The orthogonal tilt reconstruction method: An approach to generating single-class volumes with no missing cone for ab initio reconstruction of asymmetric particles
The orthogonal tilt reconstruction method: An approach to generating single-class volumes with no missing cone for ab initio reconstruction of asymmetric particles
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DOI：
10.1016/j.jsb.2005.10.012
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发表时间：
2006-03-01
影响因子：
3
通讯作者：
Nogales, E
中科院分区：
文献类型：
--
作者：
Leschziner, AE;Nogales, E
生成可靠的初始模型是三维电子显微镜重建非对称单粒子的关键步骤。如果样品中存在异质性，这尤其难以做到。随机圆锥投影（RCT）方法，可以说是目前最强大的完成这一任务，需要大量的用户干预，以解决“丢失锥”的问题。我们在这里提出了一种新的方法，称为正交倾斜重建方法，完全消除了缺失的圆锥体，使它可以直接作为初始参考的单类卷细化，而无需进一步处理。该方法涉及在+45度和-45度倾斜下收集数据，并且仅要求粒子在网格上采用相对大量的取向。一个倾斜的数据集用于对齐和分类，另一个集-它提供了正交于那些在第一-用于重建，导致在缺少一个丢失的圆锥体的情况下。我们用合成数据测试了这种方法，并将其性能与RCT方法进行了比较。我们还提出了一种方法，增加在一个异构的数据集，并确定最均匀的体积在个别的2D类（和体积）的同质性水平。(C)2005年爱思唯尔公司All rights reserved.
Generating reliable initial models is a critical step in the reconstruction of asymmetric single-particles by 3D electron microscopy. This is particularly difficult to do if heterogeneity is present in the sample. The Random Conical Tilt (RCT) method, arguably the most robust presently to accomplish this task, requires significant user intervention to solve the "missing cone" problem. We present here a novel approach, termed the orthogonal tilt reconstruction method, that eliminates the missing cone altogether, making it possible for single-class volumes to be used directly as initial references in refinement without further processing. The method involves collecting data at +45 degrees and -45 degrees tilts and only requires that particles adopt a relatively large number of orientations on the grid. One tilted data set is used for alignment and classification and the other set-which provides views orthogonal to those in the first-is used for reconstruction, resulting in the absence of a missing cone. We have tested this method with synthetic data and compared its performance to that of the RCT method. We also propose a way of increasing the level of homogeneity in individual 2D classes (and volumes) in a heterogeneous data set and identifying the most homogeneous volumes. (C) 2005 Elsevier Inc. All rights reserved.