论文标题
COVID-19抗体疗法的评论
Review of COVID-19 Antibody Therapies
论文作者
论文摘要
在2019年冠状病毒疾病引起的全球卫生紧急情况下，迫切需要高效和特定的疗法。与传统的小分子药物相比，抗体疗法相对易于开发，并且与靶向严重急性呼吸综合征冠状病毒2（SARS-COV-2）的疫苗一样具体，因此在过去几个月中引起了很多关注。这项工作回顾了七种现有的SARS-COV-2尖峰蛋白蛋白的抗体，这些抗体具有沉积在蛋白质数据库中的三维（3D）结构。评估了与SARS-COV相关的五种抗体结构，以中和SARS-COV-2的潜力。将这些抗体与S蛋白受体结合结构域（RBD）的相互作用与血管紧张素转化酶2（ACE2）和RBD复合物的相互作用。由于实验结合亲和力的差异中的数量级，我们引入了拓扑数据分析（TDA），多种网络模型以及深度学习，以分析上述四种抗体 - 抗体 - 抗原抗原复合物的结合强度和治疗潜力。还审查了当前的CoVID-19抗体临床试验，不仅限于S蛋白靶标。
Under the global health emergency caused by coronavirus disease 2019 (COVID-19), efficient and specific therapies are urgently needed. Compared with traditional small-molecular drugs, antibody therapies are relatively easy to develop and as specific as vaccines in targeting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and thus attract much attention in the past few months. This work reviews seven existing antibodies for SARS-CoV-2 spike (S) protein with three-dimensional (3D) structures deposited in the Protein Data Bank. Five antibody structures associated with SARS-CoV are evaluated for their potential in neutralizing SARS-CoV-2. The interactions of these antibodies with the S protein receptor-binding domain (RBD) are compared with those of angiotensin-converting enzyme 2 (ACE2) and RBD complexes. Due to the orders of magnitude in the discrepancies of experimental binding affinities, we introduce topological data analysis (TDA), a variety of network models, and deep learning to analyze the binding strength and therapeutic potential of the aforementioned fourteen antibody-antigen complexes. The current COVID-19 antibody clinical trials, which are not limited to the S protein target, are also reviewed.
