Maximized quantitative phosphoproteomics allows high confidence dissection of the DNA damage signaling network.
Maximized quantitative phosphoproteomics allows high confidence dissection of the DNA damage signaling network.
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DOI：
10.1038/s41598-020-74939-4
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发表时间：
2020-10-22
影响因子：
4.6
通讯作者：
Smolka MB
中科院分区：
文献类型：
--
作者：
Faca VM;Sanford EJ;Tieu J;Comstock W;Gupta S;Marshall S;Yu H;Smolka MB
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.
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影响因子：
3.4
作者：
Deutsch, Eric W.;Mendoza, Luis;Shteynberg, David;Farrah, Terry;Lam, Henry;Tasman, Natalie;Sun, Zhi;Nilsson, Erik;Pratt, Brian;Prazen, Bryan;Eng, Jimmy K.;Martin, Daniel B.;Nesvizhskii, Alexey I.;Aebersold, Ruedi
通讯作者：
Aebersold, Ruedi
影响因子：
4.8
作者：
Chen, Sheng-hong;Smolka, Marcus B.;Zhou, Huilin
通讯作者：
Zhou, Huilin
影响因子：
14.9
作者：
The Gene Ontology Consortium
通讯作者：
The Gene Ontology Consortium
影响因子：
7.5
作者：
Friedel, Anna M.;Pike, Brietta L.;Gasser, Susan M.
通讯作者：
Gasser, Susan M.
影响因子：
3.4
作者：
Eng, Jimmy K.;Jahan, Tahmina A.;Hoopmann, Michael R.
通讯作者：
Hoopmann, Michael R.