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Proteins. 2019 Jul 2. doi: 10.1002/prot.25767. [Epub ahead of print]

Estimation of model accuracy in CASP13.

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Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri.
Department of Life Science, University of Science, Pyongyang, DPR Korea.
Department of Biochemistry and Biophysics and Science for Life Laboratory, Stockholm University, Stockholm, Sweden.
School of Biological Sciences, University of Reading, Reading, UK.
Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania.
Biozentrum, University of Basel, Basel, Switzerland.
SIB Swiss Institute of Bioinformatics, Biozentrum, University of Basel, Basel, Switzerland.
Department of Physics, Chemistry, and Biology, Bioinformatics Division, Linköping University, Linköping, Sweden.


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.


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