Graduate Program in Biophysical Sciences, University of Chicago, Chicago, United States.
Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, United States.
Elife. 2020 Nov 10;9:e61393. doi: 10.7554/eLife.61393.
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.
抗体是适应性免疫的关键组成部分，能够与病原体表位高亲和力结合。抗体经过严格的选择以实现这种高亲和力，但有些抗体仍保持着额外的低亲和力基础水平，对多种表位具有广泛的反应性，这种现象被称为“多反应性”。虽然在从各种免疫部位分离的抗体中观察到了多反应性，但对于在选择用于与单一靶标高亲和力结合的蛋白质中允许混杂的生物物理特性仍不清楚。我们使用了一个包含 1000 多个多反应性和非多反应性抗体序列的数据库，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。这些决定因素包括增加环间串扰和中性结合表面的倾向，足以产生一个能够以超过 75%的准确率识别多反应性抗体的分类器。构建这个分类器的框架是可推广的，代表了未来免疫受体库分析的一种强大的、自动化的管道。
