Protein Secondary Structure Prediction using Recurrent Neural Networks
R. Thendral1, AN. Sigappi2
1Thendral R, Research Scholar, Computer Science and Engineering, Annamalai University, India.
2Dr.Sigappi.AN, Professor, Computer Science Engineering, Annamalai University, India.
Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 660-663 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6137018520/2020©BEIESP | DOI: 10.35940/ijrte.E6137.018520
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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: In bioinformatics the prediction of the secondary structure of the protein from its primary amino acid sequence is very difficult, which has a huge impact on the field of science and medicine. The hardest part is how to learn the most effective and correct protein features to improve prediction. Here, we carry out a deep learning model to enhance structure prediction. The core achievement of this paper is a group of recurrent neural networks (RNNs) that can manage high-level relational features from a pair of input protein sequence and target protein sequences. This paper contrasts the different type of recurrent network in recurrent neural networks (RNNs). In addition, the emphasis is on more advanced systems which incorporate a gating utility is called long short term memory (LSTM) unit and the newly added gated recurrent unit (GRU). This recurrent units has been calculated on the basis of predicting protein secondary structure using an amino acid sequence. The dataset has been taken from a publicly available database server (RCSB), and this study shows that advanced recurrent units LSTM is better than GRU for a long protein sequence.
Keywords: Protein Structure Prediction, Deep Learning, LSTM,GRU.
Scope of the Article: Deep Learning.