Department of Chemistry and Biochemistry, University of California Santa Barbara, Santa Barbara, CA 93106, USA.
The Scintillon Institute, La Jolla, San Diego, CA 92121, USA.
Int J Mol Sci. 2024 Jul 11;25(14):7593. doi: 10.3390/ijms25147593.
Common challenges in cryogenic electron microscopy, such as orientation bias, conformational diversity, and 3D misclassification, complicate single particle analysis and lead to significant resource expenditure. We previously introduced an in silico method using the maximum Feret diameter distribution, the Feret signature, to characterize sample heterogeneity of disc-shaped samples. Here, we expanded the Feret signature methodology to identify preferred orientations of samples containing arbitrary shapes with only about 1000 particles required. This method enables real-time adjustments of data acquisition parameters for optimizing data collection strategies or aiding in decisions to discontinue ineffective imaging sessions. Beyond detecting preferred orientations, the Feret signature approach can serve as an early-warning system for inconsistencies in classification during initial image processing steps, a capability that allows for strategic adjustments in data processing. These features establish the Feret signature as a valuable auxiliary tool in the context of single particle analysis, significantly accelerating the structure determination process.
低温电子显微镜技术常见的挑战包括取向偏差、构象多样性和 3D 分类错误等，这些问题使得单颗粒分析变得复杂，并导致大量资源的消耗。我们之前介绍了一种基于最大 Feret 直径分布和 Feret 特征的计算机模拟方法，用于描述盘状样品的异质性。在此基础上，我们将 Feret 特征方法扩展到了仅使用约 1000 个颗粒来识别任意形状的样品的优选取向。这种方法可以实时调整数据采集参数，以优化数据采集策略或辅助决定停止无效的成像过程。除了检测优选取向之外，Feret 特征方法还可以作为初始图像处理步骤中分类不一致的预警系统，这一功能允许在数据处理过程中进行策略性调整。这些特性使得 Feret 特征成为单颗粒分析中的一个有价值的辅助工具，显著加速了结构确定过程。
