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Automated refinement of macromolecular structures at low resolution using prior information

Since the ratio of the number of observations to adjustable parameters is small at low resolution, it is necessary to use complementary information for the analysis of such data. ProSMART is a program that can generate restraints for macromolecules using homologous structures, as well as generic res...

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Autores principales: Kovalevskiy, Oleg, Nicholls, Robert A., Murshudov, Garib N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Union of Crystallography 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5053141/
https://www.ncbi.nlm.nih.gov/pubmed/27710936
http://dx.doi.org/10.1107/S2059798316014534
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author Kovalevskiy, Oleg
Nicholls, Robert A.
Murshudov, Garib N.
author_facet Kovalevskiy, Oleg
Nicholls, Robert A.
Murshudov, Garib N.
author_sort Kovalevskiy, Oleg
collection PubMed
description Since the ratio of the number of observations to adjustable parameters is small at low resolution, it is necessary to use complementary information for the analysis of such data. ProSMART is a program that can generate restraints for macromolecules using homologous structures, as well as generic restraints for the stabilization of secondary structures. These restraints are used by REFMAC5 to stabilize the refinement of an atomic model. However, the optimal refinement protocol varies from case to case, and it is not always obvious how to select appropriate homologous structure(s), or other sources of prior information, for restraint generation. After running extensive tests on a large data set of low-resolution models, the best-performing refinement protocols and strategies for the selection of homologous structures have been identified. These strategies and protocols have been implemented in the Low-Resolution Structure Refinement (LORESTR) pipeline. The pipeline performs auto-detection of twinning and selects the optimal scaling method and solvent parameters. LORESTR can either use user-supplied homologous structures, or run an automated BLAST search and download homologues from the PDB. The pipeline executes multiple model-refinement instances using different parameters in order to find the best protocol. Tests show that the automated pipeline improves R factors, geometry and Ramachandran statistics for 94% of the low-resolution cases from the PDB included in the test set.
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spelling pubmed-50531412016-10-13 Automated refinement of macromolecular structures at low resolution using prior information Kovalevskiy, Oleg Nicholls, Robert A. Murshudov, Garib N. Acta Crystallogr D Struct Biol Research Papers Since the ratio of the number of observations to adjustable parameters is small at low resolution, it is necessary to use complementary information for the analysis of such data. ProSMART is a program that can generate restraints for macromolecules using homologous structures, as well as generic restraints for the stabilization of secondary structures. These restraints are used by REFMAC5 to stabilize the refinement of an atomic model. However, the optimal refinement protocol varies from case to case, and it is not always obvious how to select appropriate homologous structure(s), or other sources of prior information, for restraint generation. After running extensive tests on a large data set of low-resolution models, the best-performing refinement protocols and strategies for the selection of homologous structures have been identified. These strategies and protocols have been implemented in the Low-Resolution Structure Refinement (LORESTR) pipeline. The pipeline performs auto-detection of twinning and selects the optimal scaling method and solvent parameters. LORESTR can either use user-supplied homologous structures, or run an automated BLAST search and download homologues from the PDB. The pipeline executes multiple model-refinement instances using different parameters in order to find the best protocol. Tests show that the automated pipeline improves R factors, geometry and Ramachandran statistics for 94% of the low-resolution cases from the PDB included in the test set. International Union of Crystallography 2016-09-30 /pmc/articles/PMC5053141/ /pubmed/27710936 http://dx.doi.org/10.1107/S2059798316014534 Text en © Kovalevskiy et al. 2016 http://creativecommons.org/licenses/by/2.0/uk/ This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.
spellingShingle Research Papers
Kovalevskiy, Oleg
Nicholls, Robert A.
Murshudov, Garib N.
Automated refinement of macromolecular structures at low resolution using prior information
title Automated refinement of macromolecular structures at low resolution using prior information
title_full Automated refinement of macromolecular structures at low resolution using prior information
title_fullStr Automated refinement of macromolecular structures at low resolution using prior information
title_full_unstemmed Automated refinement of macromolecular structures at low resolution using prior information
title_short Automated refinement of macromolecular structures at low resolution using prior information
title_sort automated refinement of macromolecular structures at low resolution using prior information
topic Research Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5053141/
https://www.ncbi.nlm.nih.gov/pubmed/27710936
http://dx.doi.org/10.1107/S2059798316014534
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