Estimation of model accuracy in CASP13

Cheng, Jianlin and Choe, Myong-Ho and Elofsson, Arne and Han, Kun-Sop and Hou, Jie and Maghrabi, Ali H. A. and McGuffin, Liam J. and Menéndez-Hurtado, David and Olechnovič, Kliment and Schwede, Torsten and Studer, Gabriel and Uziela, Karolis and Venclovas, Česlovas and Wallner, Björn. (2019) Estimation of model accuracy in CASP13. Proteins, 87 (12). pp. 1361-1377.

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Official URL: https://edoc.unibas.ch/71700/

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Methods to reliably estimate the accuracy of 3D models of proteins are both a fundamental part of most protein folding pipelines and important for reliable identification of the best models when multiple pipelines are used. Here, we describe the progress made from CASP12 to CASP13 in the field of estimation of model accuracy (EMA) as seen from the progress of the most successful methods in CASP13. We show small but clear progress, that is, several methods perform better than the best methods from CASP12 when tested on CASP13 EMA targets. Some progress is driven by applying deep learning and residue-residue contacts to model accuracy prediction. We show that the best EMA methods select better models than the best servers in CASP13, but that there exists a great potential to improve this further. Also, according to the evaluation criteria based on local similarities, such as lDDT and CAD, it is now clear that single model accuracy methods perform relatively better than consensus-based methods.
Faculties and Departments:05 Faculty of Science > Departement Biozentrum > Computational & Systems Biology > Bioinformatics (Schwede)
UniBasel Contributors:Schwede, Torsten and Studer, Gabriel
Item Type:Article, refereed
Article Subtype:Research Article
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:23 Mar 2020 15:11
Deposited On:23 Mar 2020 15:11

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