Sheng Yajun, Qiu Xingye, Zhang Chen, Xu Jun, Zhang Yanping, Zheng Wei, Chen Ke
School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China.
School of Computer Science and Software Engineering, Tianjin Polytechnic University, No. 399 Binshui Road, Tianjin 300387, China.
Comput Math Methods Med. 2014;2014:715494. doi: 10.1155/2014/715494. Epub 2014 May 18.
The protein quaternary structure is very important to the biological process. Predicting their attributes is an essential task in computational biology for the advancement of the proteomics. However, the existing methods did not consider sufficient properties of amino acid. To end this, we proposed a hybrid method Quad-PRE to predict protein quaternary structure attributes using the properties of amino acid, predicted secondary structure, predicted relative solvent accessibility, and position-specific scoring matrix profiles and motifs. Empirical evaluation on independent dataset shows that Quad-PRE achieved higher overall accuracy 81.7%, especially higher accuracy 92.8%, 93.3%, and 90.6% on discrimination for trimer, hexamer, and octamer, respectively. Our model also reveals that six features sets are all important to the prediction, and a hybrid method is an optimal strategy by now. The results indicate that the proposed method can classify protein quaternary structure attributes effectively.
蛋白质四级结构对生物过程非常重要。预测其属性是蛋白质组学发展中计算生物学的一项重要任务。然而，现有方法没有充分考虑氨基酸的特性。为此，我们提出了一种混合方法Quad-PRE，利用氨基酸特性、预测的二级结构、预测的相对溶剂可及性以及位置特异性评分矩阵概况和基序来预测蛋白质四级结构属性。在独立数据集上的实证评估表明，Quad-PRE实现了更高的总体准确率81.7%，特别是在三聚体、六聚体和八聚体的区分上分别达到了更高的准确率92.8%、93.3%和90.6%。我们的模型还表明，六个特征集对预测都很重要，并且混合方法是目前的最优策略。结果表明，所提出的方法能够有效地对蛋白质四级结构属性进行分类。
