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Searching for 3D structural models from a library of biological shapes using a few 2D experimental images
BACKGROUND: Advancements in biophysical experimental techniques have pushed the limits in terms of the types of phenomena that can be characterized, the amount of data that can be produced and the resolution at which we can visualize them. Single particle techniques such as Electron Microscopy (EM)...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6134691/ https://www.ncbi.nlm.nih.gov/pubmed/30208849 http://dx.doi.org/10.1186/s12859-018-2358-0 |
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author | Tiwari, Sandhya P. Tama, Florence Miyashita, Osamu |
author_facet | Tiwari, Sandhya P. Tama, Florence Miyashita, Osamu |
author_sort | Tiwari, Sandhya P. |
collection | PubMed |
description | BACKGROUND: Advancements in biophysical experimental techniques have pushed the limits in terms of the types of phenomena that can be characterized, the amount of data that can be produced and the resolution at which we can visualize them. Single particle techniques such as Electron Microscopy (EM) and X-ray free electron laser (XFEL) scattering require a large number of 2D images collected to resolve three-dimensional (3D) structures. In this study, we propose a quick strategy to retrieve potential 3D shapes, as low-resolution models, from a few 2D experimental images by searching a library of 2D projection images generated from existing 3D structures. RESULTS: We developed the protocol to assemble a non-redundant set of 3D shapes for generating the 2D image library, and to retrieve potential match 3D shapes for query images, using EM data as a test. In our strategy, we disregard differences in volume size, giving previously unknown structures and conformations a greater number of 3D biological shapes as possible matches. We tested the strategy using images from three EM models as query images for searches against a library of 22750 2D projection images generated from 250 random EM models. We found that our ability to identify 3D shapes that match the query images depends on how complex the outline of the 2D shapes are and whether they are represented in the search image library. CONCLUSIONS: Through our computational method, we are able to quickly retrieve a 3D shape from a few 2D projection images. Our approach has the potential for exploring other types of 2D single particle structural data such as from XFEL scattering experiments, for providing a tool to interpret low-resolution data that may be insufficient for 3D reconstruction, and for estimating the mixing of states or conformations that could exist in such experimental data. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2358-0) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6134691 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-61346912018-09-13 Searching for 3D structural models from a library of biological shapes using a few 2D experimental images Tiwari, Sandhya P. Tama, Florence Miyashita, Osamu BMC Bioinformatics Methodology Article BACKGROUND: Advancements in biophysical experimental techniques have pushed the limits in terms of the types of phenomena that can be characterized, the amount of data that can be produced and the resolution at which we can visualize them. Single particle techniques such as Electron Microscopy (EM) and X-ray free electron laser (XFEL) scattering require a large number of 2D images collected to resolve three-dimensional (3D) structures. In this study, we propose a quick strategy to retrieve potential 3D shapes, as low-resolution models, from a few 2D experimental images by searching a library of 2D projection images generated from existing 3D structures. RESULTS: We developed the protocol to assemble a non-redundant set of 3D shapes for generating the 2D image library, and to retrieve potential match 3D shapes for query images, using EM data as a test. In our strategy, we disregard differences in volume size, giving previously unknown structures and conformations a greater number of 3D biological shapes as possible matches. We tested the strategy using images from three EM models as query images for searches against a library of 22750 2D projection images generated from 250 random EM models. We found that our ability to identify 3D shapes that match the query images depends on how complex the outline of the 2D shapes are and whether they are represented in the search image library. CONCLUSIONS: Through our computational method, we are able to quickly retrieve a 3D shape from a few 2D projection images. Our approach has the potential for exploring other types of 2D single particle structural data such as from XFEL scattering experiments, for providing a tool to interpret low-resolution data that may be insufficient for 3D reconstruction, and for estimating the mixing of states or conformations that could exist in such experimental data. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2358-0) contains supplementary material, which is available to authorized users. BioMed Central 2018-09-12 /pmc/articles/PMC6134691/ /pubmed/30208849 http://dx.doi.org/10.1186/s12859-018-2358-0 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Article Tiwari, Sandhya P. Tama, Florence Miyashita, Osamu Searching for 3D structural models from a library of biological shapes using a few 2D experimental images |
title | Searching for 3D structural models from a library of biological shapes using a few 2D experimental images |
title_full | Searching for 3D structural models from a library of biological shapes using a few 2D experimental images |
title_fullStr | Searching for 3D structural models from a library of biological shapes using a few 2D experimental images |
title_full_unstemmed | Searching for 3D structural models from a library of biological shapes using a few 2D experimental images |
title_short | Searching for 3D structural models from a library of biological shapes using a few 2D experimental images |
title_sort | searching for 3d structural models from a library of biological shapes using a few 2d experimental images |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6134691/ https://www.ncbi.nlm.nih.gov/pubmed/30208849 http://dx.doi.org/10.1186/s12859-018-2358-0 |
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