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Artificial Intelligence in Cryo-Electron Microscopy
Cryo-electron microscopy (cryo-EM) has become an unrivaled tool for determining the structure of macromolecular complexes. The biological function of macromolecular complexes is inextricably tied to the flexibility of these complexes. Single particle cryo-EM can reveal the conformational heterogenei...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9410485/ https://www.ncbi.nlm.nih.gov/pubmed/36013446 http://dx.doi.org/10.3390/life12081267 |
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author | Chung, Jeong Min Durie, Clarissa L. Lee, Jinseok |
author_facet | Chung, Jeong Min Durie, Clarissa L. Lee, Jinseok |
author_sort | Chung, Jeong Min |
collection | PubMed |
description | Cryo-electron microscopy (cryo-EM) has become an unrivaled tool for determining the structure of macromolecular complexes. The biological function of macromolecular complexes is inextricably tied to the flexibility of these complexes. Single particle cryo-EM can reveal the conformational heterogeneity of a biochemically pure sample, leading to well-founded mechanistic hypotheses about the roles these complexes play in biology. However, the processing of increasingly large, complex datasets using traditional data processing strategies is exceedingly expensive in both user time and computational resources. Current innovations in data processing capitalize on artificial intelligence (AI) to improve the efficiency of data analysis and validation. Here, we review new tools that use AI to automate the data analysis steps of particle picking, 3D map reconstruction, and local resolution determination. We discuss how the application of AI moves the field forward, and what obstacles remain. We also introduce potential future applications of AI to use cryo-EM in understanding protein communities in cells. |
format | Online Article Text |
id | pubmed-9410485 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94104852022-08-26 Artificial Intelligence in Cryo-Electron Microscopy Chung, Jeong Min Durie, Clarissa L. Lee, Jinseok Life (Basel) Review Cryo-electron microscopy (cryo-EM) has become an unrivaled tool for determining the structure of macromolecular complexes. The biological function of macromolecular complexes is inextricably tied to the flexibility of these complexes. Single particle cryo-EM can reveal the conformational heterogeneity of a biochemically pure sample, leading to well-founded mechanistic hypotheses about the roles these complexes play in biology. However, the processing of increasingly large, complex datasets using traditional data processing strategies is exceedingly expensive in both user time and computational resources. Current innovations in data processing capitalize on artificial intelligence (AI) to improve the efficiency of data analysis and validation. Here, we review new tools that use AI to automate the data analysis steps of particle picking, 3D map reconstruction, and local resolution determination. We discuss how the application of AI moves the field forward, and what obstacles remain. We also introduce potential future applications of AI to use cryo-EM in understanding protein communities in cells. MDPI 2022-08-19 /pmc/articles/PMC9410485/ /pubmed/36013446 http://dx.doi.org/10.3390/life12081267 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Chung, Jeong Min Durie, Clarissa L. Lee, Jinseok Artificial Intelligence in Cryo-Electron Microscopy |
title | Artificial Intelligence in Cryo-Electron Microscopy |
title_full | Artificial Intelligence in Cryo-Electron Microscopy |
title_fullStr | Artificial Intelligence in Cryo-Electron Microscopy |
title_full_unstemmed | Artificial Intelligence in Cryo-Electron Microscopy |
title_short | Artificial Intelligence in Cryo-Electron Microscopy |
title_sort | artificial intelligence in cryo-electron microscopy |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9410485/ https://www.ncbi.nlm.nih.gov/pubmed/36013446 http://dx.doi.org/10.3390/life12081267 |
work_keys_str_mv | AT chungjeongmin artificialintelligenceincryoelectronmicroscopy AT durieclarissal artificialintelligenceincryoelectronmicroscopy AT leejinseok artificialintelligenceincryoelectronmicroscopy |