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Exploiting prior knowledge about biological macromolecules in cryo-EM structure determination
Three-dimensional reconstruction of the electron-scattering potential of biological macromolecules from electron cryo-microscopy (cryo-EM) projection images is an ill-posed problem. The most popular cryo-EM software solutions to date rely on a regularization approach that is based on the prior assum...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
International Union of Crystallography
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7793004/ https://www.ncbi.nlm.nih.gov/pubmed/33520243 http://dx.doi.org/10.1107/S2052252520014384 |
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author | Kimanius, Dari Zickert, Gustav Nakane, Takanori Adler, Jonas Lunz, Sebastian Schönlieb, Carola-Bibiane Öktem, Ozan Scheres, Sjors H. W. |
author_facet | Kimanius, Dari Zickert, Gustav Nakane, Takanori Adler, Jonas Lunz, Sebastian Schönlieb, Carola-Bibiane Öktem, Ozan Scheres, Sjors H. W. |
author_sort | Kimanius, Dari |
collection | PubMed |
description | Three-dimensional reconstruction of the electron-scattering potential of biological macromolecules from electron cryo-microscopy (cryo-EM) projection images is an ill-posed problem. The most popular cryo-EM software solutions to date rely on a regularization approach that is based on the prior assumption that the scattering potential varies smoothly over three-dimensional space. Although this approach has been hugely successful in recent years, the amount of prior knowledge that it exploits compares unfavorably with the knowledge about biological structures that has been accumulated over decades of research in structural biology. Here, a regularization framework for cryo-EM structure determination is presented that exploits prior knowledge about biological structures through a convolutional neural network that is trained on known macromolecular structures. This neural network is inserted into the iterative cryo-EM structure-determination process through an approach that is inspired by regularization by denoising. It is shown that the new regularization approach yields better reconstructions than the current state of the art for simulated data, and options to extend this work for application to experimental cryo-EM data are discussed. |
format | Online Article Text |
id | pubmed-7793004 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | International Union of Crystallography |
record_format | MEDLINE/PubMed |
spelling | pubmed-77930042021-01-29 Exploiting prior knowledge about biological macromolecules in cryo-EM structure determination Kimanius, Dari Zickert, Gustav Nakane, Takanori Adler, Jonas Lunz, Sebastian Schönlieb, Carola-Bibiane Öktem, Ozan Scheres, Sjors H. W. IUCrJ Research Papers Three-dimensional reconstruction of the electron-scattering potential of biological macromolecules from electron cryo-microscopy (cryo-EM) projection images is an ill-posed problem. The most popular cryo-EM software solutions to date rely on a regularization approach that is based on the prior assumption that the scattering potential varies smoothly over three-dimensional space. Although this approach has been hugely successful in recent years, the amount of prior knowledge that it exploits compares unfavorably with the knowledge about biological structures that has been accumulated over decades of research in structural biology. Here, a regularization framework for cryo-EM structure determination is presented that exploits prior knowledge about biological structures through a convolutional neural network that is trained on known macromolecular structures. This neural network is inserted into the iterative cryo-EM structure-determination process through an approach that is inspired by regularization by denoising. It is shown that the new regularization approach yields better reconstructions than the current state of the art for simulated data, and options to extend this work for application to experimental cryo-EM data are discussed. International Union of Crystallography 2021-01-01 /pmc/articles/PMC7793004/ /pubmed/33520243 http://dx.doi.org/10.1107/S2052252520014384 Text en © Dari Kimanius et al. 2021 http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Research Papers Kimanius, Dari Zickert, Gustav Nakane, Takanori Adler, Jonas Lunz, Sebastian Schönlieb, Carola-Bibiane Öktem, Ozan Scheres, Sjors H. W. Exploiting prior knowledge about biological macromolecules in cryo-EM structure determination |
title | Exploiting prior knowledge about biological macromolecules in cryo-EM structure determination |
title_full | Exploiting prior knowledge about biological macromolecules in cryo-EM structure determination |
title_fullStr | Exploiting prior knowledge about biological macromolecules in cryo-EM structure determination |
title_full_unstemmed | Exploiting prior knowledge about biological macromolecules in cryo-EM structure determination |
title_short | Exploiting prior knowledge about biological macromolecules in cryo-EM structure determination |
title_sort | exploiting prior knowledge about biological macromolecules in cryo-em structure determination |
topic | Research Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7793004/ https://www.ncbi.nlm.nih.gov/pubmed/33520243 http://dx.doi.org/10.1107/S2052252520014384 |
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