The rise of taxon-specific epitope predictors

Brief Bioinform. 2024 Jan 22;25(2):bbae092. doi: 10.1093/bib/bbae092.ABSTRACTComputational predictors of immunogenic peptides, or epitopes, are traditionally built based on data from a broad range of pathogens without consideration for taxonomic information. While this approach may be reasonable if one aims to develop one-size-fits-all models, it may be counterproductive if the proteins for which the model is expected to generalize are known to come from a specific subset of phylogenetically related pathogens. There is mounting evidence that, for these cases, taxon-specific models can outperform generalist ones, even when trained with substantially smaller amounts of data. In this comment, we provide some perspective on the current state of taxon-specific modelling for the prediction of linear B-cell epitopes, and the challenges faced when building and deploying these predictors.PMID:38493292 | DOI:10.1093/bib/bbae092

Source: Briefings in Bioinformatics - March 17, 2024 Category: Bioinformatics Authors: Felipe Campelo Francisco P Lobo Source Type: research