Abstract
We introduce a phylogeny-aware framework for predicting linear B-cell epitope (LBCE)-containing regions within proteins. Our approach leverages evolutionary information by using a taxonomic scaffold to build models trained on hierarchically structured data. The resulting models present performance equivalent or superior to generalist methods, despite using simpler features and a fraction of the data volume required by current state-of-the-art predictors. This allows the utilization of available data for major pathogen lineages to facilitate the prediction of LBCEs for emerging infectious agents. We demonstrate the efficacy of our approach by predicting new LBCEs in the monkeypox (MPXV) and vaccinia viruses. Experimental validation of selected targets using sera from infected patients confirms the presence of LBCEs, including candidates for the differential serodiagnosis of recent MPXV infections. These results point to the use of phylogeny-aware predictors as a useful strategy to facilitate the targeted development of immunodiagnostic tools.
Original language | English |
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Article number | bbae527 |
Number of pages | 12 |
Journal | Briefings in Bioinformatics |
Volume | 25 |
Issue number | 6 |
Early online date | 6 Nov 2024 |
DOIs | |
Publication status | Published - Nov 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 License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords
- Orthopoxvirus
- Epitope prediction
- Diagnostics
- Machine Learning
- Monkeypox Virus
- Phylogeny-aware Methods
- Humans
- Vaccinia virus
- Epitopes, B-Lymphocyte
- Computational Biology
- Phylogeny