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 以及相关的免疫反应。朝着了解受体结合域是如何 (RBD)突变影响遗传性，阐明RBD突变对ACE2亲和力的影响至关重要 抗体逃逸。这一信息很重要，因为RBD突变可以强烈地调节ACE2的亲和力， 这与病毒传染性的变化和抗体逃逸有关，抗体逃逸与抗体的变化有关 中和效力。此外，这一信息也很重要，因为两者之间存在内在的权衡 ACE2亲和力和抗体逃逸，因为许多RBD突变强烈增加一种特性也强烈 减少另一个属性，这表明单独评估任一属性不太可能解释RBD如何 基因突变影响SARS-CoV-2的传播性。因此，特西尔实验室开发了机器学习 描述单个和多个RBD突变对ACE2亲和力和抗体逃逸影响的模型。这 方法使用大型但稀疏抽样的实验数据集来衡量单站点和多站点的影响 RBD突变对ACE2亲和力和抗体逃逸的影响用来训练机器学习模型。接下来，模特是 用于预测大量额外的RBD突变的影响，这些突变在实验中是没有的 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来 预测和实验评估额外的RBD突变在令人关注的变体中的影响，例如 Delta变异体，对ACE2亲和力和抗体逃逸的影响。假设是模型将能够识别 强烈调控血管紧张素转换酶2的关键变异的rbds中的额外单和多位点突变 亲和力和/或抗体逃逸。为了检验这一假设，在目标1中，预测了额外的单曲的影响 并将测试ACE2亲和力和传染性上令人担忧的变体的rbds的多点突变。这 AIM将涉及使用i)rbds的酵母表面展示和流式细胞术测量来验证这些预测 ACE2亲和力，以及ii)假病毒检测以衡量传染性。不会生成或测试任何活病毒 这项工作。接下来，在目标2中，对rbds中额外的单和多点突变的影响进行了预测。 关于抗体逃逸和中和的变种将进行测试。人类血清样本将会 可使用的是来自被感染、接种疫苗或感染并随后接种疫苗的捐赠者。这 AIM将涉及使用i)rbds的酵母表面展示和流式细胞术来测试模型预测 测量抗体结合力，以及ii)假病毒检测以测量抗体中和。预期的关键字 结果将是优化和验证可用于帮助快速识别 最具威胁的新出现的SARS-CoV-2变种。这种实验和计算相结合的方法 在改进疫苗和治疗性抗体开发方面具有巨大潜力，以解决当前和 未来的大流行。