Current approaches for automated model building into cryo-EM maps using Buccaneer with CCP-EM.

Soon Wen Hoh,Kevin Cowtan,Tom Burnley

doi:10.1107/s2059798320005513

Abstract

This work focuses on the use of the existing protein-model-building software Buccaneer to provide structural interpretation of electron cryo-microscopy (cryo-EM) maps. Originally developed for application to X-ray crystallography, the necessary steps to optimise the usage of Buccaneer with cryo-EM maps are shown. This approach has been applied to the data sets of 208 cryo-EM maps with resolutions of better than 4 Å. The results obtained also show an evident improvement in the sequencing step when the initial reference map and model used for crystallographic cases are replaced by a cryo-EM reference. All other necessary changes to settings in Buccaneer are implemented in the model-building pipeline from within the CCP-EM interface (as of version 1.4.0).

Highlights

Technological advances have brought major improvements in single-particle electron cryo-microscopy, in particular the introduction of direct electron detectors (McMullan et al, 2016)
All tests were performed on a total of 208 EM maps downloaded from the Electron Microscopy Data Bank (EMDB; Lawson et al, 2011)
An initial test was performed to examine the effects of correlation and fast modes on models built by Buccaneer

Summary

Introduction

Technological advances have brought major improvements in single-particle electron cryo-microscopy (cryo-EM), in particular the introduction of direct electron detectors (McMullan et al, 2016) This technique has allowed the study of rapidly frozen biological macromolecules without the need for crystallization. In cryo-EM, volumes produced using the single-particle analysis reconstruction technique contain both amplitude and phase information. In these EM cases the amplitudes are less accurate than those measured from X-ray diffraction (Cheng, 2015). Modifications of amplitudes via sharpening can be performed prior to model building to increase the interpretability of input maps, phases are not modified during refinement of the model. We note that the experimental phases are not without error, and it is hoped that in the future a model-based error model (or similar) could be used to improve both the amplitudes and the phases of the data collected

Methods

Results

Conclusion