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

基本信息

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
项目类别：
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

项目摘要

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 变种引起了广泛关注，因为它们可能对当前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如何发挥作用的长期目标迈进 (RBD) 突变影响传播性，阐明 RBD 突变对 ACE2 亲和力的影响至关重要和抗体逃逸。该信息很重要，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有关，而抗体逃逸则与抗体的变化有关中和效力。此外，该信息也很重要，因为之间存在固有的权衡 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增加减少另一个属性，这表明单独评估任一属性不太可能解释 RBD 如何突变影响 SARS-CoV-2 的传播能力。因此，Tessier实验室开发了机器学习模型描述单位点和多位点 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疏采样的实验数据集来衡量单个和多个站点的影响 ACE2 亲和力和抗体逃逸的 RBD 突变可用于训练机器学习模型。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并通过实验评估关注变体中额外 RBD 突变的影响，例如 Delta 变体，关于 ACE2 亲和力和抗体逃逸。假设模型将能够识别强烈调节 ACE2 的关键关注变体的 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，预测了额外单一因素的影响并将测试关注变体的 RBD 中对 ACE2 亲和力和感染性的多位点突变。这目标将包括使用 i) RBD 的酵母表面展示和流式细胞术来测试这些预测 ACE2 亲和力，以及 ii) 假病毒检测以测量感染性。不会产生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测试有关抗体逃逸和中和的关注变体。人类血清样本将使用的捐赠者要么被感染，要么接种疫苗，要么被感染然后接种疫苗。这目标将包括使用 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，以及 ii) 假病毒测定以测量抗体中和。预期的关键结果将是模型的优化和验证，这些模型可用于帮助快速识别最具威胁性的新兴 SARS-CoV-2 变种。这种综合实验和计算方法在改善疫苗和治疗性抗体开发以解决当前和未来的流行病。