Global and local model quality estimation at CASP8 using the scoring functions QMEAN and QMEANclust
Global and local model quality estimation at CASP8 using the scoring functions QMEAN and QMEANclust
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DOI：
10.1002/prot.22532
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发表时间：
2009-01-01
影响因子：
2.9
通讯作者：
Schwede, Torsten
中科院分区：
文献类型：
--
作者：
Benkert, Pascal;Tosatto, Silvio C. E.;Schwede, Torsten
在蛋白质结构预测中确定最佳候选模型至关重要。为此，已经开发了评分函数，该评分函数要么根据单个模型来计算质量估计，要么从给定序列生成的模型集合中的信息中得出分数（即共识方法）。在CASP7，共识方法的执行比在单个模型上运行的评分功能要好得多。但是，如果最佳模型远离主导结构群的中心，共识方法往往会失败。在CASP8，我们研究了混合方法QMeanclust是否可以通过将在单个模型上运行的Qmean复合评分功能与共识信息相结合来克服这一限制。我们参与了质量评估类别中的四个不同评分功能。 Qmeanclust共识评分函数既是整个模型排名的成功方法，尤其是估算每个救助模型质量的方法。在本文中，我们简要描述了两个评分函数qmean和qmeanclust，并在CASP8的对与错的背景下讨论了它们的表现。这两个评分功能均可公开获得athttp：//swissmodel.expasy.org/qmean/。
Identifying the best candidate model among an ensemble of alternatives is crucial in protein structure prediction. For this purpose, scoring functions have been developed which either calculate a quality estimate on the basis of a single model or derive a score from the information contained in the ensemble of models generated for a given sequence (i.e., consensus methods). At CASP7, consensus methods have performed considerably better than scoring functions operating on single models. However, consensus methods tend to fail if the best models are far from the center of the dominant structural cluster. At CASP8, we investigated whether our hybrid method QMEANclust may overcome this limitation by combining the QMEAN composite scoring function operating on single models with consensus information. We participated with four different scoring functions in the quality assessment category. The QMEANclust consensus scoring function turned out to be a successful method both for the ranking of entire models but especially for the estimation of the per-residue model quality. In this article, we briefly describe the two scoring functions QMEAN and QMEANclust and discuss their performance in the context of what went right and wrong at CASP8. Both scoring functions are publicly available athttp://swissmodel.expasy.org/qmean/.