{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T11:01:52Z","timestamp":1775732512927,"version":"3.50.1"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2020,9,11]],"date-time":"2020-09-11T00:00:00Z","timestamp":1599782400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"STIKO Serology"},{"name":"Federal Ministry of Education and Research (BMBF) of Germany","award":["03ZZ0820A"],"award-info":[{"award-number":["03ZZ0820A"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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\u2009\u00b1\u20090.07 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availabilityand implementation<\/jats:title>\n                    <jats:p>EpiDope is available on GitHub (http:\/\/github.com\/mcollatz\/EpiDope).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa773","type":"journal-article","created":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T07:12:43Z","timestamp":1598944363000},"page":"448-455","source":"Crossref","is-referenced-by-count":85,"title":["EpiDope: a deep neural network for linear B-cell epitope prediction"],"prefix":"10.1093","volume":"37","author":[{"given":"Maximilian","family":"Collatz","sequence":"first","affiliation":[{"name":"RNA Bioinformatics \/High Throughput Analysis , Faculty of Mathematics and Computer Science"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1791-4437","authenticated-orcid":false,"given":"Florian","family":"Mock","sequence":"additional","affiliation":[{"name":"RNA Bioinformatics \/High Throughput Analysis , Faculty of Mathematics and Computer Science"}]},{"given":"Emanuel","family":"Barth","sequence":"additional","affiliation":[{"name":"RNA Bioinformatics \/High Throughput Analysis , Faculty of Mathematics and Computer Science"},{"name":"Bioinformatics Core Facility Jena, Friedrich Schiller University Jena , Jena 07743, Germany"}]},{"given":"Martin","family":"H\u00f6lzer","sequence":"additional","affiliation":[{"name":"RNA Bioinformatics \/High Throughput Analysis , Faculty of Mathematics and Computer Science"},{"name":"RNA Bioinformatics\/High Throughput Analysis, European Virus Bioinformatics Center (EVBC) , Jena 07743, Germany"}]},{"given":"Konrad","family":"Sachse","sequence":"additional","affiliation":[{"name":"RNA Bioinformatics \/High Throughput Analysis , Faculty of Mathematics and Computer Science"}]},{"given":"Manja","family":"Marz","sequence":"additional","affiliation":[{"name":"RNA Bioinformatics \/High Throughput Analysis , Faculty of Mathematics and Computer Science"},{"name":"Bioinformatics Core Facility Jena, Friedrich Schiller University Jena , Jena 07743, Germany"},{"name":"RNA Bioinformatics\/High Throughput Analysis, European Virus Bioinformatics Center (EVBC) , Jena 07743, Germany"},{"name":"RNA Bioinformatics\/High Throughput Analysis , FLI Leibniz Institute for Age Research, Jena 07745, Germany"}]}],"member":"286","published-online":{"date-parts":[[2020,9,11]]},"reference":[{"key":"2023051706080608200_btaa773-B1","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.3201\/eid2407.171928","article-title":"Integrated serologic surveillance of population immunity and disease transmission","volume":"24","author":"Arnold","year":"2018","journal-title":"Emerg. 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