Kumar Vibhor, Heikkonen Jukka, Engelhardt Peter, Kaski Kimmo
Laboratory of Computational Engineering, Helsinki University of Technology, P.O. Box 9203, FIN-02015 HUT Helsinki, Finland.
J Struct Biol. 2004 Jan-Feb;145(1-2):41-51. doi: 10.1016/j.jsb.2003.09.036.
In order to make a high resolution model of macromolecular structures from cryo-electron microscope (cryo-EM) raw images one has to be precise at every processing step from particle picking to 3D image reconstruction. In this paper we propose a collection of novel methods for filtering cryo-EM images and for automatic picking of particles. These methods have been developed for two cases: (1) when particles can be identified and (2) when particle are not distinguishable. The advantages of these methods are demonstrated in standard purified protein samples and to generalize them we do not use any ad hoc presumption of the geometry of the particle projections. We have also suggested a filtering method to increase the signal-to-noise (S/N) ratio which has proved to be useful for other levels of reconstruction, i.e., finding orientations and 3D model reconstruction.
为了从冷冻电子显微镜（cryo-EM）原始图像制作高分子结构的高分辨率模型，从颗粒挑选到三维图像重建的每个处理步骤都必须精确。在本文中，我们提出了一系列用于过滤冷冻电镜图像和自动挑选颗粒的新方法。这些方法针对两种情况开发：（1）颗粒可识别时；（2）颗粒不可区分时。这些方法的优势在标准纯化蛋白质样本中得到了证明，并且为了推广这些方法，我们没有使用任何关于颗粒投影几何形状的特殊假设。我们还提出了一种提高信噪比（S/N）的滤波方法，该方法已被证明对其他重建级别（即寻找方向和三维模型重建）很有用。
