Prediction of global and local model quality in CASP7 using Pcons and ProQ
Prediction of global and local model quality in CASP7 using Pcons and ProQ
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DOI：
10.1002/prot.21774
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发表时间：
2007-01-01
影响因子：
2.9
通讯作者：
Elofsson, Arne
中科院分区：
文献类型：
--
作者：
Wallner, Bjorn;Elofsson, Arne
在蛋白质结构预测中，排序和选择最佳模型的能力非常重要。模型质量评估程序（MQAP）是为执行此任务而开发的程序，它们可以根据使用的信息分为三类。基于共识的方法使用与其他模型的相似性，基于结构的方法使用从结构计算的特征，基于进化的方法使用模型和模板之间的序列相似性。这些方法可以被训练来预测模型的整体全局质量，即模型可能与原生结构有多大差异。这些方法也可以被训练来确定模型中哪些局部区域可能是不正确的。在CASP7中，我们使用上述三个类别的信息参与了三个全球质量预测因子和四个局部质量预测因子。结果表明，MQAP使用共识，Pcons，是显着更好地预测全局和局部质量相比，MQAP只使用基于结构或序列的信息。
The ability to rank and select the best model is important in protein structure prediction. Model Quality Assessment Programs (MQAPs) are programs developed to perform this task They can be divided into three categories based on the information they use. Consensus based methods use the similarity to other models, structure-based methods use features calculated from the structure and evolutionary based methods use the sequence similarity between a model and a template. These methods can be trained to predict the overall global quality of a model, that is, how much a model is likely to differ from the native structure. The methods can also be trained to pinpoint which local regions in a model are likely to be incorrect. In CASP7, we participated with three predictors of global and four of local quality using information from the three categories described above. The result shows that the MQAP using consensus, Pcons, was significantly better at predicting both global and local quality compared with MQAPs using only structure or sequence based information.