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Methods for estimation of model accuracy in CASP12
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).ORCID iD: 0000-0002-7115-9751
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Number of Authors: 13
2018 (English)In: Proteins: Structure, Function, and Bioinformatics, ISSN 0887-3585, E-ISSN 1097-0134, Vol. 86, no S1, p. 361-373Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2018. Vol. 86, no S1, p. 361-373
Keyword [en]
CASP, consensus predictions, estimates of model accuracy, machine learning, protein structure prediction, quality assessment
National Category
Biological Sciences
Identifiers
URN: urn:nbn:se:su:diva-154838DOI: 10.1002/prot.25395ISI: 000425523000031PubMedID: 28975666OAI: oai:DiVA.org:su-154838DiVA, id: diva2:1196475
Available from: 2018-04-10 Created: 2018-04-10 Last updated: 2018-04-10Bibliographically approved

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Elofsson, ArneMcGuffin, Liam J.Menéndez Hurtado, DavidUziela, KarolisWallner, Björn
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Department of Biochemistry and BiophysicsScience for Life Laboratory (SciLifeLab)
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Proteins: Structure, Function, and Bioinformatics
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