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Exploiting prior knowledge about biological macromolecules in cryo-EM structure determination
MRC Lab Mol Biol, Cambridge, England..
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematics (Div.).ORCID iD: 0000-0002-7472-5325
MRC Lab Mol Biol, Cambridge, England..
DeepMind, London, England..
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2021 (English)In: IUCrJ, E-ISSN 2052-2525, Vol. 8, p. 60-75Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
International Union of Crystallography (IUCr) , 2021. Vol. 8, p. 60-75
Keywords [en]
3D reconstruction, image processing, single-particle cryo-EM, imaging, structure determination, cryo-electron microscopy
National Category
Biochemistry and Molecular Biology
Identifiers
URN: urn:nbn:se:kth:diva-289886DOI: 10.1107/S2052252520014384ISI: 000608819200007PubMedID: 33520243Scopus ID: 2-s2.0-85104968834OAI: oai:DiVA.org:kth-289886DiVA, id: diva2:1528443
Note

QC 20210215

Available from: 2021-02-15 Created: 2021-02-15 Last updated: 2022-09-28Bibliographically approved

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Zickert, GustavÖktem, Ozan

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CiteExportLink to record
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