Increased confidence in large-scale phosphoproteomics data by complementary mass spectrometric techniques and matching of phosphopeptide data sets.

Large-scale phosphoproteomics studies are of great interest due to their potential for the dissection of signaling pathways controlled by protein kinases. Recent advances in mass spectrometry (MS)-based phosphoproteomic techniques offer new opportunities to profile protein kinase activities in a comprehensive manner. However, this increasingly used approach still poses many analytical challenges. On one hand, high stringency criteria for phosphopeptide identification based on MS/MS data are needed in order to avoid false positives; however, on the other hand, these stringent criteria also result in the introduction of many false negatives. In the current report, we employ different mass spectrometric techniques for large-scale phosphoproteomics in order to reduce the presence of false negatives and enhance data confidence. A LTQ-Orbitrap LC-MS/MS platform identified approximately 3 times more phosphopeptides than Q-TOF LC-MS/MS instrumentation (4308 versus 1485 identifications, respectively). In both cases, collision induced dissociation (CID) was used to fragment peptides. Interestingly, the two platforms produced complementary data as many of the low scoring phosphopeptide ions identified by LTQ-Orbitrap MS/MS gave rise to high score identifications by Q-TOF MS/MS analysis, and vice versa. In fact, approximately 450 phosphopeptides identified by the Q-TOF instrument were not identified by the LTQ-Orbitrap. Further data comparison revealed the extent of the problem: in one experiment, the estimated number of false negatives (1066) was close to the number of identified phosphopeptides (1485). This work demonstrates that by using standard procedures for phosphopeptide identification the number of false negatives can be even greater than the number of false positives. We propose using historical phosphoproteomic data and spectral matching algorithms in order to efficiently minimize false negative rates.