Reconstruction of heterogeneous and small macromolecules by cyro-EM

冷冻电镜重建异质小分子

基本信息

批准号：
10380770
负责人：
Amit Singer
金额：
$ 31.29万
依托单位：
PRINCETON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-06-01 至 2024-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10380770
关键词：
3-Dimensional Algorithmic Software Algorithms Area Belief Biological Biological Process Bypass Collaborations Complex Computational algorithm Computer software Cryoelectron Microscopy Crystallization Data Data Set Detection Development Diffusion Dimensions Discipline Drug Design Fostering G-Protein-Coupled Receptors Heterogeneity Human Genome Image Individual Institution Investigation Ion Channel Ion Pumps Machine Learning Maps Markov Chains Markov chain Monte Carlo methodology Mathematics Methods Modeling Molecular Conformation Molecular Machines Molecular Motors Molecular Weight Motion NMR Spectroscopy Names Noise Particle Size Phase Polymerase Preparation Proteins Pythons Research Resolution Ribosomes Roentgen Rays Sampling Signal Transduction Spliceosomes Structure Techniques Time Uncertainty Update Variant Work X-Ray Crystallography base computer framework computerized data processing computerized tools data acquisition expectation flexibility high dimensionality improved insight interest macromolecule molecular mass novel strategies open source particle programs protein complex protein structure receptor reconstruction small molecule statistics success theories three dimensional structure

项目摘要

PROJECT SUMMARY Single-particle electron cryomicroscopy (cryo-EM) has recently joined X-ray crystallography and NMR spectroscopy as a high-resolution structural method for biological macromolecules. In addition, cryo-EM produces images of individual molecules, and therefore has the potential to resolve conformational changes. The proposal aims to develop new algorithms and software for extending the application of cryo-EM to molecules that are either too small or too flexible to be mapped by existing computational tools for cryo-EM. This extension requires solving two of the most challenging computational problems posed by cryo-EM. First, mapping the structural variability of macromolecules is widely recognized as the main computational challenge in cryo-EM. Structural variations are of great significance to biologists, as they provide insight into the functioning of molecular machines. Existing computational tools are limited to a small number of distinct conformations, and therefore are incapable of tackling highly mobile biomolecules with multiple, continuous spectra of conformational changes. The first area of investigation in this project is the development of a computational framework to analyze continuous variability. The proposed approach is based on a new mathematical representation of continuously changing structures and its efficient estimation using Markov chain Monte Carlo (MCMC) algorithms. MCMC algorithms have found great success in many other scientific disciplines, yet they have been mostly overlooked for cryo-EM single particle analysis. Second, a major limiting factor for present cryo-EM studies is the molecule size. Images of small molecules (below ~50kDa) have too little signal to allow existing methods to provide valid 3-D reconstructions. It is commonly believed that cryo-EM cannot be used for molecules that are too small to be reliably detected and picked from micrographs. Challenging that widespread belief, the second area of investigation focuses on developing a groundbreaking approach for reconstructing small molecules directly from micrographs without particle picking. The new approach is based on autocorrelation analysis and completely bypasses particle picking and orientation assignment and requires just one pass over the data. The single-pass approach opens new possibilities for real-time processing during data acquisition.

项目摘要单粒子电子冷冻显微镜（Cryo-EM）最近与X射线晶体学和 NMR光谱作为生物大分子的高分辨率结构方法。在此外，冷冻EM产生单个分子的图像，因此有潜力解决构象变化。该提案旨在开发新的算法和软件将冷冻EM的应用扩展到太小或太灵活的分子中由Cero-EM现有的计算工具映射。此扩展需要解决两个 Cryo-EM构成的最具挑战性的计算问题。首先，映射大分子的结构变异性被广泛认为是主要的 Cryo-EM中的计算挑战。结构变化对生物学家具有重要意义，因为它们可以深入了解分子机的功能。现有的计算工具仅限于少数不同的构象，因此无法应对具有多个连续构象变化光谱的高度移动生物分子。第一个该项目的调查领域是开发用于分析的计算框架连续变异性。提出的方法基于一个新的数学表示不断变化的结构及其使用马尔可夫链蒙特卡洛的有效估计（MCMC）算法。 MCMC算法在许多其他科学方面发现了巨大的成功学科，但是对于冷冻EM单个粒子分析而言，它们大多被忽略了。其次，当前冷冻EM研究的主要限制因素是分子大小。小图像分子（低于50kDa）的信号太少，无法允许现有方法提供有效的3-D 重建。通常认为冷冻EM不能用于也是如此的分子小，可靠地从显微照片中挑选。充满挑战的人们的信念，第二个调查领域的重点是开发一种开创性的方法直接从显微照片中重建小分子，而无需绘制颗粒。新的方法基于自相关分析，并完全绕过粒子拾取和方向分配，仅需要一个通过数据。单通道的方法在数据采集期间为实时处理打开了新的可能性。