• DocumentCode
    399586
  • Title

    Two-layer protein structure comparison

  • Author

    Park, Sung-Joon ; Yamamura, Masayuki

  • Author_Institution
    Interdisciplinary Graduate Sch. of Sci. & Eng., Tokyo Inst. of Technol., Japan
  • fYear
    2003
  • fDate
    3-5 Nov. 2003
  • Firstpage
    435
  • Lastpage
    440
  • Abstract
    Extracting biological importance from protein structures is extremely helpful to understand the molecular nature. Although methods for protein structure-based alignment have been hitherto proposed in a number of ways, each method focuses on a part of alignment possibility. We have developed a generic method for pair wise structure-based alignment utilizing the population search ability of a real-coded genetic algorithm. Our method simultaneously optimizes vector-expressed local fragment posture and global atomic superposition. Here, we report comparative results derived from the proposed method and existing methods. The experiments use three protein pairs well studied and a number of pairs derived from diverse protein families. The results show that our method provides useful two-layer similarity and statistical significance at a time to be able to capture not only the remarkable difference between local alignment and global alignment but also biologically meaningful common folds and motifs. Interestingly, we unveiled a vague region in protein structure-function relationships. It may indicate the limit of using alpha-carbon backbones.
  • Keywords
    biology computing; genetic algorithms; proteins; alpha-carbon backbone; global atomic superposition; pair wise structure-based alignment; protein structure-based alignment; protein structure-function relationship; real-coded genetic algorithm; two-layer protein structure; vector-expressed local fragment posture; Amino acids; Biology; Bonding; Chemicals; Genetic algorithms; Hydrogen; Optimization methods; Peptides; Protein engineering; Spine;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools with Artificial Intelligence, 2003. Proceedings. 15th IEEE International Conference on
  • ISSN
    1082-3409
  • Print_ISBN
    0-7695-2038-3
  • Type

    conf

  • DOI
    10.1109/TAI.2003.1250222
  • Filename
    1250222