A Refined MSAPSO Algorithm for Improving Alignment Score

Multiple Sequence Alignment (MSA) is an important part of bioinformatics domain in which two or more biological sequences, such as proteins, DNA or RNA are aligned sequentially. This Multiple Sequence Alignment plays a vital role in the generation of phylogenetic tree as well as predicting the protein structure. The protein sequences are generally used to generate the Phylogenetic tree. To attain this, we have transformed the protein sequences into numerical values using a substitution matrix and optimized those numerical values using Particle Swarm Optimization (PSO) method. The PSO is a meta-heuristic computational approach for performing optimization. The PSO uses the random values for pair-wise sequence alignment, resulting in decrease in the rate of the residues matched. This study presents how the rate of matching process can be improved by replacing the random values with the substitution matrix values if there is a positive value in the matrix. As result of this, we have also found that the score of the alignment sequence has been improved.