School of Information Science and Engineering, Lanzhou University, 730000, Lanzhou, China.
College of Computer Science and Engineering, Northwest Normal University, 730070, Lanzhou, China.
Brief Bioinform. 2022 May 13;23(3). doi: 10.1093/bib/bbac032.
Single-particle cryo-electron microscopy (cryo-EM) has become one of the mainstream technologies in the field of structural biology to determine the three-dimensional (3D) structures of biological macromolecules. Heterogeneous cryo-EM projection image classification is an effective way to discover conformational heterogeneity of biological macromolecules in different functional states. However, due to the low signal-to-noise ratio of the projection images, the classification of heterogeneous cryo-EM projection images is a very challenging task. In this paper, two novel distance measures between projection images integrating the reliability of common lines, pixel intensity and class averages are designed, and then a two-stage spectral clustering algorithm based on the two distance measures is proposed for heterogeneous cryo-EM projection image classification. In the first stage, the novel distance measure integrating common lines and pixel intensities of projection images is used to obtain preliminary classification results through spectral clustering. In the second stage, another novel distance measure integrating the first novel distance measure and class averages generated from each group of projection images is used to obtain the final classification results through spectral clustering. The proposed two-stage spectral clustering algorithm is applied on a simulated and a real cryo-EM dataset for heterogeneous reconstruction. Results show that the two novel distance measures can be used to improve the classification performance of spectral clustering, and using the proposed two-stage spectral clustering algorithm can achieve higher classification and reconstruction accuracy than using RELION and XMIPP.
单颗粒冷冻电子显微镜（cryo-EM）已成为结构生物学领域确定生物大分子三维（3D）结构的主流技术之一。异质 cryo-EM 投影图像分类是发现不同功能状态下生物大分子构象异质性的有效方法。然而，由于投影图像的信噪比低，因此异质 cryo-EM 投影图像的分类是一项极具挑战性的任务。在本文中，设计了两种新的投影图像之间的距离度量，这些距离度量综合了公共线、像素强度和类平均值的可靠性，然后提出了一种基于这两种距离度量的两阶段谱聚类算法，用于异质 cryo-EM 投影图像分类。在第一阶段，使用新的投影图像公共线和像素强度集成的距离度量通过谱聚类获得初步分类结果。在第二阶段，使用新的投影图像集成的第一个距离度量和从每组投影图像生成的类平均值的另一种新的距离度量，通过谱聚类获得最终的分类结果。所提出的两阶段谱聚类算法应用于模拟和真实 cryo-EM 数据集的异质重建。结果表明，这两种新的距离度量可以用于提高谱聚类的分类性能，并且使用所提出的两阶段谱聚类算法可以实现比 RELION 和 XMIPP 更高的分类和重建精度。
