A comparison of sequence alignment algorithms for measuring secondary structure similarity

Abstract

Methods for protein secondary structure prediction have improved significantly in recent years. This has lead to enhanced protein homology modeling efforts. Protein homology modeling involves the subtask of identifying a set of homologous proteins from a protein database when given as input the amino acid sequence of a query protein, with the ultimate goal of using the resulting set of homologous proteins as a starting point for predicting the 3D structure of the query protein. Previous work has indicated that improvements can be made when combining secondary structure sequence alignment using a 3-state structure symbol alphabet together with primary amino acid sequence alignment methods. These approaches typically use a local alignment algorithm. We compare the performance of several dynamic programming alignment algorithms on the task of aligning secondary structure sequences using an 8-state secondary structure alphabet. Our results indicate that the typical use of a local alignment algorithm may not be best when aligning protein secondary structure information.