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Likelihood-based structural analysis of electron microscopy images

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Cossio,  Pilar
Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max Planck Society;
Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia, Medellín, Colombia;

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Hummer,  Gerhard
Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max Planck Society;
Institute of Biophysics, Goethe University Frankfurt, Frankfurt am Main, Germany;

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Citation

Cossio, P., & Hummer, G. (2018). Likelihood-based structural analysis of electron microscopy images. Current Opinion in Structural Biology, 49, 162-168. doi:10.1016/j.sbi.2018.03.004.


Cite as: http://hdl.handle.net/21.11116/0000-0003-8DB9-E
Abstract
Likelihood-based analysis of single-particle electron microscopy images has contributed much to the recent improvements in resolution. By treating particle orientations and classes probabilistically, uncertainties in the reconstruction process are explicitly accounted for, and the risk of bias towards the initial model is diminished. As a result, the quality and reliability of the reconstructions have greatly improved at manageable computational cost. Likelihood-based analysis of electron microscopy images also offers a route to direct coordinate refinement for dynamic systems, as an alternative to 3D density reconstruction. Here, we review recent developments in the algorithms used for reconstructions of high-resolution maps, and in the integrative framework of combining likelihood methods with simulations to address conformational variability in cryo-electron microscopy.