Tools to determine and analyze the structures of molecular machines in motion

确定和分析运动中分子机器结构的工具

• 批准号: 10596109
• 负责人: Joseph Harry Davis
• 金额: $ 31.95万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10596109
• 关键词: 3-Dimensional; Adopted; Algorithm Design; Algorithms; Autophagosome; Benchmarking; Biochemical; Cell physiology; Cells; Communities; Community Developments; Complex; Computer software; Consensus; Coupled; Cryoelectron Microscopy; Custom; Data; Data Set; Disease; Electrons; Elements; Funding; Generations; Heterogeneity; Human; Image; Individual; Life; Machine Learning; Macromolecular Complexes; Maps; Methods; Modeling; Molecular; Molecular Conformation; Molecular Machines; Molecular Structure; Monitor; Motion; Network-based; Process; Publishing; Reaction; Research; Resolution; Ribosomes; Sampling; Structure; Testing; Therapeutic; Time; United States National Institutes of Health; Validation; Visualization; Work; comparative efficacy; computerized tools; data integration; deep learning; deep neural network; density; design; image processing; innovation; interest; learning strategy; method development; molecular dynamics; neural network; novel; particle; programs; reconstruction; structural biology; therapeutic development; three dimensional structure; tool; user-friendly

PROJECT SUMMARY/ABSTRACT Tools to determine and analyze the structures of molecular machines in motion Single particle cryo-electron microscopy (cryo-EM) has transformed our ability to rapidly determine high resolution structures of static, structurally homogeneous macromolecular complexes. However, we have not realized cryo-EM’s potential to uncover the full ensemble of heterogeneous structures these molecules adopt as they function. The overall objective of this work is to develop novel cryo-EM image processing tools to: 1) determine the complete ensemble of structural states adopted by imaged complexes; 2) quantify the relative abundance of these states; 3) monitor how the distribution of these states changes as the machine functions; and 4) use this information to understand the molecular mechanism of how these machines assemble and function. This objective is important as visualizing structural ensembles can be vital in developing and testing hypotheses for how these machines function, and in developing therapeutics to modulate their activity. Here, we specifically aim to develop two tools to facilitate achieving these overall objectives. First, we will generate ‘benchmark’ datasets that will be distributed to the methods development community to aid in building and quantitatively assessing of the fidelity of different approaches to reconstruct 3D density maps from single particle cryo-EM data. These benchmark datasets will include macromolecular complexes bearing elements of structural heterogeneity we have specifically designed for this purpose, and that we have biochemically assembled and imaged. Additionally, it will design, implement, and validate a machine learning-based computational tool that more realistically simulates the imaging process than existent software, thereby enabling users to rapidly construct custom synthetic benchmark datasets to test specific aspects of their own algorithms. Recently, as a proof-of-concept, we published the first method using deep neural networks to perform 3D reconstruction from single particle data, and this approach was particularly efficacious is revealing heterogeneous structures. Thus, our second aim is to develop this approach into a complete software package enabling users to readily reconstruct hundreds-to-thousands of density maps from a single dataset; to implement tools to focus the analysis on specific structural regions; and to deploy methods guiding the interpretation of the density maps and the construction of ensembles of associated atomic models. This work in innovative in its objective to analyze heterogeneous structural ensembles as opposed to static structures at high resolution; in our approach to model model conformational changes as originating from a continuous distribution of structures as opposed to isolated, discrete states; and in our application of deep learning methods to both the generation of benchmark datasets and in the reconstruction process itself. As a proof-of-concept, our reconstruction approach has proven significant as evidenced by its recent application in multiple structural studies, and we expect the tools we propose to develop here will be broadly impactful on a wide-array of NIH-funded research programs that rely on single particle cryo-EM.

项目总结/摘要 确定和分析运动中分子机器结构的工具 单粒子低温电子显微镜（cryo-EM）已经改变了我们快速测定高浓度的能力。 静态的、结构均匀的大分子复合物的解析结构。但我们无法 实现了cryo-EM的潜力，揭示了这些分子采用的异质结构的全部集合， 他们的功能。这项工作的总体目标是开发新的冷冻EM图像处理工具，以：1） 确定成像复合物所采用的结构状态的完整系综; 2）量化相对 3）监视这些状态的分布如何随着机器的运行而改变; 以及4）使用这些信息来理解这些机器如何组装的分子机制， 功能这一目标很重要，因为可视化结构集成在开发和测试中至关重要 这些机器如何运作的假设，以及开发治疗方法来调节它们的活动。这里我们 具体目标是开发两种工具，以促进实现这些总体目标。首先，我们将生成 “基准”数据集，将分发给方法开发社区，以帮助建立和 定量评估不同方法重建单粒子三维密度图的保真度 cryo-EM数据这些基准数据集将包括具有结构性元素的大分子复合物。 异质性，我们专门为此目的设计，我们已经生化组装， 成像。此外，它将设计，实施和验证基于机器学习的计算工具， 比现有软件更真实地模拟成像过程，从而使用户能够快速地 构建自定义合成基准数据集，以测试他们自己算法的特定方面。近日，作为 为了验证概念，我们发布了第一种使用深度神经网络执行3D重建的方法， 单颗粒数据，这种方法特别有效地揭示了异质结构。因此，在本发明中， 我们的第二个目标是将这种方法发展成一个完整的软件包，使用户能够随时 从单个数据集重建数百到数千张密度图;实施工具来集中 分析具体的结构区域;并部署指导解释密度图的方法， 相关原子模型的集合的构建。本文在客观分析上有所创新 与高分辨率的静态结构相反的异质结构集合;在我们的建模方法中， 将构象变化建模为源自结构的连续分布而不是孤立的， 离散状态;以及我们将深度学习方法应用于基准数据集的生成 以及重建过程本身。作为概念验证，我们的重建方法已经证明 重要的是，它最近在多个结构研究中的应用证明了这一点，我们希望我们使用的工具， 建议在这里开发将对大量NIH资助的研究项目产生广泛影响，这些项目依赖于 单粒子冷冻电镜