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Detalle de: Advances in image processing for single-particle analysis by electron cryomicroscopy and challenges ahead

Title Advances in image processing for single-particle analysis by electron cryomicroscopy and challenges ahead
Abstract

Electron cryomicroscopy (cryoEM) is essential for the study and functional understanding of non-crystalline macromolecules such as proteins. These molecules cannot be imaged using X-ray crystallography or other popular methods. CryoEM has been successfully used to visualize macromolecular complexes such as ribosomes, viruses, and ion channels. Determination of structural models of these at various conformational states leads to insight on how these molecules function. Recent advances in imaging technology have given cryoEM a scientific rebirth. As a result of these technological advances image processing and analysis have yielded molecular structures at atomic resolution. Nevertheless there continue to be challenges in image processing, and in this article we will touch on the most essential in order to derive an accurate three-dimensional model from noisy projection images. Traditional approaches, such as k-means clustering for class averaging, will be provided as background. We will then highlight new approaches for each image processing subproblem, including a 3D reconstruction method for asymmetric molecules using just two projection images and deep learning algorithms for automated particle picking.

Apa Reference Vilas, J., Tabassum, N., Mota, J., Maluenda, D., Jiménez-Moreno, A., Majtner, T., Carazo, J., Acton, S. & Sorzano, C. (2018). Advances in image processing for single-particle analysis by electron cryomicroscopy and challenges ahead. Current Opinion in Structural Biology, 52, 127-145. 10.1016/j.sbi.2018.11.004
Doi 10.1016/j.sbi.2018.11.004
Publication Date Oct 2018
Publication Type Article
Researcher Jose María Salvador Carazo Garcia
Ontology Artificial Intelligence, Structural bioinformatics, Experimental structure determination, Molecular flexibility, Image analysis, Deep Learning in Biology, Cryo-electron microscopy, Clustering
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