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Estimation of model accuracy in CASP13
Author(s) -
Cheng Jianlin,
Choe MyongHo,
Elofsson Arne,
Han KunSop,
Hou Jie,
Maghrabi Ali H. A.,
McGuffin Liam J.,
MenéndezHurtado David,
Olechnovič Kliment,
Schwede Torsten,
Studer Gabriel,
Uziela Karolis,
Venclovas Česlovas,
Wallner Björn
Publication year - 2019
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.25767
Subject(s) - computer science , machine learning , data mining , artificial intelligence , field (mathematics) , identification (biology) , mathematics , botany , pure mathematics , biology
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.