Alharbi, Emad, Bond, Paul orcid.org/0000-0002-8465-4823, Calinescu, Radu orcid.org/0000-0002-2678-9260 et al. (1 more author) (2021) Predicting the performance of automated crystallographic model-building pipelines. Acta crystallographica. Section D, Structural biology. pp. 1591-1601. ISSN 2059-7983
Abstract
Proteins are macromolecules that perform essential biological functions which depend on their three-dimensional structure. Determining this structure involves complex laboratory and computational work. For the computational work, multiple software pipelines have been developed to build models of the protein structure from crystallographic data. Each of these pipelines performs differently depending on the characteristics of the electron-density map received as input. Identifying the best pipeline to use for a protein structure is difficult, as the pipeline performance differs significantly from one protein structure to another. As such, researchers often select pipelines that do not produce the best possible protein models from the available data. Here, a software tool is introduced which predicts key quality measures of the protein structures that a range of pipelines would generate if supplied with a given crystallographic data set. These measures are crystallographic quality-of-fit indicators based on included and withheld observations, and structure completeness. Extensive experiments carried out using over 2500 data sets show that the tool yields accurate predictions for both experimental phasing data sets (at resolutions between 1.2 and 4.0 Å) and molecular-replacement data sets (at resolutions between 1.0 and 3.5 Å). The tool can therefore provide a recommendation to the user concerning the pipelines that should be run in order to proceed most efficiently to a depositable model.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) The University of York > Faculty of Sciences (York) > Chemistry (York) |
Funding Information: | Funder Grant number BBSRC (BIOTECHNOLOGY AND BIOLOGICAL SCIENCES RESEARCH COUNCIL) BB/S005099/1 |
Depositing User: | Pure (York) |
Date Deposited: | 08 Dec 2021 17:30 |
Last Modified: | 16 Oct 2024 18:03 |
Published Version: | https://doi.org/10.1107/S2059798321010500 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1107/S2059798321010500 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:181405 |
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Description: Predicting the performance of automated crystallographic model-building pipelines
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