Scheres Sjors H W, Valle Mikel, Carazo José-María
Centro Nacional de Biotecnología-CSIC, Campus Universidad Autónoma, Madrid, Spain.
Bioinformatics. 2005 Sep 1;21 Suppl 2:ii243-4. doi: 10.1093/bioinformatics/bti1140.
Maximum-likelihood (ML) image refinement is a promising candidate to improve attainable resolution limits in 3D-EM. However, its large CPU requirements may prohibit application to 3D-structure optimization.
We speeded up ML image refinement by reducing its search space over the alignment parameters. Application of this reduced-search approach to a cryo-EM dataset yielded practically identical results as the original approach, but in approximately one day instead of one week of CPU.
This work has been implemented in the public domain package Xmipp. Documentation and download instructions may be found at: http://www.cnb.uam.es/~bioinfo
最大似然（ML）图像细化是提高三维电子显微镜可达到分辨率极限的一个有前景的方法。然而，其对CPU的高要求可能会限制其在三维结构优化中的应用。
我们通过减少对齐参数的搜索空间来加速ML图像细化。将这种减少搜索的方法应用于冷冻电镜数据集，得到的结果与原始方法几乎相同，但所需的CPU时间约为一天而非一周。
这项工作已在公共领域软件包Xmipp中实现。文档和下载说明可在以下网址找到：http://www.cnb.uam.es/~bioinfo
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