Department of Applied Mathematics, School of Mathematical Sciences, Tel-Aviv University, Israel.
J Struct Biol. 2017 Nov;200(2):106-117. doi: 10.1016/j.jsb.2017.09.007. Epub 2017 Sep 21.
We consider the problem of estimating an unbiased and reference-free ab initio model for non-symmetric molecules from images generated by single-particle cryo-electron microscopy. The proposed algorithm finds the globally optimal assignment of orientations that simultaneously respects all common lines between all images. The contribution of each common line to the estimated orientations is weighted according to a statistical model for common lines' detection errors. The key property of the proposed algorithm is that it finds the global optimum for the orientations given the common lines. In particular, any local optima in the common lines energy landscape do not affect the proposed algorithm. As a result, it is applicable to thousands of images at once, very robust to noise, completely reference free, and not biased towards any initial model. A byproduct of the algorithm is a set of measures that allow to asses the reliability of the obtained ab initio model. We demonstrate the algorithm using class averages from two experimental data sets, resulting in ab initio models with resolutions of 20Å or better, even from class averages consisting of as few as three raw images per class.
我们考虑从单颗粒低温电子显微镜生成的图像中估计无偏倚且无参考的非对称分子从头模型的问题。所提出的算法找到同时尊重所有图像之间所有公共线的取向的全局最优分配。根据公共线检测误差的统计模型，为估计取向加权每个公共线的贡献。所提出算法的关键特性是，给定公共线，它为取向找到全局最优。特别是，公共线能量景观中的任何局部最优都不会影响所提出的算法。因此，它可以一次应用于数千张图像，对噪声非常鲁棒，完全无参考，并且不偏向任何初始模型。该算法的一个副产品是一组度量标准，允许评估获得的从头模型的可靠性。我们使用两个实验数据集的类平均值演示该算法，即使对于每个类仅包含三个原始图像的类平均值，也可以获得分辨率为 20Å 或更好的从头模型。
