• DocumentCode
    2583405
  • Title

    Stochastic model of protease-ligand reactions

  • Author

    Anderson, Paul ; Raiford, Douglas ; Sweeney, Deacon ; Doom, Travis ; Raymer, Michael

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Wright State Univ., Dayton, OH, USA
  • fYear
    2005
  • fDate
    19-21 Oct. 2005
  • Firstpage
    306
  • Lastpage
    310
  • Abstract
    Prediction of protein tertiary structure based on amino acid sequence is one of the most challenging open questions in computational molecular biology. The two most common experimental methods for determining protein structure, X-ray crystallography and nuclear magnetic resonance (NMR) are both relatively expensive and time consuming processes. Further, some proteins (such as largely hydrophobic membrane integral proteins) are resistant to crystallization. Thus, computational approaches for determination of protein conformation are an attractive alternative to experimental procedures. While many such algorithms exist, none can yet achieve the same accuracy and reliability as experimental structure determination techniques. The objective of this research is to combine experimental evidence with computational modeling for high-confidence structure prediction. Limited proteolysis and chemical modification data is used to drive the modeling process towards physically realizable structures. Here we present a stochastic simulation of a limited proteolysis experiment that models the behavior of a protease-ligand reaction at an abstracted molecular level. The results of this simulation will be used to validate rate constant prediction methods that will then be used for the selection and refinement of candidate models for computational structure determination.
  • Keywords
    biochemistry; biology computing; enzymes; molecular biophysics; molecular configurations; physiological models; stochastic processes; X-ray crystallography; amino acid sequence; chemical modification; computational modeling; computational molecular biology; high-confidence structure prediction; largely hydrophobic membrane integral proteins; limited proteolysis; nuclear magnetic resonance; protease-ligand reactions; protein conformation; protein tertiary structure; rate constant prediction; stochastic model; Amino acids; Biological system modeling; Biology computing; Computational biology; Computational modeling; Nuclear magnetic resonance; Predictive models; Proteins; Sequences; Stochastic processes;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Bioinformatics and Bioengineering, 2005. BIBE 2005. Fifth IEEE Symposium on
  • Print_ISBN
    0-7695-2476-1
  • Type

    conf

  • DOI
    10.1109/BIBE.2005.52
  • Filename
    1544486