Handle URI:
http://hdl.handle.net/10754/598250
Title:
Evaluation of model quality predictions in CASP9
Authors:
Kryshtafovych, Andriy; Fidelis, Krzysztof; Tramontano, Anna
Abstract:
CASP has been assessing the state of the art in the a priori estimation of accuracy of protein structure prediction since 2006. The inclusion of model quality assessment category in CASP contributed to a rapid development of methods in this area. In the last experiment, 46 quality assessment groups tested their approaches to estimate the accuracy of protein models as a whole and/or on a per-residue basis. We assessed the performance of these methods predominantly on the basis of the correlation between the predicted and observed quality of the models on both global and local scales. The ability of the methods to identify the models closest to the best one, to differentiate between good and bad models, and to identify well modeled regions was also analyzed. Our evaluations demonstrate that even though global quality assessment methods seem to approach perfection point (weighted average per-target Pearson's correlation coefficients are as high as 0.97 for the best groups), there is still room for improvement. First, all top-performing methods use consensus approaches to generate quality estimates, and this strategy has its own limitations. Second, the methods that are based on the analysis of individual models lag far behind clustering techniques and need a boost in performance. The methods for estimating per-residue accuracy of models are less accurate than global quality assessment methods, with an average weighted per-model correlation coefficient in the range of 0.63-0.72 for the best 10 groups.
Citation:
Kryshtafovych A, Fidelis K, Tramontano A (2011) Evaluation of model quality predictions in CASP9. Proteins: Structure, Function, and Bioinformatics 79: 91–106. Available: http://dx.doi.org/10.1002/prot.23180.
Publisher:
Wiley-Blackwell
Journal:
Proteins: Structure, Function, and Bioinformatics
KAUST Grant Number:
KUK-I1-012-43
Issue Date:
2011
DOI:
10.1002/prot.23180
PubMed ID:
21997462
PubMed Central ID:
PMC3226935
Type:
Article
ISSN:
0887-3585
Sponsors:
Grant sponsor: US National Library of Medicine (NIH/NLM); Grant number: LM007085; Grant sponsor: KAUST Award; Grant number: KUK-I1-012-43.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorKryshtafovych, Andriyen
dc.contributor.authorFidelis, Krzysztofen
dc.contributor.authorTramontano, Annaen
dc.date.accessioned2016-02-25T13:17:24Zen
dc.date.available2016-02-25T13:17:24Zen
dc.date.issued2011en
dc.identifier.citationKryshtafovych A, Fidelis K, Tramontano A (2011) Evaluation of model quality predictions in CASP9. Proteins: Structure, Function, and Bioinformatics 79: 91–106. Available: http://dx.doi.org/10.1002/prot.23180.en
dc.identifier.issn0887-3585en
dc.identifier.pmid21997462en
dc.identifier.doi10.1002/prot.23180en
dc.identifier.urihttp://hdl.handle.net/10754/598250en
dc.description.abstractCASP has been assessing the state of the art in the a priori estimation of accuracy of protein structure prediction since 2006. The inclusion of model quality assessment category in CASP contributed to a rapid development of methods in this area. In the last experiment, 46 quality assessment groups tested their approaches to estimate the accuracy of protein models as a whole and/or on a per-residue basis. We assessed the performance of these methods predominantly on the basis of the correlation between the predicted and observed quality of the models on both global and local scales. The ability of the methods to identify the models closest to the best one, to differentiate between good and bad models, and to identify well modeled regions was also analyzed. Our evaluations demonstrate that even though global quality assessment methods seem to approach perfection point (weighted average per-target Pearson's correlation coefficients are as high as 0.97 for the best groups), there is still room for improvement. First, all top-performing methods use consensus approaches to generate quality estimates, and this strategy has its own limitations. Second, the methods that are based on the analysis of individual models lag far behind clustering techniques and need a boost in performance. The methods for estimating per-residue accuracy of models are less accurate than global quality assessment methods, with an average weighted per-model correlation coefficient in the range of 0.63-0.72 for the best 10 groups.en
dc.description.sponsorshipGrant sponsor: US National Library of Medicine (NIH/NLM); Grant number: LM007085; Grant sponsor: KAUST Award; Grant number: KUK-I1-012-43.en
dc.publisherWiley-Blackwellen
dc.subjectCASPen
dc.subjectModel quality assessmenten
dc.subjectProtein structure modelingen
dc.subjectProtein structure predictionen
dc.subjectQAen
dc.subject.meshModels, Molecularen
dc.titleEvaluation of model quality predictions in CASP9en
dc.typeArticleen
dc.identifier.journalProteins: Structure, Function, and Bioinformaticsen
dc.identifier.pmcidPMC3226935en
dc.contributor.institutionGenome Center, University of California-Davis, 451 Health Sciences Drive, Davis, CA 95616, USA. akryshtafovych@ucdavis.eduen
kaust.grant.numberKUK-I1-012-43en

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