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: 10345392
• 负责人: Joseph Harry Davis
• 金额: $ 31.98万
• 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/10345392
• 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; Work; base; comparative efficacy; computerized tools; crystallinity; deep learning; deep neural network; density; design; image processing; innovation; interest; learning strategy; method development; 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 用于确定和分析运动中的分子机结构的工具 单粒子低温电子显微镜(Cryo-EM)改变了我们快速测定高能电子显微镜的能力 静态、结构均一的大分子络合物的拆分结构。然而，我们还没有 意识到了低温EM揭示这些分子所采用的全部多相结构的潜力 它们起作用了。这项工作的总体目标是开发新型的低温电磁图像处理工具，以：1) 确定成像的复合体所采用的结构状态的完整集合；2)量化相对 这些状态的丰富性；3)监控这些状态的分布如何随着机器的运行而改变； 4)使用这些信息来理解这些机器如何组装和 功能。这个目标很重要，因为可视化的结构集合在开发和测试中是至关重要的 这些机器如何运作的假说，以及开发调节它们活动的治疗方法。在这里，我们 具体目的是开发两种工具，以促进实现这些总体目标。首先，我们将生成 将被分发给方法开发社区以帮助构建和 从单个粒子重建三维密度图的不同方法的保真度定量评估 低温电磁数据。这些基准数据集将包括含有结构元素的大分子络合物 异质性是我们专门为此目的而设计的，我们已经通过生物化学组装和 已成像。此外，它将设计、实现和验证一个基于机器学习的计算工具，该工具 比现有软件更逼真地模拟成像过程，从而使用户能够快速 构建定制的合成基准数据集，以测试其自身算法的特定方面。最近，作为一个 概念验证，我们发布了第一个使用深度神经网络执行3D重建的方法 单粒子数据，这种方法在揭示非均质结构方面特别有效。因此， 我们的第二个目标是将这种方法开发成一个完整的软件包，使用户能够轻松地 从单个数据集重建数百到数千个密度贴图；实现工具以将 对特定构造区域的分析；并采用指导密度图解释和 关联原子模型系综的构建。这项工作在创新中对其目标进行了分析 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 将构象变化建模为起源于结构的连续分布，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 以及重建进程本身。作为概念验证，我们的重建方法已经证明 它最近在多个结构研究中的应用证明了它的重要性，我们希望我们的工具 提议在这里发展将对NIH资助的一系列研究项目产生广泛影响，这些项目依赖于 单粒子低温电子显微镜。