School of Engineering Mathematics and Technology, University of Bristol, Ada Lovelace Building, Tankard's Close BS8 1TW, Bristol, United Kingdom.
Post-Graduate Program in Infectious Diseases and Tropical Medicine, School of Medicine, Federal University of Minas Gerais, Av. Prof. Alfredo Balena 190, 30130-100, Belo Horizonte, Brazil.
Brief Bioinform. 2024 Sep 23;25(6). doi: 10.1093/bib/bbae527.
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.
我们引入了一种基于系统发育的框架,用于预测蛋白质中线性 B 细胞表位 (LBCE) 所在的区域。我们的方法利用进化信息,使用分类学支架在分层结构的数据上构建模型。与使用更简单的特征和当前最先进的预测器所需数据量的一小部分的一般方法相比,所得到的模型具有相当或更高的性能。这允许利用主要病原体谱系的现有数据来促进新兴传染病原体的 LBCE 预测。我们通过预测猴痘 (MPXV) 和牛痘病毒中的新 LBCE 来证明我们方法的有效性。使用感染患者的血清对选定靶标的实验验证证实了 LBCE 的存在,包括用于最近 MPXV 感染的差异血清诊断的候选物。这些结果表明,使用基于系统发育的预测器是一种有用的策略,可以促进免疫诊断工具的有针对性开发。