A Bayesian approach to single-particle electron cryo-tomography in RELION-4.0
Jasenko Zivanov, Joaquı́n Otón, Zunlong Ke, Andriko von Kügelgen, Euan Pyle, Kun Qu, Dustin R. Morado, Daniel Castaño‐Díez, Giulia Zanetti, Tanmay A. M. Bharat, John A. G. Briggs, Sjors H. W. Scheres
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 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.