Acquired raw data were analyzed with the integrated MaxQuant software v1.6.1.0 using the Andromeda search engine (53, 54). In MaxQuant, the estimated FDR of all peptide identifications was set to a maximum of 1%. The main search was performed with a mass tolerance of 7 parts per million (ppm). Enzyme specificity was set to trypsin/P. A maximum of three missed cleavages was permitted, and the minimum peptide length was fixed at seven amino acids. Carbamidomethylation of cysteine was set as a fixed modification. The 2018_07 (18 July 2018) version of the UniProt sequence was used for peptide identification.

To assign and quantify SILAC methyl peptides, all raw data were processed, indicating N-terminal acetylation, methionine oxidation, mono-methyl-K/R, and di-methyl-K/R as variable modifications. The MaxQuant evidence.txt output file was then filtered: Potential contaminants and reverse sequences were removed, and methyl peptides were required to have an Andromeda score ≥ 25 and a PTM localization probability ≥ 0.75. For the methyl peptides quantified more than once, the median SILAC ratio was calculated. Last, methyl peptide SILAC ratios were normalized on the respective protein SILAC ratios extracted from the proteinGroups.txt MaxQuant output file. These were calculated using unmodified peptides in the “input” experiment. To define up- or down-regulated methyl peptides by GSK591, we used mean (μ) and SD (σ) based on the distribution of the unmodified peptide SILAC ratios calculated separately in the forward and reverse experiments and applied a μ ± 3σ cutoff to the distributions of the modified peptides of the respective replicate. To assess which of the regulated peptides were statistically significant, we used the limma algorithm in the “DEP” R package (24, 25) and applied the following filters: log2 fold change >0.6 (fold change >1.5 on a linear scale) and adjusted P value [corrected with Benjamini-Hochberg method (55)] <0.05.

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