TY - JOUR
T1 - Phylogeny-aware linear B-cell epitope predictor detects targets associated with immune response to orthopoxviruses
AU - Campelo, Felipe
AU - de Oliveira, Ana Laura Grossi
AU - Reis-Cunha, João
AU - Fraga, Vanessa Gomes
AU - Bastos, Pedro Henrique
AU - Ashford, Jodie
AU - Ekárt, Anikó
AU - Adelino, Talita Emile Ribeiro
AU - Silva, Marcos Vinicius Ferreira
AU - de Melo Iani, Felipe Campos
AU - de Jesus, Augusto César Parreiras
AU - Bartholomeu, Daniella Castanheira
AU - de Souza Trindade, Giliane
AU - Fujiwara, Ricardo Toshio
AU - Bueno, Lilian Lacerda
AU - Lobo, Francisco Pereira
N1 - 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.
PY - 2024/11
Y1 - 2024/11
N2 - 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.
AB - 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.
KW - Orthopoxvirus
KW - Epitope prediction
KW - Diagnostics
KW - Machine Learning
KW - Monkeypox Virus
KW - Phylogeny-aware Methods
KW - Humans
KW - Vaccinia virus
KW - Epitopes, B-Lymphocyte
KW - Computational Biology
KW - Phylogeny
UR - https://academic.oup.com/bib/article/25/6/bbae527/7877279
UR - http://www.scopus.com/inward/record.url?scp=85208586771&partnerID=8YFLogxK
U2 - 10.1093/bib/bbae527
DO - 10.1093/bib/bbae527
M3 - Article
SN - 1477-4054
VL - 25
JO - Briefings in Bioinformatics
JF - Briefings in Bioinformatics
IS - 6
M1 - bbae527
ER -