A large-scale systematic survey reveals recurring molecular features of public antibody responses to SARS-CoV-2.
A large-scale systematic survey reveals recurring molecular features of public antibody responses to SARS-CoV-2.
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DOI：
10.1016/j.immuni.2022.03.019
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发表时间：
2022-06-14
期刊：
影响因子：
32.4
通讯作者：
Wu NC
中科院分区：
文献类型：
--
作者：
Wang Y;Yuan M;Lv H;Peng J;Wilson IA;Wu NC
全球抗击 COVID-19 大流行的研究已经分离和表征了数千种针对 SARS-CoV-2 刺突蛋白的人类抗体，为研究抗体对单一抗原的反应提供了前所未有的机会。利用来自 88 篇研究出版物和 13 项专利的信息，我们收集了来自超过 200 名捐赠者的约 8,000 种针对 SARS-CoV-2 刺突蛋白的人类抗体的数据集。通过分析免疫球蛋白 V 和 D 基因的使用、互补决定区 H3 序列和体细胞超突变，我们证明对刺突蛋白不同结构域的共同（公共）反应有很大不同。我们进一步使用这些序列来训练深度学习模型，以准确地区分 SARS-CoV-2 刺突蛋白的人类抗体和流感血凝素蛋白的人类抗体。总的来说，这项研究为抗体研究提供了信息资源，并增强了我们对公共抗体反应的分子理解。收集了来自超过 200 个供体的约 8,000 个已发表的 SARS-CoV-2 S 抗体的数据集 RBD、NTD 和 S2 的抗体具有独特的收敛序列和分子特征 公共抗体克隆型显示重复的亲和力成熟途径 为使用深度学习的抗体特异性预测提供了概念证明 自 COVID-19 大流行开始以来，SARS-CoV-2 刺突蛋白抗体的分离一直是主要的研究重点。王等人。分析了约 8,000 种已发表的针对刺突蛋白的人类单克隆抗体，并确定了针对 SARS-CoV-2 的公共抗体反应的序列和分子特征。结果使得能够构建基于序列的深度学习模型来预测抗体特异性。
Global research to combat the COVID-19 pandemic has led to the isolation and characterization of thousands of human antibodies to the SARS-CoV-2 spike protein, providing an unprecedented opportunity to study the antibody response to a single antigen. Using the information derived from 88 research publications and 13 patents, we assembled a dataset of ∼8,000 human antibodies to the SARS-CoV-2 spike protein from >200 donors. By analyzing immunoglobulin V and D gene usages, complementarity-determining region H3 sequences, and somatic hypermutations, we demonstrated that the common (public) responses to different domains of the spike protein were quite different. We further used these sequences to train a deep-learning model to accurately distinguish between the human antibodies to SARS-CoV-2 spike protein and those to influenza hemagglutinin protein. Overall, this study provides an informative resource for antibody research and enhances our molecular understanding of public antibody responses. Assembled a dataset of ∼8,000 published antibodies to SARS-CoV-2 S from >200 donors Antibodies to RBD, NTD, and S2 have distinct convergent sequence and molecular features Public antibody clonotypes show recurring affinity maturation pathway Provided a proof of concept for antibody specificity prediction using deep learning Since the start of the COVID-19 pandemic, the isolation of antibodies to SARS-CoV-2 spike protein has been a major research focus. Wang et al. analyzed ∼8,000 published human monoclonal antibodies to the spike protein and identified sequence and molecular features of the public antibody responses to SARS-CoV-2. The results enable the construction of a sequence-based, deep-learning model to predict antibody specificity.
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