Protein structure comparison using iterated double dynamic programming

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Abstract
A protein structure comparison method is described that allows the generation of large populations of high-scoring alternate alignments. This was achieved by incorporating a random element into an iterative double dynamic programming algorithm. The maximum scores from repeated comparisons of a pair of structures converged on a value that was taken as the global maximum. This lay 15% over the score obtained from the single fixed (unrandomized) calculation. The effect of the gap penalty was observed through the shift of the alignment populations, characterized by their alignment length and root-mean-square deviation (RMSD). The best (lowest RMSD) values found in these populations provided a base-line against which other methods were compared.