Evaluating the accuracy of classifying heterogeneous single-particle cryo-EM datasets

Abstract

One important advantage of cryogenic electron microscopy (cryo-EM) is its ability to determine high-resolution structures from samples containing heterogeneous ensembles of a macromolecule in different conformational or compositional states. As resolutions and data processing algorithms continue to improve, one outstanding question is how well can we determine subtle differences in protein states, such as the same protein bound to different ligands at the same binding site? Importantly, how reliable is the separation of these states and can we derive quantitative information such as binding kinetics? In this study, we address these questions by assessing the classification of synthetic heterogenous datasets of TRPV1.