Designing neutralization antibodies against Sars-Cov-2

项目摘要 新冠肺炎已成为一种世界性流行病，其迅速传播和死亡率威胁着数以百万计的生命 以及全球经济体系。开发有效的治疗方法，如中和抗体是当务之急 需要。我们建议开发一种新的方法来设计与SARS-CoV-2强结合的抗体 病毒进入人体细胞所必需的受体结合域(RBD)。我们将写一部小说 将定向进化、深度排序和可解释神经网络模型相结合的方法 高效识别强而特异的抗体。该方法将允许分析大型测序数据集 针对SARS-CoV-2 RBD的抗体变异体的研究，以获得在 原创图书馆。通过定向进化和计算设计的迭代将有效地识别 中和抗体候选，可作为治疗新冠肺炎的有效疗法。

Project Summary COVID-19 has become a worldwide pandemic whose rapid spread and mortality rate threatens millions of lives and the global economic system. Developing effective treatment such as neutralization antibodies is an urgent need. We propose here to develop a new method to design antibodies strongly bind to the SARS-CoV-2 receptor binding domain (RBD) that is necessary for viral entrance to human cells. We will develop a novel approach that combines directed evolution, deep sequencing and interpretable neural network models to efficiently identify strong and specific antibodies. This method will allow analyzing large sequencing data sets of antibody variants against the SARS-CoV-2 RBD in order to derive superior binders that do not exist in the original library. Iteration through directed evolution and computational design will efficiently identify neutralization antibody candidates that can be used as potent therapeutics to treat COVID-19.