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arXiv 2011.03112q-bio.PEq-bio.QM
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使用soNNia的T和B细胞受体库的深度生成选择模型

Deep generative selection models of T and B cell receptor repertoires with soNNia

Giulio Isacchini, Aleksandra M Walczak, Thierry Mora, Armita Nourmohammad

摘要:

淋巴细胞的亚类具有不同的功能作用,共同产生免疫反应和持久的免疫力。除了这些功能作用之外,T和B细胞淋巴细胞还依赖于其受体链的多样性来识别不同的病原体。淋巴细胞亚类来自在选择过程中具有相同多样性的受体产生的共同祖先。在这里,我们利用受体生成的生物物理模型与选择的机器学习模型来识别功能性淋巴细胞库和亚库的特定序列特征。具体地说,仅使用库水平的序列信息,我们对CD4 $^+$和CD8 $^+$ T细胞进行分类,发现在选择过程中产生的受体链之间的相关性,并鉴定作为致病性表位靶标的T细胞亚群。我们还展示了简单的线性分类器以及更复杂的机器学习方法的例子。

英文摘要:

Subclasses of lymphocytes carry different functional roles to work together to produce an immune response and lasting immunity. Additionally to these functional roles, T and B-cell lymphocytes rely on the diversity of their receptor chains to recognize different pathogens. The lymphocyte subclasses emerge from common ancestors generated with the same diversity of receptors during selection processes. Here we leverage biophysical models of receptor generation with machine learning models of selection to identify specific sequence features characteristic of functional lymphocyte repertoires and subrepertoires. Specifically using only repertoire level sequence information, we classify CD4$^+$ and CD8$^+$ T-cells, find correlations between receptor chains arising during selection and identify T-cells subsets that are targets of pathogenic epitopes. We also show examples of when simple linear classifiers do as well as more complex machine learning methods.

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。

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