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

为了预防特定病毒的感染，重要的是要了解驱动病毒进入宿主细胞的分子细节，在那里它们复制，使更多的病毒颗粒扩散到感染个体的其他细胞。该奖项将有助于了解SARS-CoV 2感染性的机制，该病毒是目前COVID-19大流行的罪魁祸首，通过使用机器学习算法制作参与驱动其感染的关键蛋白质的电影。越来越多的证据表明，病毒蛋白存在于一系列结构中，称为构象，并且这些结构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用于在近原子水平上确定关键SARS-CoV 2蛋白的构象景观，有和没有抗体参与。 对SAR-CoV 2蛋白质构象变化的原子洞察有望帮助澄清该病毒及其后继者毒力的结构基础，最终为开发针对冠状病毒的合适治疗策略提供基础。 使用实验cryo-EM快照，该项目将绘制关键SARS-CoV 2蛋白的功能相关构象异质性，以更深入地了解这种大流行病毒中构象异质性的作用。 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性冷冻EM单粒子快照，以确定有和没有抗体参与的关键SARS-CoV 2蛋白的能量景观;（2）确定相关能量景观上功能重要的构象路径;（3）将沿着功能路径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;和（5）使结果广泛获得，以帮助促进治疗策略的发展。该RAPID奖由生物基础设施部（DBI）使用冠状病毒援助，救济，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.