Identify Protein 8-Class Secondary Structure with Quadratic Discriminant Algorithm based on the Feature Combination

Background: The research of protein structure is one of the most important subjects in the 21 st century. However, the prediction of protein secondary structure is a key step in the prediction of protein three-dimensional structure. Protein eight-class secondary structure (SS) prediction has gained less attention and the implementation of three-class secondary structure (SS) prediction has been done in the past. Method: We introduced a model for the prediction of protein eight-class secondary structure using quadratic discriminant algorithm (QDA) based on the feature combination. We combined chemical shifts with the measure of diversity as features. The measure of diversity is based on the hydrophilic-hydrophobic residues and their dipeptides respectively. Firstly, we extracted the chemical shifts in protein as features. Then, we implemented the eight-class secondary structures prediction using these chemical shifts as features. In order to improve the accuracy, we constructed the measure of diversity based on the hydrophilic-hydrophobic residue. Finally, we combined chemical shifts with the measure of diversity to predict protein eight-class secondary structures. Results: We achieved the best accuracy of eight-class secondary structures (Q(8)) 80.7% in seven-fold cross-validation combining chemical shifts with the measure of diversity. In the same data set, we performed the prediction by C8-Scorpion sever, support vector machine (SVM) and random forest (RF) and the results showed that our prediction model is superior to other algorithms in terms of accuracy. Conclusion: The finding suggested that our model is an effective model for the prediction of protein eight-class secondary structures.