Data mining of supersecondary structure in protein

Abstract

We have proposed a conformational code for the description of conformations of all kinds of chemical compounds based on structural analysis using vibrational circular dichroism (VCD) of chiral bioactive compounds and have developed the auto-conversion techniques of conformational information needed for structural homology between proteins, which can be represented by 20 symbols of amino acid residues. The fuzzy search and data mining techniques using “h”, “s”, and “o” fragment patterns to convert 3D data to 1D data reflect well the shape of main chain and show the characteristic fragment patterns “hhshshh” in the armadillo repeat region of β-catenin. Characteristic fragment patterns “shhshss”, which are sandwiched between two amino acid residues His and Pro, as HisXaaXaaXaaXaaXaaPro (Xaa: an amino acid residue), have been found in light chains of various types of immunogloblins and MHC class I and II molecules including HFE. However, human IgM rheumatoid factor, GM-CSF autoantibody, TSHR autoantibody, CD1D, and MIC-A,B do not clearly indicate these patterns. In this presentation, the comparison result of the new method with the DSSP method to standardize secondary structure assignment is also discussed.