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Methods for estimation of model accuracy in CASP12
Author(s) -
Elofsson Arne,
Joo Keehyoung,
Keasar Chen,
Lee Jooyoung,
Maghrabi Ali H. A.,
Manavalan Balachandran,
McGuffin Liam J.,
Ménendez Hurtado David,
Mirabello Claudio,
Pilstål Robert,
Sidi Tomer,
Uziela Karolis,
Wallner Björn
Publication year - 2018
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.25395
Subject(s) - computer science , estimation , quality (philosophy) , selection (genetic algorithm) , model selection , domain (mathematical analysis) , data mining , machine learning , artificial intelligence , mathematics , engineering , mathematical analysis , philosophy , systems engineering , epistemology
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 article, 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 3 top methods (MESHI, ProQ3, SVMQA) all perform better than the top method of CASP11 (ProQ2). Although 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.