• DocumentCode
    1943327
  • Title

    Prediction of Long-range Contacts from Sequence Profile

  • Author

    Chen, Peng ; Wang, Bing ; Wong, Hau-San ; Huang, De-Shuang

  • Author_Institution
    Chinese Acad. of Sci., Hefei
  • fYear
    2007
  • fDate
    12-17 Aug. 2007
  • Firstpage
    938
  • Lastpage
    943
  • Abstract
    Theoretic study in this paper shows that we can obtain exact long-range contacts by adopting one classifier if the centers of sequence profiles of residue pairs for long-range contacts and non-long-range contacts are known. The adopted classifier, referred to as multiple conditional probability mass function classifier (MCPMFC), can find an optimized transformation of the variables for each of the classes and therefore resulting in K separate classifiers. As a result, about 44.48% long-range contacts are around at the sequence profile (SP) centre for long-range contacts and about 20.9% long-range contacts are correctly predicted when considering the top L/5 (L is the protein sequence length) predicted contacts and the residue pair with 24 apart. The highest cluster result gives us a clue that SP center should be a sound pathway to investigate contact map in protein structures.
  • Keywords
    pattern classification; probability; proteins; long-range contact prediction; multiple conditional probability mass function classifier; protein structures; sequence profile; Accuracy; Amino acids; Computational modeling; Computer simulation; Genetic programming; Hidden Markov models; Neural networks; Predictive models; Protein sequence; Stability;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2007. IJCNN 2007. International Joint Conference on
  • Conference_Location
    Orlando, FL
  • ISSN
    1098-7576
  • Print_ISBN
    978-1-4244-1379-9
  • Electronic_ISBN
    1098-7576
  • Type

    conf

  • DOI
    10.1109/IJCNN.2007.4371084
  • Filename
    4371084