Department of Biological Systems Engineering, Virginia Tech, Blacksburg, Virginia, United States of America.
PLoS One. 2022 Mar 29;17(3):e0265644. doi: 10.1371/journal.pone.0265644. eCollection 2022.
The human leukocyte antigen (HLA) gene complex, one of the most diverse gene complexes found in the human genome, largely dictates how our immune systems recognize pathogens. Specifically, HLA genetic variability has been linked to vaccine effectiveness in humans and it has likely played some role in the shortcomings of the numerous human vaccines that have failed clinical trials. This variability is largely impossible to evaluate in animal models, however, as their immune systems generally 1) lack the diversity of the HLA complex and/or 2) express major histocompatibility complex (MHC) receptors that differ in specificity when compared to human MHC. In order to effectively engage the majority of human MHC receptors during vaccine design, here, we describe the use of HLA population frequency data from the USA and MHC epitope prediction software to facilitate the in silico mining of universal helper T cell epitopes and the subsequent design of a universal human immunogen using these predictions. This research highlights a novel approach to using in silico prediction software and data processing to direct vaccine development efforts.
人类白细胞抗原（HLA）基因复合体是人类基因组中发现的最多样化的基因复合体之一，在很大程度上决定了我们的免疫系统如何识别病原体。具体来说，HLA 的遗传变异性与人类疫苗的有效性有关，而且它可能在许多临床试验失败的人类疫苗的缺陷中发挥了一定作用。然而，这种变异性在动物模型中基本上是不可能评估的，因为它们的免疫系统通常 1）缺乏 HLA 复合体的多样性，或者 2）表达的主要组织相容性复合体（MHC）受体在特异性上与人类 MHC 不同。为了在疫苗设计过程中有效地与大多数人类 MHC 受体结合，在这里，我们描述了使用来自美国的 HLA 群体频率数据和 MHC 表位预测软件，以促进通用辅助 T 细胞表位的计算机挖掘，并使用这些预测随后设计通用的人类免疫原。这项研究强调了一种使用计算机预测软件和数据处理来指导疫苗开发工作的新方法。
