Huang Yin-Fu, Chen Shu-Ying
Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, 123 University Road, Section 3, Touliu, Yunlin 640, Taiwan.
ScientificWorldJournal. 2013 May 14;2013:347106. doi: 10.1155/2013/347106. Print 2013.
We propose a protein secondary structure prediction method based on position-specific scoring matrix (PSSM) profiles and four physicochemical features including conformation parameters, net charges, hydrophobic, and side chain mass. First, the SVM with the optimal window size and the optimal parameters of the kernel function is found. Then, we train the SVM using the PSSM profiles generated from PSI-BLAST and the physicochemical features extracted from the CB513 data set. Finally, we use the filter to refine the predicted results from the trained SVM. For all the performance measures of our method, Q 3 reaches 79.52, SOV94 reaches 86.10, and SOV99 reaches 74.60; all the measures are higher than those of the SVMpsi method and the SVMfreq method. This validates that considering these physicochemical features in predicting protein secondary structure would exhibit better performances.
我们提出了一种基于位置特异性得分矩阵（PSSM）概况和包括构象参数、净电荷、疏水性和侧链质量在内的四种物理化学特征的蛋白质二级结构预测方法。首先，找到具有最佳窗口大小和核函数最佳参数的支持向量机（SVM）。然后，我们使用从PSI-BLAST生成的PSSM概况和从CB513数据集中提取的物理化学特征来训练SVM。最后，我们使用过滤器对训练好的SVM的预测结果进行优化。对于我们方法的所有性能指标，Q3达到79.52，SOV94达到86.10，SOV99达到74.60；所有指标均高于SVMpsi方法和SVMfreq方法。这证实了在预测蛋白质二级结构时考虑这些物理化学特征会表现出更好的性能。
