multiFLEX-LF: A Computational Approach to Quantify the Modification Stoichiometries in Label-free Proteomics Datasets

In high-throughput LC-MS/MS-based proteomics, information about the presence and stoichiometry of post-translational modifications is normally not readily available. To overcome this problem we developed multiFLEX-LF, a computational tool that builds upon FLEXIQuant and FLEXIQuant-LF, which detect modified peptides and quantify their modification extent by monitoring the differences between observed and expected intensities of the unmodified peptides. To this end, multiFLEX-LF relies on robust linear regression to calculate the modification extent of a given peptide relative to a within-study reference. multiFLEX-LF can analyze entire label-free discovery proteomics datasets. Furthermore, to analyze modification dynamics and co-regulated modifications, the peptides of all proteins are hierarchically clustered based on their computed relative modification scores. To demonstrate the versatility of multiFLEX-LF we applied it on a cell-cycle time series dataset acquired using data-independent acquisition. The clustering of the peptides highlighted several groups of peptides with different modification dynamics across the four analyzed time points providing evidence of the kinases involved in the cell-cycle. Overall, multiFLEX-LF enables fast identification of potentially differentially modified peptides and quantification of their differential modification extent in large datasets. Additionally, multiFLEX-LF can drive large-scale investigation of modification dynamics of peptides in time series and case-control studies. multiFLEX-LF is available at https://gitlab.com/SteenOmicsLab/multiflex-lf.