Multiple Proteins Sequence Alignment Based on Progressive Methods with New Guide Tree

Multiple proteins sequence alignment is one of the important research topics of bioinformatics. In multiple sequence alignment, it is emphasized to find optimal alignment for a group of sequences. All sequences are constituted of residues i.e. nucleotides for DNA/RNA, or amino acids for proteins. The objective is to maximize the similarities between them by adding and shuffling gaps. To do this, we propose a guide tree based on new distance definition. This distance is based on a Sequence Feature Vector (SFV). The SFV is built using a similarities descriptor of the sequence. We are studying the progressive alignment methodology with the proposed guide tree which is constructed using the similarity of all possible pairs of sequences. The proposed guide tree is simple to implement and give good result's performance. The comparison between the proposed guide tree and the tree built using pairwise distance is analyzed and then obtained solution qualities are reported. The Results of our testing in all dataset show that the proposed guide tree is as good as Clustalw in most cases.