• DocumentCode
    2687981
  • Title

    Prediction of Protein Catalytic Residues by Local Structural Rigidity

  • Author

    Chien, Yu-Tung ; Huang, Shao-Wei

  • Author_Institution
    Dept. of Med. Inf., Tzu Chi Univ., Hualien, Taiwan
  • fYear
    2012
  • fDate
    4-6 July 2012
  • Firstpage
    592
  • Lastpage
    596
  • Abstract
    Due to the large number of protein structures whose functions are unknown, it becomes increasing important to study the structural characteristics of catalytic residues. Here, we use a novel method to calculate the local structural rigidity (LSR) of protein. Based on a dataset of 760 proteins, the results show that catalytic residues have distinct structural properties. They are shown to be extremely rigid based on the calculation of LSR. Finally, we present a machine-learning based method to predict catalytic residues from protein structure using LSR as primary input feature. The prediction sensitivity and specificity are 0.86 and 0.86, respectively, and the Matthew´s correlation coefficient is 0.72.
  • Keywords
    biology computing; catalysis; learning (artificial intelligence); molecular biophysics; molecular configurations; proteins; LSR; Matthew correlation coefficient; local structural rigidity; machine-learning based method; prediction sensitivity; protein catalytic residues; protein dataset; protein structure; structural characteristics; Amino acids; Bioinformatics; Proteins; Sensitivity; Solvents; Support vector machines; catalytic site prediction; local structural rigidity; support vector machine;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Complex, Intelligent and Software Intensive Systems (CISIS), 2012 Sixth International Conference on
  • Conference_Location
    Palermo
  • Print_ISBN
    978-1-4673-1233-2
  • Type

    conf

  • DOI
    10.1109/CISIS.2012.99
  • Filename
    6245633