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Prediction of models for ordered solvent in macromolecular structures by a classifier based upon resolution-independent projections of local feature data

Current software tools for the automated building of models for macro­molecular X-ray crystal structures are capable of assembling high-quality models for ordered macromolecule and small-molecule scattering components with minimal or no user supervision. Many of these tools also incorporate robust f...

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Autores principales: Jones, Laurel, Tynes, Michael, Smith, Paul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Union of Crystallography 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6677017/
https://www.ncbi.nlm.nih.gov/pubmed/31373570
http://dx.doi.org/10.1107/S2059798319008933
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author Jones, Laurel
Tynes, Michael
Smith, Paul
author_facet Jones, Laurel
Tynes, Michael
Smith, Paul
author_sort Jones, Laurel
collection PubMed
description Current software tools for the automated building of models for macro­molecular X-ray crystal structures are capable of assembling high-quality models for ordered macromolecule and small-molecule scattering components with minimal or no user supervision. Many of these tools also incorporate robust functionality for modelling the ordered water molecules that are found in nearly all macromolecular crystal structures. However, no current tools focus on differentiating these ubiquitous water molecules from other frequently occurring multi-atom solvent species, such as sulfate, or the automated building of models for such species. PeakProbe has been developed specifically to address the need for such a tool. PeakProbe predicts likely solvent models for a given point (termed a ‘peak’) in a structure based on analysis (‘probing’) of its local electron density and chemical environment. PeakProbe maps a total of 19 resolution-dependent features associated with electron density and two associated with the local chemical environment to a two-dimensional score space that is independent of resolution. Peaks are classified based on the relative frequencies with which four different classes of solvent (including water) are observed within a given region of this score space as determined by large-scale sampling of solvent models in the Protein Data Bank. Designed to classify peaks generated from difference density maxima, PeakProbe also incorporates functionality for identifying peaks associated with model errors or clusters of peaks likely to correspond to multi-atom solvent, and for the validation of existing solvent models using solvent-omit electron-density maps. When tasked with classifying peaks into one of four distinct solvent classes, PeakProbe achieves greater than 99% accuracy for both peaks derived directly from the atomic coordinates of existing solvent models and those based on difference density maxima. While the program is still under development, a fully functional version is publicly available. PeakProbe makes extensive use of cctbx libraries, and requires a PHENIX licence and an up-to-date phenix.python environment for execution.
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spelling pubmed-66770172019-08-08 Prediction of models for ordered solvent in macromolecular structures by a classifier based upon resolution-independent projections of local feature data Jones, Laurel Tynes, Michael Smith, Paul Acta Crystallogr D Struct Biol Research Papers Current software tools for the automated building of models for macro­molecular X-ray crystal structures are capable of assembling high-quality models for ordered macromolecule and small-molecule scattering components with minimal or no user supervision. Many of these tools also incorporate robust functionality for modelling the ordered water molecules that are found in nearly all macromolecular crystal structures. However, no current tools focus on differentiating these ubiquitous water molecules from other frequently occurring multi-atom solvent species, such as sulfate, or the automated building of models for such species. PeakProbe has been developed specifically to address the need for such a tool. PeakProbe predicts likely solvent models for a given point (termed a ‘peak’) in a structure based on analysis (‘probing’) of its local electron density and chemical environment. PeakProbe maps a total of 19 resolution-dependent features associated with electron density and two associated with the local chemical environment to a two-dimensional score space that is independent of resolution. Peaks are classified based on the relative frequencies with which four different classes of solvent (including water) are observed within a given region of this score space as determined by large-scale sampling of solvent models in the Protein Data Bank. Designed to classify peaks generated from difference density maxima, PeakProbe also incorporates functionality for identifying peaks associated with model errors or clusters of peaks likely to correspond to multi-atom solvent, and for the validation of existing solvent models using solvent-omit electron-density maps. When tasked with classifying peaks into one of four distinct solvent classes, PeakProbe achieves greater than 99% accuracy for both peaks derived directly from the atomic coordinates of existing solvent models and those based on difference density maxima. While the program is still under development, a fully functional version is publicly available. PeakProbe makes extensive use of cctbx libraries, and requires a PHENIX licence and an up-to-date phenix.python environment for execution. International Union of Crystallography 2019-07-30 /pmc/articles/PMC6677017/ /pubmed/31373570 http://dx.doi.org/10.1107/S2059798319008933 Text en © Jones et al. 2019 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 Research Papers
Jones, Laurel
Tynes, Michael
Smith, Paul
Prediction of models for ordered solvent in macromolecular structures by a classifier based upon resolution-independent projections of local feature data
title Prediction of models for ordered solvent in macromolecular structures by a classifier based upon resolution-independent projections of local feature data
title_full Prediction of models for ordered solvent in macromolecular structures by a classifier based upon resolution-independent projections of local feature data
title_fullStr Prediction of models for ordered solvent in macromolecular structures by a classifier based upon resolution-independent projections of local feature data
title_full_unstemmed Prediction of models for ordered solvent in macromolecular structures by a classifier based upon resolution-independent projections of local feature data
title_short Prediction of models for ordered solvent in macromolecular structures by a classifier based upon resolution-independent projections of local feature data
title_sort prediction of models for ordered solvent in macromolecular structures by a classifier based upon resolution-independent projections of local feature data
topic Research Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6677017/
https://www.ncbi.nlm.nih.gov/pubmed/31373570
http://dx.doi.org/10.1107/S2059798319008933
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