Bioinformatics-Based Predictions of Peptide Binding to Disease-Associated HLA Proteins Suggest Explanation for Shared Autoimmunity

J. Glutting, P. Reche, M. Fridkis-Hareli
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Abstract

Aim This study was designed to examine the immunogenetic basis for shared autoimmunity, resulting in autoantigen presentation that leads to the production of two or more disease-specific autoantibodies. Methods A bioinformatics approach based on peptide binding predictions to disease-associated HLA determinants has been developed and tested here using 11 disease associations between autoimmune systemic and mucocutaneous blistering disorders. Various HLAs associated with antigens within a given “disease model” (set of HLA class II and protein sequences known to be associated with a specific autoimmune disease) were tested and ranked against the antigenic proteins, first with proteins they are known to associate with and then with proteins known to be implicated in a second disease model. In every case binding predictions were compared for different proteins binding to the same HLA. Subsequently, disease-related autoantigens have been tested for their binding affinity against each disease-specific HLA class II protein. Results For a single HLA haplotype, several binders have been generated from a related autoantigen with the variable binding score. In most cases, the binding score corresponding to the interactions between the autoantigen-derived epitope and the HLA associated with one disease was similar or lower than the interactions between the epitope from proteins associated with the second disease and the same HLA. Notably, there was no compelling promiscuity in peptide binding to each of the HLA molecules, in spite of the promiscuous nature of HLA class II binding. Conclusions The data suggest that, in susceptible individuals, shared autoimmunity might be initiated by two types of HLA/peptide interaction; first between an autoantigen-derived epitope and its disease-associated HLA molecules, and second, between a different peptide of the same autoantigen and HLA proteins specific for the second disease.
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基于生物信息学的多肽结合疾病相关HLA蛋白的预测为共同自身免疫提供了解释
目的:本研究旨在探讨共同自身免疫的免疫遗传学基础,共同自身免疫导致自身抗原呈递,从而产生两种或多种疾病特异性自身抗体。方法一种基于多肽结合预测疾病相关HLA决定因素的生物信息学方法已经开发出来,并在这里使用自身免疫系统和粘膜皮肤水泡疾病之间的11种疾病关联进行了测试。在给定的“疾病模型”(一组已知与特定自身免疫性疾病相关的HLA II类和蛋白质序列)中检测与抗原相关的各种HLA,并根据抗原蛋白进行排序,首先与已知与它们相关的蛋白质进行排序,然后与已知与第二种疾病模型相关的蛋白质进行排序。在每种情况下,结合预测比较不同的蛋白质结合到相同的HLA。随后,疾病相关的自身抗原已被检测其对每种疾病特异性HLA II类蛋白的结合亲和力。结果对于单个HLA单倍型,从相关的自身抗原中产生了多种结合物,其结合分数不同。在大多数情况下,自身抗原衍生表位与一种疾病相关的HLA相互作用对应的结合评分与第二种疾病相关蛋白的表位与相同HLA的相互作用相似或更低。值得注意的是,尽管HLA II类结合具有混杂性,但肽与每个HLA分子的结合没有明显的混杂性。结论:在易感人群中,共享自身免疫可能由两种类型的HLA/肽相互作用启动;首先是自身抗原衍生表位与其疾病相关的HLA分子之间的差异,其次是同一自身抗原的不同肽与第二种疾病特异性的HLA蛋白之间的差异。
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