Methods for estimation of model accuracy in CASP12

Autor: Arne Elofsson, David Menéndez Hurtado, Chen Keasar, Ali H. A. Maghrabi, Balachandran Manavalan, Jooyoung Lee, Björn Wallner, Keehyoung Joo, Liam J. McGuffin, Tomer Sidi, Robert Pilstål, Karolis Uziela, Claudio Mirabello
Rok vydání: 2017
Předmět:
0301 basic medicine
Models
Molecular
Computer science
Protein Conformation
media_common.quotation_subject
3d model
CAD
Machine learning
computer.software_genre
Biochemistry
03 medical and health sciences
Protein structure
Structural Biology
Sequence Analysis
Protein
Humans
Quality (business)
CASP
Databases
Protein
Molecular Biology
Life Scientists
media_common
Estimation
Bioinformatics (Computational Biology)
030102 biochemistry & molecular biology
business.industry
Model selection
Computational Biology
Proteins
Protein structure prediction
030104 developmental biology
consensus predictions
estimates of model accuracy
machine learning
protein structure prediction
quality assessment
Bioinformatik (beräkningsbiologi)
Data mining
Artificial intelligence
business
Estimation methods
computer
Sequence Alignment
Zdroj: Proteins. 86
ISSN: 1097-0134
2012-5046
Popis: 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. Funding Agencies|Swedish Research Council [VR-NT 2012-5046, 2012-5270]; Swedish e-Science Research Center; National Research Foundation of Korea (NRF) - Korea government (MEST) [2008-0061987]; Saudi Arabian Government; United States-Israel Binational Science Foundation (BSF) [2009432]; Israel Science Foundation (ISF) [1122/14]
Databáze: OpenAIRE
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