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 ,
3- Biophysics Department, Biological Sciences Faculty, Tarbiat Modares University,Tehran, Iran
Abstract: (5439 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
References
1. Hirose S, Yokota K, Kuroda Y, Wako H, Endo S, Kanai S, et al. Prediction of protein motions from amino acid sequence and its application to protein-protein interaction. BMC Struct Biol. 2010;10:20. [Link] [DOI:10.1186/1472-6807-10-20]
2. Chen K, Kurgan LA, Ruan J. Prediction of flexible/rigid regions from protein sequences using k-spaced amino acid pairs. BMC Struct Biol. 2007;7:25. [Link] [DOI:10.1186/1472-6807-7-25]
3. Karplus PA, Schulz GE. Prediction of chain flexibility in proteins. Naturwissenschaften. 1985;72(4):212-3. [Link] [DOI:10.1007/BF01195768]
4. Vihinen M, Torkkila E, Riikonen P. Accuracy of protein flexibility predictions. Proteins. 1994;19(2):141-9. [Link] [DOI:10.1002/prot.340190207]
5. Smith DK, Radivojac P, Obradovic Z, Dunker AK, Zhu G. Improved amino acid flexibility parameters. Protein Sci. 2003;12(5):1060-72. [Link] [DOI:10.1110/ps.0236203]
6. Schlessinger A, Rost B. Protein flexibility and rigidity predicted from sequence. Proteins. 2005;61(1):115-26. [Link] [DOI:10.1002/prot.20587]
7. Schlessinger A, Yachdav G, Rost B. PROFbval: Predict flexible and rigid residues in proteins. Bioinformatics. 2006;22(7):891-3. [Link] [DOI:10.1093/bioinformatics/btl032]
8. Pan XY, Shen HB. Robust prediction of B-factor profile from sequence using two-stage SVR based on random forest feature selection. Protein Pept Lett. 2009;16(12):1447-54. [Link] [DOI:10.2174/092986609789839250]
9. De Brevern AG, Bornot A, Craveur P, Etchebest C, Gelly JC. PredyFlexy: Flexibility and local structure prediction from sequence. Nucleic Acids Res. 2012;40(Web Server issue):W317-22. [Link]
10. Bornot A, Etchebest C, De Brevern AG. Predicting protein flexibility through the prediction of local structures. Proteins. 2011;79(3):839-52. [Link] [DOI:10.1002/prot.22922]
11. Trott O, Siggers K, Rost B, Palmer AG. Protein conformational flexibility prediction using machine learning. J Magn Reson. 2008;192(1):37-47. [Link] [DOI:10.1016/j.jmr.2008.01.011]
12. Zhang T, Faraggi E, Zhou Y. Fluctuations of backbone torsion angles obtained from NMR-determined structures and their prediction. Proteins. 2010;78(16):3353-62. [Link] [DOI:10.1002/prot.22842]
13. Arab SS, Sadeghi M, ESlahchi C, Pezeshk H. Prediction of flexibility in proteins based on sequence of amino acids. Biotechnol Tarbiat Modares Univ. 2010;0(1):1-10. [Persian] [Link]
14. Chen XW, Chen J. Protein flexibility modeling using kernel based methods. International Joint Conference on Neural Networks. Montreal: IEEE; 2005. [Link]
15. Yuan Z, Bailey TL, Teasdale RD. Prediction of protein flexibility profile [Internet]. St Lucia: The University of Queensland; 2014 [cited 2015 May]. Available from: https://www.researchgate.net/publication/228899091_Prediction_of_Protein_Flexibility_Profile [Link]
16. Kuznetsov IB, McDuffie M. FlexPred: A web-server for predicting residue positions involved in conformational switches in proteins. Bioinformation. 2008.3(3):134-6. [Link]
17. Hinsen K. Structural flexibility in proteins: Impact of the crystal environment. Bioinformatics. 2008;24(4):521-8. [Link] [DOI:10.1093/bioinformatics/btm625]
18. Jamroz M, Kolinski A, Kmiecik S. CABS-flex predictions of protein flexibility compared with NMR ensembles. Bioinformatics. 2014;30(15):2150-4. [Link] [DOI:10.1093/bioinformatics/btu184]
19. Markvoort AJ. Coarse‐grained molecular dynamics. In: Van Santen RA, Sautet P, editors. Computational methods in catalysis and materials science: An introduction for scientists and engineers. New Jersey: John Wiley & Sons; 2009. pp. 151-66. [Link] [DOI:10.1002/9783527625482.ch8]
20. Jamroz M, Orozco M, Kolinski A, Kmiecik S. Consistent view of protein fluctuations from all-atom molecular dynamics and coarse-grained dynamics with knowledge-based force-field. J Chem Theory Comput. 2013;9(1):119-25. [Link] [DOI:10.1021/ct300854w]
21. Jamroz M, Kolinski A, Kmiecik S. CABS-flex: Server for fast simulation of protein structure fluctuations. Nucleic Acids Res. 2013;41(Web Server issue):W427-31. [Link]
22. Noble WS. What is a support vector machine?. Nat Biotechnol. 2006;24(12):1565-7. [Link] [DOI:10.1038/nbt1206-1565]
23. Meyer D. Support vector machines, the interface to libsvm in package e1071 [Internet]. Wien: FH Technikum Wien; 2014 [cited 2015 May]. Available from: https://cran.r-project.org/web/packages/e1071/vignettes/svmdoc.pdf [Link]
24. Bank PD. Protein data bank. Nat New Biol. 1971;233:223. [Link] [DOI:10.1038/newbio233223b0]
25. Huang Y, Niu B, Gao Y, Fu L, Li W. CD-HIT Suite: A web server for clustering and comparing biological sequences. Bioinformatics. 2010;26(5):680-2. [Link] [DOI:10.1093/bioinformatics/btq003]
26. Kotsiantis S, Kanellopoulos D, Pintelas P. Handling imbalanced datasets: A review. GESTS Int Trans Comput Sci Eng. 2006;30. 29- Garnier J, Robson B. The GOR method for predicting secondary structures in proteins. In: Fasman GD, editor. Prediction of protein structure and the principles of protein conformation. Berlin: Springer Science & Business Media; 1989. pp. 417-65. [Link]
27. Zahiri J, Yaghoubi O, Mohammad Noori M, Ebrahimpour R, Masoudi Nejad A. PPIevo: Protein-protein interaction prediction from PSSM based evolutionary information. Genomics. 2013;102(4):237-42. [Link] [DOI:10.1016/j.ygeno.2013.05.006]
28. Pollastri G, Baldi P, Fariselli P, Casadio R. Prediction of coordination number and relative solvent accessibility in proteins. Proteins. 2002;47(2):142-53. [Link] [DOI:10.1002/prot.10069]
29. 29- Garnier J, Robson B. The GOR method for predicting secondary structures in proteins. In: Fasman GD, editor. Prediction of protein structure and the principles of protein conformation. Berlin: Springer Science & Business Media; 1989. pp. 417-65. [Link] [DOI:10.1007/978-1-4613-1571-1_10]
30. Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res. 1997;25(17):3389-402. [Link] [DOI:10.1093/nar/25.17.3389]
31. Meiler J, Müller M, Zeidler A, Schmäschke F. Generation and evaluation of dimension-reduced amino acid parameter representations by artificial neural networks. Mol Model Annu. 2001;7(9):360-9. [Link] [DOI:10.1007/s008940100038]
32. Magnan CN, Baldi P. SSpro/ACCpro 5: Almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity. Bioinformatics. 2014;30(18):2592-7. [Link] [DOI:10.1093/bioinformatics/btu352]
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