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Gentle and fast all-atom model refinement to cryo-EM densities via a maximum likelihood approach
Better detectors and automated data collection have generated a flood of high-resolution cryo-EM maps, which in turn has renewed interest in improving methods for determining structure models corresponding to these maps. However, automatically fitting atoms to densities becomes difficult as their re...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10427019/ https://www.ncbi.nlm.nih.gov/pubmed/37523411 http://dx.doi.org/10.1371/journal.pcbi.1011255 |
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author | Blau, Christian Yvonnesdotter, Linnea Lindahl, Erik |
author_facet | Blau, Christian Yvonnesdotter, Linnea Lindahl, Erik |
author_sort | Blau, Christian |
collection | PubMed |
description | Better detectors and automated data collection have generated a flood of high-resolution cryo-EM maps, which in turn has renewed interest in improving methods for determining structure models corresponding to these maps. However, automatically fitting atoms to densities becomes difficult as their resolution increases and the refinement potential has a vast number of local minima. In practice, the problem becomes even more complex when one also wants to achieve a balance between a good fit of atom positions to the map, while also establishing good stereochemistry or allowing protein secondary structure to change during fitting. Here, we present a solution to this challenge using a maximum likelihood approach by formulating the problem as identifying the structure most likely to have produced the observed density map. This allows us to derive new types of smooth refinement potential—based on relative entropy—in combination with a novel adaptive force scaling algorithm to allow balancing of force-field and density-based potentials. In a low-noise scenario, as expected from modern cryo-EM data, the relative-entropy based refinement potential outperforms alternatives, and the adaptive force scaling appears to aid all existing refinement potentials. The method is available as a component in the GROMACS molecular simulation toolkit. |
format | Online Article Text |
id | pubmed-10427019 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-104270192023-08-16 Gentle and fast all-atom model refinement to cryo-EM densities via a maximum likelihood approach Blau, Christian Yvonnesdotter, Linnea Lindahl, Erik PLoS Comput Biol Research Article Better detectors and automated data collection have generated a flood of high-resolution cryo-EM maps, which in turn has renewed interest in improving methods for determining structure models corresponding to these maps. However, automatically fitting atoms to densities becomes difficult as their resolution increases and the refinement potential has a vast number of local minima. In practice, the problem becomes even more complex when one also wants to achieve a balance between a good fit of atom positions to the map, while also establishing good stereochemistry or allowing protein secondary structure to change during fitting. Here, we present a solution to this challenge using a maximum likelihood approach by formulating the problem as identifying the structure most likely to have produced the observed density map. This allows us to derive new types of smooth refinement potential—based on relative entropy—in combination with a novel adaptive force scaling algorithm to allow balancing of force-field and density-based potentials. In a low-noise scenario, as expected from modern cryo-EM data, the relative-entropy based refinement potential outperforms alternatives, and the adaptive force scaling appears to aid all existing refinement potentials. The method is available as a component in the GROMACS molecular simulation toolkit. Public Library of Science 2023-07-31 /pmc/articles/PMC10427019/ /pubmed/37523411 http://dx.doi.org/10.1371/journal.pcbi.1011255 Text en © 2023 Blau et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Blau, Christian Yvonnesdotter, Linnea Lindahl, Erik Gentle and fast all-atom model refinement to cryo-EM densities via a maximum likelihood approach |
title | Gentle and fast all-atom model refinement to cryo-EM densities via a maximum likelihood approach |
title_full | Gentle and fast all-atom model refinement to cryo-EM densities via a maximum likelihood approach |
title_fullStr | Gentle and fast all-atom model refinement to cryo-EM densities via a maximum likelihood approach |
title_full_unstemmed | Gentle and fast all-atom model refinement to cryo-EM densities via a maximum likelihood approach |
title_short | Gentle and fast all-atom model refinement to cryo-EM densities via a maximum likelihood approach |
title_sort | gentle and fast all-atom model refinement to cryo-em densities via a maximum likelihood approach |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10427019/ https://www.ncbi.nlm.nih.gov/pubmed/37523411 http://dx.doi.org/10.1371/journal.pcbi.1011255 |
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