An Integrated-OFFT Model for the Prediction of Protein Secondary Structure Class

Proteins are the utmost multi-purpose macromolecules, which play a crucial function in many aspects of biological processes. For a long time, sequence arrangement of amino acid has been utilized for the prediction of protein secondary structure. Besides, in major methods for the prediction of protein secondary structure class, the impact of Gaussian noise on sequence representation of amino acids has not been considered until now; which is one of the important constraints for the functionality of a protein.

In the present research, the prediction of protein secondary structure class was accomplished by integrated application of Stockwell transformation and Amino Acid Composition (AAC), on equivalent Electron-ion Interaction Potential (EIIP) representation of raw amino acid sequence. The introduced method was evaluated by using 4 benchmark datasets of low sequence homology, namely PDB25, 498, 277, and 204. Furthermore, random forest algorithm together with the out-of-bag error estimate and Support Vector Machine (SVM), using k-fold cross validation demonstrated high feature representation potential of our reported approach.

The overall prediction accuracy for PDB25, 498, 277, and 204 datasets with randomforest classifier was 92.5%, 94.79%, 92.45%, and 88.04% respectively, whereas with SVM, the results were 84.66%, 95.32%, 89.29%, and 84.37% respectively.

An integrated-order-function-frequency-time (OFFT) model has been proposed for the prediction of protein secondary structure class. For the first time, we reported the effect of Gaussian noise on the prediction accuracy of protein secondary structure class and proposed a robust integrated- OFFT model, which is effectively noise resistant.