DocumentCode
2377822
Title
Prediction of protein active and/or binding site using time-frequency analysis: Application to ras oncogene proteins
Author
Pirogova, Elena ; Vojisavljevic, Vuk ; Cosic, Irena
Author_Institution
Sch. of Electr. & Comput. Eng., RMIT Univ., Melbourne, VIC, Australia
fYear
2012
fDate
9-11 Jan. 2012
Firstpage
1
Lastpage
4
Abstract
Cancer cells contain genetic damage that can lead to tumorigenesis. Genetic damage found in cancer cells is of two types: dominant and the genes are termed proto-oncogenes; and recessive and the genes are termed tumor or growth suppressors, recessive oncogenes or anti-oncogenes. Oncogene proteins are a specific group of growth factors that promotes uncontrolled cell growth and proliferation. These proteins are derived from normal proto-oncogenes via a limited number of modifications, i.e mutations, insertions or deletions. Because proto-oncogenes control the cell cycle, it is obvious that should a proto-oncogene be mutated the potential for an unregulated cell cycle results. Therefore, a structure-function analysis of oncogene proteins is of great importance in understanding cell transformation that causes cancer development. In this paper we present and discuss the use of two related computational techniques for analysis of ras oncongene protein example. We showed that the methods are efficient for accurate prediction of the protein active/binding site locations critical for its bioactivity, i.e. cell transformation.
Keywords
biology computing; cancer; cellular biophysics; genetics; molecular biophysics; proteins; proteomics; time-frequency analysis; cancer cells; cell cycle; cell transformation; genetic damage; protein active site; protein binding site; protooncogenes; ras oncogene proteins; structure-function analysis; time-frequency analysis; tumor; tumorigenesis;
fLanguage
English
Publisher
ieee
Conference_Titel
Biosignals and Biorobotics Conference (BRC), 2012 ISSNIP
Conference_Location
Manaus
Print_ISBN
978-1-4673-2476-2
Type
conf
DOI
10.1109/BRC.2012.6222173
Filename
6222173
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