ArticleJournal of proteome research2022
multiFLEX-LF: A Computational Approach to Quantify the Modification Stoichiometries in Label-Free Proteomics Data Sets.
Article in Journal of proteome research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
In liquid-chromatography-tandem-mass-spectrometry-based proteomics, information about the presence and stoichiometry of protein modifications is not readily available. To overcome this problem, we developed multiFLEX-LF, a computational tool that builds upon FLEXIQuant, which detects modified peptide precursors and quantifies their modification extent by monitoring the differences between observed and expected intensities of the unmodified precursors. multiFLEX-LF relies on robust linear regression to calculate the modification extent of a given precursor relative to a within-study reference. multiFLEX-LF can analyze entire label-free discovery proteomics data sets in a precursor-centric manner without preselecting a protein of interest. To analyze modification dynamics and coregulated modifications, we hierarchically clustered the precursors of all proteins based on their computed relative modification scores. We applied multiFLEX-LF to a data-independent-acquisition-based data set acquired using the anaphase-promoting complex/cyclosome (APC/C) isolated at various time points during mitosis. The clustering of the precursors allows for identifying varying modification dynamics and ordering the modification events. Overall, multiFLEX-LF enables the fast identification of potentially differentially modified peptide precursors and the quantification of their differential modification extent in large data sets using a personal computer. Additionally, multiFLEX-LF can drive the large-scale investigation of the modification dynamics of peptide precursors in time-series and case-control studies. multiFLEX-LF is available at https://gitlab.com/SteenOmicsLab/multiflex-lf.
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