Prediction of hydrophobic regions effectively intransmembrane proteins using digital filter
ABSTRACT
The hydrophobic effect is the major factor that drives a protein molecule towards folding and to a great degree the stability of protein structures. Therefore the knowledge of hydrophobic regions and its prediction is of great help in understanding the structure and function of the protein. Hence determination of membrane buried region is a computationally intensive task in bioinformatics. Several prediction methods have been reported but there are some deficiencies in prediction accuracy and adaptability of these methods. Of these proteins that are found embedded in cellular membranes, called as membrane proteins, are of particular importance because they form targets for over 60% of drugs on the market. 20-30% of all the proteins in any organism are membrane proteins. Thus transmembrane protein plays important role in the life activity of the cells. Hence prediction of membrane buried segments in transmembrane proteins is of particular importance. In this paper we have proposed signal processing algorithms based on digital filter for prediction of hydrophobic regions in the transmembrane proteins and found improved prediction efficiency than the existing methods. Hydrophobic regions are extracted by assigning physico-chemical parameter such as hydrophobicity and hydration energy index to each amino acid residue and the resulting numerical representation of the protein is subjected to digital low pass filter. The proposed method is validated on transmembrane proteins using Orientation of Proteins in Membranes (OPM) dataset with various prediction measures and found better prediction accuracy than the existing methods.
The hydrophobic effect is the major factor that drives a protein molecule towards folding and to a great degree the stability of protein structures. Therefore the knowledge of hydrophobic regions and its prediction is of great help in understanding the structure and function of the protein. Hence determination of membrane buried region is a computationally intensive task in bioinformatics. Several prediction methods have been reported but there are some deficiencies in prediction accuracy and adaptability of these methods. Of these proteins that are found embedded in cellular membranes, called as membrane proteins, are of particular importance because they form targets for over 60% of drugs on the market. 20-30% of all the proteins in any organism are membrane proteins. Thus transmembrane protein plays important role in the life activity of the cells. Hence prediction of membrane buried segments in transmembrane proteins is of particular importance. In this paper we have proposed signal processing algorithms based on digital filter for prediction of hydrophobic regions in the transmembrane proteins and found improved prediction efficiency than the existing methods. Hydrophobic regions are extracted by assigning physico-chemical parameter such as hydrophobicity and hydration energy index to each amino acid residue and the resulting numerical representation of the protein is subjected to digital low pass filter. The proposed method is validated on transmembrane proteins using Orientation of Proteins in Membranes (OPM) dataset with various prediction measures and found better prediction accuracy than the existing methods.
Cite this paper
nullMeher, J. , Raval, M. , Dash, G. and Meher, P. (2011) Prediction of hydrophobic regions effectively intransmembrane proteins using digital filter. Journal of Biomedical Science and Engineering, 4, 562-568. doi: 10.4236/jbise.2011.48072.
nullMeher, J. , Raval, M. , Dash, G. and Meher, P. (2011) Prediction of hydrophobic regions effectively intransmembrane proteins using digital filter. Journal of Biomedical Science and Engineering, 4, 562-568. doi: 10.4236/jbise.2011.48072.
References
[1] Ramachandran, G., Ramakrishnan, C. and Sasisekharan, V. (1963) Stereochemistry of polypeptide chain configuration. J. Mol. Biol., 7: 95-99. [2] Cordes, F., Bright, J.and Sansom, M. (2002) Proline induced distortions of transmembrane helices. J. Mol. Biol., 323: 951-960. [3] Anfinsen, C.B. (1973) Principles that govern the folding of protein chains. Science, 181(96), 223-230. [4] Rose, G.D. (1978) Prediction of chain turns in globular proteins on a hydrophobic basis. Nature, 272, 586-590. [5] Qian, H. (1996) Prediction of _-helices in proteins based on thermodynamic parameters from solution chemistry. J. Mol. Biol., 256, 663-666. [6] Mohapatra, P., Khamari, A. and Raval, M. (2004) A method for structural analysis of α-helices of membrane proteins J. Mol. Model., 10: 393-398,. [7] Heijne, G. V. (1991)Proline kinks in transmembrane α-helices J. Mol. Biol., 218: 499-503. [8] Swindells, M.B. (1995) A procedure for the automatic determination of hydrophobic cores in protein structures. Protein Science, 4, 93-102. [9] Desjarlais, J.R. and Handel, T.M. (1995) De novo design of the hydrophobic cores of proteins. Protein Science, 4, 2006-2018. [10] Rost, B. and Sander, C. (1994) Combining evolutionary information and neural networks to predict secondary structure. PROTEINS: Structure, Function, and Genetics, 19, 55-72. [11] Kyte, J. and Doolittle, R.F. (1982) A simple method for displaying the hydropathic character of a protein. J. Mol. Biol., 157, 105-132. [12] Cornette, J.L., Cease, K.B., Margalit, H., Spouge, J.L., Berzofsky, J.A. and DeLisi, C. (1987) Hydrophobicity scales and computational techniques for detecting amphipathic structures in proteins. J. Mol. Biol., 195, 659-685. [13] Liò, P. (2003) Wavelets in bioinformatics and computational biology: state of art and perspectives,” Bioinformatics, Vol. 19(1), pp. 2-9. [14] Hirakawa, H. and Kuhara, S. (1997) Prediction of hydrophobic cores of proteins using wavelet analysis. Genome Inform. Ser Workshop Genome Inform, 8: 61-70,. [15] Hirakawa, H., Muta, S. and Kuhara, S. (1999) The hydrophobic cores of proteins predicted by wavelet analysis. Bioinformatics, 15: 141-148. [16] de Trad, C., Fang, Q. and Cosic, I. (2002) Protein sequence comparision based on the wavelet transform approach. Protein Eng., 15: 193-203. [17] Murray, K. B., Gorse, D. and Thornton, J.(2002) Wavelet transforms for the characterization and detection of repeating motifs. J. Mol. Biol., 316: 341-363. [18] Yu, B., Meng, X. H. and Liu, H. J. (2006) Prediction of transmembrane helical segments in transmembrane proteins based on wavelet transform Journal of Shanghai University (English Edition), Vol. 10, 2006, pp. 308-318. [19] Bin, Y. and Yan, Z. (2010) On the Prediction of Transmembrane Helical Segments in Membrane Protein International Journal of Mathematical and Computer Sciences, 6:4, pp. 192-195. [20] Kuntz, I.D. (1972) Protein folding. J. Am. Chem. Soc., 94, 4009-4012. [21] Qian, H. (1996) Prediction of α-helices in proteins based on thermodynamic parameters from solution chemistry. J. Mol. Biol., 256, 663-666. [22] S. K. Mitra (2006) Digital signal processing Tata McGraw-Hill. [23] Oppenheim, A. V. and Schafer, R. W. (1999) Discrete-Time Signal Processing Prentice-Hall, Inc., NJ.
