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3D Structure From 2D Microscopy Images Using Deep Learning
Understanding the structure of a protein complex is crucial in determining its function. However, retrieving accurate 3D structures from microscopy images is highly challenging, particularly as many imaging modalities are two-dimensional. Recent advances in Artificial Intelligence have been applied...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581024/ https://www.ncbi.nlm.nih.gov/pubmed/36303741 http://dx.doi.org/10.3389/fbinf.2021.740342 |
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author | Blundell, Benjamin Sieben, Christian Manley, Suliana Rosten, Ed Ch’ng, QueeLim Cox, Susan |
author_facet | Blundell, Benjamin Sieben, Christian Manley, Suliana Rosten, Ed Ch’ng, QueeLim Cox, Susan |
author_sort | Blundell, Benjamin |
collection | PubMed |
description | Understanding the structure of a protein complex is crucial in determining its function. However, retrieving accurate 3D structures from microscopy images is highly challenging, particularly as many imaging modalities are two-dimensional. Recent advances in Artificial Intelligence have been applied to this problem, primarily using voxel based approaches to analyse sets of electron microscopy images. Here we present a deep learning solution for reconstructing the protein complexes from a number of 2D single molecule localization microscopy images, with the solution being completely unconstrained. Our convolutional neural network coupled with a differentiable renderer predicts pose and derives a single structure. After training, the network is discarded, with the output of this method being a structural model which fits the data-set. We demonstrate the performance of our system on two protein complexes: CEP152 (which comprises part of the proximal toroid of the centriole) and centrioles. |
format | Online Article Text |
id | pubmed-9581024 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95810242022-10-26 3D Structure From 2D Microscopy Images Using Deep Learning Blundell, Benjamin Sieben, Christian Manley, Suliana Rosten, Ed Ch’ng, QueeLim Cox, Susan Front Bioinform Bioinformatics Understanding the structure of a protein complex is crucial in determining its function. However, retrieving accurate 3D structures from microscopy images is highly challenging, particularly as many imaging modalities are two-dimensional. Recent advances in Artificial Intelligence have been applied to this problem, primarily using voxel based approaches to analyse sets of electron microscopy images. Here we present a deep learning solution for reconstructing the protein complexes from a number of 2D single molecule localization microscopy images, with the solution being completely unconstrained. Our convolutional neural network coupled with a differentiable renderer predicts pose and derives a single structure. After training, the network is discarded, with the output of this method being a structural model which fits the data-set. We demonstrate the performance of our system on two protein complexes: CEP152 (which comprises part of the proximal toroid of the centriole) and centrioles. Frontiers Media S.A. 2021-10-28 /pmc/articles/PMC9581024/ /pubmed/36303741 http://dx.doi.org/10.3389/fbinf.2021.740342 Text en Copyright © 2021 Blundell, Sieben, Manley, Rosten, Ch’ng and Cox. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Bioinformatics Blundell, Benjamin Sieben, Christian Manley, Suliana Rosten, Ed Ch’ng, QueeLim Cox, Susan 3D Structure From 2D Microscopy Images Using Deep Learning |
title | 3D Structure From 2D Microscopy Images Using Deep Learning |
title_full | 3D Structure From 2D Microscopy Images Using Deep Learning |
title_fullStr | 3D Structure From 2D Microscopy Images Using Deep Learning |
title_full_unstemmed | 3D Structure From 2D Microscopy Images Using Deep Learning |
title_short | 3D Structure From 2D Microscopy Images Using Deep Learning |
title_sort | 3d structure from 2d microscopy images using deep learning |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581024/ https://www.ncbi.nlm.nih.gov/pubmed/36303741 http://dx.doi.org/10.3389/fbinf.2021.740342 |
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