Evaluation of model quality predictions in CASP9.
Evaluation of model quality predictions in CASP9.
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DOI：
10.1002/prot.23180
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发表时间：
2011
影响因子：
2.9
通讯作者：
Tramontano, Anna
中科院分区：
文献类型：
--
作者：
Kryshtafovych, Andriy;Fidelis, Krzysztof;Tramontano, Anna
CASP在2006年以来一直在蛋白质结构预测准确性的先验估计中评估最新技术。在CASP中包含模型质量评估类别在上一个实验中有助于快速开发方法。评估组测试了他们的方法，以估计蛋白质模型的整体和/或均衡基础。在全球和局部尺度上观察到的模型的质量评估表明，即使全球质量评估方法似乎接近完美点（最佳组的平均平均每个目标皮尔逊的相关系数高达0.97），但仍有首先，所有绩效的方法都使用共识方法来产生质量估计，并且该策略具有其自身的局限性和缺陷在性能中。最佳10组的0.63–0.72。
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 forty six 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 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 and deficiencies. Second, the methods that are based on the analysis of individual models lag far behind clustering methods 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.
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影响因子：
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