Title |
Methods for estimation of model accuracy in CASP12
|
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Published in |
Proteins: Structure, Function, and Bioinformatics, October 2017
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DOI | 10.1002/prot.25395 |
Pubmed ID | |
Authors |
Arne Elofsson, Keehyoung Joo, Chen Keasar, Jooyoung Lee, Ali H. A. Maghrabi, Balachandran Manavalan, Liam J. McGuffin, David Ménendez Hurtado, Claudio Mirabello, Robert Pilstål, Tomer Sidi, Karolis Uziela, Björn Wallner |
Abstract |
Methods to reliably estimate the quality of 3D models of proteins are essential drivers for the wide adoption and serious acceptance of protein structure predictions by life scientists. In this paper, the most successful groups in CASP12 describe their latest methods for Estimates of Model Accuracy (EMA). We show that pure single model accuracy estimation methods have shown clear progress since CASP11; the three top methods (MESHI, ProQ3, SVMQA) all perform better than the top method of CASP11 (ProQ2). While the pure single model accuracy estimation methods outperform quasi-single (ModFOLD6 variations) and consensus methods (Pcons, ModFOLDclust2, Pcomb-domain and Wallner) in model selection, they are still not as good as those methods in absolute model quality estimation and predictions of local quality. Finally, we show that when using contact based model quality measures (CAD, lDDT) the single model quality methods perform relatively better. This article is protected by copyright. All rights reserved. |
X Demographics
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United States | 1 | 100% |
Demographic breakdown
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 41 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Master | 8 | 20% |
Student > Ph. D. Student | 7 | 17% |
Researcher | 5 | 12% |
Student > Bachelor | 5 | 12% |
Professor > Associate Professor | 3 | 7% |
Other | 7 | 17% |
Unknown | 6 | 15% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 10 | 24% |
Chemistry | 8 | 20% |
Agricultural and Biological Sciences | 5 | 12% |
Computer Science | 4 | 10% |
Psychology | 3 | 7% |
Other | 5 | 12% |
Unknown | 6 | 15% |