Pairwise sequence alignment using a PROSITE pattern-derived similarity score

Abstract

Existing methods for alignments are based on edition costs computed additionally position by position, according to a fixed substitution matrix: a substitution always has the same weight regardless of the position. Nevertheless the biologist favours a similarity according to his knowledge of the structure or the function of the sequences considered. In the particular case of proteins, we present a method consisting in integrating other information, such as patterns of the PROSITE databank, in the classical dynamic programming algorithm. The method consists in making an alignment by dynamic programming taking a decision not only letter by letter as in the Smith & Waterman algorithm but also by giving a reward when aligning patterns.