Electrostatic Similarity Determination Using Multiresolution Analysis

Molecular similarity is an important tool in protein and drug design for analyzing the quantitative relationships between physicochemical properties of two molecules. We present a family of similarity measures which exploits the ability of wavelet transformation to analyze the spectral components of physicochemical properties and suggests a sensitive way for measuring similarities of biological molecules. In order to investigate how effective wavelet-based similarity measures were against conventional measures, we defined several patterns which involve scalar or topological changes in the distribution of electrostatic properties. The wavelet-based measures were more successful in discriminating these patterns in contrast to the current state-of-art similarity measures. We also present the validity of wavelet-based similarity measures through the hierarchical clustering of two protein datasets consisting of families of homologous domains and alanine scan mutants. This type of similarity analysis is useful for protein structure-function studies and protein design.