Background: Protein secondary structure is vital to predicting the tertiary structure,
which is essential in deciding protein function and drug designing. Therefore, there is a high
requirement of computational methods to predict secondary structure from their primary sequence.
Protein primary sequences represented as a linear combination of twenty amino acid characters and
contain the contextual information for secondary structure prediction.
Objective and Methods: Protein secondary structure predicted from their primary sequences using a
deep recurrent neural network. Protein secondary structure depends on local and long-range residues
in primary sequences. In the proposed work, the local contextual information of amino acid residues
captures with character n-gram. A dense embedding vector represents this local contextual
information. Furthermore, the bidirectional long short-term memory (Bi-LSTM) model is used to
capture the long-range contexts by extracting the past and future residues information in primary
Results: The proposed deep recurrent architecture is evaluated for its efficacy for datasets, namely
ss.txt, RS126, and CASP9. The model shows the Q3 accuracies of 88.45%, 83.48%, and 86.69% for
ss.txt, RS126, and CASP9, respectively. The performance of the proposed model is also compared
with other state-of-the-art methods available in the literature.
Conclusion: After a comparative analysis, it was observed that the proposed model is performing
better in comparison to state-of-art methods.