Automated protein sequence database classification. I. Integration of compositional similarity search, local similarity search, and multiple sequence alignment
Motivation: Genome sequencing projects require the periodic application of analysis tools that can classify and multiply align related protein sequence domains. Full automation of this task requires an efficient integration of similarity and alignment techniques. Results: We have developed a fully automated process that classifies entire protein sequence databases, resulting in alignment of the homologous sequences. The successive steps of the procedure are based on compositional and local
... nce similarity searches followed by multiple sequence alignments. Global similarities are detected from the pairwise comparison of amino acid and dipeptide compositions of each protein. After the elimination of all but one sequence from each detected cluster of closely related proteins, the remaining sequences are compiled in a suffix tree which is self-compared to detect local sequence similarities. Sets of proteins which share similar sequence segments are then weighted according to their closeness and multiply aligned using a fast hierarchical dynamic programming algorithm. Computational strategies were devised to minimize computer processing time and memory space requirements. The accuracy of the sequence classifications has been evaluated for 12 462 primary structures distributed over 341 known families. The percentage of sequences with missed or incorrect family assignments was 6.8% on the test set. This low error level is only twice that of the manually constructed PROSITE database (3.4%) and is substantially better than that found for the automatically built PRODOM database (34.9%).