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Research

Investigating MS²/MS³ Matching Statistics

A Model For Coupling Consecutive Stage Mass Spectrometry Data For Increased Peptide Identification Confidence^*,S

Peter J. Ulintz^,, Bernd Bodenmiller^¶,||, Philip C. Andrews, Ruedi Aebersold^¶,**, and Alexey I. Nesvizhskii^,,¶¶

From the Departments of Biological Chemistry and Pathology and Bioinformatics Program, University of Michigan, Ann Arbor, Michigan, 48103, ^¶ Institute of Molecular Systems Biology, Swiss Federal Institute of Technology Zurich, 8093 Zurich, Switzerland, ^** Institute for Systems Biology, Seattle, Washington 98103, and Faculty of Science, University of Zurich, 8057 Zurich, Switzerland

Improvements in ion trap instrumentation have made n-dimensional mass spectrometry more practical. The overall goal of the study was to describe a model for making use of MS² and MS³ information in mass spectrometry experiments. We present a statistical model for adjusting peptide identification probabilities based on the combined information obtained by coupling peptide assignments of consecutive MS² and MS³ spectra. Using two data sets, a mixture of known proteins and a complex phosphopeptide-enriched sample, we demonstrate an increase in discriminating power of the adjusted probabilities compared with models using MS² or MS³ data only. This work also addresses the overall value of generating MS³ data as compared with an MS²-only approach with a focus on the analysis of phosphopeptide data.