Improving pairwise sequence alignment accuracy using near-optimal protein sequence alignments |
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| Authors: | Michael L Sierk Michael E Smoot Ellen J Bass William R Pearson |
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| Affiliation: | (1) Bioinformatics Program and Chemistry Department, Saint Vincent College, Latrobe, PA 15650, USA;(2) Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA;(3) Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA 22908, USA;(4) Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA |
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| Abstract: | Background While the pairwise alignments produced by sequence similarity searches are a powerful tool for identifying homologous proteins - proteins that share a common ancestor and a similar structure; pairwise sequence alignments often fail to represent accurately the structural alignments inferred from three-dimensional coordinates. Since sequence alignment algorithms produce optimal alignments, the best structural alignments must reflect suboptimal sequence alignment scores. Thus, we have examined a range of suboptimal sequence alignments and a range of scoring parameters to understand better which sequence alignments are likely to be more structurally accurate. |
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