The Influence of Dipeptide Composition on Protein Folding Rates

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Abstract:

Understanding the relationship between amino acid sequences and folding rates of proteins is an important challenge in computational and molecular biology. All existing algorithms for predicting protein folding rates have never taken into account the sequence coupling effects. In this work, a novel algorithm was developed for predicting the protein folding rates from amino acid sequences. The prediction was achieved on the basis of dipeptide composition, in which the sequence coupling effects are explicitly included through a series of conditional probability elements. Based on a non-redundant dataset of 99 proteins, the proposed method was found to provide an excellent agreement between the predicted and experimental folding rates of proteins when evaluated with the jackknife test. The correlation coefficient was 87.7% and the standard error was 2.04, which indicated the important contribution from sequence coupling effects to the determination of protein folding rates.

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Advanced Materials Research (Volumes 378-379)

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157-160

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October 2011

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] K. W. Plaxco, K. T. Simons, and D. Baker, J. Mol. Biol. vol. 277 (1998), p.985.

Google Scholar

[2] M. M. Gromiha, and S. Selvaraj, J. Mol. Biol. vol. 310 (2001), p.27.

Google Scholar

[3] H. Zhou, and Y. Zhou, Biophys. J. vol. 82 (2002), p.458.

Google Scholar

[4] H. Gong, D. G. Isom, R. Srinivasan, and G. D. Rose, J. Mol. Biol. vol. 327 (2003), p.1149.

Google Scholar

[5] D. N. Ivankov, and A. V. Finkelstein, Proc. Nat. Acad. Sci. USA. vol. 101 (2004), p.8942.

Google Scholar

[6] O. V. Galzitskaya, S. O. Garbuzynskiy, D. N. Ivankov, and A. V. Finkelstein, Proteins vol. 51 (2003), p.162.

Google Scholar

[7] J. T. Huang, and T. Jing, Proteins vol. 63 (2006), p.551.

Google Scholar

[8] B. G. Ma, J. X. Guo, and H. Y. Zhang, Proteins vol. 65 (2006), p.362.

Google Scholar

[9] M. M. Gromiha, A. M. Thangakani, and S. Selvaraj, Nucleic Acids Res. vol. 34 (2006), p.70.

Google Scholar

[10] Y. Jiang, P. Iglinski, and L. Kurgan, J. Comput. Chem. vol. 30 (2009), p.772.

Google Scholar

[11] L. T. Huang, and M. M. Gromiha, J. Comput. Chem. vol. 29 (2008), p.1675.

Google Scholar

[12] M. M. Gromiha, and S. Selvaraj, Current Bioinformatics vol. 3 (2008), p.1.

Google Scholar

[13] J. X. Guo, B. G. Ma, and H. Y. Zhang, Acta Biophysica Sinica vol. 22 (2006), p.89.

Google Scholar

[14] J. X. Guo, N. N. Rao, G. X. Liu, Y. Yang, and G. Wang, J. Comput. Chem. vol. 32 (2011), p.1612.

Google Scholar

[15] J. X. Guo, N. N. Rao, G. X. Liu, J. Li, and Y. H. Wang, Progress in Biochemistry and Biophysics vol. 37 (2010), p.1331.

Google Scholar

[16] K. C. Chou, Current Proteomics vol. 6 (2009), p.262.

Google Scholar