Bayesian analysis of individual electron microscopy images: Towards structures of dynamic and heterogeneous biomolecular assemblies
Bayesian analysis of individual electron microscopy images: Towards structures of dynamic and heterogeneous biomolecular assemblies
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DOI：
10.1016/j.jsb.2013.10.006
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发表时间：
2013-12-01
影响因子：
3
通讯作者：
Hummer, Gerhard
中科院分区：
文献类型：
--
作者：
Cossio, Pilar;Hummer, Gerhard
我们开发了一种方法来提取结构信息的电子显微镜（EM）图像的动态和异质分子组装。为了克服成像结构中的无序挑战，我们单独分析每个图像，通过聚类或平均来避免信息丢失。贝叶斯推断EM（BioEM）方法使用基于似然的概率度量来量化每个EM图像和给定结构模型之间的一致性。似然函数考虑了分子位置和方向的不确定性、相对强度的变化以及实验图像中的噪声。BioEM形式主义在物理上是直观的，在数学上是简单的。我们表明，实验GroEL图像，BioEM正确识别结构根据功能状态。排名最高的结构是对应的X射线晶体结构，随后是先前从这里使用的EM图像的超集生成的EM结构。为了分析高度柔性分子的EM图像，我们提出了一种系综细化程序，并使用ESCRT-I-II超复合物的合成EM图来验证它。正确地识别了系综的大小及其结构成员。BioEM为3D重建方法提供了一种替代方法，为高度灵活的结构及其组件提取准确的群体分布。我们讨论的方法的局限性，以及可能的应用超越合奏细化，包括交叉验证和公正的模型结构后评估，和传统方法失败的系统的结构表征。总的来说，我们的研究结果表明，BioEM框架可用于分析有序和无序分子系统的EM图像。(C)2013 Elsevier Inc. All rights reserved.
We develop a method to extract structural information from electron microscopy (EM) images of dynamic and heterogeneous molecular assemblies. To overcome the challenge of disorder in the imaged structures, we analyze each image individually, avoiding information loss through clustering or averaging. The Bayesian inference of EM (BioEM) method uses a likelihood-based probabilistic measure to quantify the consistency between each EM image and given structural models. The likelihood function accounts for uncertainties in the molecular position and orientation, variations in the relative intensities and noise in the experimental images. The BioEM formalism is physically intuitive and mathematically simple. We show that for experimental GroEL images, BioEM correctly identifies structures according to the functional state. The top-ranked structure is the corresponding X-ray crystal structure, followed by an EM structure generated previously from a superset of the EM images used here. To analyze EM images of highly flexible molecules, we propose an ensemble refinement procedure, and validate it with synthetic EM maps of the ESCRT-I-II supercomplex. Both the size of the ensemble and its structural members are identified correctly. BioEM offers an alternative to 3D-reconstruction methods, extracting accurate population distributions for highly flexible structures and their assemblies. We discuss limitations of the method, and possible applications beyond ensemble refinement, including the cross-validation and unbiased post-assessment of model structures, and the structural characterization of systems where traditional approaches fail. Overall, our results suggest that the BioEM framework can be used to analyze EM images of both ordered and disordered molecular systems. (C) 2013 Elsevier Inc. All rights reserved.