A Bayesian approach to beam-induced motion correction in cryo-EM single-particle analysis

A new method to estimate the trajectories of particle motion and the amount of cumulative beam damage in electron cryo-microscopy (cryo-EM) single-particle analysis is presented. The motion within the sample is modelled through the use of Gaussian process regression. This allows a prior likelihood t...

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Bibliographic Details
Published in:IUCrJ Vol. 6; no. 1; pp. 5 - 17
Main Authors: Zivanov, Jasenko, Nakane, Takanori, Scheres, Sjors H. W.
Format: Journal Article
Language:English
Published: England International Union of Crystallography 01.01.2019
Subjects:
Algorithms
Bayesian analysis
Bayesian particle polishing
beam-induced motion correction
cryo-EM
Damage assessment
electron cryo-microscopy
Gaussian process
Microscopy
Parameter estimation
Particle motion
Particle trajectories
Polishing
Radiation (Physics)
Radiation damage
Radiation dosage
Regression analysis
Research Papers
single-particle analysis
Smoothness
State of the art
Trajectory analysis
electron cryo-microscopy
Bayesian particle polishing
beam-induced motion correction
cryo-EM
single-particle analysis
ISSN:2052-2525, 2052-2525
Online Access:Get full text
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Summary:A new method to estimate the trajectories of particle motion and the amount of cumulative beam damage in electron cryo-microscopy (cryo-EM) single-particle analysis is presented. The motion within the sample is modelled through the use of Gaussian process regression. This allows a prior likelihood that favours spatially and temporally smooth motion to be associated with each hypothetical set of particle trajectories without imposing hard constraints. This formulation enables the a posteriori likelihood of a set of particle trajectories to be expressed as a product of that prior likelihood and an observation likelihood given by the data, and this a posteriori likelihood to then be maximized. Since the smoothness prior requires three parameters that describe the statistics of the observed motion, an efficient stochastic method to estimate these parameters is also proposed. Finally, a practical algorithm is proposed that estimates the average amount of cumulative radiation damage as a function of radiation dose and spatial frequency, and then fits relative B factors to that damage in a robust way. The method is evaluated on three publicly available data sets, and its usefulness is illustrated by comparison with state-of-the-art methods and previously published results. The new method has been implemented as Bayesian polishing in RELION -3, where it replaces the existing particle-polishing method, as it outperforms the latter in all tests conducted.
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ISSN:2052-2525
2052-2525
DOI:10.1107/S205225251801463X