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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.

has been cited by the following article:

  • TITLE: Prediction of hydrophobic regions effectively in transmembrane proteins using digital filter

    AUTHORS: Jayakishan Meher, Mukesh Kumar Raval, Gananath Dash, Pramod Kumar Meher

    KEYWORDS: Hydrophobic Region; Transmembrane Protein; Wavelet Transform; Physico-Chemical Parameter; Digital Filter

    JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.4 No.8, August 22, 2011

    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.