Protein Fold Recognition using n-Gram Strict Position Specific Scoring Matrix and Structural based Feature Extraction Technique
Kailash Shaw1, Sashikala Mishra2, Debahuti Mishra3
1Dr. Kailash Shaw, Department of Computer Engineering, D Y Patil College of Engineering, Akurdi, SPPU University, Pune, India.
2Dr. Sashikala Mishra, Department of Computer Engineering, International Institute of Engineering Technology, SPPU University, Pune, India.
3Prof. Dr. Debahuti Mishra, Department of Computer Science & Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India.
Manuscript received on 11 April 2019 | Revised Manuscript received on 16 May 2019 | Manuscript published on 30 May 2019 | PP: 719-727 | Volume-8 Issue-1, May 2019 | Retrieval Number: F2847037619/19©BEIESP
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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: The decoding of tertiary structure of a protein is proved to be a difficult and important task in the study of biological science. In last few decades initiative has been taken care of predicting the tertiary structure by using different feature extracting techniques to discover the significant information from protein primary sequences and utilizing suitable classifiers to recognize different fold of unknown protein sequences. But in spite of several feature extraction techniques the limited accuracy has been achieved. In this study, we proposed a new feature extraction technique to discover the score matrix using evolutionary and structural information from primary sequence and utilize support vector machine classifier to classify protein folding. The proposed technique is compared with the recent feature extraction techniques such as; tri-gram, bi-gram, PSSM, and n-form with strict position specific scoring matrix achieved ~3.4-11.4% more accuracy.
Index Terms: Classification, Fold Recognition, Protein Structure Prediction, Position Specific Scoring Matrix.
Scope of the Article: Classification