A Bayesian approach to single-particle electron cryo-tomography in RELION-4.0
- MRC Laboratory of Molecular Biology, United Kingdom
- ALBA Synchrotron, Spain
- Birkbeck, University of London, United Kingdom
- University of Basel, Switzerland
Abstract
We present a new approach for macromolecular structure determination from multiple particles in electron cryo-tomography (cryo-ET) data sets. Whereas existing subtomogram averaging approaches are based on 3D data models, we propose to optimise a regularised likelihood target that approximates a function of the 2D experimental images. In addition, analogous to Bayesian polishing and contrast transfer function (CTF) refinement in single-particle analysis, we describe approaches that exploit the increased signal-to-noise ratio in the averaged structure to optimise tilt series alignments, beam-induced motions of the particles throughout the tilt series acquisition, defoci of the individual particles, as well as higher-order optical aberrations of the microscope. Implementation of our approaches in the open-source software package RELION aims to facilitate their general use, in particular for those researchers who are already familiar with its single-particle analysis tools. We illustrate for three applications that our approaches allow structure determination from cryo-ET data to resolutions sufficient for de novo atomic modelling.
Data availability
We have only used previously published cryo-EM data sets for testing our software.Reconstructed maps and atomic models generated in this study have been submitted to the EMDB and PDB, with entry codes as indicated in Table 1.
Article and author information
Author details
Andriko von Kügelgen
Funding
UK Research and Innovation (MC_UP_A025_1013)
- Sjors HW Scheres
UK Research and Innovation (MC_UP_1201/16)
- John AG Briggs
European Research Council (ERC-CoG-2014 grant 648432)
- John AG Briggs
European Research Council (ERC-StG-2019 grant 852915)
- Giulia Zanetti
Swiss National Science Foundation (205321_179041/1)
- Daniel Castaño-Díez
UK Research and Innovation (BBSRC grant BB/T002670/1)
- Giulia Zanetti
European Research Council (ERC-AdG-2015 grant 692726)
- Jasenkio Zivanov
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2022, Zivanov et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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