Browsing by Subject "Epitopes"

Now showing 1 - 2 of 2
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    BEST: Improved Prediction of B-Cell Epitopes from Antigen Sequences
    (Public Library of Science, 2012) Gao, Jianzhao; Faraggi, Eshel; Zhou, Yaoqi; Ruan, Jishou; Kurgan, Lukasz; Physics, School of Science
    Accurate identification of immunogenic regions in a given antigen chain is a difficult and actively pursued problem. Although accurate predictors for T-cell epitopes are already in place, the prediction of the B-cell epitopes requires further research. We overview the available approaches for the prediction of B-cell epitopes and propose a novel and accurate sequence-based solution. Our BEST (B-cell Epitope prediction using Support vector machine Tool) method predicts epitopes from antigen sequences, in contrast to some method that predict only from short sequence fragments, using a new architecture based on averaging selected scores generated from sliding 20-mers by a Support Vector Machine (SVM). The SVM predictor utilizes a comprehensive and custom designed set of inputs generated by combining information derived from the chain, sequence conservation, similarity to known (training) epitopes, and predicted secondary structure and relative solvent accessibility. Empirical evaluation on benchmark datasets demonstrates that BEST outperforms several modern sequence-based B-cell epitope predictors including ABCPred, method by Chen et al. (2007), BCPred, COBEpro, BayesB, and CBTOPE, when considering the predictions from antigen chains and from the chain fragments. Our method obtains a cross-validated area under the receiver operating characteristic curve (AUC) for the fragment-based prediction at 0.81 and 0.85, depending on the dataset. The AUCs of BEST on the benchmark sets of full antigen chains equal 0.57 and 0.6, which is significantly and slightly better than the next best method we tested. We also present case studies to contrast the propensity profiles generated by BEST and several other methods.
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    Inhibition of weak-affinity epitope-IgE interactions prevents mast cell degranulation
    (Nature Publishing Group, 2013-12) Handlogten, Michael W; Kiziltepe, Tanyel; Serezani, Ana P; Kaplan, Mark H; Bilgicer, Basar; Department of Pediatrics, IU School of Medicine
    Development of specific inhibitors of allergy has had limited success, in part, owing to a lack of experimental models that reflect the complexity of allergen-IgE interactions. We designed a heterotetravalent allergen (HtTA) system, which reflects epitope heterogeneity, polyclonal response and number of immunodominant epitopes observed in natural allergens, thereby providing a physiologically relevant experimental model to study mast cell degranulation. The HtTA design revealed the importance of weak-affinity epitopes in allergy, particularly when presented with high-affinity epitopes. The effect of selective inhibition of weak-affinity epitope-IgE interactions was investigated with heterobivalent inhibitors (HBIs) designed to simultaneously target the antigen- and nucleotide-binding sites on the IgE Fab. HBI demonstrated enhanced avidity for the target IgE and was a potent inhibitor of degranulation in vitro and in vivo. These results demonstrate that partial inhibition of allergen-IgE interactions was sufficient to prevent mast cell degranulation, thus establishing the therapeutic potential of the HBI design.