使用公共数据库数据预测B细胞受体替代谱
Predicting B Cell Receptor Substitution Profiles Using Public Repertoire Data
摘要：
B细胞在亲和力成熟过程中产生高亲和力受体，这是一个突变和选择的循环过程。在亲和力成熟结束时，许多共享相同祖先（即在相同的“克隆家族”中）的细胞从生发中心释放，它们的氨基酸频率分布反映了每个位置上允许和不允许的取代。这些克隆家族特异性频率谱，称为“取代谱”，可用于研究亲和力成熟过程以及抗体工程目的。然而，在测序实验中，通常仅从每个克隆家族回收单个序列，使得不可能构建克隆家族特异性取代谱。鉴于许多高质量的大型B细胞受体数据集的公开发布，人们可能会问是否有可能在克隆家族特异性取代谱的预测模型中使用这些数据。在本文中，我们提出了方法“使用相关家族的替代配置文件”（SPURF），惩罚张量回归框架，集成了丰富的数据集组合的信息，以预测任何单一输入序列的克隆家族特异性替代配置文件。使用这个框架，我们表明，类似的克隆家族的替代配置文件可以利用模拟的替代配置文件和种系基因序列信息，以提高预测。我们在一个大型公共数据集上拟合了这个模型，并在外部数据集上验证了我们的方法的鲁棒性。此外，我们在开源软件包（https：//github.com/krdav/SPURF）中提供了一个命令行工具，可以实现这些想法，并使用我们的预拟合模型提供简单的预测。
英文摘要：
B cells develop high affinity receptors during the course of affinity maturation, a cyclic process of mutation and selection. At the end of affinity maturation, a number of cells sharing the same ancestor (i.e. in the same "clonal family") are released from the germinal center, their amino acid frequency profile reflects the allowed and disallowed substitutions at each position. These clonal-family-specific frequency profiles, called "substitution profiles", are useful for studying the course of affinity maturation as well as for antibody engineering purposes. However, most often only a single sequence is recovered from each clonal family in a sequencing experiment, making it impossible to construct a clonal-family-specific substitution profile. Given the public release of many high-quality large B cell receptor datasets, one may ask whether it is possible to use such data in a prediction model for clonal-family-specific substitution profiles. In this paper, we present the method "Substitution Profiles Using Related Families" (SPURF), a penalized tensor regression framework that integrates information from a rich assemblage of datasets to predict the clonal-family-specific substitution profile for any single input sequence. Using this framework, we show that substitution profiles from similar clonal families can be leveraged together with simulated substitution profiles and germline gene sequence information to improve prediction. We fit this model on a large public dataset and validate the robustness of our approach on an external dataset. Furthermore, we provide a command-line tool in an open-source software package (https://github.com/krdav/SPURF) implementing these ideas and providing easy prediction using our pre-fit models.
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。
