Protein Secondary Structure Prediction using Character n-gram Embedding and Bi-LSTM

(E-pub Ahead of Print)

Author(s): Ashish Kumar Sharma*, Rajeev Srivastava

Journal Name: Current Bioinformatics

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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 state-of-arts methods.

Keywords: Proteomics, Protein Secondary Structure, Amino Acids Sequence, Character N-gram Embedding, Deep Learning, Bidirectional Long Short-Term Memory.

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(E-pub Ahead of Print)
DOI: 10.2174/1574893615999200601122840
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