Cascaded Bidirectional Recurrent Neural Networks for Protein Secondary Structure Prediction

Protein secondary structure (PSS) prediction is an important topic in bioinformatics. Our study on a large set of nonhomologous proteins shows that long-range interactions commonly exist and negatively affect PSS prediction. Besides, we also reveal strong correlations between secondary structure (SS) elements. In order to take into account the long-range interactions and SS-SS correlations, we propose a novel prediction system based on a cascaded bidirectional recurrent neural network (BRNN). We compare the cascaded BRNN against two other BRNN architectures, namely, the original BRNN architecture used for speech recognition and Pollastri´s BRNN, which was proposed for PSS prediction. Our cascaded BRNN achieves an overall three-state accuracy Q3 of 74.38 percent and reaches a high Segment Overlap (SOV) of 66.0455. It outperforms the original BRNN and Pollastri´s BRNN in both Q3 and SOV. Specifically, it improves the SOV score by 4-6 percent.