• DocumentCode
    2118045
  • Title

    Prediction of Selenoproteins Based on Motif Recognition

  • Author

    Tao, Lan ; Liu, Geng ; Wang, Xiaoli ; Zhang, Lei

  • Author_Institution
    Coll. of Comput. & Software, Shenzhen Univ., Shenzhen, China
  • fYear
    2009
  • fDate
    17-19 Oct. 2009
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    At present available computational methods can not predict selenoproteins correctly because of the special features of selenoproteins. It is known that there are some conservative sections around U in selenoproteins from previous research. So we bring forward a new method to predict selenoproteins based on motif recognition, we use Multiple Em for Motif Elicitation (MEME) to discover motif around U in selenoproteins and then predict selenoproteins based on the motif. The new method found all the selenoproteins in 9 seleno families expect one false positive in family of GPX1 and one in SelS. From the experiment, it is showed that this method can effectively predict almost all the selenoproteins in the known seleno families, and better than the methods of locating the position of U and blasting the Sec/Cys pairing based on handcraft.
  • Keywords
    proteins; proteomics; GPX1 family; Multiple Em for Motif Elicitation; motif recognition; selenoproteins prediction; Amino acids; Educational institutions; Humans; Learning systems; Organisms; Predictive models; Proteins; Pulse width modulation; Sequences; Software;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Biomedical Engineering and Informatics, 2009. BMEI '09. 2nd International Conference on
  • Conference_Location
    Tianjin
  • Print_ISBN
    978-1-4244-4132-7
  • Electronic_ISBN
    978-1-4244-4134-1
  • Type

    conf

  • DOI
    10.1109/BMEI.2009.5302720
  • Filename
    5302720