使用B细胞受体谱系结构预测亲和力
Using B cell receptor lineage structures to predict affinity
摘要：
我们经常面对大量的抗体，并希望选择对其同源抗原具有最高亲和力的抗体。例如，当开发一种新病原体的一线治疗方法时，我们可能会在已经康复的患者中寻找这种抗体。存在有效的实验方法来实现这一点，例如细胞分选和诱饵;然而，它们耗时且昂贵。下一代B细胞受体（BCR）库测序提供了一个额外的序列来源，如果我们有一个可靠的方法来选择那些编码最好的抗体，可以利用。在本文中，我们介绍了一种方法，该方法使用来自共享一个幼稚祖先的相关序列家族的进化信息来预测每个所得抗体对其抗原的亲和力。当结合抗原的身份信息时，这种方法应该提供有效的新抗体的来源。我们还介绍了一个相关任务的方法：给定一个感兴趣的抗体及其推断的祖先谱系，树中的哪些分支可能含有关键的亲和力增加突变？这些方法是作为partis BCR推理包的持续开发的一部分实现的，可在https://github.com/psathyrella/partis上获得。
英文摘要：
We are frequently faced with a large collection of antibodies, and want to select those with highest affinity for their cognate antigen. When developing a first-line therapeutic for a novel pathogen, for instance, we might look for such antibodies in patients that have recovered. There exist effective experimental methods of accomplishing this, such as cell sorting and baiting; however they are time consuming and expensive. Next generation sequencing of B cell receptor (BCR) repertoires offers an additional source of sequences that could be tapped if we had a reliable method of selecting those coding for the best antibodies. In this paper we introduce a method that uses evolutionary information from the family of related sequences that share a naive ancestor to predict the affinity of each resulting antibody for its antigen. When combined with information on the identity of the antigen, this method should provide a source of effective new antibodies. We also introduce a method for a related task: given an antibody of interest and its inferred ancestral lineage, which branches in the tree are likely to harbor key affinity-increasing mutations? These methods are implemented as part of continuing development of the partis BCR inference package, available at https://github.com/psathyrella/partis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
