GAMSA-ALIGN: A NOVEL APPROACH FOR EFFICIENT PROTEIN SEQUENCE ALIGNMENT USING GENETIC ALGORITHM

Bioinformatics has played an important role in discovering medicine because whole-genome sequences can help to find out the many genetic diseases. Bioinformatics uses computational tools to manage, store and analyze the data. Multiple sequence alignment (MSA) seems to be a very useful process in molecular and evolutionary biology. There are a variety of software’s and methods for it. It's used to find conserved patterns, identify protein domains, identify 2D and 3D structures using homology, and conduct evolutionary research. There are several methods for aligning multiple sequences. Many strategies are designed to enhance speed while ignoring the quality of the resulting alignment. Similarly, several strategies are designed to enhance accuracy while ignoring speed. As a result, finding the best method for alignment accuracy and computing cost has become an important factor in choosing the best MSA method. Genetic Algorithms GAMSA-Align have also shown promise in optimizing the multiple sequence alignment process, offering potential improvements in both speed and accuracy. In this study assessed the cost and accuracy of nine common MSA methods against the BAliBASE v4.0 benchmark alignment datasets, including ProbCons 1.12, T-Coffee 9.03, MAFFT 7.031, MUSCLE 3.8.31, Clustal1.1.0, Probalign 1.4, and ProDa, Kalign, and Prank. The two standard scoring procedures, TC score and SP score, are used to calculate alignment accuracy, and computing costs were evaluated by measuring peak memory consumption and CPU execution time. The results indicated that the ProbCons and ProbAlign MSA methods that are based on the progressive consistency approach were first and second, but these tools had a high execution cost.