Dong Qiwen, Wang Xiaolong, Lin Lei, Wang Yadong
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
Proteins. 2008 Jul;72(1):163-72. doi: 10.1002/prot.21904.
In recent years, protein structure prediction using local structure information has made great progress. Many fragment libraries or structure alphabets have been developed. In this study, the entropies and correlations of local structures are first calculated. The results show that neighboring local structures are strongly correlated. Then, a dual-layer model has been designed for protein local structure prediction. The position-specific score matrix, generated by PSI-BLAST, is inputted to the first-layer classifier, whose output is further enhanced by a second-layer classifier. The neural network is selected as the classifier. Two structure alphabets are explored, which are represented in Cartesian coordinate space and in torsion angles space respectively. Testing on the nonredundant dataset shows that the dual-layer model is an efficient method for protein local structure prediction. The Q-scores are 0.456 and 0.585 for the two structure alphabets, which is a significant improvement in comparison with related works.
近年来，利用局部结构信息进行蛋白质结构预测取得了很大进展。已经开发了许多片段库或结构字母表。在本研究中，首先计算了局部结构的熵和相关性。结果表明，相邻的局部结构具有很强的相关性。然后，设计了一个双层模型用于蛋白质局部结构预测。由PSI-BLAST生成的位置特异性得分矩阵被输入到第一层分类器，其输出由第二层分类器进一步增强。选择神经网络作为分类器。探索了两种结构字母表，它们分别在笛卡尔坐标空间和扭转角空间中表示。在非冗余数据集上的测试表明，双层模型是一种有效的蛋白质局部结构预测方法。两种结构字母表的Q值分别为0.456和0.585，与相关工作相比有显著改进。
