Roseman A M
Medical Research Council-Laboratory of Molecular Biology, Hills Road, England, Cambridge CB2 2QH, UK.
J Struct Biol. 2004 Jan-Feb;145(1-2):91-9. doi: 10.1016/j.jsb.2003.11.007.
The FindEM particle picking program was tested on the publicly available keyhole limpet hemocyanin (KLH) dataset, and the results were submitted for the "bakeoff" contest at the recent particle picking workshop (Zhu et al., 2003b). Two alternative ways of using the program are demonstrated and the results are compared. The first of these approximates exhaustive projection matching with a full set of expected views, which need to be known. This could correspond to the task of extending a known structure to higher resolution, for which many 1000's of additional images are required. The second procedure illustrates use of multivariate statistical analysis (MSA) to filter a preliminary set of candidate particles containing a high proportion of false particles. This set was generated using the FindEM program to search with one template that crudely represents the expected views. Classification of the resultant set of candidate particles then allows the desired classes to be selected while the rest can be ignored. This approach requires no prior information of the structure and is suitable for the initial investigation of an unknown structure--the class averages indicate the symmetry and oligomeric state of the particles. Potential improvements in speed and accuracy are discussed.
FindEM颗粒挑选程序在公开可用的钥孔戚血蓝蛋白（KLH）数据集上进行了测试，其结果已提交至最近的颗粒挑选研讨会上的“烘焙赛”（Zhu等人，2003b）。展示了使用该程序的两种替代方法并比较了结果。其中第一种方法通过一整套已知的预期视图来近似穷举投影匹配。这可能对应于将已知结构扩展到更高分辨率的任务，为此需要数千张额外的图像。第二种方法说明了如何使用多元统计分析（MSA）来过滤一组初步的候选颗粒，这些颗粒中含有高比例的假颗粒。该组是使用FindEM程序，用一个粗略代表预期视图的模板进行搜索生成的。然后对生成的候选颗粒集进行分类，从而可以选择所需的类别，而忽略其余的类别。这种方法不需要结构的先验信息，适用于对未知结构的初步研究——类别平均图像表明了颗粒的对称性和寡聚状态。文中还讨论了在速度和准确性方面可能的改进。
