The Use of Search Workflows in Peptide Assignment from MS/MS Data (ABRF 2002)

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Denny, R;Neeson, K;Rennie, C;Richardson, K;Leicester, S;Swainston, N;Worroll, J;Young, P
ABRF; Austin; 9th-12th March; 2002
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Whilst significant advances have been made in the identification of trypatic peptides and thus proteins from tandem mass spectrometry data, it is apparent that many spectra remain unassigned. Reasons for lack of assignment of the spectra include databank incompleteness, point mutations, post translational modifications not selected by the user, and non-specific cleavage by trypsin. In an effort to increase the assignment rate of tandem mass spectrometry data to peptides, we have implemented a new algorithm known as Automod, which on input of protein sequences, generates all possible peptides from a sequence, and then using a library of post-translational modifications and point mutations attempts to assign tandem mass spectrometry data to the generated peptides. Furthermore, we have extended our de novo peptide sequencing algorithm to include batch processing, sothat more than more spectrum can be interpreted in a search. By using the same peptide fragmentation model and scoring scheme dor databank searching, automod and de novo sequencing (Figure 1), we are able to chain these different search straegies together into a data interpretation workflow. In this poster we present example data illustrating the power of this approach

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