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.

Bioinformatics (2021) doi: 10.1093/bioinformatics/btaa773 In the originally published version of this manuscript, there was an erroneous omission in the Funding section. The section should read: “This work was funded in the framework of the national research network InfectControl, project "Molecular serology for rapid determination of vaccination titers (STIKO Serology)", which was financially supported by the Federal Ministry of Education and Research (BMBF) of Germany under grant 03ZZ0820A. This work was further supported by the Collaborative Research Center/Transregio 124 (FungiNet; number 210879364), project B5, funded by Deutsche Forschungsgemeinschaft (DFG). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.” instead of: “This work was funded in the framework of the national research network InfectControl, project ‘Molecular serology for rapid determination of vaccination titers (STIKO Serology)’, which was financially supported by the Federal Ministry of Education and Research (BMBF) of Germany [03ZZ0820A]. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.” This error has now been corrected online.

MOTIVATION: Zoonosis, the natural transmission of infections from animals to humans, is a far-reaching global problem. The recent outbreaks of Zikavirus, Ebolavirus and Coronavirus are examples of viral zoonosis, which occur more frequently due to globalization. In case of a virus outbreak, it is helpful to know which host organism was the original carrier of the virus to prevent further spreading of viral infection. Recent approaches aim to predict a viral host based on the viral genome, often in combination with the potential host genome and arbitrarily selected features. These methods are limited in the number of different hosts they can predict or the accuracy of the prediction. RESULTS: Here, we present a fast and accurate deep learning approach for viral host prediction, which is based on the viral genome sequence only. We tested our deep neural network (DNN) on three different virus species (influenza A virus, rabies lyssavirus and rotavirus A). We achieved for each virus species an AUC between 0.93 and 0.98, allowing highly accurate predictions while using only fractions (100-400 bp) of the viral genome sequences. We show that deep neural networks are suitable to predict the host of a virus, even with a limited amount of sequences and highly unbalanced available data. The trained DNNs are the core of our virus-host prediction tool VIrus Deep learning HOst Prediction (VIDHOP). VIDHOP also allows the user to train and use models for other viruses. AVAILABILITY AND IMPLEMENTATION: VIDHOP is freely available under https://github.com/flomock/vidhop. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.