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. Further, 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 sequences.
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 literature.
Conclusion: After a comparative analysis, it was observed that the proposed model is performing better in comparison to