Protein single-model quality assessment by feature-based probability density functions.
Protein single-model quality assessment by feature-based probability density functions.
复制标题
DOI：
10.1038/srep23990
复制
发表时间：
2016-04-04
影响因子：
4.6
通讯作者：
Cheng J
中科院分区：
文献类型：
--
作者：
Cao R;Cheng J
蛋白质质量评估（QA）在蛋白质结构预测中发挥了重要作用。我们开发了一种新颖的单模型质量评估方法——Qprob。 Qprob 计算每个蛋白质特征值相对于蛋白质结构模型的真实质量分数（即 GDT-TS 分数）的绝对误差，并使用它们来估计其概率密度分布以进行质量评估。 Qprob 已作为 MULTICOM-NOVEL 服务器在第 11 届蛋白质结构预测技术关键评估 (CASP11) 上进行了盲测。官方 CASP 结果显示，Qprob 是顶级单模型 QA 方法之一。此外，Qprob 对我们的蛋白质三级结构预测器 MULTICOM 做出了贡献，该预测器在 143 个预测器中正式排名第三。良好的性能表明Qprob擅长评估硬目标模型的质量。这些结果表明，这种新的基于概率密度分布的方法对于蛋白质单模型质量评估是有效的，并且对于蛋白质结构预测是有用的。 Qprob 的网络服务器位于：http://calla.rnet.missouri.edu/qprob/。该软件现已在 Qprob 的网络服务器上免费提供。
Protein quality assessment (QA) has played an important role in protein structure prediction. We developed a novel single-model quality assessment method–Qprob. Qprob calculates the absolute error for each protein feature value against the true quality scores (i.e. GDT-TS scores) of protein structural models, and uses them to estimate its probability density distribution for quality assessment. Qprob has been blindly tested on the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM-NOVEL server. The official CASP result shows that Qprob ranks as one of the top single-model QA methods. In addition, Qprob makes contributions to our protein tertiary structure predictor MULTICOM, which is officially ranked 3rd out of 143 predictors. The good performance shows that Qprob is good at assessing the quality of models of hard targets. These results demonstrate that this new probability density distribution based method is effective for protein single-model quality assessment and is useful for protein structure prediction. The webserver of Qprob is available at: http://calla.rnet.missouri.edu/qprob/. The software is now freely available in the web server of Qprob.