通过似然优化对电子子断层平均和随机锥形倾斜重建。
Averaging of electron subtomograms and random conical tilt reconstructions through likelihood optimization.
机构信息
Biocomputing Unit, Centro Nacional de Biotecnología - CSIC, Darwin 3, Cantoblanco, 28049, Madrid, Spain.
Biocomputing Unit, Centro Nacional de Biotecnología - CSIC, Darwin 3, Cantoblanco, 28049, Madrid, Spain.
出版信息
Structure. 2009 Dec 9;17(12):1563-1572. doi: 10.1016/j.str.2009.10.009.
Abstract
The reference-free averaging of three-dimensional electron microscopy (3D-EM) reconstructions with empty regions in Fourier space represents a pressing problem in electron tomography and single-particle analysis. We present a maximum likelihood algorithm for the simultaneous alignment and classification of subtomograms or random conical tilt (RCT) reconstructions, where the Fourier components in the missing data regions are treated as hidden variables. The behavior of this algorithm was explored using tests on simulated data, while application to experimental data was shown to yield unsupervised class averages for subtomograms of groEL/groES complexes and RCT reconstructions of p53. The latter application served to obtain a reliable de novo structure for p53 that may resolve uncertainties about its quaternary structure.
摘要
无参考平均化三维电子显微镜（3D-EM）重构与傅里叶空间中的空区代表电子断层扫描和单颗粒分析中的一个紧迫问题。我们提出了一种最大似然算法，用于亚断层或随机锥形倾斜（RCT）重构的同时对准和分类，其中缺失数据区域的傅里叶分量被视为隐藏变量。通过对模拟数据的测试，研究了该算法的行为，而对实验数据的应用则表明，可以对 groEL/groES 复合物的亚断层和 p53 的 RCT 重构进行无监督分类平均。后一种应用有助于获得 p53 的可靠从头结构，这可能解决其四元结构的不确定性。
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