Grey Wolf Optimization Algorithm for Solving Multiple Sequence Alignment (MSA)

Abstract

Multiple Sequence Alignment (MSA) is a critical task in bioinformatics, involving the arrangement of sequences to identify similarities and differences, which can be crucial for understanding evolutionary relationships, protein structure, and function. Traditional MSA methods, while effective, can struggle with computational complexity and the accuracy of large datasets. In this paper, we explore the utilization of Grey Wolf Optimization (GWO), a powerful nature-inspired algorithm, to the MSA problem. GWO mimics the hunting behavior and social hierarchy of grey wolves to find optimal solutions in complex search spaces. However, the performance of GWO in MSA can be limited by its exploration and exploitation balance. To address this, we propose an improved GWO operator that refines the algorithm's search capabilities, enhancing its ability to identify higher-quality sequence alignments.

Our methodology involves adapting GWO to the MSA problem by representing sequence alignments as solutions and employing a fitness function that measures alignment quality. The proposed operator modification enhances the algorithm’s convergence speed and accuracy, ensuring more reliable alignment results. We compare the improved GWO approach with traditional MSA techniques and demonstrate that it consistently outperforms existing methods in both accuracy and computational efficiency.

The results of our experiments show that the enhanced GWO algorithm offers a promising alternative to traditional MSA methods, especially when contending with large, complex sequence datasets. This work contributes to the growing field of computational biology by providing a more efficient and effective tool for sequence alignment, with the potential to support various bioinformatics applications, such as gene prediction, phylogenetic analysis, and functional genomics.