Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications.

Prediction of which peptides can bind major histocompatibility complex (MHC) molecules is commonly used to assist in the identification of T cell epitopes. However, because of the large numbers of different MHC molecules of interest, each associated with different predictive tools, tool generation and evaluation can be a very resource intensive task. A methodology commonly used to predict MHC binding affinity is the matrix or linear coefficients method. Herein, we described Average Relative Binding (ARB) matrix methods that directly predict IC50 values allowing combination of searches involving different peptide sizes and alleles into a single global prediction. A computer program was developed to automate the generation and evaluation of ARB predictive tools. Using an in-house MHC binding database, we generated a total of 85 and 13 MHC class I and class II matrices, respectively. Results from the automated evaluation of tool efficiency are presented. We anticipate that this automation framework will be generally applicable to the generation and evaluation of large numbers of MHC predictive methods and tools, and will be of value to centralize and rationalize the process of evaluation of MHC predictions. MHC binding predictions based on ARB matrices were made available at http://epitope.liai.org:8080/matrix web server.

Ab and T cell epitopes of influenza A virus, knowledge and opportunities

The Immune Epitope Database and Analysis Resources (IEDB) (www.immuneepitope.org) was recently developed to capture epitope related data. IEDB also hosts various bioinformatics tools that can be used to identify novel epitopes as well as to analyze and visualize existing epitope data. Herein, a comprehensive analysis was undertaken (i) to compile and inventory existing knowledge regarding influenza A epitopes and (ii) to determine possible cross-reactivities of identified epitopes among avian H5N1 and human influenza strains. At present, IEDB contains >600 different epitopes derived from 58 different strains and 10 influenza A proteins. By using the IEDB analysis resources, conservancy analyses were performed, and several conserved and possibly cross-reactive epitopes were identified. Significant gaps in the current knowledge were also revealed, including paucity of Ab epitopes in comparison with T cell epitopes, limited number of epitopes reported for avian influenza strains/subtypes, and limited number of epitopes reported from proteins other than hemagglutinin and nucleoprotein. This analysis provides a resource for researchers to access existing influenza epitope data. At the same time, the analysis illustrates gaps in our collective knowledge that should inspire directions for further study of immunity against the influenza A virus.

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.

Immunomic Analysis of the Repertoire of T-Cell Specificities for Influenza A Virus in Humans

ABSTRACT Continuing antigenic drift allows influenza viruses to escape antibody-mediated recognition, and as a consequence, the vaccine currently in use needs to be altered annually. Highly conserved epitopes recognized by effector T cells may represent an alternative approach for the generation of a more universal influenza virus vaccine. Relatively few highly conserved epitopes are currently known in humans, and relatively few epitopes have been identified from proteins other than hemagglutinin and nucleoprotein. This prompted us to perform a study aimed at identifying a set of human T-cell epitopes that would provide broad coverage against different virus strains and subtypes. To provide coverage across different ethnicities, seven different HLA supertypes were considered. More than 4,000 peptides were selected from a panel of 23 influenza A virus strains based on predicted high-affinity binding to HLA class I or class II and high conservancy levels. Peripheral blood mononuclear cells from 44 healthy human blood donors were tested for reactivity against HLA-matched peptides by using gamma interferon enzyme-linked immunospot assays. Interestingly, we found that PB1 was the major target for both CD4+ and CD8+ T-cell responses. The 54 nonredundant epitopes (38 class I and 16 class II) identified herein provided high coverage among different ethnicities, were conserved in the majority of the strains analyzed, and were consistently recognized in multiple individuals. These results enable further functional studies of T-cell responses during influenza virus infection and provide a potential base for the development of a universal influenza vaccine.

A Quantitative Analysis of the Variables Affecting the Repertoire of T Cell Specificities Recognized after Vaccinia Virus Infection

Many components contribute to immunodominance in the response to a complex virus, but their relative importance is unclear. This was addressed using vaccinia virus and HLA-A*0201 as the model system. A comprehensive analysis of 18 viral proteins recognized by CD8+ T cell responses demonstrated that approximately one-fortieth of all possible 9- to 10-mer peptides were high-affinity HLA-A*0201 binders. Peptide immunization and T cell recognition data generated from 90 peptides indicated that about one-half of the binders were capable of eliciting T cell responses, and that one-seventh of immunogenic peptides are generated by natural processing. Based on these results, we estimate that vaccinia virus encodes ∼150 dominant and subdominant epitopes restricted in by HLA-A*0201. However, of all these potential epitopes, only 15 are immunodominant and actually recognized in vivo during vaccinia virus infection of HLA-A*0201 transgenic mice. Neither peptide-binding affinity, nor complex stability, nor TCR avidity, nor amount of processed epitope appeared to strictly correlate with immunodominance status. Additional experiments suggested that vaccinia infection impairs the development of responses directed against subdominant epitopes. This suggested that additional factors, including immunoregulatory mechanisms, restrict the repertoire of T cell specificities after vaccinia infection by a factor of at least 10.

Predicting population coverage of T-cell epitope-based diagnostics and vaccines

BackgroundT cells recognize a complex between a specific major histocompatibility complex (MHC) molecule and a particular pathogen-derived epitope. A given epitope will elicit a response only in individuals that express an MHC molecule capable of binding that particular epitope. MHC molecules are extremely polymorphic and over a thousand different human MHC (HLA) alleles are known. A disproportionate amount of MHC polymorphism occurs in positions constituting the peptide-binding region, and as a result, MHC molecules exhibit a widely varying binding specificity. In the design of peptide-based vaccines and diagnostics, the issue of population coverage in relation to MHC polymorphism is further complicated by the fact that different HLA types are expressed at dramatically different frequencies in different ethnicities. Thus, without careful consideration, a vaccine or diagnostic with ethnically biased population coverage could result.ResultsTo address this issue, an algorithm was developed to calculate, on the basis of HLA genotypic frequencies, the fraction of individuals expected to respond to a given epitope set, diagnostic or vaccine. The population coverage estimates are based on MHC binding and/or T cell restriction data, although the tool can be utilized in a more general fashion. The algorithm was implemented as a web-application available at http://epitope.liai.org:8080/tools/population.ConclusionWe have developed a web-based tool to predict population coverage of T-cell epitope-based diagnostics and vaccines based on MHC binding and/or T cell restriction data. Accordingly, epitope-based vaccines or diagnostics can be designed to maximize population coverage, while minimizing complexity (that is, the number of different epitopes included in the diagnostic or vaccine), and also minimizing the variability of coverage obtained or projected in different ethnic groups.

Development of an epitope conservancy analysis tool to facilitate the design of epitope-based diagnostics and vaccines

BackgroundIn an epitope-based vaccine setting, the use of conserved epitopes would be expected to provide broader protection across multiple strains, or even species, than epitopes derived from highly variable genome regions. Conversely, in a diagnostic and disease monitoring setting, epitopes that are specific to a given pathogen strain, for example, can be used to monitor responses to that particular infectious strain. In both cases, concrete information pertaining to the degree of conservancy of the epitope(s) considered is crucial.ResultsTo assist in the selection of epitopes with the desired degree of conservation, we have developed a new tool to determine the variability of epitopes within a given set of protein sequences. The tool was implemented as a component of the Immune Epitope Database and Analysis Resources (IEDB), and is directly accessible at http://tools.immuneepitope.org/tools/conservancy.ConclusionAn epitope conservancy analysis tool was developed to analyze the variability or conservation of epitopes. The tool is user friendly, and is expected to aid in the design of epitope-based vaccines and diagnostics.