Evolutionary computation techniques for multiple sequence alignment

Given a collection of biologically related protein or DNA sequences, the basic multiple sequence alignment problem is to determine the most biologically plausible alignment of these sequences. Under the assumption that the collection of sequences arose from some common ancestor, an alignment can be used to infer the evolutionary history among the sequences, i.e., the most likely pattern of insertions, deletions and mutations that transformed one sequence into another. The general multiple sequence alignment problem is known to be NP-hard, and hence the problem of finding the best possible multiple sequence alignment is intractable. However, this does not preclude the possibility of developing algorithms that produce near optimal multiple sequence alignments in polynomial time. We examine techniques to combine efficient algorithms for near optimal global and local multiple sequence alignment with evolutionary computation techniques to search for better near optimal sequence alignments. We describe our evolutionary computation approach to multiple sequence alignment and present preliminary simulation results on a set of 17 clusters of orthologous groups of proteins (COGs). We compare the fitness of the alignments given by the proposed techniques with the fitness of CLUSTAL W alignments given in the COG database