Campelo, Felipe, de Oliveira, Ana Laura Grossi, Reis-Cunha, João, Fraga, Vanessa Gomes, Bastos, Pedro Henrique, Ashford, Jodie, Ekárt, Anikó, Adelino, Talita Emile Ribeiro, Silva, Marcos Vinicius Ferreira, de Melo Iani, Felipe Campos, de Jesus, Augusto César Parreiras, Bartholomeu, Daniella Castanheira, de Souza Trindade, Giliane, Fujiwara, Ricardo Toshio, Bueno, Lilian Lacerda and Lobo, Francisco Pereira (2024). Phylogeny-aware linear B-cell epitope predictor detects targets associated with immune response to orthopoxviruses. Briefings in Bioinformatics, 25 (6),
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.
Publication DOI: | https://doi.org/10.1093/bib/bbae527 |
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Divisions: | College of Engineering & Physical Sciences > School of Computer Science and Digital Technologies > Applied AI & Robotics |
Funding Information: | J.A. was supported by the EPSRC/UK (PhD fees and stipend). J.R.-C. was supported by the UK Medical Research Council (MR/T016019/1). The Brazilian authors would like to acknowledge the following sources of financial support: MCTI/Brazil (C\u00E2mara Pox, 4 |
Additional Information: | 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. |
Uncontrolled Keywords: | Orthopoxvirus,Epitope prediction,Diagnostics,Machine Learning,Monkeypox Virus,Phylogeny-aware Methods,Humans,Vaccinia virus,Epitopes, B-Lymphocyte,Computational Biology,Phylogeny |
Publication ISSN: | 1477-4054 |
Last Modified: | 28 Mar 2025 08:11 |
Date Deposited: | 04 Dec 2024 17:05 |
Full Text Link: | |
Related URLs: |
https://academi ... bbae527/7877279
(Publisher URL) http://www.scop ... tnerID=8YFLogxK (Scopus URL) |
PURE Output Type: | Article |
Published Date: | 2024-11 |
Published Online Date: | 2024-11-06 |
Accepted Date: | 2024-09-01 |
Authors: |
Campelo, Felipe
(![]() de Oliveira, Ana Laura Grossi Reis-Cunha, João Fraga, Vanessa Gomes Bastos, Pedro Henrique Ashford, Jodie Ekárt, Anikó ( ![]() Adelino, Talita Emile Ribeiro Silva, Marcos Vinicius Ferreira de Melo Iani, Felipe Campos de Jesus, Augusto César Parreiras Bartholomeu, Daniella Castanheira de Souza Trindade, Giliane Fujiwara, Ricardo Toshio Bueno, Lilian Lacerda Lobo, Francisco Pereira |