QMEANDisCo - Distance Constraints Applied on Model Quality Estimation

Studer, Gabriel and Rempfer, Christine and Waterhouse, Andrew M. and Gumienny, Rafal and Haas, Juergen and Schwede, Torsten. (2019) QMEANDisCo - Distance Constraints Applied on Model Quality Estimation. Bioinformatics, 36 (6). pp. 1765-1771.

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Official URL: https://edoc.unibas.ch/72647/

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Methods that estimate the quality of a 3-dimensional protein structure model in absence of an experimental reference structure are crucial to determine a modelâEurotms 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 score (DisCo).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.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 data are available at Bioinformatics online.
Faculties and Departments:05 Faculty of Science > Departement Biozentrum > Computational & Systems Biology > Bioinformatics (Schwede)
UniBasel Contributors:Schwede, Torsten and Studer, Gabriel and Waterhouse, Andrew Mark and Gumienny, Rafal Wojciech and Haas, Jürgen and Rempfer, Christine
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Oxford University Press
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:20 Aug 2020 12:18
Deposited On:20 Aug 2020 12:18

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