细胞电子断层扫描图中的从头结构模式挖掘。
De Novo Structural Pattern Mining in Cellular Electron Cryotomograms.
机构信息
Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Institute for Quantitative and Computational Biosciences, Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA 90095, USA; Quantitative and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.
出版信息
Structure. 2019 Apr 2;27(4):679-691.e14. doi: 10.1016/j.str.2019.01.005. Epub 2019 Feb 7.
Electron cryotomography enables 3D visualization of cells in a near-native state at molecular resolution. The produced cellular tomograms contain detailed information about a plethora of macromolecular complexes, their structures, abundances, and specific spatial locations in the cell. However, extracting this information in a systematic way is very challenging, and current methods usually rely on individual templates of known structures. Here, we propose a framework called "Multi-Pattern Pursuit" for de novo discovery of different complexes from highly heterogeneous sets of particles extracted from entire cellular tomograms without using information of known structures. These initially detected structures can then serve as input for more targeted refinement efforts. Our tests on simulated and experimental tomograms show that our automated method is a promising tool for supporting large-scale template-free visual proteomics analysis.
电子晶体学断层扫描技术能够以接近自然状态的分子分辨率对细胞进行 3D 可视化。所生成的细胞断层图像包含了大量关于大分子复合物的详细信息，包括它们的结构、丰度以及在细胞中的特定空间位置。然而，以系统的方式提取这些信息极具挑战性，目前的方法通常依赖于已知结构的单个模板。在这里，我们提出了一种称为“多模式追踪”的框架，用于从整个细胞断层图像中提取的高度异质的粒子集中从头发现不同的复合物，而无需使用已知结构的信息。这些最初检测到的结构随后可以作为更有针对性的细化工作的输入。我们在模拟和实验断层图像上的测试表明，我们的自动化方法是支持大规模无模板可视化蛋白质组学分析的有前途的工具。
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