Protein secondary structure prediction based on physicochemical features and PSSM by SVM

In this paper, we propose a protein secondary structure prediction method based on the support vector machine (SVM) with 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, Q₃ 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 would exhibit better performances.