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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,...

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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Union of Crystallography 2018
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.
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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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