Quantitative Analysis in Iterative Classification Schemes for Cryo-EM Application

Abstract

Over the past three decades, cryogenic electron microscopy (cryo-EM) and single-particle reconstruction (SPR) techniques have evolved into a powerful toolbox for determining biological macromolecular structures. In its original form, the SPR requires a homogeneous sample, i.e., all the projection images represent identical copies of the macromolecules (Frank, Three-dimensional electron microscopy of macromolecular assemblies: visualization of biological molecules in their native state, Oxford University Press, Oxford, 2006). Recent developments in computational classification methods have made it possible to determine multiple conformations/structures of the macromolecules from cryo-EM data obtained from a single biological sample (Agirrezabala et al., Proc Natl Acad Sci 109:6094–6099, 2012; Fischer et al., Nature 466:329–333, 2010; Scheres, J Struct Biol 180:519–530, 2012). However, the existing classification methods involve different amounts of arbitrary decisions, which may lead to ambiguities of the classification results. In this work, we propose a quantitative way of analyzing the results obtained with iterative classification of cryo-EM data. Based on the logs of iterative particle classification, this analysis can provide quantitative criteria for determining the iteration of convergence and the number of distinguishable conformations/structures in a heterogeneous cryo-EM data set. To show its applicability, we tailored this analysis to the classification results of the program RELION (Scheres, Methods Enzymol 482:295–320, 2010; Scheres, J Mol Biol 415:406–418, 2011) using both benchmark and experimental data sets of ribosomes.