Hierarchical motif vectors for amino acid sequence alignment

Abstract

We present a new framework for global and local alignment of amino acid sequences based on hierarchical motif vectors that characterize local amino acid configurations. The motif vectors are constructed by carrying out wavelet decomposition on numeric property sequences obtained by replacing each amino acid in a sequence with their respective properties, and concatenating such profiles obtained for a large number of physico-chemical properties into a single column vector. We then formulate different schemes for aligning amino acid sequences based on their respective motif vectors globally as well as locally subject to measures of statistical significance. Experiment results indicate that the motif vectors accurately capture the amino acid composition at and around individual sites along sequences and allow associating sequence segments sharing similar functional attributes.