TY - JOUR
T1 - Phylogeny-aware linear B-cell epitope predictor detects targets associated with immune response to orthopoxviruses
AU - Campelo, Felipe
AU - Grossi de Oliveira, Ana Laura
AU - Reis-Cunha, João
AU - Gomes Fraga, Vanessa
AU - Bastos, Pedro Henrique
AU - Ashford, Jodie
AU - Ekárt, Anikó
AU - Ribeiro Adelino, Talita
AU - Silva, Marcos Vinicius
AU - de Melo Iani, Felipe
AU - Parreiras de Jesus, Augusto Cesar
AU - Bartholomeu, Daniella
AU - de Souza Trindade, Giliane
AU - Fujiwara, Ricardo
AU - Lacerda Bueno, Lilian
AU - Pereira Lobo, Francisco
N1 - Publisher Copyright:
© 2024 The Author(s). Published by Oxford University Press.
PY - 2024/11/6
Y1 - 2024/11/6
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 (VACV) 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 phylogenyaware 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 (VACV) 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 phylogenyaware predictors as a useful strategy to facilitate the targeted development of immunodiagnostic tools.
U2 - 10.1093/bib/bbae527
DO - 10.1093/bib/bbae527
M3 - Article (Academic Journal)
C2 - 39503522
SN - 1467-5463
VL - 25
JO - Briefings in Bioinformatics
JF - Briefings in Bioinformatics
IS - 6
M1 - bbae527
ER -