Computational protocol: Stoichiometry of chromatin-associated protein complexes revealed by label-free quantitative mass spectrometry-based proteomics

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Protocol publication

[…] Raw data were analyzed by MaxQuant (version () using standard settings with the additional options match between runs, LFQ and iBAQ selected. The generated ‘proteingroups.txt’ table was filtered for contaminants, reverse hits, number of unique peptides (>0) and number of peptides (>1) in Perseus (from MaxQuant package) or R.For interactor identification, t-test-based statistics was applied on LFQ as described earlier (). First, the logarithm (log 2) of the LFQ values were taken, resulting in a Gaussian distribution of the data (Supplementary Figure S1). This allowed imputation of missing values by normal distribution (width = 0.3, shift = 1.8), assuming these proteins were close to the detection limit. Statistical outliers for the GFP pull-down of the BAC HeLa compared to HeLa WT were then determined using two-tailed t-test. Multiple testing correction was applied by using a permutation-based false discovery rate (FDR) method in Perseus.To determine the stoichiometry of the identified complexes, we compared the relative abundance of the identified interactors as measured by the iBAQ intensities. The background binding level of proteins as measured by the iBAQ intensity in the different control samples were subtracted from the BAC HeLa GFP pulldown iBAQ intensity. Next, these relative abundance values were scaled to the obtained abundance of the bait protein which was set to 1. […]

Pipeline specifications

Software tools MaxQuant, Perseus
Application MS-based untargeted proteomics