High Resolution in Single Particle Reconstruction

描述（由申请人提供）：我们描述了一项研究计划，以开发用于单粒子分析的扩展图像形成模型和伴随的计算方法，其目标是在生物大分子的电子显微镜结构研究中实现近原子分辨率。虽然它已经有可能达到分辨率精细于10在一些试点研究，仍然没有计算工具，用于常规的高分辨率电子显微镜结构测定。我们的目标是提高现有的能力，以提取4-7 A分辨率的3-D结构从固有的噪声图像的单个颗粒通过开发算法的鲁棒性和准确的图像处理和误差分析。我们首先证明，目前的电子显微镜图像形成模型是矛盾的实验证据，其次，高分辨率的结构确定所需的大量数据处理将难以实现使用现有的图像处理工具。在这个建议的框架内，我们将详细说明和实验测试的图像形成模型的电子显微镜，占一些非线性效应。具体地说，我们认为应该有一个信号依赖的噪声，因此将取决于相对数量的蛋白质存在于网格上。新模型将为电磁数据的信噪比估计和对准方法的改进提供基础。为了减少数据量，我们将开发一个高精度的算法库，用于从投影进行3-D重建和使用新的插值技术进行2-D图像处理。我们还将开发用于计算从投影集重建的结构中的真实的空间方差/协方差的算法。这些方法的目的是在本地化的构象变化，使用冷冻EM和提高准确性的对接的X-射线晶体结构域到3-D EM地图。该软件的开发方式将确保充分的可移植性，并将在SPARX和SPIDER主要软件包内在整个环境管理界传播。

DESCRIPTION (provided by applicant): We describe a research plan to develop an extended image formation model for single particle analysis and accompanying computational methods with the goal of achieving near atomic resolution in the electron microscopy structural studies of biological macromolecules. Although it has been possible to attain resolution finer than 10 A in a number of pilot studies, there are still no computational tools for routine high-resolution electron microscopy structure determination. Our goal is to advance the existing capabilities in order to extract 4-7 A resolution 3-D structures from the inherently noisy images of single particles by developing algorithms for robust and accurate image processing and error analysis. We demonstrate first that the current image formation model in electron microscopy is contradicted by the experimental evidence, and secondly that the massive data processing required for the high-resolution structure determination will be difficult to achieve using existing image processing tools. Within the framework of this proposal, we will detail and experimentally test an image formation model of electron microscopy that accounts for some non-linear effects. Specifically, we suggest that there should be a signal dependent noise, which therefore will depend on the relative amount of protein present on the grid. The new model will provide a basis for the signal-to-noise estimation for EM data and for improvements of alignment methods. In order to reduce the volume of the data we will develop a library of highly accurate algorithms for 3-D reconstruction from projections and for 2-D image manipulation using novel interpolation techniques. We will also develop algorithms for the calculation of the real space variance/covariance in structures reconstructed from sets of their projections. These methods are aimed at the localization of conformational variability using cryo-EM and at improving the accuracy of the docking of X-ray crystallographic domains into 3-D EM maps. The software will be developed in ways that assure full portability and will be disseminated throughout the EM community within the leading software packages SPARX and SPIDER.