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 nearly 1,500 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 an "inoffensive" 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.

中文翻译：

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通过基于生物信息学的CDR环分析揭示抗体多反应性的生化模式。

抗体是适应性免疫的关键组成部分，与病原体表位具有高亲和力。抗体经过严格选择以实现这种高亲和力，但有些抗体仍维持较低的亲和力的基础水平，对各种表位具有广泛的反应性，这种现象称为“多反应性”。尽管在从各种免疫小生境分离的抗体中观察到了多反应性，但尚不清楚允许以高亲和力结合到单个靶标选择的蛋白质混杂的生物物理特性。使用包含近1,500个多反应性和非多反应性抗体序列的数据库，我们创建了一条生物信息学流水线以分离多反应性的关键决定因素。这些决定因素包括回路间串扰的增加和“进攻性”倾向 结合表面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。建立该分类器的框架是可推广的，并且代表了用于将来的免疫库分析的强大，自动化的管道。