Mutational Analysis of Tradeoffs between Receptor Affinity and Antibody Escape for SARS-CoV-2 Variants of Concern

新出现的SARS-CoV-2变种引起了广泛的兴趣，因为它们可能对当前的疫苗更具抵抗力 和相关的免疫反应。为了了解受体结合结构域 (RBD)突变影响遗传性，因此阐明RBD突变对ACE 2亲和力的影响至关重要 抗体逃逸这一信息很重要，因为RBD突变可以强烈调节ACE 2亲和力， 这与病毒感染性的变化有关，抗体逃逸与抗体的变化有关， 中和效力。此外，该信息也很重要，因为在以下方面之间存在固有的权衡： ACE 2亲和力和抗体逃逸，因为许多强烈增加一种性质的RBD突变也强烈增加 减少另一个属性，这表明孤立地评估任何一个属性都不太可能解释RBD如何 突变影响SARS-CoV-2的传播性。因此，Tessier实验室开发了机器学习 模型来描述单位点和多位点RBD突变对ACE 2亲和力和抗体逃逸的影响。这 该方法使用大型但稀疏采样的实验数据集来测量单站点和多站点的影响 RBD突变对ACE 2亲和力和抗体逃逸的影响，以训练机器学习模型。接下来，模型是 用于预测实验中不存在的大量额外RBD突变的影响。 数据集。该提案的目标是使用机器学习模型和多种实验技术， 预测和实验评估其他RBD突变对关注变体的影响，例如 δ变体，对ACE 2亲和力和抗体逃逸的影响。假设模型能够识别 在强烈调节ACE 2的关键关注变异体的RBD中存在额外的单位点和多位点突变 亲和力和/或抗体逃逸。为了检验这一假设，在目标1中，预测额外的单一 并且将测试关注变体的RBD中对ACE 2亲和力和感染性的多位点突变。这 目的将涉及使用i）RBD的酵母表面展示和流式细胞术来测量 ACE 2亲和力，和ii）假病毒测定以测量感染性。不会生成或测试活病毒， 这项工作接下来，在目标2中，预测RBD中额外的单位点和多位点突变的影响， 将检测抗体逃逸和中和相关变体。人类血清样本 可以使用的供体来自感染、接种或感染并随后接种的供体。这 目的将涉及使用i）RBD的酵母表面展示和流式细胞术来测试模型预测， 测量抗体结合，和ii）假病毒测定以测量抗体中和。需要一个键 结果将是优化和验证模型，可用于帮助快速识别 最具威胁性的新出现的SARS-CoV-2变种。这种综合的实验和计算方法 在改进疫苗和治疗性抗体开发中具有巨大的潜力， 未来的流行病

Emerging SARS-CoV-2 variants are of broad interest because they may be more resistant to current vaccines and associated immune responses. Toward the long-term goal of understanding how receptor-binding domain (RBD) mutations impact transmissibility, it is critical to elucidate the impacts of RBD mutations on ACE2 affinity and antibody escape. This information is important because RBD mutations can strongly modulate ACE2 affinity, which is linked to changes in viral infectivity, and antibody escape, which is linked to changes in antibody neutralization potency. Moreover, this information is also important because of the inherent tradeoffs between ACE2 affinity and antibody escape, as many RBD mutations that strongly increase one property also strongly decrease the other property, suggesting that evaluating either property in isolation is unlikely to explain how RBD mutations impact SARS-CoV-2 transmissibility. Therefore, the Tessier lab has developed machine learning models to describe the impact of single and multisite RBD mutations on ACE2 affinity and antibody escape. This approach uses large but sparsely sampled experimental datasets that measure the impact of single and multisite RBD mutations on ACE2 affinity and antibody escape to train machine learning models. Next, the models are used to predict the impact of vast numbers of additional RBD mutations that are absent in the experimental datasets. The goal of this proposal is to use machine learning models and multiple experimental techniques to predict and experimentally evaluate the impacts of additional RBD mutations in Variants of Concern, such as the Delta variant, on ACE2 affinity and antibody escape. The hypothesis is that the models will be able to identify additional single and multisite mutations in the RBDs of key Variants of Concern that strongly modulate ACE2 affinity and/or antibody escape. To test this hypothesis, in Aim 1, predictions of the impact of additional single and multisite mutations in the RBDs of Variants of Concern on ACE2 affinity and infectivity will be tested. This Aim will involve testing these predictions using i) yeast surface display of RBDs and flow cytometry to measure ACE2 affinity, and ii) pseudovirus assays to measure infectivity. No live viruses will be generated or tested in this work. Next, in Aim 2, predictions of the impact of additional single and multisite mutations in the RBDs of Variants of Concern on antibody escape and neutralization will be tested. The human serum samples that will be used are from donors that were either infected, vaccinated, or infected and subsequently vaccinated. This Aim will involve testing the model predictions using i) yeast surface display of RBDs and flow cytometry to measure antibody binding, and ii) pseudovirus assays to measure antibody neutralization. A key expected outcome will be the optimization and validation of models that can be used to aid in the rapid identification of the most threatening emerging SARS-CoV-2 variants. This integrated experimental and computational approach holds great potential for use in improving vaccine and therapeutic antibody development to address current and future pandemics.