Protein Secondary Structural Class Prediction Using Effective Feature Modeling and Machine Learning Techniques

Abstract

Protein Secondary Structural Class (PSSC) prediction is an important step to find its further folds, tertiary structure and functions, which in turn have potential applications in drug discovery. Various computational methods have been developed to predict the PSSC, however, predicting PSSC on the basis of protein sequences is still a challenging task. In this study, we propose an effective approach to extract features using two techniques (i) SkipXGram bi-gram: in which skipped bi-gram features are extracted and (ii) Character embedded features: in which features are extracted using word embedding approach. The combined feature sets from the proposed feature modeling approach are explored using various machine learning classifiers. The best performing classifier (i.e. Random Forest) is benchmarked against state-of-the-art PSSC prediction models. The proposed model was assessed on two low sequence similarity benchmark datasets i.e. 25PDB and FC699. The performance analysis demonstrates that the proposed model consistently outperformed state-of-the-art models by a factor of 3% to 23% and 4% to 6% for 25PDB and FC699 datasets respectively.