Volume 9, Issue 4 (2018)
JMBS 2018, 9(4): 549-555 |
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Mozafari Lagha M, Arab S, Zahiri J. Flexibility Prediction of Protein Structures Using Support Vector Machine. JMBS 2018; 9 (4) :549-555
URL: http://biot.modares.ac.ir/article-22-24333-en.html
URL: http://biot.modares.ac.ir/article-22-24333-en.html
1- Biological Sciences Faculty, Tarbiat Modares University,Tehran, Iran
2- Biophysics Department, Biological Sciences Faculty, Tarbiat Modares University,Tehran, Iran, Tarbiat Modares University, Nasr Bridge, Jalal-Al-Ahmad Highway, Tehran, Iran , sh.arab@modares.ac.ir
3- Biophysics Department, Biological Sciences Faculty, Tarbiat Modares University,Tehran, Iran
2- Biophysics Department, Biological Sciences Faculty, Tarbiat Modares University,Tehran, Iran, Tarbiat Modares University, Nasr Bridge, Jalal-Al-Ahmad Highway, Tehran, Iran , sh.arab@modares.ac.ir
3- Biophysics Department, Biological Sciences Faculty, Tarbiat Modares University,Tehran, Iran
Abstract: (4958 Views)
Aims: Information of the protein structure is essential to understand the protein functions. Flexibility is one of the most important characteristics related to protein functions. Knowledge about flexibility of the protein structures can be helpful to improve protein structure prediction and comprehend their function. This study was conducted with the aim of investigating the flexibility prediction of protein structures, using support vector machine.
Materials and Methods: In this study, a balanced dataset containing 95 proteins was used. The features used in the present study for modeling amino acids formed a 33-dimensional vector. Some of them were obtained by crawling a window with the length of 17 focusing on the target amino acid on the protein chain, and some were only related to the target amino acid. To define the flexibility factor, the characteristics based on the information derived from the two-dimensional angular variations was used. The information was calculated for each amino acid by considering the position of each amino acid alone and for the adjacent amino acid pairs in a seventeenth window, and the support vector machine method was used for prediction.
Findings: The accuracy was 73.1%, F-measure was 71%, precision was 73%, and sensitivity was 73.2%. Acceptable superiority of the proposed method was confirmed in comparison with the current methods. The angular representation of each protein was able to accurately demonstrate the 3D characteristics and properties of the protein structure.
Conclusion: The accuracy is 73.1%, F-measure is 71%, precision is 73%, and sensitivity is 73.2% and angular aspect is the best descriptor for flexibility prediction. Angular representation of each protein can accurately reflect the 3D characteristics and properties of the protein structure.
Materials and Methods: In this study, a balanced dataset containing 95 proteins was used. The features used in the present study for modeling amino acids formed a 33-dimensional vector. Some of them were obtained by crawling a window with the length of 17 focusing on the target amino acid on the protein chain, and some were only related to the target amino acid. To define the flexibility factor, the characteristics based on the information derived from the two-dimensional angular variations was used. The information was calculated for each amino acid by considering the position of each amino acid alone and for the adjacent amino acid pairs in a seventeenth window, and the support vector machine method was used for prediction.
Findings: The accuracy was 73.1%, F-measure was 71%, precision was 73%, and sensitivity was 73.2%. Acceptable superiority of the proposed method was confirmed in comparison with the current methods. The angular representation of each protein was able to accurately demonstrate the 3D characteristics and properties of the protein structure.
Conclusion: The accuracy is 73.1%, F-measure is 71%, precision is 73%, and sensitivity is 73.2% and angular aspect is the best descriptor for flexibility prediction. Angular representation of each protein can accurately reflect the 3D characteristics and properties of the protein structure.
Keywords: Flexibility Prediction, Two-dimensional Angular Variations, Information Theory, Support Vector Machine
Subject:
Agricultural Biotechnology
Received: 2016/05/10 | Accepted: 2017/06/9 | Published: 2018/12/21
Received: 2016/05/10 | Accepted: 2017/06/9 | Published: 2018/12/21
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