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.

项目摘要/摘要 确定和分析分子机器中的结构的工具 单粒子冷冻电子显微镜（Cryo-EM）已转化了我们快速确定高的能力 静态，结构均匀分子复合物的分辨率结构。但是，我们没有 认识到Cryo-Em有潜力揭示这些分子所采用的全异构结构的完整合奏 它们起作用。这项工作的总体目的是开发新型的Cryo-Em图像处理工具：1） 确定成像复合物采用的结构状态的完整合奏； 2）量化相对 这些状态的抽象； 3）监控这些状态的分布如何随着机器的功能而变化； 4）使用此信息来了解这些机器如何组装和 功能。这个目标很重要，因为可视化结构合奏对于开发和测试至关重要 假设这些机器如何发挥作用以及开发用于调节其活性的治疗。在这里，我们 首先，我们将生成 将分发给方法开发社区的“基准”数据集，以帮助建造和 定量评估不同方法的保真度，以重建单个粒子的3D密度图 冷冻-EM数据。这些基准数据集将包括带有结构元素的大分子复合物 我们专门为此目的设计的异质性，并且我们已经组装了生化，并且 成像。此外，它将设计，实现和验证基于机器学习的计算工具 比现有软件更现实地模拟成像过程，从而使用户能够快速 构建自定义合成基准数据集以测试其自己算法的特定方面。最近，作为 概念证明，我们发布了第一种使用深神经网络来执行3D重建的方法 单个粒子数据，这种方法特别有效地揭示了异质结构。那， 我们的第二个目的是将这种方法开发为完整的软件包，使用户可以轻松地 从一个数据集中重建了数十万的密度图；实施工具以关注 对特定结构区域的分析；并部署指导密度图的解释和 相关原子模型的集合的构建。这项创新的工作目的是分析 与高分辨率的静态结构相反，异质结构合奏；在我们的模型方法中 模型会议变化是由结构的连续分布而不是孤立的 离散国家；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。作为概念证明，我们的重建方法已证明 它在多种结构研究中的最新应用所证明的是重要的，我们希望我们的工具 在这里开发的提议将对依赖NIH资助的研究计划的广泛阵容具有广泛的影响 单粒子冷冻EM。