Proteome discoverer version

Raw MS/MS spectra files were searched against Drosophila melanogaster RefSeq protein database (release 70; 30,513 entries) appended with the known contaminants using SEQUEST and MASCOT (version 2.4.1) search engines in the Proteome Discoverer version 2.0 suite (Thermo Scientific, Germany). A precursor ion mass range of 600–5000 Da and a signal-to-noise ratio of 1.5 was used for the searches. Enzyme specificity was set to trypsin, allowing for a maximum of one missed cleavage. Variable (oxidation of methionine and phosphorylation of serine, threonine and tyrosine) and fixed (carbamidomethylation of cysteine; iTRAQ-labeling at N terminus of the peptide and lysine) modifications were selected. Mass tolerance was set to 15 ppm and 0.1 Da for precursor and fragment ions, respectively. Peptide lists were filtered to remove known contaminants such as BSA and human keratin proteins. To maximize the coverage of identifications, 1% FDR cut-off was used at PSM level for all the identifications as calculated by percolator algorithm using decoy search approach. Data analysis was performed using custom scripts in R.

Data analysis was performed with Proteome Discoverer Version 2.5.0.400 (Thermo Scientific). Reporter ion integration was carried out with 20 ppm tolerance and most confident centroid was set as integration method. Peptides were identified using Sequest™ search engine with UniProtKB Swiss-Prot (TaxID = 9606, Homo sapiens) as database. The following search settings were applied: precursor Δm tolerance = 10 ppm, fragment Δm tolerance = 0.02 Da (MS2 mode), 0.6 Da (MS3 mode), missed cleavages = 2, fixed modifications = carbamidomethyl, TMTpro (peptide N-terminus, K residues). For peptide scoring, Percolator was employed with an identification threshold of 1% false discovery rate (FDR). Peptide to protein summarization was performed as implemented by Proteome Discoverer [21 ], adapted from McAlister et al. [18 (link)]. Protein abundances are therein calculated as simple summation of their associated peptide group abundances. Peptide groups were considered for quantification based on their uniqueness (unique peptides) and in accordance with the principle of parsimony (razor peptides). No imputation of missing values was performed. For evaluation of TMT labeling efficiency, TMTpro was set as variable modification (peptide N-terminus, K residues).

The MS/MS data was searched against the Rat UniProt database using Proteome Discoverer version 1.4 (ThermoFisher, San Jose, CA, USA) which also extracted the quantitation data from the 10 TMT tags. The data was imported into Scaffold Q+ (Proteome Software, Portland OR, USA) for label based quantitative analysis. Protein identifications were accepted if they contained at least 2 identified peptides above 95% tandem mass spectrometry confidence (with 0% decoy false discovery rate (FDR)). Differentially expressed proteins were determined by applying t-Test with unadjusted significance level p < 0.05 corrected by Benjamini–Hochberg.

Identification and relative quantification of protein abundance was performed using Proteome Discoverer version 2.2 (Thermo Fisher Scientific). The E. coli MG1655 and S. enterica serovar Typhimurium LT2 databases were downloaded from Uniprot (December 2017) and supplemented with the common proteomic contaminants. A database search was performed using the Mascot search engine version 2.5.1 (Matrix Science, London, UK) with MS peptide tolerance of 10 ppm and fragment ion tolerance of 0.6 Da. Tryptic peptides were accepted with 1 missed cleavage; methionine oxidation was set as a variable modification; and cysteine methylthiolation, TMT-6 on lysine, and peptide N-termini were set as fixed modifications. Percolator was used for peptide-spectrum match validation with the false discovery rate threshold of 1%. TMT reporter ions were identified in the MS³ HCD spectra with 3 mmu mass tolerance, and the TMT reporter intensity values for each sample were normalized on the total peptide amount. Only the unique identified peptides were considered for the relative quantification, which was our sole analysis. We did not perform a systems-level investigation of the mutant proteomes, but this should be possible with the data sets provided in S1 Data.