1990) to collect homologs of a query sequence and construct its profile by iteratively scanning a sequence database (Altschul et al. The PSI-BLAST program relies on the BLAST algorithm (Altschul et al. For proteins, a sequence profile lists a preference for the 20 standard amino acid residue types at each position in a given multiple sequence alignment. One of the most significant improvements in alignment accuracy was achieved through the use of multiple sequence alignments and the corresponding sequence profiles (Gribskov et al. Since its inception, the scoring function and its optimization by dynamic programming have been improved for alignment accuracy and speed, and applied to a variety of alignment problems. The original and still widely used optimization method for sequence alignment is based on dynamic programming (Needleman and Wunsch 1970 Sellers 1974). The alignment score is usually a sum of the gap penalties that depend linearly on the gap lengths, and the pairwise substitution scores that depend on the matched residue types. The two common ingredients of the scoring function are a gap penalty function and a matrix of substitution scores for matching every residue in one sequence to every residue in the other sequence. An alignment between two sequences of residues is usually calculated by optimizing an alignment scoring function. Nucleic acid and protein sequence alignments are central to many problems in biology, including gene assignment, phylogeny construction, protein structure modeling, protein design, and functional annotation of proteins (Barton 1996, 1998 Gotoh 1999). The new method is currently applied to large-scale comparative protein structure modeling of all known sequences. For example, the fraction of the correctly aligned residues relative to the structure-based alignment by the best protocol is 56%, which can be compared with the accuracies of 26%, 42%, 43%, 48%, 50%, 49%, 43%, and 43% for the other methods, respectively. The alignment accuracies of the best new protocols were significantly better than those of the other tested methods. A test set of 200 pairwise, structure-based alignments with sequence identities below 40% is used to benchmark the 13 protocols as well as a number of previously described sequence alignment methods, including heuristic pairwise sequence alignment by BLAST, pairwise sequence alignment by global dynamic programming with an affine gap penalty function by the ALIGN command of MODELLER, sequence-profile alignment by PSI-BLAST, Hidden Markov Model methods implemented in SAM and LOBSTER, pairwise sequence alignment relying on predicted local structure by SEA, and multiple sequence alignment by CLUSTALW and COMPASS. Thirteen different protocols for creating and comparing profiles corresponding to the multiple sequence alignments are implemented in the SALIGN command of MODELLER. We optimize and benchmark such an approach that relies on aligning two multiple sequence alignments, each one including one of the two protein sequences. The accuracy of an alignment between two protein sequences can be improved by including other detectably related sequences in the comparison.
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