151. A Bayesian approach to beam-induced motion correction in cryo-EM single-particle analysis
- Author
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Takanori Nakane, Jasenko Zivanov, and Sjors H.W. Scheres
- Subjects
Computer science ,Bayesian probability ,Single particle analysis ,macromolecular substances ,010402 general chemistry ,01 natural sciences ,Biochemistry ,Set (abstract data type) ,Quantitative Biology::Subcellular Processes ,03 medical and health sciences ,0302 clinical medicine ,Bayesian particle polishing ,Kriging ,single-particle analysis ,General Materials Science ,beam-induced motion correction ,Magnetosphere particle motion ,030304 developmental biology ,0303 health sciences ,Quantitative Biology::Biomolecules ,Smoothness (probability theory) ,Crystallography ,General Chemistry ,Function (mathematics) ,Condensed Matter Physics ,Research Papers ,0104 chemical sciences ,QD901-999 ,Computer Science::Computer Vision and Pattern Recognition ,A priori and a posteriori ,cryo-EM ,Spatial frequency ,electron cryo-microscopy ,Algorithm ,030217 neurology & neurosurgery - Abstract
A Bayesian approach to estimate the trajectories of particle motion in electron cryo-microscopy single-particle analysis is presented., 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.
- Published
- 2019