A Stochastic Hill Climbing Approach for Simultaneous 2D Alignment and Clustering of Cryogenic Electron Microscopy Images

• Published in: Structure (London) Vol. 24; no. 6; pp. 988 - 996
• Main Authors: Reboul, Cyril F., Bonnet, Frederic, Elmlund, Dominika, Elmlund, Hans
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 07.06.2016
• Subjects: Algorithms; alignment; Cluster Analysis; clustering; cryo-EM; Cryoelectron Microscopy - methods; electron microscopy; Image Processing, Computer-Assisted; Imaging, Three-Dimensional - methods; Models, Molecular; single-particle; stochastic; electron microscopy; stochastic; clustering; single-particle; alignment; cryo-EM
• ISSN: 0969-2126; 1878-4186
• DOI: 10.1016/j.str.2016.04.006

A critical step in the analysis of novel cryogenic electron microscopy (cryo-EM) single-particle datasets is the identification of homogeneous subsets of images. Methods for solving this problem are important for data quality assessment, ab initio 3D reconstruction, and analysis of population diversity due to the heterogeneous nature of macromolecules. Here we formulate a stochastic algorithm for identification of homogeneous subsets of images. The purpose of the method is to generate improved 2D class averages that can be used to produce a reliable 3D starting model in a rapid and unbiased fashion. We show that our method overcomes inherent limitations of widely used clustering approaches and proceed to test the approach on six publicly available experimental cryo-EM datasets. We conclude that, in each instance, ab initio 3D reconstructions of quality suitable for initialization of high-resolution refinement are produced from the cluster centers. •Generation of improved 2D class averages from large single-particle cryo-EM datasets•Production of a reliable 3D starting model in a rapid and unbiased fashion•Overcoming inherent limitations in widely used clustering approaches•The method is many times faster than other widely used approaches Reboul et al. describe a new algorithm for simultaneous 2D alignment and clustering of cryo-EM images that generates improved 2D class averages that can be used to produce a reliable 3D starting model in a rapid and unbiased fashion.