The rise of taxon-specific epitope predictors

Felipe Campelo*, Francisco P. Lobo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Computational 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.
Original languageEnglish
Article numberbbae092
Number of pages3
JournalBriefings in Bioinformatics
Issue number2
Early online date16 Mar 2024
Publication statusPublished - 16 Mar 2024

Bibliographical note

Copyright © The Author(s) 2024. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (, which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected]


  • data mining
  • epitope prediction
  • machine learning
  • phylogeny-aware modelling


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