Nyárády Z, Czömpöly T, Bosze Sz, Nagy G, Petrohai A, Pál J, Hudecz F, Berki T, Németh P
Department of Immunology and Biotechnology, University of Pécs, Szigeti út 12, H-7643 Pécs, Faculty of Medicine, Hungary.
Mol Immunol. 2006 Mar;43(7):830-8. doi: 10.1016/j.molimm.2005.06.044. Epub 2005 Aug 8.
In silico antibody-antigen binding predictions are generally employed in research to rationalize epitope development. These techniques are widely spread despite their technical limitations. To validate the results of these bioinformatic calculations evidence based comparative in vitro studies are necessary. We have used a well-conserved mitochondrial inner membrane antigen-citrate synthase to develop a model for comparative analysis of the predicted and the immunoserologically verified epitopes of circulating autoantibodies. Epitopes were predicted using accepted tools: the GCG Wisconsin package and TEPITOPE 2000. An overlapping multipin ELISA assay--covering 49% of the citrate synthase molecule--was developed to map autoantibody epitopes of individuals (healthy, systemic autoimmune, and heart transplanted) in different immunopathological conditions. From the 40 synthesized decapeptides 34 were predicted in silico and 27 were validated in vitro. Thirty-two percent of epitopes were recognized by majority of sera 47% by at least one sera. False positive predictions were 21%. There was major difference in the recognized epitope pattern under different immunopathological conditions. Our results suggest that special databases are needed for training and weighing prediction methods by clinically well-characterized samples, due to the differences in the immune response under different health status. The development of these special algorithms needs a new approach. A high number of samples under these special immunological conditions are to be mapped and then used for the "fine tuning" of different prediction algorithms.
在计算机上进行抗体 - 抗原结合预测通常用于研究中,以使表位开发合理化。尽管存在技术局限性,但这些技术仍广泛应用。为了验证这些生物信息学计算的结果,基于证据的比较体外研究是必要的。我们使用一种高度保守的线粒体内膜抗原 - 柠檬酸合酶来建立一个模型,用于比较分析循环自身抗体的预测表位和免疫血清学验证的表位。使用公认的工具(GCG Wisconsin软件包和TEPITOPE 2000)预测表位。开发了一种覆盖柠檬酸合酶分子49%的重叠多针ELISA检测方法,以绘制不同免疫病理条件下个体(健康、系统性自身免疫和心脏移植个体)的自身抗体表位图谱。在40个合成的十肽中,34个在计算机上被预测,27个在体外得到验证。32%的表位被大多数血清识别,47%的表位被至少一种血清识别。假阳性预测率为21%。在不同免疫病理条件下,识别的表位模式存在主要差异。我们的结果表明,由于不同健康状况下免疫反应的差异,需要特殊的数据库来通过临床特征明确的样本训练和权衡预测方法。这些特殊算法的开发需要一种新方法。需要对这些特殊免疫条件下的大量样本进行图谱绘制,然后用于不同预测算法的“微调”。