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：
10594985
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 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10594985
关键词：
3-Dimensional 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 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.

项目概要单粒子电子冷冻显微镜 (cryo-EM) 最近加入了 X 射线晶体学和核磁共振波谱作为生物大分子的高分辨率结构方法。在此外，冷冻电镜可生成单个分子的图像，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电镜的应用扩展到太小或太柔韧的分子由现有的冷冻电镜计算工具绘制。此扩展需要解决以下两个问题冷冻电镜提出的最具挑战性的计算问题。首先，绘制大分子的结构变异性被广泛认为是主要的冷冻电镜中的计算挑战。结构变异对于生物学家来说具有重要意义，因为它们提供了对分子机器功能的深入了解。现有的计算工具仅限于少数不同的构象，因此无法解决具有多种连续构象变化光谱的高度流动性生物分子。第一个该项目的研究领域是开发一个计算框架来分析连续可变性。所提出的方法基于新的数学表示连续变化结构及其马尔可夫链蒙特卡罗的高效估计（MCMC）算法。 MCMC 算法在许多其他科学领域取得了巨大成功学科，但它们在冷冻电镜单粒子分析中大多被忽视。其次，目前冷冻电镜研究的一个主要限制因素是分子大小。小的图片分子（低于~50kDa）的信号太少，现有方法无法提供有效的 3-D 重建。人们普遍认为冷冻电镜不能用于过于复杂的分子。小到可以从显微照片中可靠地检测和挑选。挑战这种普遍的信念，第二个研究领域的重点是开发一种突破性的方法直接从显微照片重建小分子，无需粒子拾取。新的该方法基于自相关分析，完全绕过粒子拾取和方向分配并且只需要一次传递数据。单遍方法为数据采集期间的实时处理开辟了新的可能性。