Available from: Gajendra Pal Singh Raghava, Jun 16, 2015
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    ABSTRACT: 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.
    Nucleic Acids Research 05/2012; 40(Web Server issue):W525-30. DOI:10.1093/nar/gks438 · 8.81 Impact Factor
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    ABSTRACT: The Bacille-Calmette Guérin (BCG) vaccine does not provide consistent protection against adult pulmonary tuberculosis (TB) worldwide. As novel TB vaccine candidates advance in studies and clinical trials, it will be critically important to evaluate their global coverage by assessing the impact of host and pathogen variability on vaccine efficacy. In this study, we focus on the impact that host genetic variability may have on the protective effect of TB vaccine candidates Ag85B-ESAT-6, Ag85B-TB10.4, and Mtb72f. We use open-source epitope binding prediction programs to evaluate the binding of vaccine epitopes to Class I HLA (A, B, and C) and Class II HLA (DRB1) alleles. Our findings suggest that Mtb72f may be less consistently protective than either Ag85B-ESAT-6 or Ag85B-TB10.4 in populations with a high TB burden, while Ag85B-TB10.4 may provide the most consistent protection. The findings of this study highlight the utility of bioinformatics as a tool for evaluating vaccine candidates before the costly stages of clinical trials and informing the development of new vaccines with the broadest possible population coverage.
    PLoS ONE 10/2012; 7(7):e40882. DOI:10.1371/journal.pone.0040882 · 3.53 Impact Factor
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    ABSTRACT: HLA class I molecules reflect the health state of cells to cytotoxic T cells by presenting a repertoire of endogenously derived peptides. However, the extent to which the proteome shapes the peptidome is still largely unknown. Here we present a high-throughput mass spectrometry based workflow that allows stringent and accurate identification of thousands of such peptides and direct determination of binding motifs. Applying the workflow to seven cancer cell lines and primary cells, yielded more than 22,000 unique HLA peptides across different allelic binding specificities. By computing a score representing the HLA class I sampling density, we show a strong link between protein abundance and HLA-presentation (P<0.0001). When analyzing over-presented proteins, those with at least five-fold higher density score than expected for their abundance, we noticed that they are degraded almost 3 hours faster than similar but non-presented proteins (top 20% abundance class; median half-life 20.8h vs. 23.6h, p<0.0001). This validates protein degradation as an important factor for HLA presentation. Ribosomal, mitochondrial respiratory chain and nucleosomal proteins are particularly well presented. Taking a set of proteins associated with cancer, we compared the predicted immunogenicity of previously validated T cell epitopes with other peptides from these proteins in our dataset. The validated epitopes indeed tend to have higher immunogenic scores than the other detected HLA peptides, suggesting the usefulness of combining MS-analysis with immunogenesis prediction for ranking and selecting peptides for therapeutic use. Copyright © 2015, The American Society for Biochemistry and Molecular Biology.
    Molecular &amp Cellular Proteomics 01/2015; 14(3). DOI:10.1074/mcp.M114.042812 · 7.25 Impact Factor