KAUST Grant NumberKUK-I1-012-43
Online Publication Date2011-10-14
Print Publication Date2011
Permanent link to this recordhttp://hdl.handle.net/10754/598250
MetadataShow full item record
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.
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.
SponsorsGrant sponsor: US National Library of Medicine (NIH/NLM); Grant number: LM007085; Grant sponsor: KAUST Award; Grant number: KUK-I1-012-43.
PubMed Central IDPMC3226935
CollectionsPublications Acknowledging KAUST Support
- Assessment of the assessment: evaluation of the model quality estimates in CASP10.
- Authors: Kryshtafovych A, Barbato A, Fidelis K, Monastyrskyy B, Schwede T, Tramontano A
- Issue date: 2014 Feb
- Assessment of predictions in the model quality assessment category.
- Authors: Cozzetto D, Kryshtafovych A, Ceriani M, Tramontano A
- Issue date: 2007
- Assessment of template based protein structure predictions in CASP9.
- Authors: Mariani V, Kiefer F, Schmidt T, Haas J, Schwede T
- Issue date: 2011
- FunFOLDQA: a quality assessment tool for protein-ligand binding site residue predictions.
- Authors: Roche DB, Buenavista MT, McGuffin LJ
- Issue date: 2012
- Evaluation of CASP8 model quality predictions.
- Authors: Cozzetto D, Kryshtafovych A, Tramontano A
- Issue date: 2009