https://www.socictopen.socict.org/files/original/9cb38e3cfbc8e34051a7ef8b46c30a87.pdf b0d2fb2d159d835aa12e60a4801c37cf Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A general approach for predicting protein epitopes targeted by antibody repertoires using whole proteomes. Creator An entity primarily responsible for making the resource Michael L Paull, Tim Johnston, Kelly N Ibsen, Joel D Bozekowski, Patrick S Daugherty Description An account of the resource Antibodies are essential to functional immunity, yet the epitopes targeted by antibody repertoires remain largely uncharacterized. To aid in characterization, we developed a generalizable strategy to predict antibody-binding epitopes within individual proteins and entire proteomes. Specifically, we selected antibody-binding peptides for 273 distinct sera out of a random library and identified the peptides using next-generation sequencing. To predict antibody-binding epitopes and the antigens from which these epitopes were derived, we tiled the sequences of candidate antigens into short overlapping subsequences of length k (k-mers). We used the enrichment over background of these k-mers in the antibody-binding peptide dataset to predict antibody-binding epitopes. As a positive control, we used this approach, termed K-mer Tiling of Protein Epitopes (K-TOPE), to predict epitopes targeted by monoclonal and polyclonal antibodies of well-characterized specificity, accurately recovering their known epitopes. K-TOPE characterized a commonly targeted antigen from Rhinovirus A, predicting four epitopes recognized by antibodies present in 87% of sera (n = 250). An analysis of 2,908 proteins from 400 viral taxa that infect humans predicted seven enterovirus epitopes and five Epstein-Barr virus epitopes recognized by >30% of specimens. Analysis of Staphylococcus and Streptococcus proteomes similarly predicted 22 epitopes recognized by >30% of specimens. Twelve of these common viral and bacterial epitopes agreed with previously mapped epitopes with p-values < 0.05. Additionally, we predicted 30 HSV2-specific epitopes that were 100% specific against HSV1 in novel and previously reported antigens. Experimentally validating these candidate epitopes could help identify diagnostic biomarkers, vaccine components, and therapeutic targets. The K-TOPE approach thus provides a powerful new tool to elucidate the organisms, antigens, and epitopes targeted by human antibody repertoires. Date A point or period of time associated with an event in the lifecycle of the resource 2019 Identifier An unambiguous reference to the resource within a given context DOI: 10.1371/journal.pone.0217668 Source A related resource from which the described resource is derived PLoS ONE Publisher An entity responsible for making the resource available Public Library of Science (PLoS) Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Science, Medicine Language A language of the resource EN