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Abstract:
MOTIVATION: Structural alignments of proteins are important for identification
of structural similarities, homology detection and functional annotation. The
structural alignment problem is well studied and computationally difficult.
Many different scoring schemes for structural similarity as well as many
algorithms for finding high-scoring alignments have been proposed. Algorithms
using contact map overlap (CMO) as scoring function are currently the only
practical algorithms able to compute provably optimal alignments. RESULTS: We
propose a new mathematical model for the alignment of inter-residue distance
matrices, building upon previous work on maximum CMO. Our model includes all
elements needed to emulate various scoring schemes for the alignment of protein
distance matrices. The algorithm that we use to compute alignments is practical
only for sparse distance matrices. Therefore, we propose a more effective
scoring function, which uses a distance threshold and only positive structural
scores. We show that even under these restrictions our approach is in terms of
alignment accuracy competitive with state-of-the-art structural alignment
algorithms, whereas it additionally either proves the optimality of an
alignment or returns bounds on the optimal score. Our novel method is freely
available and constitutes an important promising step towards truly provably
optimal structural alignments of proteins. AVAILABILITY: An executable of our
program PAUL is available at http://planet-lisa.net/.
