Abstract
The functions of transmembrane proteins are attributed by kinks (bends) in helices. Kinked helices are believed to be required for appropriate helix-helix and protein-protein interaction in membrane protein complexes. Therefore, knowledge of kink and its prediction from amino acid sequences is of great help in understanding the function of proteins. However, determination of kink in transmembrane α-helices is a computationally intensive task. In this paper we have developed signal processing algorithms based on discrete Fourier transform and wavelet transform for prediction of kink in the helices with a prediction efficiency of ~80%. The numerical representation of the protein in terms of probability of occurrence of amino acids constituted in kinked helices contains most of the necessary information in determining the kink location, and the signal processing methods capture this information more effectively than existing statistical and machine learning methods.
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Meher, J.K., Mishra, N., Mohapatra, P.K., Raval, M.K., Meher, P.K., Dash, G. (2011). Signal Processing Approach for Prediction of Kink in Transmembrane α-Helices. In: Das, V.V., Thomas, G., Lumban Gaol, F. (eds) Information Technology and Mobile Communication. AIM 2011. Communications in Computer and Information Science, vol 147. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20573-6_27
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DOI: https://doi.org/10.1007/978-3-642-20573-6_27
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