Localized reconstruction in Scipion expedites the analysis of symmetry mismatches in cryo-EM data

Technological advances in transmission electron microscopes and detectors have turned cryogenic electron microscopy (cryo-EM) into an essential tool for structural biology. A commonly used cryo-EM data analysis method, single particle analysis, averages hundreds of thousands of low-dose images of individual macromolecular complexes to determine a density map of the complex. The presence of symmetry in the complex is beneficial since each projection image can be assigned to multiple views of the complex. However, data processing that applies symmetry can average out asymmetric features and consequently data analysis methods are required to resolve asymmetric structural features. Scipion is a cryo-EM image processing framework that integrates functions from different image processing packages as plugins. To extend its functionality for handling symmetry mismatches, we present here a Scipion plugin termed LocalRec implementing the localized reconstruction method. When tested on an adenovirus data set, the plugin enables resolving the symmetry-mismatched trimeric fibre bound to the five-fold vertices of the capsid. Furthermore, it improves the structure determination of the icosahedral capsid by dealing with the defocus gradient across the particle. LocalRec is expected to be widely applicable in a range of cryo-EM investigations of flexible and symmetry mismatched complexes. (C) 2020 The Authors. Published by Elsevier Ltd.

Automated summary

Technological advances in transmission electron microscopes and detectors have made cryogenic electron microscopy an essential tool for structural biology. A method called single particle analysis is commonly used to determine the density map of macromolecular complexes, but dealing with symmetry mismatches in structures can be a challenge. Scipion, a cryo-EM image processing framework, has introduced a plugin called LocalRec to handle localized reconstruction for resolving asymmetric features.