IDENTIFICATION OF HOTSPOTS IN PROTEIN SEQUENCES USING CPNR AND DWT

G. Anitha Mary, G. AnjanBabu, G. Aparna Raghava Rao,

Abstract


Protein-protein interactions controls most of all the biological activities and therefore the key functions of proteins. The interaction layout of protein rely on the organic compound sequence. Computationally assessing the purposeful affinities between proteins is a crucial job of bioinformatics analysis. It will favor molecular biologists share information on few proteins to others and thus scale back the quantity of tedious and valuable bench work. Hence, identification of protein performance from its primary sequence may be an important and tiring task in bioinformatics.Identification of the amino acids (hotspots) that ends up in the characteristic frequency signifying a particular biological perform is de facto associate in nursing annoying task in Genomic signal process. Since experimental procedures of protein hotspot identification are still financially very rigorous and time taking, there's a wrench to supply enough reliable process procedures for this specific task. Signal process demands the sequence to be in numerical illustration, therefore protein sequences are mapped (encoding) into digital signal.  Amino acids are the building blocks of proteins plays a major role achieve this job.For signal processing the sequences need to converted into numerical sequence, for which mapping is necessary.From literature experimented results incontestable among EIIP and CPNR mapping, CPNR achieves better performance than EIIP. So, CPNR mapping is taken into account. With the recent advances in Genomic signal process (GSP) domain, researchers are applying digital signal process (DSP) techniques in raw genomic information for extracting the hidden features among proteins. Wavelet transformation has been a really novel strategy for the analysis and process of non-stationary signals like bio signals within which each time and frequency data area unit required. In this paper, we have incorporated complex prime numerical representation (CPNR) used for mapping of protein sequence into digital signal and discrete wavelet transform (DWT) strategies to spot the hotspots. A new proposed scheme using CPNR & DWT is introduced.

 

 


Keywords


Complex prime numerical representation (CPNR), discrete wavelet transform (DWT), protein sequence, and Genomic signal process (GSP).

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References


George TP, Thomas T, “Discrete wavelet transform de-noising in eukaryotic gene splicing,” BMC Bioinformatics, 2010.

Rafale C. Gonzalez, Richard E Woods’s _Digital image processing, Second Edition page 410.

Tuncbag, N, Gursoy, A &Keskin, O 2009, ‘Identification ofcomputational hot spots in protein interfaces: combining solvent accessibility and inter-residue potentials improves the accuracy’,Bioinformatics, vol. 25, no. 12, pp. 1513–1520

Yadav, Y &Wadhwani, S 2011, ‘Determination of characteristicfrequency for identification of hot spots in proteins’, InternationalJournal of Electrical and Electronics Engineering (IJEEE), vol.1, no.1,pp. 1-4

National Centre for Biotechnology Information (NCBI).Available: http://www.ncbi.nlm.nih.gov/.

DUO CHEN, JIASONG WANG, MING YAN, and FORREST SHENG BAO, ‘A Complex Prime Numerical

Representation of Amino Acids for Protein Function,

Volume 23, Number 8, 2016.

Shu-ching Chen, “Wavelet analysis in current cancer Genome Research: A Survey”, IEEE /ACM transactions on computational biology and bioinformatics, vol. 10, pp. 567-570, 2013

P. P. Vaidyanathan and B. J. Yoon, “The role of signal processing concepts in genomics and proteomics,” Journal of the Franklin Institute, Vol. 341, pp. 111-135,

Sahu, SS & Panda, G 2011, ‘Efficient localization of hot spots inProteins using a novel S-transform based filtering approach’,IEEE/ACM Transactions on computational biology and bioinformatics,vol.8, no.05, pp. 1235-1246




DOI: https://doi.org/10.26483/ijarcs.v9i3.6108

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