Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
Department of Aquaculture, National Penghu University of Science and Technology, Penghu 88046, Taiwan.
Viruses. 2022 Jun 22;14(7):1357. doi: 10.3390/v14071357.
infection is one of the leading causes of death in commercial fish. Although many vaccines against this virus family have been developed, their efficacies are relatively low. are categorized into three subfamilies: alphanodavirus (infects insects), betanodavirus (infects fish), and gammanodavirus (infects prawns). These three subfamilies possess host-specific characteristics that could be used to identify effective linear epitopes (LEs).
A multi-expert system using five existing LE prediction servers was established to obtain initial LE candidates. Based on the different clustered pathogen groups, both conserved and exclusive LEs among the family could be identified. The advantages of undocumented cross infection among the different host species for the family were applied to re-evaluate the impact of LE prediction. The surface structural characteristics of the identified conserved and unique LEs were confirmed through 3D structural analysis, and concepts of surface patches to analyze the spatial characteristics and physicochemical propensities of the predicted segments were proposed. In addition, an intelligent classifier based on the Immune Epitope Database (IEDB) dataset was utilized to review the predicted segments, and enzyme-linked immunosorbent assays (ELISAs) were performed to identify host-specific LEs.
We predicted 29 LEs for . The analysis of the surface patches showed common tendencies regarding shape, curvedness, and PH features for the predicted LEs. Among them, five predicted exclusive LEs for fish species were selected and synthesized, and the corresponding ELISAs for antigenic feature analysis were examined.
Five identified LEs possessed antigenicity and host specificity for grouper fish. We demonstrate that the proposed method provides an effective approach for in silico LE prediction prior to vaccine development and is especially powerful for analyzing antigen sequences with exclusive features among clustered antigen groups.
感染是商业鱼类死亡的主要原因之一。尽管已经开发了许多针对这种病毒家族的疫苗，但它们的疗效相对较低。正粘病毒科分为三个亚科：甲型（感染昆虫）、乙型（感染鱼类）和丙型（感染虾类）。这三个亚科具有宿主特异性特征，可以用来识别有效的线性表位（LE）。
建立了一个使用五个现有 LE 预测服务器的多专家系统，以获得初始 LE 候选物。基于不同的聚类病原体组，可以识别 家族中保守和独特的 LE。针对不同宿主物种之间未记录的交叉感染的优势，用于重新评估 LE 预测的影响。通过 3D 结构分析，确定了鉴定出的保守和独特 LE 的表面结构特征，并提出了用于分析预测片段的空间特征和理化倾向的表面斑块概念。此外，利用基于免疫表位数据库（IEDB）数据集的智能分类器对预测片段进行了回顾，并且进行了酶联免疫吸附测定（ELISA）以鉴定宿主特异性 LE。
我们预测了 29 个 LE。对表面斑块的分析表明，预测的 LE 具有形状、弯曲度和 PH 特征的共同趋势。其中，选择并合成了五个针对鱼类物种的预测独特 LE，并对其进行了抗原特性分析的 ELISA 检测。
五个鉴定的 LE 对石斑鱼具有抗原性和宿主特异性。我们证明，所提出的方法为疫苗开发之前的计算机 LE 预测提供了一种有效方法，并且对于分析聚类抗原组中具有独特特征的抗原序列特别有效。
