• DocumentCode
    3732156
  • Title

    Summary of Statistical Prediction of Posttranslational Modification Sites in Proteins

  • Author

    Li-Shu Wen;Cai-Lian Yao

  • Author_Institution
    Sch. of Sci., Shenyang Aerosp. Univ., Shenyang, China
  • fYear
    2015
  • Firstpage
    805
  • Lastpage
    808
  • Abstract
    Most proteins perform their functions after post-translational modifications (PTMs). Post-translational modifications play crucial roles in various cell functions and biological process. Computational identification of PTMs is an important way in contrast to experimental approach with its convenience. This review gave the process for prediction of Post-translational modification sites including benchmark dataset, feature construction, algorithm, evaluation measurement, and online web-server. Feature construction which reflects intrinsic correlation with proteins was emphasized mainly. Seven feature construction methods containing physiochemical properties and sequence information have been illustrated principally. Last nitro tyrosine was an application to design a site-specific predictor. The procedure was clearly described.
  • Keywords
    "Proteins","Peptides","Amino acids","Support vector machines","Yttrium","Benchmark testing","Machine learning algorithms"
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Transportation, Big Data and Smart City (ICITBS), 2015 International Conference on
  • Type

    conf

  • DOI
    10.1109/ICITBS.2015.203
  • Filename
    7384149