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• W2069969703 abstract "The ability to effectively search databases for the identification of protein spots from two-dimensional electrophoresis gels has become an essential step in the study of microbial proteomes. A variety of analytical techniques are currently being employed during protein characterisation. A number of algorithms used to search databases, accessible via the World Wide Web, depend upon information concerning N- and C-terminal microsequence, amino acid composition, and peptide-mass fingerprinting. The effectiveness of nine such algorithms, as well as COMBINED (software developed in this laboratory for identifying proteins across species boundaries) was examined. Fifty-four ribosomal proteins from the Mycoplasma genitalium genome, and 72 amino acyl tRNA synthetases from the Haemophilus influenzae, M. genitalium and Methanococcus jannaschii genomes were chosen for study. These proteins were selected because they represent a wide range of sequence identities across species boundaries (22.7–100% identity), as detected by standard sequence alignment tools. Such sequence variation allowed for a statistical comparison of algorithm success measured against published sequence identity. The ability of analytical techniques used in protein characterisation and associated database query programs to detect identity at the functional group level was examined for proteins with low levels of homology at the gene/protein sequence level. The significance of these theoretical data manipulations provided the means to predict the utility of data acquired experimentally for non-sequence-dependent software in proteome analysis. The data obtained also predicted that ‘sequence tagging’ of peptide fingerprints would need to be accompanied by at least 11–20 residues of amino acid sequence for it to be widely used for protein characterisation across species boundaries." @default.
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• W2069969703 date "1997-01-01" @default.
• W2069969703 modified "2023-10-16" @default.
• W2069969703 title "Evaluation of algorithms used for cross-species proteome characterisation" @default.
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• W2069969703 doi "https://doi.org/10.1002/elps.1150180816" @default.
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