ENG  Vol.5 No.10 B , October 2013
Using the Support Vector Machine Algorithm to Predict β-Turn Types in Proteins

The structure and function of proteins are closely related, and protein structure decides its function, therefore protein structure prediction is quite important.β-turns are important components of protein secondary structure. So development of an accurate prediction method ofβ-turn types is very necessary. In this paper, we used the composite vector with position conservation scoring function, increment of diversity and predictive secondary structure information as the input parameter of support vector machine algorithm for predicting theβ-turn types in the database of 426 protein chains, obtained the overall prediction accuracy of 95.6%, 97.8%, 97.0%, 98.9%, 99.2%, 91.8%, 99.4% and 83.9% with the Matthews Correlation Coefficient values of 0.74, 0.68, 0.20, 0.49, 0.23, 0.47, 0.49 and 0.53 for types I, II, VIII, I’, II’, IV, VI and nonturn respectively, which is better than other prediction.

Cite this paper: Shi, X. and Hu, X. (2013) Using the Support Vector Machine Algorithm to Predict β-Turn Types in Proteins. Engineering, 5, 386-390. doi: 10.4236/eng.2013.510B078.

[1]   K. C. Chou, “Prediction of Tight Turns and Their Types in Proteins,” Analytical Biochemistry, Vol. 286, 2000, pp. 1-16.

[2]   X. Z. Hu and Q. Z. Li, “Using Support Vector Machine to Predict β- and γ-Turns and in Proteins,” Journal of Computational Chemistry, Vol. 10, 2008, pp. 1-9.

[3]   K. C. Chou and J. R. Blinn, “Classification and Prediction of Beta-turn Types,” Journal of Protein Chemistry, Vol. 16, 1997, pp. 575-595.

[4]   K. S. Kaur and G. P. Raghava, “A Neural Network Method for Pre-diction of Beta-Turn Types in Proteins using Evolutionary Information,” Bioinformatics, Vol. 16, 2004, pp. 2751-2758.

[5]   P. F. J. Fuchs and A. J. P. Alix, “High Accuracy Prediction of β-Turn and Their Types Using Propensities and Multiple Alignments,” Proteins, Vol. 59, 2005, pp. 828- 839.

[6]   E. G. Hutchinson and J. M. Thornton, “A Revised Set of Potentials for Beta-turn Formation in Proteins,” Protein Science, Vol. 3, 1994, pp. 2207-2216.

[7]   A. Kirschner and D. Frishman, “Prediction of β-Turns and β-Turn Types by a Novel Bidirectional Elman-Type Recurrent Neural Network with Multiple Output Layers,” Gene, Vol. 422, 2008, pp. 22-29.

[8]   A. J. Shepherd, D. Gorse and J. M. Thornton, “Prediction of the Location and Type of Beta-turns in Proteins using Neural Networks,” Protein Science, Vol. 8, 1999, pp. 1045-1055.

[9]   C. M. Wilmot and J. M. Thornton, “Analysis and Prediction of the Different Types of Beta-turn in Proteins,” Journal of Molecular Biology, Vol. 203, 1988, pp. 221- 232.

[10]   Y. D. Cai, X. J. Liu, Y. X. Li, X. B. Xu and K. C. Chou, “Support Vector Machines for the Classification and Prediction of Beta-Turn Types,” Journal of Peptide Science, Vol. 8, 2002, pp. 297-301.

[11]   K. Guruprasad and S. Rajkumar, “Beta-and Gamma-Turns in Proteins Revisited: A New Set of Amino Acid Turn- Type Dependent Positional Preferences and Potentials,” Journal of Bioscience, Vol. 25, 2000, pp. 143-156.

[12]   Q. Z. Li and Z. Q. Lu, “The Prediction of the Structural Class of Protein: Application of the Measure of Diversity,” Journal of Theoretical Biology, Vol. 213, 2001, pp. 493- 502.

[13]   Y. L. Chen and Q. Z. Li, “Prediction of the Subcellular Lo- cation of Apoptosis Proteins,” Journal of Theoretical Bi- ology, Vol. 245, 2007, pp. 775-783.

[14]   C. Cortes and V. Vapnik, “Support Vector Network,” Machine Learning, Vol. 20, 1995, pp. 273-293.

[15]   V. Vapnik, “Statistical Learning Theory,” Wiley-InterScience, New York, 1998.

[16]   D. T. Jones, “Protein Secondary Structure Prediction Based on Position-Speck Scoring Matrices,” Journal of Molecular Biology, Vol. 292, 1999, pp. 195-202.