Biochemical patterns of antibody polyreactivity revealed through a bioinformatics-based analysis of CDR loops.
Biochemical patterns of antibody polyreactivity revealed through a bioinformatics-based analysis of CDR loops.
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DOI：
10.7554/elife.61393
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发表时间：
2020-11-10
期刊：
影响因子：
7.7
通讯作者：
Adams EJ
中科院分区：
文献类型：
--
作者：
Boughter CT;Borowska MT;Guthmiller JJ;Bendelac A;Wilson PC;Roux B;Adams EJ
抗体是获得性免疫的关键组分，以高亲和力结合致病性表位。抗体经过严格的选择以实现这种高亲和力，但有些抗体保持额外的低亲和力基础水平，对不同表位具有广泛的反应性，这种现象称为“多反应性”。虽然已在从各种免疫学小生境分离的抗体中观察到多反应性，但允许选择用于高亲和力结合单一靶标的蛋白质中的混杂的生物物理性质仍不清楚。使用超过1000个多反应性和非多反应性抗体序列的数据库，我们创建了一个生物信息学管道来分离多反应性的关键决定因素。这些决定因素包括环间串扰的增加和中性结合表面的倾向，足以产生能够以超过75%的准确度鉴定多反应性抗体的分类器。构建该分类器的框架是可推广的，并且代表了用于未来免疫库分析的强大的自动化管道。为了抵御细菌和病毒，身体依赖于一组称为抗体的蛋白质。每个抗体子集都经过严格的训练方案，以确保它能够很好地识别单个表位-也就是外来有害生物表面上的一个特定区域。大多数抗体非常紧密地粘附在它们的一个独特的表位上，但有些抗体也可以与与它们的主要训练目标截然不同的分子微弱地结合。这种被称为多反应性的特征在某些情况下可以帮助免疫系统对抗多种病毒株。另一方面，当抗体在实验室中被设计用于治疗疾病时，这种特性有时会导致临床前试验的失败。然而，目前还不清楚为什么有些抗体是多反应性的，而另一些则不是。为了研究这个问题，Boughter等人比较了来自大型数据库的1,000多个多反应性和非多反应性抗体序列，揭示了抗体与表位连接区域的物理性质差异。利用这些定义特征，Boughter等人继续设计了一种新的免费自动软件，可以预测哪些抗体在75%以上的时间内是多反应性的。这种软件最终可以帮助指导基于抗体的治疗方法的设计，同时绕过昂贵的实验室测试。
Antibodies are critical components of adaptive immunity, binding with high affinity to pathogenic epitopes. Antibodies undergo rigorous selection to achieve this high affinity, yet some maintain an additional basal level of low affinity, broad reactivity to diverse epitopes, a phenomenon termed ‘polyreactivity’. While polyreactivity has been observed in antibodies isolated from various immunological niches, the biophysical properties that allow for promiscuity in a protein selected for high-affinity binding to a single target remain unclear. Using a database of over 1000 polyreactive and non-polyreactive antibody sequences, we created a bioinformatic pipeline to isolate key determinants of polyreactivity. These determinants, which include an increase in inter-loop crosstalk and a propensity for a neutral binding surface, are sufficient to generate a classifier able to identify polyreactive antibodies with over 75% accuracy. The framework from which this classifier was built is generalizable, and represents a powerful, automated pipeline for future immune repertoire analysis. To defend itself against bacteria and viruses, the body depends on a group of proteins known as antibodies. Each subset of antibodies undergoes a rigorous training regimen to ensure it recognizes a single epitope well – that is, one specific region on the surface of foreign, harmful organisms. Most antibodies stick extremely tightly to their one unique epitope, but some can also weakly bind to molecules that are vastly different from their main trained targets. This feature – known as polyreactivity – can in some cases help the immune system fight against multiple strains of viruses. On the other hand, when antibodies are designed in the laboratory to treat diseases, this characteristic can sometimes lead to the failure of pre-clinical trials. Yet it is currently unclear why some antibodies are polyreactive when others are not. To investigate this question, Boughter et al. compared over 1,000 polyreactive and non-polyreactive antibody sequences from a large database, revealing differences in the physical properties of the region of the antibodies that attaches to epitopes. Using these defining features, Boughter et al. went on to design a new piece of freely available, automated software that could predict which antibodies would be polyreactive more than 75% of the time. Such software could ultimately help to guide the design of antibody-based treatments, while bypassing the need for costly laboratory tests.