A hybrid deep learning and handcrafted feature approach for the prediction of protein structural class

Abstract

The knowledge of the protein structural class is one of the most important sources of information related to protein structure or that about function analysis and drug design. But researchers still face difficulties to predict the protein structural class when it is a question about low-similarity sequences. In this paper, we propose to make the prediction using a hybrid deep learning method and handcrafted features instead of shallow classifier. We input only nine features, mostly from predicted secondary structure information, to a feed-forward deep neural network. The latter will automatically explore and extend those features through many layers and discover the representations needed for classification. The obtained results, when applying the jackknife test on two low-similarity benchmark datasets (25PDB and FC699), proved to be very significant. After comparing our method to others, it has turned out that using deep learning methods affords satisfactory performance in the field of protein structural class prediction.