eLife (Dec 2022)

A Bayesian approach to single-particle electron cryo-tomography in RELION-4.0

  • Jasenko Zivanov,
  • Joaquín Otón,
  • Zunlong Ke,
  • Andriko von Kügelgen,
  • Euan Pyle,
  • Kun Qu,
  • Dustin Morado,
  • Daniel Castaño-Díez,
  • Giulia Zanetti,
  • Tanmay AM Bharat,
  • John AG Briggs,
  • Sjors HW Scheres

DOI
https://doi.org/10.7554/eLife.83724
Journal volume & issue
Vol. 11

Abstract

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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 the 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, particularly 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.

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