EAGER: Functionally Relevant Structural Heterogeneity in Coronavirus SARS-CoV2 Proteins

EAGER：冠状病毒 SARS-CoV2 蛋白的功能相关结构异质性

• 批准号: 2029533
• 负责人: Abbas Ourmazd
• 金额: $ 29.97万
• 依托单位: University of Wisconsin-Milwaukee
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2029533&HistoricalAwards=false
• 关键词: EAGER; Functionally; Relevant; Structural; Heterogeneity

In order to prevent infections by specific viruses, it is important to understand the molecular details that drive the virus into host cells where they replicate, making more viral particles that spread to other cells in the infected individual. This award will help understand the mechanisms of the SARS-CoV2 infectivity, the virus responsible for the current COVID-19 pandemic, by employing machine learning algorithms to make movies of key proteins involved in driving its infection. There is mounting evidence that viral proteins exist in a range of structures, known as conformations, and that these can play a critical role in their function. In this project, recently developed machine-learning techniques will be used to determine the conformational landscape of key SARS-CoV2 proteins at near-atomic level, with and without antibody involvement. Atomistic insight into the conformational changes in SAR-CoV2 proteins is expected to help clarify the structural basis of virulence in this virus and its successors, ultimately providing a foundation for the development of suitable therapeutic strategies against coronaviruses. Using experimental cryo-EM snapshots, this project will map the functionally relevant conformational heterogeneities of key SARS-CoV2 proteins to gain a deeper understanding of the role of conformational heterogeneity in this pandemic virus. The specific goals of this project are as follows: (1) Apply advanced machine-learning algorithms to experimental cryo-EM single-particle snapshots in order to determine the energy landscapes of key SARS-CoV2 proteins with and without antibody involvement; (2) Identify the functionally important conformational paths on the relevant energy landscapes; (3) Compare and contrast motions along functional paths with those inferred by discrete clustering methods; (4) Determine the biological implications of conformational motions associated with function; and (5) Make the results widely accessible in order to help facilitate the development of therapeutic strategies.This RAPID award is made by the Division of Biological Infrastructure (DBI) using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

为了防止被特定病毒感染，重要的是了解驱动病毒进入宿主细胞的分子细节，在宿主细胞中复制，制造更多的病毒颗粒，传播到感染者的其他细胞。该奖项将通过使用机器学习算法制作驱动其感染的关键蛋白质的电影，帮助理解SARS-CoV2传染性的机制，这种病毒是导致当前新冠肺炎大流行的病毒。越来越多的证据表明，病毒蛋白存在于一系列称为构象的结构中，这些结构可以在其功能中发挥关键作用。在这个项目中，最近开发的机器学习技术将被用来在近原子水平上确定关键的SARS-CoV2蛋白的构象图景，在有和没有抗体参与的情况下。对SAR-CoV2蛋白构象变化的原子学洞察有望有助于阐明该病毒及其继任者毒力的结构基础，最终为开发适合的冠状病毒治疗策略提供基础。使用实验性的低温EM快照，该项目将绘制关键SARS-CoV2蛋白的功能相关构象异质性图，以更深入地了解构象异质性在这种大流行病毒中的作用。该项目的具体目标如下：(1)将先进的机器学习算法应用于实验低温EM单粒子快照，以确定关键的SARS-CoV2蛋白在抗体参与和不参与的情况下的能量景观；(2)确定相关能量景观上功能上重要的构象路径；(3)将功能路径上的运动与离散聚类方法推断的运动进行比较和对比；(4)确定与功能相关的构象运动的生物学意义；这一快速奖项由生物基础设施部(DBI)利用冠状病毒援助、救济和经济安全法案(CARE)的资金做出。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。