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EpiDope: A Deep neural network for linear B-cell epitope prediction

By Maximilian Collatz, Florian Mock, Martin Hölzer, Emanuel Barth, Konrad Sachse, Manja Marz

Posted 13 May 2020
bioRxiv DOI: 10.1101/2020.05.12.090019

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 shortened by using in silico prediction. Here we present EpiDope, a python tool which uses a deep neural network to detect B-cell epitope regions on individual protein sequences ([github.com/mcollatz/EpiDope][1]). With an area under the curve (AUC) between 0.67 ± 0.07 in the ROC curve, EpiDope exceeds all other currently used B-cell prediction tools. Moreover, for AUC10% (AUC for a false-positive rate < 0.1), EpiDope improves the prediction accuracy in comparison to other state-of-the-art methods. 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. ### Competing Interest Statement The authors have declared no competing interest. [1]: https://github.com/mcollatz/EpiDope

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