[1] Ramachandran, G., Ramakrishnan, C. and Sasisekharan, V. (1963) Stereochemistry of polypeptide chain configuration. J. Mol. Biol., 7: 95-99. [2] Cordes, F., Bright, J.and Sansom, M. (2002) Proline induced distortions of transmembrane helices. J. Mol. Biol., 323: 951-960. [3] Anfinsen, C.B. (1973) Principles that govern the folding of protein chains. Science, 181(96), 223-230. [4] Rose, G.D. (1978) Prediction of chain turns in globular proteins on a hydrophobic basis. Nature, 272, 586-590. [5] Qian, H. (1996) Prediction of _-helices in proteins based on thermodynamic parameters from solution chemistry. J. Mol. Biol., 256, 663-666. [6] Mohapatra, P., Khamari, A. and Raval, M. (2004) A method for structural analysis of α-helices of membrane proteins J. Mol. Model., 10: 393-398,. [7] Heijne, G. V. (1991)Proline kinks in transmembrane α-helices J. Mol. Biol., 218: 499-503. [8] Swindells, M.B. (1995) A procedure for the automatic determination of hydrophobic cores in protein structures. Protein Science, 4, 93-102. [9] Desjarlais, J.R. and Handel, T.M. (1995) De novo design of the hydrophobic cores of proteins. Protein Science, 4, 2006-2018. [10] Rost, B. and Sander, C. (1994) Combining evolutionary information and neural networks to predict secondary structure. PROTEINS: Structure, Function, and Genetics, 19, 55-72. [11] Kyte, J. and Doolittle, R.F. (1982) A simple method for displaying the hydropathic character of a protein. J. Mol. Biol., 157, 105-132. [12] Cornette, J.L., Cease, K.B., Margalit, H., Spouge, J.L., Berzofsky, J.A. and DeLisi, C. (1987) Hydrophobicity scales and computational techniques for detecting amphipathic structures in proteins. J. Mol. Biol., 195, 659-685. [13] Liò, P. (2003) Wavelets in bioinformatics and computational biology: state of art and perspectives,” Bioinformatics, Vol. 19(1), pp. 2-9. [14] Hirakawa, H. and Kuhara, S. (1997) Prediction of hydrophobic cores of proteins using wavelet analysis. Genome Inform. Ser Workshop Genome Inform, 8: 61-70,. [15] Hirakawa, H., Muta, S. and Kuhara, S. (1999) The hydrophobic cores of proteins predicted by wavelet analysis. Bioinformatics, 15: 141-148. [16] de Trad, C., Fang, Q. and Cosic, I. (2002) Protein sequence comparision based on the wavelet transform approach. Protein Eng., 15: 193-203. [17] Murray, K. B., Gorse, D. and Thornton, J.(2002) Wavelet transforms for the characterization and detection of repeating motifs. J. Mol. Biol., 316: 341-363. [18] Yu, B., Meng, X. H. and Liu, H. J. (2006) Prediction of transmembrane helical segments in transmembrane proteins based on wavelet transform Journal of Shanghai University (English Edition), Vol. 10, 2006, pp. 308-318. [19] Bin, Y. and Yan, Z. (2010) On the Prediction of Transmembrane Helical Segments in Membrane Protein International Journal of Mathematical and Computer Sciences, 6:4, pp. 192-195. [20] Kuntz, I.D. (1972) Protein folding. J. Am. Chem. Soc., 94, 4009-4012. [21] Qian, H. (1996) Prediction of α-helices in proteins based on thermodynamic parameters from solution chemistry. J. Mol. Biol., 256, 663-666. [22] S. K. Mitra (2006) Digital signal processing Tata McGraw-Hill. [23] Oppenheim, A. V. and Schafer, R. W. (1999) Discrete-Time Signal Processing Prentice-Hall, Inc., NJ.