Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7260, USA.
Proteins. 2011 Mar;79(3):1002-9. doi: 10.1002/prot.22941. Epub 2011 Jan 3.
We present a computational approach that can quickly search a large protein structural database to identify structures that fit a given electron density, such as determined by cryo-electron microscopy. We use geometric invariants (fingerprints) constructed using 3D Zernike moments to describe the electron density, and reduce the problem of fitting of the structure to the electron density to simple fingerprint comparison. Using this approach, we are able to screen the entire Protein Data Bank and identify structures that fit two experimental electron densities determined by cryo-electron microscopy.
我们提出了一种计算方法，可以快速搜索大型蛋白质结构数据库，以识别符合给定电子密度的结构，例如通过低温电子显微镜确定的结构。我们使用基于 3D Zernike 矩的几何不变量（指纹）来描述电子密度，并将结构与电子密度的拟合问题简化为简单的指纹比较。使用这种方法，我们能够筛选整个蛋白质数据库，并识别出与通过低温电子显微镜确定的两个实验电子密度匹配的结构。
