Reconstruction of heterogeneous and small macromolecules by cyro-EM

冷冻电镜重建异质小分子

• 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
• 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

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 单粒子电子低温显微镜（cryo-EM）最近加入了X射线晶体学， 核磁共振波谱学作为生物大分子的高分辨率结构方法。在 此外，cryo-EM产生单个分子的图像，因此具有潜力， 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而不能被 由现有的计算工具绘制的冷冻EM。此扩展需要解决两个 最具挑战性的计算问题所提出的冷冻EM。 首先，绘制大分子的结构可变性被广泛认为是主要的 cryo-EM中的计算挑战。结构变异对生物学家来说意义重大， 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 高移动的生物分子，具有多个连续的构象变化谱。第一 在这个项目的调查领域是一个计算框架的发展，分析 连续可变性所提出的方法是基于一个新的数学表示， 连续变化结构及其有效估计的马尔可夫链蒙特卡罗方法 （MCMC）算法。MCMC算法已经在许多其他科学领域取得了巨大成功。 尽管它们在低温EM单粒子分析中大多被忽视。 其次，目前冷冻EM研究的主要限制因素是分子大小。图像小 分子（低于~ 50 kDa）具有太少的信号以允许现有方法提供有效的3-D 重建。人们普遍认为，冷冻EM不能用于分子太 小到无法从显微照片中可靠地检测和挑选。支持这种普遍的信念， 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颗粒拾取。新 该方法基于自相关分析，完全绕过粒子拾取， 方向分配，只需要一遍数据。单程方法 为数据采集期间的实时处理开辟了新的可能性。