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

SARS-CoV-2 相关变体的受体亲和力与抗体逃逸之间权衡的突变分析

• 批准号: 10647809
• 负责人: Peter M Tessier
• 金额: $ 18.57万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-16 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10647809
• 关键词: 2019-nCoV; ACE2; Acceleration; Address; Affinity; Antibodies; Binding; Biological Assay; COVID-19 pandemic; COVID-19 vaccine; Computer Models; Data Set; Development; Evaluation; Flow Cytometry; Future; Goals; Human; Humanities; Immune response; Infection; Link; Measures; Modeling; Mutation; Mutation Analysis; Outcome; Property; Proteins; Research; Resistance; SARS-CoV-2 B.1.617.2; SARS-CoV-2 P.1; SARS-CoV-2 variant; Sampling; Serum; Site; Surface; Techniques; Testing; Therapeutic antibodies; Time; Training; Vaccinated; Vaccination; Vaccines; Validation; Variant; Viral; Virus; Work; Yeasts; current pandemic; deep sequencing; defined contribution; future pandemic; improved; interest; machine learning model; machine learning prediction; mutant; neutralizing antibody; novel; novel strategies; predictive modeling; receptor; receptor binding; response; vaccine distribution; vaccine-induced antibodies; variants of concern

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.

新兴的SARS-COV-2变体具有广泛的兴趣，因为它们可能对当前疫苗具有更大的抵抗力 和相关的免疫反应。朝着理解受体结合领域的长期目标 （RBD）突变会影响传播性，阐明RBD突变对ACE2亲和力的影响至关重要 和抗体逃脱。此信息很重要，因为RBD突变可以强烈调节ACE2亲和力， 这与病毒感染性的变化和抗体逃脱有关，这与抗体的变化有关 中和效力。此外，此信息也很重要，因为 ACE2亲和力和抗体逃脱，因为许多RBD突变也强烈增加了一个特性 降低另一个特性，表明在孤立中评估任何一个特性都不可能解释RBD 突变会影响SARS-COV-2的传播。因此，Tessier实验室已经开发了机器学习 描述单个和多站点RBD突变对ACE2亲和力和抗体逃逸的影响。这 方法使用大型但稀少的实验数据集，这些数据集测量了单一和多站点的影响 关于ACE2亲和力和抗体的RBD突变逃避了训练机器学习模型。接下来，模型是 用于预测实验中不存在的大量其他RBD突变的影响 数据集。该建议的目的是使用机器学习模型和多种实验技术来 预测和实验评估其他RBD突变在关注的变体中的影响，例如 delta变体，在ACE2亲和力和抗体逃逸。假设是模型将能够识别 关键变体的RBD中的其他单一和多站点突变，这些变体强烈调节ACE2 亲和力和/或抗体逃脱。在AIM 1中检验这一假设，预测了其他单一的影响 将测试有关ACE2亲和力和感染性的RBD中的多站点突变。这 AIM将涉及使用i）酵母表面显示RBD和流式细胞术的酵母表面显示这些预测 ACE2亲和力，ii）假病毒测定法测量感染力。不会在 这项工作。接下来，在AIM 2中，预测了其他单点突变在RBD中的影响 将测试对抗体逃生和中和的关注变体。人类血清样品将 使用的是从感染，接种疫苗或感染并随后接种疫苗的捐助者。这 AIM将涉及使用i）酵母表面显示RBD和流式细胞术的模型预测 测量抗体结合，ii）假病毒测定法测量抗体中和。预期的关键 结果将是可用于快速识别的模型的优化和验证 最威胁性的新兴SARS-COV-2变体。这种集成的实验和计算方法 在改善疫苗和治疗性抗体开发方面拥有巨大的潜力，以解决当前和 未来的大流行。