Literature DB >> 21182191

Fragmentation-free LC-MS can identify hundreds of proteins.

Pascal Bochet1, Frank Rügheimer, Tina Guina, Peter Brooks, David Goodlett, Peter Clote, Benno Schwikowski.   

Abstract

One of the most common approaches for large-scale protein identification is LC, followed by MS. If more than a few proteins are to be identified, the additional fragmentation of individual peptides has so far been considered as indispensable, and thus, the associated costs, in terms of instrument time and infrastructure, as unavoidable. Here, we present evidence to the contrary. Using a combination of (i) highly accurate and precise mass measurements, (ii) modern retention time prediction, and (iii) a robust scoring algorithm, we were able to identify 257 proteins of Francisella tularensis from a single LC-MS experiment in a fragmentation-free approach (i.e. without experimental fragmentation spectra). This number amounts to 59% of the number of proteins identified in a standard fragmentation-based approach, when executed with the same false discovery rate. Independent evidence supports at least 27 of a set of 31 proteins that were identified only in the fragmentation-free approach. Our results suggest that additional developments in retention time prediction, measurement technology, and scoring algorithms may render fragmentation-free approaches an interesting complement or an alternative to fragmentation-based approaches.
Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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Year:  2010        PMID: 21182191     DOI: 10.1002/pmic.200900765

Source DB:  PubMed          Journal:  Proteomics        ISSN: 1615-9853            Impact factor:   3.984


  2 in total

1.  Mass fingerprinting of complex mixtures: protein inference from high-resolution peptide masses and predicted retention times.

Authors:  Luminita Moruz; Michael R Hoopmann; Magnus Rosenlund; Viktor Granholm; Robert L Moritz; Lukas Käll
Journal:  J Proteome Res       Date:  2013-10-11       Impact factor: 4.466

2.  DART-ID increases single-cell proteome coverage.

Authors:  Albert Tian Chen; Alexander Franks; Nikolai Slavov
Journal:  PLoS Comput Biol       Date:  2019-07-01       Impact factor: 4.475

  2 in total

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