Computational Methods Toward Unbiased Pattern Mining and Structure Determination in Cryo-Electron Tomography Data
Computational Methods Toward Unbiased Pattern Mining and Structure Determination in Cryo-Electron Tomography Data
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DOI：
10.1016/j.jmb.2023.168068
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发表时间：
2023-05-04
影响因子：
5.6
通讯作者：
Chang，Yi-Wei
中科院分区：
文献类型：
--
作者：
Kim，Hannah Hyun-Sook;Uddin，Mostofa Rafid;Chang，Yi-Wei
冷冻电子断层扫描可以独特地探测天然细胞环境中的大分子结构。断层图像以复杂的数据为特征，这些数据具有密度不同、密集的大分子络合物、低信噪比和缺失楔形效应等伪影。这种数据的后处理通常涉及从断层图像中分离出感兴趣的区域或颗粒，将它们组织成相关的组，并通过亚断层图像平均来呈现最终的结构。模板匹配和基于参考的结构确定是流行的分析方法，但容易受到偏差的影响，通常需要大量用户输入。最重要的是，这些方法无法识别驻留在成像细胞环境中的新复合体。因此，为了可靠地提取和解析感兴趣的结构，高效和不偏不倚的方法具有重要价值。这篇综述重点介绍了著名的计算软件，并讨论了它们如何有助于使自动结构模式发现成为可能。还提出了强调功能对于用户友好性和可访问性的重要性的观点。
Cryo-electron tomography can uniquely probe the native cellular environment for macromolecular structures. Tomograms feature complex data with densities of diverse, densely crowded macromolecular complexes, low signal-to-noise, and artifacts such as the missing wedge effect. Post-processing of this data generally involves isolating regions or particles of interest from tomograms, organizing them into related groups, and rendering final structures through subtomogram averaging. Template-matching and reference-based structure determination are popular analysis methods but are vulnerable to biases and can often require significant user input. Most importantly, these approaches cannot identify novel complexes that reside within the imaged cellular environment. To reliably extract and resolve structures of interest, efficient and unbiased approaches are therefore of great value. This review highlights notable computational software and discusses how they contribute to making automated structural pattern discovery a possibility. Perspectives emphasizing the importance of features for user-friendliness and accessibility are also presented.