论文标题
数学人工智能设计耐突变的covid-19单克隆抗体
Mathematical artificial intelligence design of mutation-proof COVID-19 monoclonal antibodies
论文作者
论文摘要
新兴的严重急性呼吸综合症冠状病毒2（SARS-COV-2）变体损害了现有的疫苗，并对2019年冠状病毒病（COVID-19）提出了巨大的挑战，预防，控制和全球经济复苏。对于Covid-19患者，最有效的Covid-19药物之一是单克隆抗体（MAB）疗法。美国食品药品监督管理局（美国FDA）已将紧急用途授权（EUA）授予了一些mAB，包括来自Regeneron，Eli Elly等的MAB。但是，SARS-COV-2突变也受到了破坏。必须开发有效的防突变mAB，以治疗所有新兴变体和/或原始SARS-COV-2感染的COVID-19患者。我们进行了深度的突变扫描，以使用代数拓扑和人工智能（AI）呈现此类mAB的蓝图。为了降低与临床试验相关的衰竭的风险，我们选择了五个用FDA EUA或临床试验的mAB作为我们的起点。我们证明，拓扑AI设计的mAB对世界卫生组织（WHO）以及原始SARS-COV-2指定的关注和感兴趣的变体有效。我们的拓扑AI方法已经通过数万个深突变数据来验证，它们的预测已通过数十实验实验室和数十万患者的基因组分离株的人群级统计的结果证实。
Emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have compromised existing vaccines and posed a grand challenge to coronavirus disease 2019 (COVID-19) prevention, control, and global economic recovery. For COVID-19 patients, one of the most effective COVID-19 medications is monoclonal antibody (mAb) therapies. The United States Food and Drug Administration (U.S. FDA) has given the emergency use authorization (EUA) to a few mAbs, including those from Regeneron, Eli Elly, etc. However, they are also undermined by SARS-CoV-2 mutations. It is imperative to develop effective mutation-proof mAbs for treating COVID-19 patients infected by all emerging variants and/or the original SARS-CoV-2. We carry out a deep mutational scanning to present the blueprint of such mAbs using algebraic topology and artificial intelligence (AI). To reduce the risk of clinical trial-related failure, we select five mAbs either with FDA EUA or in clinical trials as our starting point. We demonstrate that topological AI-designed mAbs are effective to variants of concerns and variants of interest designated by the World Health Organization (WHO), as well as the original SARS-CoV-2. Our topological AI methodologies have been validated by tens of thousands of deep mutational data and their predictions have been confirmed by results from tens of experimental laboratories and population-level statistics of genome isolates from hundreds of thousands of patients.
