Why is the Needleman-Wunsch algorithm too slow for very large sequence databases?

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Computer-generated model of anthrax toxin showing seven differently colored protein units, with a small gold molecule representing a drug bound to the protein structure. — bioinformatics This computerized image of anthrax shows the various structural relationships of seven units within the protein and demonstrates the interaction of a drug (shown in yellow) bound to the protein to block the so-called lethal factor unit. Bioinformatics plays an important role in enabling scientists to predict where a drug molecule will bind within a protein, given the individual structures of the molecules.

The Needleman-Wunsch algorithm is a dynamic programming method that guarantees the identification of the optimal global alignment between two sequences by breaking the task into smaller, manageable segments.1

While highly effective for comparing pairs of sequences, it is computationally intensive and too slow for searching very large sequence databases. Because the algorithm must perform an exhaustive comparison to ensure the optimal result, it cannot keep pace with the massive volume of data currently stored in biological archives.1

To address this, bioinformatics researchers utilize faster heuristic alternatives, such as BLAST (Basic Local Alignment Search Tool). These programs prioritize speed and efficiency, allowing scientists to scan vast databases for sequence similarities that would be impractical to process using the more rigorous Needleman-Wunsch approach.1