Inference and quantification of peptidoforms in large sample cohorts by SWATH-MS: 14-3-3 data set
This data set represent an experiment assessing protein-protein interaction dynamics of the 14-3-3 with a focus on phosphorylation.
Sample Processing Protocol
The data of the original publication was reanalyzed (Collins et al., PMID: 24162925).
Data Processing Protocol
Assay library generation using DDA data: All raw data was analyzed in a combined setting with MaxQuant (22.214.171.124) using primarily the default parameters: The non-redundant reviewed human protein FASTA was obtained from the UniProtKB/Swiss-Prot (2016-12-19) and appended with iRT peptide sequences and searched with static C (Carbamidomethyl), variable M (Oxidation) and variable STY (Phospho) modifications. “Match-between-runs” and the MaxLFQ algorithm were enabled. All specific parameters are provided in the file mqpar.xml in the ProteomeXchange repository. To derive peptide query parameters, we selected the best scoring spectrum per peptidoform as reported by Andromeda in the file “msms.txt”. RT calibration was conducted using the spiked-in iRT-kit per run. OpenSwathAssayGenerator and OpenSwathDecoyGenerator (OpenMS 2.1) were run as described above. For all other analyses, we used the reported confidence values and intensities from the file “Phospho (STY)Sites.txt”. OpenSWATH / PyProphet: OpenSwathWorkflow (OpenMS 2.1) was run with the following parameters -min_upper_edge_dist 1 - mz_extraction_window 0.05 -rt_extraction_window 600 - extra_rt_extraction_window 100 -min_rsq 0.95 -min_coverage 0.6 - use_ms1_traces -enable_uis_scoring -Scoring:uis_threshold_peak_area 0 - Scoring:uis_threshold_sn -1 -Scoring: stop_report_after_feature 5 -tr_irt hroest_DIA_iRT.TraML. The following subset of scores was used on MS2-level: xx_swath_prelim_score library_corr yseries_score xcorr_coelution_weighted massdev_score norm_rt_score library_rmsd bseries_score intensity_score xcorr_coelution log_sn_score isotope_overlap_score massdev_score_weighted xcorr_shape_weighted isotope_correlation_score xcorr_shape. All MS1 and UIS scores were used for pyprophet. pyprophet was run individually on all files with the following parameters: --final_statistics.emp_p --qvality.enable --qvality.generalized -- ms1_scoring.enable --uis_scoring.enable --semi_supervised_learner.num_iter=20 --xeval.num_iter=20 --ignore.invalid_score_columns. TRIC was run with the following parameters: feature_alignment.py: --file_format openswath --fdr_cutoff 0.01 --max_fdr_quality 0.2 --mst:useRTCorrection True --mst:Stdev_multiplier 3.0 --method LocalMST --max_rt_diff 30 --alignment_score 0.0001 --frac_selected 0 --realign_method lowess_cython --disable_isotopic_grouping
George Rosenberger, Columbia University
Ruedi Aebersold, ETH Zurich Prof. Dr. Ruedi Aebersold Institute of Molecular Systems Biology Head of Department of Biology HPT E 78 Auguste-Piccard-Hof 1 CH-8093 Zurich Switzerland ( lab head )
Collins BC, Gillet LC, Rosenberger G, Röst HL, Vichalkovski A, Gstaiger M, Aebersold R. Quantifying protein interaction dynamics by SWATH mass spectrometry: application to the 14-3-3 system. Nat Methods. 2013 Dec;10(12):1246-53 PubMed: 24162925
Rosenberger G, Liu Y, Röst HL, Ludwig C, Buil A, Bensimon A, Soste M, Spector TD, Dermitzakis ET, Collins BC, Malmström L, Aebersold R. Inference and quantification of peptidoforms in large sample cohorts by SWATH-MS. Nat Biotechnol. 2017 Jun 12 PubMed: 28604659