We propose a modified MSA algorithm on quantum annealers with applications in areas of bioinformatics and genetic sequencing. To understand the human genome, researchers compare extensive sets of these genetic sequences -or their protein counterparts -to identify patterns. This comparison begins with the alignment of the set of (multiple) sequences. However, this alignment problem is considered nondeterministically-polynomial time complete and, thus, current classical algorithms at best rely on brute force or heuristic methods to find solutions. Quantum annealing algorithms are able to bypass this need for sheer brute force due to their use of quantum mechanical properties. However, due to the novelty of these algorithms, many are rudimentary in nature and limited by hardware restrictions. We apply progressive alignment techniques to modify annealing algorithms, achieving a linear reduction in spin usage whilst introducing more complex heuristics to the algorithm. This opens the door for further exploration into quantum computing-based bioinformatics, potentially allowing for a deeper understanding of disease detection and monitoring.