Evolutionary Analysis and Motif Discovery in Rhodopsin from Vertebrates

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Evolutionary Analysis and Motif Discovery in Rhodopsin from Vertebrates

Author Affiliations

¹Department of Bioinformatics, Uttaranchal College of Science and Technology, Dehradun, INDIA
² Forest Pathology Division, Forest Research Institute, Dehradun, INDIA

Int. Res. J. Biological Sci., Volume 2, Issue (7), Pages 6-11, July,10 (2013)

Abstract

In the present investigation, total twenty different protein sequences of rhodopsin from different organisms of vertebrates were obtained from GenPept database and only 347 characters of each sequence were considered for motif discovery, motif family analysis and phylogenetic analysis. Three different motifs were discovered by MEME program. The Pfam analysis of these motifs result revealed that two motifs belonged to 7 transmembrane receptor family. Two major clusters of all retrieved sequences were obtained after phylogenetic analysis.

References

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Edwards S.C., Involvement of cGMP and calcium in the photoresponse in vertebrate photoreceptor cells, The Journal of the Florida Medical Association, 82(7), 485–8 (1995)
Maghtheh M., Gregory C., Inglehearn C., et al., Rhodopsin mutations in autosomal dominant retinitis pigmentosa, Hum. Mutat.,2(4), 249–55 (1993)
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Kumar S., Dudley J., Nei M., and Tamura K.,MEGA: a biologist-centric software for evolutionary analysis ofDNAand protein sequences, Briefings in Bioinformatics, 299-306 (2008)
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[ref1] Humphries P., Kenna P. and Farrar G.J., On the molecular genetics of retinitis pigmentosa, Science, 256 (5058), 804–(1992)

[ref2] Edwards S.C., Involvement of cGMP and calcium in the photoresponse in vertebrate photoreceptor cells, The Journal of the Florida Medical Association, 82(7), 485–8 (1995)

[ref3] Maghtheh M., Gregory C., Inglehearn C., et al., Rhodopsin mutations in autosomal dominant retinitis pigmentosa, Hum. Mutat.,2(4), 249–55 (1993)

[ref4] Garriga P., Manyosa J., The eye photoreceptor protein rhodopsin. Structural implications for retinal disease, FEBS Lett., 528(1–3), 17–22 (2002)

[ref5] Kumar S., Dudley J., Nei M., and Tamura K.,MEGA: a biologist-centric software for evolutionary analysis ofDNAand protein sequences, Briefings in Bioinformatics, 299-306 (2008)

[ref6] Bailey T.L. and Elkan C., Unsupervised learning of multiple motifs in biopolymers using expectation maximization, Mach Learn, 21(51), 80-33 (1995)

[ref7] Punta M., Coggill P.C., Eberhardt R.Y., Mistry J., Tate J., Boursnell C., Pang N., Forslund K., Ceric G., Clements J., Heger A., Holm L., Sonnhammer E.L.L., Eddy S.R., Bateman A., and Finn R.D. The Pfam Protein Families Database, Nucleic Acids Research Database (2012)

[ref8] Thompson J.D., Higgins D.G., Gibson T.J., CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position specific gap penalties and weight matrix choice, Nucleic Acids Res., 22(22), 4673-80 (1994)

[ref9] Dwivedi V.D., Arora S., Kumar A. and Mishra S.K., Computational analysis of xanthine dehydrogenase enzyme from different source organisms, Network Modeling Analysis in Health Informatics and Bioinformatics, DOI : 10.1007/s13721-013-0029-7(2012)

[ref10] Dhar D.V., Tanuj S., Kumar M.S. and Kumar P.A., Insights to Sequence Information of Lactoylglutathione Lyase Enzyme from Different Source Organisms, I. Res. J. Biological Sci., 1(6), 38-42 (2012)

[ref11] Dhar D.V., Tanuj S., Amit P. and Kumar M.S., Insights to Sequence Information of Alpha Amylase Enzyme from Different Source Organisms, International Journal of Advanced Biotechnology and Bioinformatics, 1(1), 87-91 (2012)