Methods for estimation of model accuracy in CASP12

Elofsson, A., Joo, K., Keasar, C., Lee, J., Maghrabi, A. H. A., Manavalan, B., McGuffin, L. J. ORCID: https://orcid.org/0000-0003-4501-4767, Ménendez Hurtado, D., Mirabello, C., Pilstål, R., Sidi, T., Uziela, K. and Wallner, B. (2018) Methods for estimation of model accuracy in CASP12. Proteins: Structure, Function, and Bioinformatics, 86. pp. 361-373. ISSN 1097-0134 doi: 10.1002/prot.25395

Abstract/Summary

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.

Additional Information	Special Issue: Twelfth Meeting on the Critical Assessment of Techniques for Protein Structure Prediction
Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/72973
Identification Number/DOI	10.1002/prot.25395
Refereed	Yes
Divisions	Interdisciplinary centres and themes > Institute for Cardiovascular and Metabolic Research (ICMR) Interdisciplinary centres and themes > Reading Systems Biology Network (RSBN) Life Sciences > School of Biological Sciences > Biomedical Sciences
Additional Information	Special Issue: Twelfth Meeting on the Critical Assessment of Techniques for Protein Structure Prediction
Publisher	Wiley
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