The maintenance of genomic stability relies on DNA damage sensor kinases that detect DNA lesions and phosphorylate an extensive network of substrates. The Mec1/ATR kinase is one of the primary sensor kinases responsible for orchestrating DNA damage responses. Despite the importance of Mec1/ATR, the current network of its identified substrates remains incomplete due, in part, to limitations in mass spectrometry-based quantitative phosphoproteomics. Phosphoproteomics suffers from lack of redundancy and statistical power for generating high confidence datasets, since information about phosphopeptide identity, site-localization, and quantitation must often be gleaned from a single peptide-spectrum match (PSM). Here we carefully analyzed the isotope label swapping strategy for phosphoproteomics, using data consistency among reciprocal labeling experiments as a central filtering rule for maximizing phosphopeptide identification and quantitation. We demonstrate that the approach allows drastic reduction of false positive quantitations and identifications even from phosphopeptides with a low number of spectral matches. Application of this approach identifies new Mec1/ATR-dependent signaling events, expanding our understanding of the DNA damage signaling network. Overall, the proposed quantitative phosphoproteomic approach should be generally applicable for investigating kinase signaling networks with high confidence and depth.
21 22 considered the most accurate, although TMT-based analyses yielded better coverage of the 58 phosphoproteome [6]. SILAC is based on peptide precursor ion quantification to detect and 59 quantify, in relative terms, the ratio between heavy isotope-labeled or unlabeled (light) amino 60 acids incorporated metabolically into cells [20], [21]. Such an approach allows early mixing of 61 labeled protein extracts in phosphoproteomic workflows to minimize technical variation. 62 63Despite the broad use of mass spectrometry for investigating kinase-mediated phosphorylation 64 signaling responses, major challenges remain for systematically achieving high confidence 65Maximizing Quantitative Phosphoproteomics of Kinase Signaling 4 identification and quantitation analysis of phosphopeptides. The key difference between 66 proteomics and phosphoproteomics is that when analyzing protein abundance and/or 67 interactions (as in affinity-purification mass spectrometry), the analysis is based on identification 68 and quantification of multiple redundant representative peptides for a given protein, whereas in 69 phosphoproteomics, phosphopeptides are often unique (non-redundant) species represented by 70 one or a few peptide spectral matches (PSMs) in the dataset,. The lack of multiple redundant 71 events for informing identification, quantification and phospho-site localization hobbles the 72 acquisition of high-quality data due to the low numbers of PSMs per phosphopeptide. The ability 73 of acquiring high quality identification and quantification data is further complicated by the fact 74 that many key phosphopeptides of biological interest are present at very low levels in the pool of 75 phosphopeptides enriched from whole cell lysates. Even in cases when identification of a 76 phosphopeptide based on one or two PSMs is successful, the associated quantitative 77 information can suffer from signal interference derived from sample complexity and other 78 intrinsic technical noise [22]-[25]. As a result, a significant part of the generated 79 phosphoproteomic data is not suited for reliable quantitative analysis and biological inference, 80 representing one of the major bottlenecks in large-scale quantitative phosphoproteomic analysis 81 of kinase-mediated signaling. 82 83Here we report a phosphoproteomic approach for increasing reliability in phosphopeptide 84 identification and quantification, while minimizing loss of data from phosphopeptides with low 85 PSM counts. The approach relies on quantitation consistency among reversed isotopically 86 labeled samples as a filtering step for removing false positive identifications and erroneous 87 quantifications. We find that most experimental error or biological variation in phosphopeptide 88 quantitation does not exhibit reverted quantitation values once light and heavy media are 89 swapped in control experiments. This allows the systematic exclusion of non-reverting data-90 points from the dataset to reduce not only quantitation error and variation, but also to reduce
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