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A new MR-SAD algorithm for the automatic building of protein models from low-resolution X-ray data and a poor starting model
Determining macromolecular structures from X-ray data with resolution worse than 3 Å remains a challenge. Even if a related starting model is available, its incompleteness or its bias together with a low observation-to-parameter ratio can render the process unsuccessful or very time-consuming. Yet,...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
International Union of Crystallography
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5947721/ https://www.ncbi.nlm.nih.gov/pubmed/29765606 http://dx.doi.org/10.1107/S2052252517017961 |
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author | Skubák, Pavol Araç, Demet Bowler, Matthew W. Correia, Ana R. Hoelz, Andre Larsen, Sine Leonard, Gordon A. McCarthy, Andrew A. McSweeney, Sean Mueller-Dieckmann, Christoph Otten, Harm Salzman, Gabriel Pannu, Navraj S. |
author_facet | Skubák, Pavol Araç, Demet Bowler, Matthew W. Correia, Ana R. Hoelz, Andre Larsen, Sine Leonard, Gordon A. McCarthy, Andrew A. McSweeney, Sean Mueller-Dieckmann, Christoph Otten, Harm Salzman, Gabriel Pannu, Navraj S. |
author_sort | Skubák, Pavol |
collection | PubMed |
description | Determining macromolecular structures from X-ray data with resolution worse than 3 Å remains a challenge. Even if a related starting model is available, its incompleteness or its bias together with a low observation-to-parameter ratio can render the process unsuccessful or very time-consuming. Yet, many biologically important macromolecules, especially large macromolecular assemblies, membrane proteins and receptors, tend to provide crystals that diffract to low resolution. A new algorithm to tackle this problem is presented that uses a multivariate function to simultaneously exploit information from both an initial partial model and low-resolution single-wavelength anomalous diffraction data. The new approach has been used for six challenging structure determinations, including the crystal structures of membrane proteins and macromolecular complexes that have evaded experts using other methods, and large structures from a 3.0 Å resolution F(1)-ATPase data set and a 4.5 Å resolution SecYEG–SecA complex data set. All of the models were automatically built by the method to R (free) values of between 28.9 and 39.9% and were free from the initial model bias. |
format | Online Article Text |
id | pubmed-5947721 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | International Union of Crystallography |
record_format | MEDLINE/PubMed |
spelling | pubmed-59477212018-05-15 A new MR-SAD algorithm for the automatic building of protein models from low-resolution X-ray data and a poor starting model Skubák, Pavol Araç, Demet Bowler, Matthew W. Correia, Ana R. Hoelz, Andre Larsen, Sine Leonard, Gordon A. McCarthy, Andrew A. McSweeney, Sean Mueller-Dieckmann, Christoph Otten, Harm Salzman, Gabriel Pannu, Navraj S. IUCrJ Research Papers Determining macromolecular structures from X-ray data with resolution worse than 3 Å remains a challenge. Even if a related starting model is available, its incompleteness or its bias together with a low observation-to-parameter ratio can render the process unsuccessful or very time-consuming. Yet, many biologically important macromolecules, especially large macromolecular assemblies, membrane proteins and receptors, tend to provide crystals that diffract to low resolution. A new algorithm to tackle this problem is presented that uses a multivariate function to simultaneously exploit information from both an initial partial model and low-resolution single-wavelength anomalous diffraction data. The new approach has been used for six challenging structure determinations, including the crystal structures of membrane proteins and macromolecular complexes that have evaded experts using other methods, and large structures from a 3.0 Å resolution F(1)-ATPase data set and a 4.5 Å resolution SecYEG–SecA complex data set. All of the models were automatically built by the method to R (free) values of between 28.9 and 39.9% and were free from the initial model bias. International Union of Crystallography 2018-01-25 /pmc/articles/PMC5947721/ /pubmed/29765606 http://dx.doi.org/10.1107/S2052252517017961 Text en © Pavol Skubak et al. 2018 http://creativecommons.org/licenses/by/2.0/uk/ 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.http://creativecommons.org/licenses/by/2.0/uk/ |
spellingShingle | Research Papers Skubák, Pavol Araç, Demet Bowler, Matthew W. Correia, Ana R. Hoelz, Andre Larsen, Sine Leonard, Gordon A. McCarthy, Andrew A. McSweeney, Sean Mueller-Dieckmann, Christoph Otten, Harm Salzman, Gabriel Pannu, Navraj S. A new MR-SAD algorithm for the automatic building of protein models from low-resolution X-ray data and a poor starting model |
title | A new MR-SAD algorithm for the automatic building of protein models from low-resolution X-ray data and a poor starting model |
title_full | A new MR-SAD algorithm for the automatic building of protein models from low-resolution X-ray data and a poor starting model |
title_fullStr | A new MR-SAD algorithm for the automatic building of protein models from low-resolution X-ray data and a poor starting model |
title_full_unstemmed | A new MR-SAD algorithm for the automatic building of protein models from low-resolution X-ray data and a poor starting model |
title_short | A new MR-SAD algorithm for the automatic building of protein models from low-resolution X-ray data and a poor starting model |
title_sort | new mr-sad algorithm for the automatic building of protein models from low-resolution x-ray data and a poor starting model |
topic | Research Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5947721/ https://www.ncbi.nlm.nih.gov/pubmed/29765606 http://dx.doi.org/10.1107/S2052252517017961 |
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