University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Biopolymers. 2012 Sep;97(9):732-41. doi: 10.1002/bip.22041.
Cryo-electron microscopy (cryo-EM) experiments yield low-resolution (3-30 Å) 3D-density maps of macromolecules. These density maps are segmented to identify structurally distinct proteins, protein domains, and subunits. Such partitioning aids the inference of protein motions and guides fitting of high-resolution atomistic structures. Cryo-EM density map segmentation has traditionally required tedious and subjective manual partitioning or semisupervised computational methods, whereas validation of resulting segmentations has remained an open problem in this field. We introduce a network-based hierarchical segmentation (Nhs) method, that provides a multi-scale partitioning, reflecting local and global clustering, while requiring no user input. This approach models each map as a graph, where map voxels constitute nodes and weighted edges connect neighboring voxels. Nhs initiates Markov diffusion (or random walk) on the weighted graph. As Markov probabilities homogenize through diffusion, an intrinsic segmentation emerges. We validate the segmentations with ground-truth maps based on atomistic models. When implemented on density maps in the 2010 Cryo-EM Modeling Challenge, Nhs efficiently and objectively partitions macromolecules into structurally and functionally relevant subregions at multiple scales.
低温电子显微镜（cryo-EM）实验产生大分子的低分辨率（3-30Å）3D 密度图。这些密度图被分割以识别结构上不同的蛋白质、蛋白质结构域和亚基。这种划分有助于推断蛋白质运动并指导高分辨率原子结构的拟合。低温电子显微镜密度图分割传统上需要繁琐和主观的手动分割或半监督计算方法，而分割结果的验证仍然是该领域的一个未解决问题。我们引入了一种基于网络的层次分割（Nhs）方法，该方法提供了一种多尺度分割，反映了局部和全局聚类，同时不需要用户输入。该方法将每个地图建模为一个图，其中地图体素构成节点，加权边连接相邻体素。Nhs 在加权图上启动马尔可夫扩散（或随机游走）。随着马尔可夫概率在扩散过程中均匀化，内在的分割就会出现。我们使用基于原子模型的地面实况地图来验证分割。当在 2010 年低温电子显微镜建模挑战赛中的密度图上实现时，Nhs 能够高效且客观地将大分子在多个尺度上划分为结构和功能上相关的子区域。
