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Volumetric macromolecule identification in cryo-electron tomograms using capsule networks

BACKGROUND: Despite recent advances in cellular cryo-electron tomography (CET), developing automated tools for macromolecule identification in submolecular resolution remains challenging due to the lack of annotated data and high structural complexities. To date, the extent of the deep learning meth...

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Autores principales: Hajarolasvadi, Noushin, Sunkara, Vikram, Khavnekar, Sagar, Beck, Florian, Brandt, Robert, Baum, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9429335/
https://www.ncbi.nlm.nih.gov/pubmed/36042418
http://dx.doi.org/10.1186/s12859-022-04901-w
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author Hajarolasvadi, Noushin
Sunkara, Vikram
Khavnekar, Sagar
Beck, Florian
Brandt, Robert
Baum, Daniel
author_facet Hajarolasvadi, Noushin
Sunkara, Vikram
Khavnekar, Sagar
Beck, Florian
Brandt, Robert
Baum, Daniel
author_sort Hajarolasvadi, Noushin
collection PubMed
description BACKGROUND: Despite recent advances in cellular cryo-electron tomography (CET), developing automated tools for macromolecule identification in submolecular resolution remains challenging due to the lack of annotated data and high structural complexities. To date, the extent of the deep learning methods constructed for this problem is limited to conventional Convolutional Neural Networks (CNNs). Identifying macromolecules of different types and sizes is a tedious and time-consuming task. In this paper, we employ a capsule-based architecture to automate the task of macromolecule identification, that we refer to as 3D-UCaps. In particular, the architecture is composed of three components: feature extractor, capsule encoder, and CNN decoder. The feature extractor converts voxel intensities of input sub-tomograms to activities of local features. The encoder is a 3D Capsule Network (CapsNet) that takes local features to generate a low-dimensional representation of the input. Then, a 3D CNN decoder reconstructs the sub-tomograms from the given representation by upsampling. RESULTS: We performed binary and multi-class localization and identification tasks on synthetic and experimental data. We observed that the 3D-UNet and the 3D-UCaps had an [Formula: see text] score mostly above 60% and 70%, respectively, on the test data. In both network architectures, we observed degradation of at least 40% in the [Formula: see text] -score when identifying very small particles (PDB entry 3GL1) compared to a large particle (PDB entry 4D8Q). In the multi-class identification task of experimental data, 3D-UCaps had an [Formula: see text] -score of 91% on the test data in contrast to 64% of the 3D-UNet. The better [Formula: see text] -score of 3D-UCaps compared to 3D-UNet is obtained by a higher precision score. We speculate this to be due to the capsule network employed in the encoder. To study the effect of the CapsNet-based encoder architecture further, we performed an ablation study and perceived that the [Formula: see text] -score is boosted as network depth is increased which is in contrast to the previously reported results for the 3D-UNet. To present a reproducible work, source code, trained models, data as well as visualization results are made publicly available. CONCLUSION: Quantitative and qualitative results show that 3D-UCaps successfully perform various downstream tasks including identification and localization of macromolecules and can at least compete with CNN architectures for this task. Given that the capsule layers extract both the existence probability and the orientation of the molecules, this architecture has the potential to lead to representations of the data that are better interpretable than those of 3D-UNet. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04901-w.
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spelling pubmed-94293352022-09-01 Volumetric macromolecule identification in cryo-electron tomograms using capsule networks Hajarolasvadi, Noushin Sunkara, Vikram Khavnekar, Sagar Beck, Florian Brandt, Robert Baum, Daniel BMC Bioinformatics Research BACKGROUND: Despite recent advances in cellular cryo-electron tomography (CET), developing automated tools for macromolecule identification in submolecular resolution remains challenging due to the lack of annotated data and high structural complexities. To date, the extent of the deep learning methods constructed for this problem is limited to conventional Convolutional Neural Networks (CNNs). Identifying macromolecules of different types and sizes is a tedious and time-consuming task. In this paper, we employ a capsule-based architecture to automate the task of macromolecule identification, that we refer to as 3D-UCaps. In particular, the architecture is composed of three components: feature extractor, capsule encoder, and CNN decoder. The feature extractor converts voxel intensities of input sub-tomograms to activities of local features. The encoder is a 3D Capsule Network (CapsNet) that takes local features to generate a low-dimensional representation of the input. Then, a 3D CNN decoder reconstructs the sub-tomograms from the given representation by upsampling. RESULTS: We performed binary and multi-class localization and identification tasks on synthetic and experimental data. We observed that the 3D-UNet and the 3D-UCaps had an [Formula: see text] score mostly above 60% and 70%, respectively, on the test data. In both network architectures, we observed degradation of at least 40% in the [Formula: see text] -score when identifying very small particles (PDB entry 3GL1) compared to a large particle (PDB entry 4D8Q). In the multi-class identification task of experimental data, 3D-UCaps had an [Formula: see text] -score of 91% on the test data in contrast to 64% of the 3D-UNet. The better [Formula: see text] -score of 3D-UCaps compared to 3D-UNet is obtained by a higher precision score. We speculate this to be due to the capsule network employed in the encoder. To study the effect of the CapsNet-based encoder architecture further, we performed an ablation study and perceived that the [Formula: see text] -score is boosted as network depth is increased which is in contrast to the previously reported results for the 3D-UNet. To present a reproducible work, source code, trained models, data as well as visualization results are made publicly available. CONCLUSION: Quantitative and qualitative results show that 3D-UCaps successfully perform various downstream tasks including identification and localization of macromolecules and can at least compete with CNN architectures for this task. Given that the capsule layers extract both the existence probability and the orientation of the molecules, this architecture has the potential to lead to representations of the data that are better interpretable than those of 3D-UNet. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04901-w. BioMed Central 2022-08-30 /pmc/articles/PMC9429335/ /pubmed/36042418 http://dx.doi.org/10.1186/s12859-022-04901-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Hajarolasvadi, Noushin
Sunkara, Vikram
Khavnekar, Sagar
Beck, Florian
Brandt, Robert
Baum, Daniel
Volumetric macromolecule identification in cryo-electron tomograms using capsule networks
title Volumetric macromolecule identification in cryo-electron tomograms using capsule networks
title_full Volumetric macromolecule identification in cryo-electron tomograms using capsule networks
title_fullStr Volumetric macromolecule identification in cryo-electron tomograms using capsule networks
title_full_unstemmed Volumetric macromolecule identification in cryo-electron tomograms using capsule networks
title_short Volumetric macromolecule identification in cryo-electron tomograms using capsule networks
title_sort volumetric macromolecule identification in cryo-electron tomograms using capsule networks
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9429335/
https://www.ncbi.nlm.nih.gov/pubmed/36042418
http://dx.doi.org/10.1186/s12859-022-04901-w
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