EpiDope: a deep neural network for linear B-cell epitope prediction
Author(s) -
Maximilian Collatz,
Florian Mock,
Emanuel Barth,
Martin Hölzer,
Konrad Sachse,
Manja Marz
Publication year - 2020
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btaa773
Subject(s) - epitope , artificial neural network , computer science , artificial intelligence , deep neural networks , computational biology , biology , genetics , antibody
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.
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