Cleaning up the masses : Exclusion lists to reduce contamination with HPLC-MS/MS
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Mass spectrometry, in the past five years, has increased in speed, accuracy and use. With the ability of the mass spectrometers to identify increasing numbers of proteins the identification of undesirable peptides (those not from the protein sample) has also increased. Most undesirable contaminants originate in the laboratory and come from either the user (e.g. keratin from hair and skin), or from reagents (e.g. trypsin), that are required to prepare samples for analysis. We found that a significant amount of MS instrument time was spent sequencing peptides from abundant contaminant proteins. While completely eliminating non-specific protein contamination is not feasible, it is possible to reduce the sequencing of these contaminants. For example, exclusion lists can provide a list of masses that can be used to instruct the mass spectrometer to 'ignore' the undesired contaminant peptides in the list. We empirically generated be-spoke exclusion lists for several model organisms (Homo sapiens, Caenorhabditis elegans, Saccharomyces cerevisiae and Xenopus laevis), utilising information from over 500 mass spectrometry runs and cumulative analysis of these data. Here we show that by employing these empirically generated lists, it was possible to reduce the time spent analysing contaminating peptides in a given sample thereby facilitating more efficient data acquisition and analysis. Biological significance. Given the current efficacy of the Mass Spectrometry instrumentation, the utilisation of data from ~500 mass spec runs to generate be-spoke exclusion lists and optimise data acquisition is the significance of this manuscript. This article is part of a Special Issue entitled: EUPA 2012: NEW HORIZONS.
Hodge , K , Have , S T , Hutton , L & Lamond , A I 2013 , ' Cleaning up the masses : Exclusion lists to reduce contamination with HPLC-MS/MS ' Journal of Proteomics , vol 8 , pp. 92-103 . DOI: 10.1016/j.jprot.2013.02.023
Journal of Proteomics
© 2013 Elsevier B.V. All rights reserved. Published as open access.
The work was funded by the Wellcome Trust grant 073980/Z/03/Z (to A.I Lamond).
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