Heterogeneous Cryo-EM Projection Image Classification Based on Common Lines

Reconstruction of heterogeneous cryo-electron microscopy (cryo-EM) structures is a very challenging task for discovering conformational heterogeneity of biological macro-molecules and their complexes in different functional states, where heterogeneous projection image classification is a key technology. In this paper, three heterogeneous projection image classification algorithms based on the similarity and reliability of common lines are proposed, in which the reliability of common lines is calculated according to two voting algorithms. The similarity and reliability of common lines are converted into adjacency matrices using a k-nearest neighbor algorithm and a shared nearest neighbor algorithm. The adjacency matrices are used as the input of a normalized spectral clustering algorithm to perform the classification of heterogeneous projection images. The proposed algorithms are applied to two heterogeneous cryo-EM datasets to demonstrate their classification performance. Experimental results show that the proposed algorithms can achieve higher classification accuracy in comparison with R ELION and XMIPP, indicating that they are effective in the 3D reconstruction of heterogeneous cryo-EM structures.