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Likelihood-based docking of models into cryo-EM maps
Optimized docking of models into cryo-EM maps requires exploiting an understanding of the signal expected in the data to minimize the calculation time while maintaining sufficient signal. The likelihood-based rotation function used in crystallography can be employed to establish plausible orientatio...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
International Union of Crystallography
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10071562/ https://www.ncbi.nlm.nih.gov/pubmed/36920336 http://dx.doi.org/10.1107/S2059798323001602 |
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author | Millán, Claudia McCoy, Airlie J. Terwilliger, Thomas C. Read, Randy J. |
author_facet | Millán, Claudia McCoy, Airlie J. Terwilliger, Thomas C. Read, Randy J. |
author_sort | Millán, Claudia |
collection | PubMed |
description | Optimized docking of models into cryo-EM maps requires exploiting an understanding of the signal expected in the data to minimize the calculation time while maintaining sufficient signal. The likelihood-based rotation function used in crystallography can be employed to establish plausible orientations in a docking search. A phased likelihood translation function yields scores for the placement and rigid-body refinement of oriented models. Optimized strategies for choices of the resolution of data from the cryo-EM maps to use in the calculations and the size of search volumes are based on expected log-likelihood-gain scores computed in advance of the search calculation. Tests demonstrate that the new procedure is fast, robust and effective at placing models into even challenging cryo-EM maps. |
format | Online Article Text |
id | pubmed-10071562 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | International Union of Crystallography |
record_format | MEDLINE/PubMed |
spelling | pubmed-100715622023-04-05 Likelihood-based docking of models into cryo-EM maps Millán, Claudia McCoy, Airlie J. Terwilliger, Thomas C. Read, Randy J. Acta Crystallogr D Struct Biol Research Papers Optimized docking of models into cryo-EM maps requires exploiting an understanding of the signal expected in the data to minimize the calculation time while maintaining sufficient signal. The likelihood-based rotation function used in crystallography can be employed to establish plausible orientations in a docking search. A phased likelihood translation function yields scores for the placement and rigid-body refinement of oriented models. Optimized strategies for choices of the resolution of data from the cryo-EM maps to use in the calculations and the size of search volumes are based on expected log-likelihood-gain scores computed in advance of the search calculation. Tests demonstrate that the new procedure is fast, robust and effective at placing models into even challenging cryo-EM maps. International Union of Crystallography 2023-03-15 /pmc/articles/PMC10071562/ /pubmed/36920336 http://dx.doi.org/10.1107/S2059798323001602 Text en © Claudia Millán et al. 2023 https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited. |
spellingShingle | Research Papers Millán, Claudia McCoy, Airlie J. Terwilliger, Thomas C. Read, Randy J. Likelihood-based docking of models into cryo-EM maps |
title | Likelihood-based docking of models into cryo-EM maps |
title_full | Likelihood-based docking of models into cryo-EM maps |
title_fullStr | Likelihood-based docking of models into cryo-EM maps |
title_full_unstemmed | Likelihood-based docking of models into cryo-EM maps |
title_short | Likelihood-based docking of models into cryo-EM maps |
title_sort | likelihood-based docking of models into cryo-em maps |
topic | Research Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10071562/ https://www.ncbi.nlm.nih.gov/pubmed/36920336 http://dx.doi.org/10.1107/S2059798323001602 |
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