CAREER: Constructing Multi-scale Dynamical Ensembles of Ribonucleic Acids (RNAs)

职业：构建核糖核酸 (RNA) 的多尺度动态整体

• 批准号: 2046005
• 负责人: Aaron Frank
• 金额: $ 109.99万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046005&HistoricalAwards=false
• 关键词: CAREER; Constructing; Multi; scale; Dynamical

The ability of ribonucleic acids (RNAs) to sample distinct structures underlie many of their biological roles. Many RNAs that regulate gene expression in bacteria, viruses, and humans do so by changing their structure in response to stimuli. In bacteria, for instance, RNA elements switch their molecular structure in the presence of specific metabolites, such as vitamin B12. The binding of such metabolites and the subsequent structural changes enable organisms that express these RNAs to respond to their environment and turn ON or OFF gene expression as needed. However, visualizing the structures that RNAs adopt remains a fundamental challenge in molecular biophysics. This project seeks to develop and apply new methods to integrate experimental and computational data to describe the structure of RNAs. This project will also tightly integrate research and education to train high school, undergraduate, and graduate students in the fundamentals of applied computational molecular biophysics. During this project, students will leverage modern machine learning techniques, similar to techniques used in facial recognition software, to extract RNA structural information from biophysical measurements. Outreach activities will be aimed at actively engaging high school students in computationally-intensive research, especially those from historically under-recruited groups. Specifically, this project aims to develop methods to determine the dynamical ensemble, i.e., the collection of conformational states that a given RNA can populate. To understand how RNAs fold and function, one must determine their ensembles and how they change during the recognition of proteins or ligands or during the introduction of mutations or chemical modifications. Characterizing dynamical ensembles of RNA, however, remains a formidable task because the number of unknown variables exceeds that which can be measured experimentally. Additionally, many conformers exist in low-abundance and have short lifetimes and are therefore difficult to detect experimentally. Even more challenging is determining how different conformers in an ensemble interconvert as this requires characterization of exceptionally lowly-populated transition states and complex pathways that can be difficult to tease out. In this project, the dynamical ensembles of RNAs will be determined using novel integrative approaches that are initialized using readily available experimental data. In this project, NMR chemical shifts will be used to guide simulations of RNA and to determine their dynamical ensembles. Therefore, the first objective is to develop multi-scale methods to estimate chemical shifts from structures of RNA. The second objective is to leverage multi-scale chemical shift prediction methods to construct multi-scale dynamical ensembles of RNA. The last objective is to develop a methodology that uses multi-scale dynamical ensembles to construct transition-path ensembles of RNA. Ultimately, this project will lead to robust, streamlined, and integrated methodologies that go beyond static structure determination to construct dynamical ensembles that capture the range of conformational states accessible to functional RNAs, while simultaneously describing the transitions between these states. This structural information will advance the understanding of RNA-driven biochemical processes from descriptive to predictive.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.

核糖核酸（rna）取样不同结构的能力是其许多生物学作用的基础。在细菌、病毒和人类中，许多调节基因表达的rna是通过改变它们的结构来响应刺激的。例如，在细菌中，RNA元素在特定代谢物（如维生素B12）的存在下改变其分子结构。这些代谢物的结合和随后的结构变化使表达这些rna的生物体能够对环境做出反应，并根据需要打开或关闭基因表达。然而，可视化rna所采用的结构仍然是分子生物物理学中的一个基本挑战。该项目旨在开发和应用新的方法来整合实验和计算数据来描述rna的结构。该项目还将紧密结合研究和教育，培养高中生、本科生和研究生应用计算分子生物物理学的基础知识。在这个项目中，学生将利用现代机器学习技术，类似于面部识别软件中使用的技术，从生物物理测量中提取RNA结构信息。外展活动的目的是让高中生积极参与计算密集型研究，特别是那些历史上未被招募的群体。具体来说，该项目旨在开发确定动态集合的方法，即给定RNA可以填充的构象态的集合。为了理解rna的折叠和功能，我们必须确定它们的集合体，以及它们在识别蛋白质或配体或引入突变或化学修饰时是如何变化的。然而，表征RNA的动态集合仍然是一项艰巨的任务，因为未知变量的数量超过了可以通过实验测量的数量。此外，许多构象以低丰度存在，寿命短，因此难以通过实验检测。更具有挑战性的是确定一个整体中不同的构象是如何相互转换的，因为这需要表征异常低密度的过渡态和复杂的途径，这很难梳理出来。在这个项目中，rna的动态集成将使用新的综合方法来确定，这些方法使用现成的实验数据进行初始化。在这个项目中，核磁共振化学位移将用于指导RNA的模拟，并确定它们的动力学集合。因此，第一个目标是开发多尺度方法来估计RNA结构的化学转移。第二个目标是利用多尺度化学位移预测方法构建RNA的多尺度动力学集成。最后一个目标是开发一种使用多尺度动态集成来构建RNA过渡路径集成的方法。最终，该项目将带来强大、精简和集成的方法，超越静态结构确定，构建动态集成，捕获功能性rna可访问的构象状态范围，同时描述这些状态之间的转换。这种结构信息将促进对rna驱动的生化过程的理解，从描述性到预测性。该奖项反映了美国国家科学基金会的法定使命，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。