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. 2018 Jul 1;25(7):726–739. doi: 10.1089/cmb.2017.0267

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Copyright 2018, Mary Ann Liebert, Inc.

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FIG. 4. — BBK* used MS and SS- bounds to prune up to 99.99999% of the sequence space. (A) Sequence space reduction due to MS pruning. The fraction of unpruned sequences (gray) normalized to the total number of sequences (green). BBK* used MS bounds to provably prune up to 99.9% of the sequence space. BBK* does not compute SS- bounds for pruned sequences. (B) The fraction of BBK* SS- bounds (blue for the best sequence and orange for the best five sequences) normalized to the number of sequences not pruned by MS bound pruning (gray). To compute the best sequences, BBK* calculated SS- bounds for as few as 0.01% of the unpruned sequences. The remaining 99.99% of these sequences were pruned at mere SS- accuracy.

Inline graphic — BBK* used MS and SS- bounds to prune up to 99.99999% of the sequence space. (A) Sequence space reduction due to MS pruning. The fraction of unpruned sequences (gray) normalized to the total number of sequences (green). BBK* used MS bounds to provably prune up to 99.9% of the sequence space. BBK* does not compute SS- bounds for pruned sequences. (B) The fraction of BBK* SS- bounds (blue for the best sequence and orange for the best five sequences) normalized to the number of sequences not pruned by MS bound pruning (gray). To compute the best sequences, BBK* calculated SS- bounds for as few as 0.01% of the unpruned sequences. The remaining 99.99% of these sequences were pruned at mere SS- accuracy.