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Journal Article

Bayesian inference of initial models in cryo-electron microscopy using pseudo-atoms.

MPS-Authors
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Habeck,  M.
Research Group of Statistical Inverse-Problems in Biophysics, MPI for Biophysical Chemistry, Max Planck Society;

Fulltext (public)

2139298.pdf
(Publisher version), 2MB

Supplementary Material (public)

2139298_Suppl_1.pdf
(Supplementary material), 653KB

2139298_Suppl_2.pdf
(Supplementary material), 3MB

Citation

Joubert, P., & Habeck, M. (2015). Bayesian inference of initial models in cryo-electron microscopy using pseudo-atoms. Biophysical Journal, 108(5), 1165-1175. doi:10.1016/j.bpj.2014.12.054.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0026-B033-E
Abstract
Single-particle cryo-electron microscopy is widely used to study the structure of macromolecular assemblies. Tens of thousands of noisy two-dimensional images of the macromolecular assembly viewed from different directions are used to infer its three-dimensional structure. The first step is to estimate a low-resolution initial model and initial image orientations. This is a challenging global optimization problem with many unknowns, including an unknown orientation for each two-dimensional image. Obtaining a good initial model is crucial for the success of the subsequent refinement step. We introduce a probabilistic algorithm for estimating an initial model. The algorithm is fast, has very few algorithmic parameters, and yields information about the precision of estimated model parameters in addition to the parameters themselves. Our algorithm uses a pseudo-atomic model to represent the low-resolution three-dimensional structure, with isotropic Gaussian components as moveable pseudo-atoms. This leads to a significant reduction in the number of parameters needed to represent the three-dimensional structure, and a simplified way of computing two-dimensional projections. It also contributes to the speed of the algorithm. We combine the estimation of the unknown three-dimensional structure and image orientations in a Bayesian framework. This ensures that there are very few parameters to set, and specifies how to combine different types of prior information about the structure with the given data in a systematic way. To estimate the model parameters we use Markov chain Monte Carlo sampling. The advantage is that instead of just obtaining point estimates of model parameters, we obtain an ensemble of models revealing the precision of the estimated parameters. We demonstrate the algorithm on both simulated and real data.