Alternate Representation of Distance Matrices for Characterization of Protein Structure

The most suitable method for the automated classification of protein structures remains an open problem in computational biology. In order to classify a protein structure with any accuracy, an effective representation must be chosen. Here we present two methods of representing protein structure. One involves representing the distances between the Cα atoms of a protein as a two-dimensional matrix and creating a model of the resulting surface with Zernike polynomials. The second uses a wavelet-based approach. We convert the distances between a protein’s Cα atoms into a one-dimensional signal which is then decomposed using a discrete wavelet transformation. Using the Zernike coefficients and the approximation coefficients of the wavelet decomposition as feature vectors, we test the effectiveness of our representation with two different classifiers on a dataset of more than 600 proteins taken from the 27 mostpopulated SCOP folds. We find that the wavelet decomposition greatly out...

• Type: Conference
• Year: 2005
• Where: ICDM
• Authors: Keith Marsolo, Srinivasan Parthasarathy
• Keywords: Cα Atoms, Data Mining, ICDM 2005, Protein Structure, Wavelet Decomposition