ProQ3: Improved model quality assessments using Rosetta energy terms.

ProQ3: Improved model quality assessments using Rosetta energy terms.
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DOI:
10.1038/srep33509
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发表时间:
2016-10-04
期刊:
影响因子:
4.6
通讯作者:
Elofsson A
Elofsson A
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Uziela K;Shu N;Wallner B;Elofsson A

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除了模型本身的结构之外,不使用其他信息的蛋白质模型的质量评估已经被证明对于结构预测是有用的。在这里,我们介绍了两种新的方法,ProQRosFA和ProQRosCen,受到最先进的方法ProQ2的启发,但使用了完全不同的蛋白质模型描述。ProQ2使用从模型计算的接触和其他特征,而新的预测值基于Rosetta能量:ProQRosFA使用考虑所有原子的全原子能量函数,而ProQRosCen使用粗粒度质心能量函数。这两个新的预测因子还包括残基守恒和与预测的二级结构和表面积相一致的模型项,如在ProQ2中。我们表明,这些预测指标的性能与ProQ2持平,明显好于所有其他模型质量评估程序。此外,我们还表明,结合所有三个预测器的输入特征,所得到的预测器ProQ3的性能比任何单独的方法都要好。ProQ3、ProQRosFA和ProQRosCen作为网络服务器和独立程序可在http://proq3.bioinfo.se/.免费获得
Quality assessment of protein models using no other information than the structure of the model itself has been shown to be useful for structure prediction. Here, we introduce two novel methods, ProQRosFA and ProQRosCen, inspired by the state-of-art method ProQ2, but using a completely different description of a protein model. ProQ2 uses contacts and other features calculated from a model, while the new predictors are based on Rosetta energies: ProQRosFA uses the full-atom energy function that takes into account all atoms, while ProQRosCen uses the coarse-grained centroid energy function. The two new predictors also include residue conservation and terms corresponding to the agreement of a model with predicted secondary structure and surface area, as in ProQ2. We show that the performance of these predictors is on par with ProQ2 and significantly better than all other model quality assessment programs. Furthermore, we show that combining the input features from all three predictors, the resulting predictor ProQ3 performs better than any of the individual methods. ProQ3, ProQRosFA and ProQRosCen are freely available both as a webserver and stand-alone programs at http://proq3.bioinfo.se/.
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发表时间: 1993-09-01
期刊: PROTEIN SCIENCE
影响因子: 8
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