College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China.
School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.
Int J Mol Sci. 2023 May 6;24(9):8380. doi: 10.3390/ijms24098380.
Heterogeneous three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy (cryo-EM) is an important but very challenging technique for recovering the conformational heterogeneity of flexible biological macromolecules such as proteins in different functional states. Heterogeneous projection image classification is a feasible solution to solve the structural heterogeneity problem in single-particle cryo-EM. The majority of heterogeneous projection image classification methods are developed using supervised learning technology or require a large amount of a priori knowledge, such as the orientations or common lines of the projection images, which leads to certain limitations in their practical applications. In this paper, an unsupervised heterogeneous cryo-EM projection image classification algorithm based on autoencoders is proposed, which only needs to know the number of heterogeneous 3D structures in the dataset and does not require any labeling information of the projection images or other a priori knowledge. A simple autoencoder with multi-layer perceptrons trained in iterative mode and a complex autoencoder with residual networks trained in one-pass learning mode are implemented to convert heterogeneous projection images into latent variables. The extracted high-dimensional features are reduced to two dimensions using the uniform manifold approximation and projection dimensionality reduction algorithm, and then clustered using the spectral clustering algorithm. The proposed algorithm is applied to two heterogeneous cryo-EM datasets for heterogeneous 3D reconstruction. Experimental results show that the proposed algorithm can effectively extract category features of heterogeneous projection images and achieve high classification and reconstruction accuracy, indicating that the proposed algorithm is effective for heterogeneous 3D reconstruction in single-particle cryo-EM.
在单颗粒冷冻电子显微镜（cryo-EM）中进行异质三维（3D）重建是一种重要但极具挑战性的技术，可用于恢复蛋白质等灵活生物大分子在不同功能状态下的构象异质性。异质投影图像分类是解决单颗粒 cryo-EM 中结构异质性问题的一种可行方法。大多数异质投影图像分类方法都是使用监督学习技术开发的，或者需要大量的先验知识，例如投影图像的取向或公共线，这在其实际应用中存在一定的局限性。本文提出了一种基于自动编码器的无监督异质 cryo-EM 投影图像分类算法，该算法仅需要知道数据集内异质 3D 结构的数量，而不需要投影图像的任何标记信息或其他先验知识。实现了两种自动编码器：一种是在迭代模式下训练的具有多层感知机的简单自动编码器，另一种是在一次学习模式下训练的具有残差网络的复杂自动编码器，用于将异质投影图像转换为潜在变量。使用等距映射和投影降维算法提取高维特征，并使用谱聚类算法对其进行聚类。将所提出的算法应用于两个异质 cryo-EM 数据集进行异质 3D 重建。实验结果表明，所提出的算法可以有效地提取异质投影图像的类别特征，并实现高分类和重建精度，表明该算法对单颗粒 cryo-EM 中的异质 3D 重建是有效的。
