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      A Bayesian View on Cryo-EM Structure Determination

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          Abstract

          Three-dimensional (3D) structure determination by single-particle analysis of cryo-electron microscopy (cryo-EM) images requires many parameters to be determined from extremely noisy data. This makes the method prone to overfitting, that is, when structures describe noise rather than signal, in particular near their resolution limit where noise levels are highest. Cryo-EM structures are typically filtered using ad hoc procedures to prevent overfitting, but the tuning of arbitrary parameters may lead to subjectivity in the results. I describe a Bayesian interpretation of cryo-EM structure determination, where smoothness in the reconstructed density is imposed through a Gaussian prior in the Fourier domain. The statistical framework dictates how data and prior knowledge should be combined, so that the optimal 3D linear filter is obtained without the need for arbitrariness and objective resolution estimates may be obtained. Application to experimental data indicates that the statistical approach yields more reliable structures than existing methods and is capable of detecting smaller classes in data sets that contain multiple different structures.

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          Highlights

          ► Limiting overfitting in cryo-EM reconstruction requires user expertise. ► A Bayesian approach may limit overfitting without arbitrary decisions, providing novel insights in the Wiener filter for 3D reconstruction, yielding better structures and improved classifications, and contributing to increase the objectivity in cryo-EM reconstruction.

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          Most cited references 34

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          FREALIGN: high-resolution refinement of single particle structures.

          The refinement of three-dimensional reconstructions and correction for the contrast transfer function of the microscope are important steps in the determination of macromolecular structures by single particle electron microscopy. The algorithms implemented in the computer program FREALIGN are optimized to perform these tasks efficiently. A general overview and details on how to use FREALIGN are provided. The program is free and available for download on the author's web page.
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            XMIPP: a new generation of an open-source image processing package for electron microscopy.

            X-windows based microscopy image processing package (Xmipp) is a specialized suit of image processing programs, primarily aimed at obtaining the 3D reconstruction of biological specimens from large sets of projection images acquired by transmission electron microscopy. This public-domain software package was introduced to the electron microscopy field eight years ago, and since then it has changed drastically. New methodologies for the analysis of single-particle projection images have been added to classification, contrast transfer function correction, angular assignment, 3D reconstruction, reconstruction of crystals, etc. In addition, the package has been extended with functionalities for 2D crystal and electron tomography data. Furthermore, its current implementation in C++, with a highly modular design of well-documented data structures and functions, offers a convenient environment for the development of novel algorithms. In this paper, we present a general overview of a new generation of Xmipp that has been re-engineered to maximize flexibility and modularity, potentially facilitating its integration in future standardization efforts in the field. Moreover, by focusing on those developments that distinguish Xmipp from other packages available, we illustrate its added value to the electron microscopy community.
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              Image processing for electron microscopy single-particle analysis using XMIPP.

              We describe a collection of standardized image processing protocols for electron microscopy single-particle analysis using the XMIPP software package. These protocols allow performing the entire processing workflow starting from digitized micrographs up to the final refinement and evaluation of 3D models. A particular emphasis has been placed on the treatment of structurally heterogeneous data through maximum-likelihood refinements and self-organizing maps as well as the generation of initial 3D models for such data sets through random conical tilt reconstruction methods. All protocols presented have been implemented as stand-alone, executable python scripts, for which a dedicated graphical user interface has been developed. Thereby, they may provide novice users with a convenient tool to quickly obtain useful results with minimum efforts in learning about the details of this comprehensive package. Examples of applications are presented for a negative stain random conical tilt data set on the hexameric helicase G40P and for a structurally heterogeneous data set on 70S Escherichia coli ribosomes embedded in vitrified ice.
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                Author and article information

                Journal
                J Mol Biol
                J. Mol. Biol
                Journal of Molecular Biology
                Elsevier
                0022-2836
                1089-8638
                13 January 2012
                13 January 2012
                : 415
                : 2-4
                : 406-418
                Affiliations
                MRC Laboratory of Molecular Biology, Hills Road, Cambridge CB2 0QH, UK
                Article
                YJMBI63392
                10.1016/j.jmb.2011.11.010
                3314964
                22100448
                a2a36235-2925-4099-b8d1-c7b4d4c1c126
                © 2012 Elsevier Ltd.

                This document may be redistributed and reused, subject to certain conditions.

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