Protein structural model selection by combining consensus and single scoring methods.
Protein structural model selection by combining consensus and single scoring methods.
复制标题
DOI：
10.1371/journal.pone.0074006
复制
发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Xu D
中科院分区：
文献类型：
--
作者：
He Z;Alazmi M;Zhang J;Xu D
预测蛋白质结构模型的质量评估（QA）是蛋白质结构预测中一个重要且具有挑战性的研究问题。共识全局距离测试 (CGDT) 方法根据每个诱饵（预测的结构模型）与诱饵集中所有其他诱饵的结构相似性来评估每个诱饵，并且已被证明在良好诱饵位于多数簇中时效果良好。评分函数根据每个诱饵的结构特性对其进行评估。两种方法都有其优点和局限性。在本文中，我们提出了一种称为 PWCom 的新颖方法，该方法由两个神经网络依次组成，结合了 CGDT 和单模型评分方法（例如 RW、DDFire 和 OPUS-Ca）。具体来说，对于每对诱饵，相应特征向量的差异被输入到第一个神经网络，这使得人们能够预测诱饵对的 GDT 分数是否与本机有显着差异。如果是，则使用第二个神经网络来决定两者中哪一个更接近本机结构。池中每个诱饵的质量分数基于成对比较期间的获胜次数。对不同模型生成方法的三个基准数据集的测试结果表明，PWCom 比共识 GDT 和单一评分方法有显着改进。应用该方法的 QA 服务器（MUFOLD-Server）在 CASP 10 QA 类别中在 Pearson 和 Spearman 相关性能方面排名第二。
Quality assessment (QA) for predicted protein structural models is an important and challenging research problem in protein structure prediction. Consensus Global Distance Test (CGDT) methods assess each decoy (predicted structural model) based on its structural similarity to all others in a decoy set and has been proved to work well when good decoys are in a majority cluster. Scoring functions evaluate each single decoy based on its structural properties. Both methods have their merits and limitations. In this paper, we present a novel method called PWCom, which consists of two neural networks sequentially to combine CGDT and single model scoring methods such as RW, DDFire and OPUS-Ca. Specifically, for every pair of decoys, the difference of the corresponding feature vectors is input to the first neural network which enables one to predict whether the decoy-pair are significantly different in terms of their GDT scores to the native. If yes, the second neural network is used to decide which one of the two is closer to the native structure. The quality score for each decoy in the pool is based on the number of winning times during the pairwise comparisons. Test results on three benchmark datasets from different model generation methods showed that PWCom significantly improves over consensus GDT and single scoring methods. The QA server (MUFOLD-Server) applying this method in CASP 10 QA category was ranked the second place in terms of Pearson and Spearman correlation performance.
登录
查看更多内容
影响因子：
3
作者：
Ray A;Lindahl E;Wallner B
通讯作者：
Wallner B
影响因子：
14.9
作者：
Cheng, J;Randall, AZ;Sweredoski, MJ;Baldi, P
通讯作者：
Baldi, P
影响因子：
5.6
作者：
Lazaridis, T;Karplus, M
通讯作者：
Karplus, M
影响因子：
2.9
作者：
Benkert, Pascal;Tosatto, Silvio C. E.;Schwede, Torsten
通讯作者：
Schwede, Torsten
影响因子：
14.8
作者：
通讯作者：
--