Computational analysis and classification of p53 mutants according to primary structure

Widely used multiple alignment based techniques can give false results for single base mutation as the primary sequence of mutants and that of the wild types are very similar. We present a technique that uses signal processing methods along with biochemical properties of individual amino acids for the analysis of proteins. Each amino acid of mutant protein is replaced with the corresponding biochemical properties and generates a set of biochemical signals. These signals are used to extract signal processing features like complexity, mobility, fractal dimension, and wavelet transformation. In an experimental study of p53 protein, mutants resulting from single mutation of eight residue of the β-strand 326-33 to alanine were analyzed for their ability to stimulate transcription, to inhibit the growth of Saos-2 cells, and to repress the promoter of multidrug resistance gene. Our classification results, merely based on the analysis of primary sequences, are matching with those of the experiential studies.