Bayesian probability

Physics

Geology

Meteorology

Scanning transmission electron microscopy

Mathematics

Single particle analysis

Evolutionary biology

Biology

Algorithm

Optics

Statistical physics

Oceanography

Function (biology)

Artificial intelligence

Tomography

Cryo-electron microscopy

Programming language

Geometry

Software

Cryo-electron tomography

Particle (ecology)

Aerosol

Tilt (camera)

Computer science

Nuclear magnetic resonance

Electron tomography

Electron microscope

eLife

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.

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