Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
Program in Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
Nat Methods. 2024 Aug;21(8):1525-1536. doi: 10.1038/s41592-024-02210-z. Epub 2024 Mar 8.
Cryo-electron tomography (cryo-ET) enables observation of macromolecular complexes in their native, spatially contextualized cellular environment. Cryo-ET processing software to visualize such complexes at nanometer resolution via iterative alignment and averaging are well developed but rely upon assumptions of structural homogeneity among the complexes of interest. Recently developed tools allow for some assessment of structural diversity but have limited capacity to represent highly heterogeneous structures, including those undergoing continuous conformational changes. Here we extend the highly expressive cryoDRGN (Deep Reconstructing Generative Networks) deep learning architecture, originally created for single-particle cryo-electron microscopy analysis, to cryo-ET. Our new tool, tomoDRGN, learns a continuous low-dimensional representation of structural heterogeneity in cryo-ET datasets while also learning to reconstruct heterogeneous structural ensembles supported by the underlying data. Using simulated and experimental data, we describe and benchmark architectural choices within tomoDRGN that are uniquely necessitated and enabled by cryo-ET. We additionally illustrate tomoDRGN's efficacy in analyzing diverse datasets, using it to reveal high-level organization of human immunodeficiency virus (HIV) capsid complexes assembled in virus-like particles and to resolve extensive structural heterogeneity among ribosomes imaged in situ.
低温电子断层扫描（cryo-ET）使人们能够在其天然的、空间上下文化的细胞环境中观察大分子复合物。用于通过迭代对准和平均以纳米分辨率可视化此类复合物的低温电子断层扫描处理软件已经得到了很好的开发，但依赖于所关注复合物在结构上的均匀性假设。最近开发的工具允许对结构多样性进行一些评估，但代表高度异质结构的能力有限，包括那些正在经历连续构象变化的结构。在这里，我们将高度表达的 cryoDRGN（深度重构生成网络）深度学习架构扩展到 cryo-ET，该架构最初是为单颗粒低温电子显微镜分析而创建的。我们的新工具 tomoDRGN 学习了 cryo-ET 数据集结构异质性的连续低维表示，同时还学习了重建由基础数据支持的异构结构集合。使用模拟和实验数据，我们描述并基准测试了 tomoDRGN 中的体系结构选择，这些选择是 cryo-ET 所特有的需要和启用的。我们还说明了 tomoDRGN 在分析各种数据集方面的功效，使用它来揭示在病毒样颗粒中组装的人类免疫缺陷病毒（HIV）衣壳复合物的高级组织，以及解决原位成像的核糖体之间广泛的结构异质性。
