Prediction of global and local quality of CASP8 models by MULTICOM series
Prediction of global and local quality of CASP8 models by MULTICOM series
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DOI：
10.1002/prot.22487
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发表时间：
2009-01-01
影响因子：
2.9
通讯作者：
Eickholt, Jesse
中科院分区：
文献类型：
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作者：
Cheng, Jianlin;Wang, Zheng;Eickholt, Jesse
评估蛋白质结构模型的质量对于选择和使用模型非常重要。在这里，我们描述了 MULTICOM 系列模型质量预测器，其中包含在 CASP8 实验中测试的三个预测器。我们在 120 个 CASP8 目标上评估了这些预测因子。两种半聚类方法（MULTICOM 和 MULTICOM-CLUSTER）和一种单模型从头计算方法（MULTICOM-CMFR）的预测 GDT-TS 分数与实际 GDT-TS 分数之间的平均相关性分别为 0.90、0.89 和 0.74；排名靠前的模型和最佳模型的全局 GDT-TS 分数之间的平均差异（或 GDT-TS 损失）分别为 0.05、0.06 和 0.07。半聚类方法的预测局部质量得分与实际局部质量得分之间的平均相关性高于 0.64。我们的结果表明，将模型与排名靠前的参考模型进行比较的新颖半聚类方法可以提高从头计算方法和简单元方法生成的初始质量分数。
Evaluating the quality of protein structure models is important for selecting and using models. Here, we describe the MULTICOM series of model quality predictors which contains three predictors tested in the CASP8 experiments. We evaluated these predictors on 120 CASP8 targets. The average correlations between predicted and real GDT-TS scores of the two semi-clustering methods (MULTICOM and MULTICOM-CLUSTER) and the one single-model ab initio method (MULTICOM-CMFR) are 0.90, 0.89, and 0.74, respectively; and their average difference (or GDT-TS loss) between the global GDT-TS scores of the top-ranked models and the best models are 0.05, 0.06, and 0.07, respectively. The average correlation between predicted and real local quality scores of the semi-clustering methods is above 0.64. Our results show that the novel semi-clustering approach that compares a model with top ranked reference models can improve initial quality scores generated by the ab initio method and a simple meta approach.