Fig. 6.

3D multiple regression model-based calculation of phosphorylation stoichiometry. a Phosphorylation stoichiometry can be extracted by feeding phospho-, non-phospho- and protein-intensity data into a 3D multiple regression model (3DMM). More detailed explanations are given in Supplementary Note 1. b For benchmarking stoichiometry calculation via MS²- and MS³-based TMT, yeast and HeLa phosphopeptides were each half dephosphorylated with Rapid alkaline phosphatase. Yeast phospho- and non-phospho-peptides were then diluted in fixed ratios to create samples with set phosphopeptide stoichiometry, and added to equal amounts of HeLa phospho- and non-phospho-peptides serving as a contaminating background. The sample was measured three times as technical replicates each with MS²- and OT MC MS³-based TMT quantification. In this setup, protein intensities were set to 1 in the 3DMM. c 3DMM-extracted p-values describing the significance of the slope being non-zero were correlated against the difference of MS²- and MS³-estimated stoichiometry vs. the true value of 10%. d Scatter plots showing estimated stoichiometry determined in TMT MS² and MS³ mode, with three different levels of 3DMM p-value cutoffs. e Mean squared errors were calculated as a sum of positive bias and variance for all replicates of both MS²- and MS³-based TMT at different 3DMM p-value cutoffs