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Pairwise running of automated crystallographic model-building pipelines

For the last two decades, researchers have worked independently to automate protein model building, and four widely used software pipelines have been developed for this purpose: ARP/wARP, Buccaneer, Phenix AutoBuild and SHELXE. Here, the usefulness of combining these pipelines to improve the built p...

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Detalles Bibliográficos
Autores principales: Alharbi, Emad, Calinescu, Radu, Cowtan, Kevin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Union of Crystallography 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7466752/
https://www.ncbi.nlm.nih.gov/pubmed/32876057
http://dx.doi.org/10.1107/S2059798320010542
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author Alharbi, Emad
Calinescu, Radu
Cowtan, Kevin
author_facet Alharbi, Emad
Calinescu, Radu
Cowtan, Kevin
author_sort Alharbi, Emad
collection PubMed
description For the last two decades, researchers have worked independently to automate protein model building, and four widely used software pipelines have been developed for this purpose: ARP/wARP, Buccaneer, Phenix AutoBuild and SHELXE. Here, the usefulness of combining these pipelines to improve the built protein structures by running them in pairwise combinations is examined. The results show that integrating these pipelines can lead to significant improvements in structure completeness and R (free). In particular, running Phenix AutoBuild after Buccaneer improved structure completeness for 29% and 75% of the data sets that were examined at the original resolution and at a simulated lower resolution, respectively, compared with running Phenix AutoBuild on its own. In contrast, Phenix AutoBuild alone produced better structure completeness than the two pipelines combined for only 7% and 3% of these data sets.
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spelling pubmed-74667522020-09-15 Pairwise running of automated crystallographic model-building pipelines Alharbi, Emad Calinescu, Radu Cowtan, Kevin Acta Crystallogr D Struct Biol Ccp4 For the last two decades, researchers have worked independently to automate protein model building, and four widely used software pipelines have been developed for this purpose: ARP/wARP, Buccaneer, Phenix AutoBuild and SHELXE. Here, the usefulness of combining these pipelines to improve the built protein structures by running them in pairwise combinations is examined. The results show that integrating these pipelines can lead to significant improvements in structure completeness and R (free). In particular, running Phenix AutoBuild after Buccaneer improved structure completeness for 29% and 75% of the data sets that were examined at the original resolution and at a simulated lower resolution, respectively, compared with running Phenix AutoBuild on its own. In contrast, Phenix AutoBuild alone produced better structure completeness than the two pipelines combined for only 7% and 3% of these data sets. International Union of Crystallography 2020-08-19 /pmc/articles/PMC7466752/ /pubmed/32876057 http://dx.doi.org/10.1107/S2059798320010542 Text en © Alharbi et al. 2020 http://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.http://creativecommons.org/licenses/by/4.0/
spellingShingle Ccp4
Alharbi, Emad
Calinescu, Radu
Cowtan, Kevin
Pairwise running of automated crystallographic model-building pipelines
title Pairwise running of automated crystallographic model-building pipelines
title_full Pairwise running of automated crystallographic model-building pipelines
title_fullStr Pairwise running of automated crystallographic model-building pipelines
title_full_unstemmed Pairwise running of automated crystallographic model-building pipelines
title_short Pairwise running of automated crystallographic model-building pipelines
title_sort pairwise running of automated crystallographic model-building pipelines
topic Ccp4
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7466752/
https://www.ncbi.nlm.nih.gov/pubmed/32876057
http://dx.doi.org/10.1107/S2059798320010542
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