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QMEANDisCo—distance constraints applied on model quality estimation

MOTIVATION: Methods that estimate the quality of a 3D protein structure model in absence of an experimental reference structure are crucial to determine a model’s utility and potential applications. Single model methods assess individual models whereas consensus methods require an ensemble of models...

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Autores principales: Studer, Gabriel, Rempfer, Christine, Waterhouse, Andrew M, Gumienny, Rafal, Haas, Juergen, Schwede, Torsten
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7075525/
https://www.ncbi.nlm.nih.gov/pubmed/31697312
http://dx.doi.org/10.1093/bioinformatics/btz828
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author Studer, Gabriel
Rempfer, Christine
Waterhouse, Andrew M
Gumienny, Rafal
Haas, Juergen
Schwede, Torsten
author_facet Studer, Gabriel
Rempfer, Christine
Waterhouse, Andrew M
Gumienny, Rafal
Haas, Juergen
Schwede, Torsten
author_sort Studer, Gabriel
collection PubMed
description MOTIVATION: Methods that estimate the quality of a 3D protein structure model in absence of an experimental reference structure are crucial to determine a model’s utility and potential applications. Single model methods assess individual models whereas consensus methods require an ensemble of models as input. In this work, we extend the single model composite score QMEAN that employs statistical potentials of mean force and agreement terms by introducing a consensus-based distance constraint (DisCo) score. RESULTS: DisCo exploits distance distributions from experimentally determined protein structures that are homologous to the model being assessed. Feed-forward neural networks are trained to adaptively weigh contributions by the multi-template DisCo score and classical single model QMEAN parameters. The result is the composite score QMEANDisCo, which combines the accuracy of consensus methods with the broad applicability of single model approaches. We also demonstrate that, despite being the de-facto standard for structure prediction benchmarking, CASP models are not the ideal data source to train predictive methods for model quality estimation. For performance assessment, QMEANDisCo is continuously benchmarked within the CAMEO project and participated in CASP13. For both, it ranks among the top performers and excels with low response times. AVAILABILITY AND IMPLEMENTATION: QMEANDisCo is available as web-server at https://swissmodel.expasy.org/qmean. The source code can be downloaded from https://git.scicore.unibas.ch/schwede/QMEAN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-70755252020-03-19 QMEANDisCo—distance constraints applied on model quality estimation Studer, Gabriel Rempfer, Christine Waterhouse, Andrew M Gumienny, Rafal Haas, Juergen Schwede, Torsten Bioinformatics Original Papers MOTIVATION: Methods that estimate the quality of a 3D protein structure model in absence of an experimental reference structure are crucial to determine a model’s utility and potential applications. Single model methods assess individual models whereas consensus methods require an ensemble of models as input. In this work, we extend the single model composite score QMEAN that employs statistical potentials of mean force and agreement terms by introducing a consensus-based distance constraint (DisCo) score. RESULTS: DisCo exploits distance distributions from experimentally determined protein structures that are homologous to the model being assessed. Feed-forward neural networks are trained to adaptively weigh contributions by the multi-template DisCo score and classical single model QMEAN parameters. The result is the composite score QMEANDisCo, which combines the accuracy of consensus methods with the broad applicability of single model approaches. We also demonstrate that, despite being the de-facto standard for structure prediction benchmarking, CASP models are not the ideal data source to train predictive methods for model quality estimation. For performance assessment, QMEANDisCo is continuously benchmarked within the CAMEO project and participated in CASP13. For both, it ranks among the top performers and excels with low response times. AVAILABILITY AND IMPLEMENTATION: QMEANDisCo is available as web-server at https://swissmodel.expasy.org/qmean. The source code can be downloaded from https://git.scicore.unibas.ch/schwede/QMEAN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2020-03-15 2019-11-07 /pmc/articles/PMC7075525/ /pubmed/31697312 http://dx.doi.org/10.1093/bioinformatics/btz828 Text en © The Author(s) 2019. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Papers
Studer, Gabriel
Rempfer, Christine
Waterhouse, Andrew M
Gumienny, Rafal
Haas, Juergen
Schwede, Torsten
QMEANDisCo—distance constraints applied on model quality estimation
title QMEANDisCo—distance constraints applied on model quality estimation
title_full QMEANDisCo—distance constraints applied on model quality estimation
title_fullStr QMEANDisCo—distance constraints applied on model quality estimation
title_full_unstemmed QMEANDisCo—distance constraints applied on model quality estimation
title_short QMEANDisCo—distance constraints applied on model quality estimation
title_sort qmeandisco—distance constraints applied on model quality estimation
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7075525/
https://www.ncbi.nlm.nih.gov/pubmed/31697312
http://dx.doi.org/10.1093/bioinformatics/btz828
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