A Bayesian View on Cryo-EM Structure Determination
A Bayesian View on Cryo-EM Structure Determination
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DOI：
10.1016/j.jmb.2011.11.010
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发表时间：
2012-01-13
影响因子：
5.6
通讯作者：
Scheres, Sjors H. W.
中科院分区：
文献类型：
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作者：
Scheres, Sjors H. W.
通过低温电子显微镜（cryo-EM）图像的单粒子分析来确定三维（3D）结构需要从极其嘈杂的数据中确定许多参数。这使得该方法容易出现过拟合，也就是说，当结构描述噪声而不是信号时，特别是在噪声水平最高的分辨率极限附近。Cryo-EM结构通常使用特殊程序过滤以防止过拟合，但任意参数的调整可能导致结果的主观性。我描述了冷冻电镜结构测定的贝叶斯解释，其中重构密度的平滑是通过傅里叶域中的高斯先验施加的。统计框架规定了数据和先验知识如何结合，从而在不需要随意性的情况下获得最优的三维线性滤波器，并可以获得客观的分辨率估计。对实验数据的应用表明，统计方法比现有方法产生更可靠的结构，并且能够在包含多个不同结构的数据集中检测更小的类。(C) 2011 Elsevier Ltd.版权所有。
Three-dimensional (3D) structure determination by single-particle analysis of cryo-electron microscopy (cryo-EM) images requires many parameters to be determined from extremely noisy data. This makes the method prone to overfitting, that is, when structures describe noise rather than signal, in particular near their resolution limit where noise levels are highest. Cryo-EM structures are typically filtered using ad hoc procedures to prevent overfitting, but the tuning of arbitrary parameters may lead to subjectivity in the results. I describe a Bayesian interpretation of cryo-EM structure determination, where smoothness in the reconstructed density is imposed through a Gaussian prior in the Fourier domain. The statistical framework dictates how data and prior knowledge should be combined, so that the optimal 3D linear filter is obtained without the need for arbitrariness and objective resolution estimates may be obtained. Application to experimental data indicates that the statistical approach yields more reliable structures than existing methods and is capable of detecting smaller classes in data sets that contain multiple different structures. (C) 2011 Elsevier Ltd. All rights reserved.