Maximilian Collatz1, Florian Mock1, Emanuel Barth1,2, Martin Hölzer1,3, Konrad Sachse1, Manja Marz1,2,3,4. 1. RNA Bioinformatics /High Throughput Analysis, Faculty of Mathematics and Computer Science. 2. Bioinformatics Core Facility Jena, Friedrich Schiller University Jena, Jena 07743, Germany. 3. RNA Bioinformatics/High Throughput Analysis, European Virus Bioinformatics Center (EVBC), Jena 07743, Germany. 4. RNA Bioinformatics/High Throughput Analysis, FLI Leibniz Institute for Age Research, Jena 07745, Germany.
Abstract
MOTIVATION: By binding to specific structures on antigenic proteins, the so-called epitopes, B-cell antibodies can neutralize pathogens. The identification of B-cell epitopes is of great value for the development of specific serodiagnostic assays and the optimization of medical therapy. However, identifying diagnostically or therapeutically relevant epitopes is a challenging task that usually involves extensive laboratory work. In this study, we show that the time, cost and labor-intensive process of epitope detection in the lab can be significantly reduced using in silico prediction. RESULTS: Here, we present EpiDope, a python tool which uses a deep neural network to detect linear B-cell epitope regions on individual protein sequences. With an area under the curve between 0.67 ± 0.07 in the receiver operating characteristic curve, EpiDope exceeds all other currently used linear B-cell epitope prediction tools. Our software is shown to reliably predict linear B-cell epitopes of a given protein sequence, thus contributing to a significant reduction of laboratory experiments and costs required for the conventional approach. AVAILABILITYAND IMPLEMENTATION: EpiDope is available on GitHub (http://github.com/mcollatz/EpiDope). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: By binding to specific structures on antigenic proteins, the so-called epitopes, B-cell antibodies can neutralize pathogens. The identification of B-cell epitopes is of great value for the development of specific serodiagnostic assays and the optimization of medical therapy. However, identifying diagnostically or therapeutically relevant epitopes is a challenging task that usually involves extensive laboratory work. In this study, we show that the time, cost and labor-intensive process of epitope detection in the lab can be significantly reduced using in silico prediction. RESULTS: Here, we present EpiDope, a python tool which uses a deep neural network to detect linear B-cell epitope regions on individual protein sequences. With an area under the curve between 0.67 ± 0.07 in the receiver operating characteristic curve, EpiDope exceeds all other currently used linear B-cell epitope prediction tools. Our software is shown to reliably predict linear B-cell epitopes of a given protein sequence, thus contributing to a significant reduction of laboratory experiments and costs required for the conventional approach. AVAILABILITYAND IMPLEMENTATION: EpiDope is available on GitHub (http://github.com/mcollatz/EpiDope). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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