Prediction of protein structural class for low-similarity sequences using Chou's pseudo amino acid composition and wavelet denoising

Prediction of protein structural class plays an important role in protein structure and function analysis, drug design and many other biological applications. Prediction of protein structural class for low-similarity sequences is still a challenging task. Based on the theory of wavelet denoising, this paper presents a novel method of prediction of protein structural class for the first time. Firstly, the features of the protein sequence are extracted by using Chou's pseudo amino acid composition (PseAAC). Then the extracted feature information is denoised by two-dimensional (2D) wavelet. Finally, the optimal feature vectors are input to support vector machine (SVM) classifier to predict protein structural classes. We obtained significant predictive results using jackknife test on three low-similarity protein structural class datasets 25PDB, 1189 and 640, and compared our method with previous methods The results indicate that the method proposed in this paper can effectively improve the prediction accuracy of protein structural class, which will be a reliable tool for prediction of protein structural class, especially for low-similarity sequences.