A Bayesian View on Cryo-EM Structure Determination
A Bayesian View on Cryo-EM Structure Determination
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DOI：
10.1109/isbi.2012.6235807
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发表时间：
2012-01
影响因子：
5.6
通讯作者：
S. Scheres
中科院分区：
文献类型：
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作者：
S. Scheres
通过低温电子显微镜(Cryo-EM)图像的单粒子分析来确定三维结构需要从极其嘈杂的数据中确定许多参数。这使得该方法容易过拟合，即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。低温电磁结构通常使用特别程序进行过滤，以防止过度匹配，但对任意参数的调整可能会导致结果的主观性。我描述了低温电磁结构确定的贝叶斯解释，其中重建密度的光滑性是通过傅里叶域中的高斯先验强加的。统计框架规定应该如何组合数据和先验知识，从而在不需要随意性的情况下获得最佳的3D线性滤波器，并且可以获得客观的分辨率估计。对实验数据的应用表明，该统计方法产生的结构比现有方法更可靠，并且能够从包含多个不同结构的数据集中检测出较小的类。
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.