Protein Secondary Structure Prediction
Priyanka B V1, Rachitha K T2, Sanchitha N3, Srinidhi H S4, Pavankumar S P5, Shashank N6
1Priyanka B V, Vidyavardhaka College of Engineering, Mysuru, India
2Rachitha K T, Vidyavardhaka College of Engineering, Mysuru, India
3Sanchitha N, Vidyavardhaka College of Engineering, Mysuru, India
4Srinidhi H S, Vidyavardhaka College of Engineering, Mysuru, India
5Pavankumar S P, Vidyavardhaka College of Engineering, Mysuru, India
6Shashank N, Vidyavardhaka College of Engineering, Mysuru, India
Manuscript received on 30 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 1370-1244| Volume-8 Issue-9, July 2019 | Retrieval Number I7775078919/19©BEIESP | DOI: 10.35940/ijitee.I7775.078919
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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: Proteins are made up of basic units called amino acids which are held together by bonds namely hydrogen and ionic bond. The way in which the amino acids are sequenced has been categorized into two dimensional and three dimensional structures. The main advantage of predicting secondary structure is to produce tertiary structure likelihoods that are in great demand for continuous detection of proteins. This paper reviews the different methods adopted for predicting the protein secondary structure and provides a comparative analysis of accuracies obtained from various input datasets.
Index Terms: Protein secondary structure, auto encoder, Bayes classifier, Margin Infused Relaxed Algorithm(MIRA), Deep Neural Residual Network (DeepNRN), PSI-BLAST, CullPDB, support vector machines, Position Specific Scoring Matrix(PSSM).
Scope of the Article: Behaviour of Structures