Stacked Bidirectional Long Short Term Memory Models To Predict Protein Secondary Structure
1R.Thendral*, Research Scholar, Computer Science and Engineering, Annamalai University, India.
2AN.Sigappi, Ph.D, Computer Science and Engineering from Annamalai University, India.
Manuscript received on December 14, 2019. | Revised Manuscript received on December 25, 2019. | Manuscript published on January 10, 2020. | PP: 1605-1608 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8368019320/2020©BEIESP | DOI: 10.35940/ijitee.C8368.019320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Protein Secondary Structure (PSS) is one of most complex problem in biology PSS is important for determining tertiary structure in the future, for studying protein fiction and drug design. However, Experimental PSS approaches are time consuming and difficult to implement, and its most essential to establish effective computing methods for predicting on protein sequence structure. Accuracy of prediction performance has been recently improved due to the rapid expansion of protein sequences and the design of libraries in deep learning techniques. In this research proposed a deep recurring network unit method called stacked bidirectional long-term memory (Stacked BLSTM) units to predict 3-class protein secondary structure from protein sequence information using a bidirectional LSTM layer. To evaluate the output of Stacked BLSTM, using publicly available datasets from the RCSB server. This study indicates that performance of our method is better than the of that latest stranded public dataset. The accuracy achieved is more than 89%.
Keywords: Amino Acid Sequence, Protein Secondary Structure, Deep Bidirectional LSTM.
Scope of the Article: Behaviour of Structures