Cryo-electron tomography provides the opportunity for unsupervised discovery of endogenous complexes in situ. This process usually requires particle picking, clustering and alignment of subtomograms to produce an average structure of the complex. When applied to heterogeneous samples, template-free clustering and alignment of subtomograms can potentially lead to the discovery of structures for unknown endogenous complexes. However, such methods require scoring functions to measure and accurately rank the quality of aligned subtomogram clusters, which can be compromised by contaminations from misclassified complexes and alignment errors. Here, we provide the first study to assess the effectiveness of more than 15 scoring functions for evaluating the quality of subtomogram clusters, which differ in the amount of structural misalignments and contaminations due to misclassified complexes. We assessed both experimental and simulated subtomograms as ground truth data sets. Our analysis showed that the robustness of scoring functions varies largely. Most scores were sensitive to the signal-to-noise ratio of subtomograms and often required Gaussian filtering as preprocessing for improved performance. Two scoring functions, Spectral SNR-based Fourier Shell Correlation and Pearson Correlation in the Fourier domain with missing wedge correction, showed a robust ranking of subtomogram clusters without any preprocessing and irrespective of SNR levels of subtomograms. Of these two scoring functions, Spectral SNR-based Fourier Shell Correlation was fastest to compute and is a better choice for handling large numbers of subtomograms. Our results provide a guidance for choosing an accurate scoring function for template-free approaches to detect complexes from heterogeneous samples.

冷冻电子断层扫描为原位内源性复合物的无监督发现提供了机会。该过程通常需要对子断层图进行粒子拾取、聚类和对齐，以产生复合物的平均结构。当应用于异质样品时，子断层图的无模板聚类和对齐可能会导致发现未知内源性复合物的结构。然而，这种方法需要评分函数来测量和准确排列对齐的子断层图簇的质量，这可能会受到错误分类复合物和对齐错误的污染的影响。在这里，我们提供了第一项研究来评估超过 15 种评分函数在评估亚断层图簇质量方面的有效性，由于错误分类的复合体，其结构错位和污染的数量不同。我们评估了实验和模拟的子断层图作为地面实况数据集。我们的分析表明，评分函数的稳健性差异很大。大多数分数对子断层图的信噪比很敏感，并且通常需要高斯滤波作为预处理以提高性能。两个评分函数，基于光谱 SNR 的傅里叶壳相关性和傅里叶域中缺少楔形校正的 Pearson 相关性，显示了无需任何预处理且不考虑子图的 SNR 级别的子图簇的稳健排名。在这两个评分函数中，基于光谱 SNR 的傅里叶壳相关计算速度最快，是处理大量子图的更好选择。