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FLEXIQuant-LF: Robust Regression to quantify protein modification extent in label-free proteomics data

ABSTRACT: Improvements in LC-MS/MS methods and technology have enabled the identification of thousands of modified peptides in a single experiment. However, protein regulation by post-translational modifications (PTMs) is not binary, making methods to quantify the modification extent crucial to fully understand the role of PTMs. Here, we introduce FLEXIQuant-LF, a software tool for large-scale identification of differentially modified peptides and quantification of their modification extent without prior knowledge of the type of modification. We developed FLEXIQuant-LF using label-free quantification of unmodified peptides and robust linear regression to quantify the modification extent of peptides. As proof of concept, we applied FLEXIQuant-LF to data-independent-acquisition (DIA) data of the anaphase promoting complex/cyclosome (APC/C) during mitosis. The unbiased approach of FLEXIQuant-LF to assess the modification extent in quantitative proteomics data provides a novel platform to better understand the function and regulation of PTMs in new experiments and reanalyzed data. The software is available at https://github.com/SteenOmicsLab/FLEXIQuantLF.

INSTRUMENT(S): TripleTOF 5600

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Christoph Schlaffner

LAB HEAD: Hanno Steen

PROVIDER: PXD018411 | Pride | 2020-12-04

REPOSITORIES: Pride

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Project description:SUMO modification of proteins (sumoylation) is essential for mitotic progression from yeast to humans, but only a limited number of sumoylated proteins with functions in mitosis have been discovered. Vertebrates express three SUMO paralogs, SUMO-1, SUMO-2 and SUMO-3, but only SUMO-2 and -3 are chromosome-associated in mitosis. In this study, we used chromosome spreads to more precisely define the localization of endogenous SUMO-2 and -3 to the centromere as well as the chromosome protein scaffold. Furthermore, we developed methodologies for the immunopurification of endogenous SUMO-2 and -3 modified proteins from cell extracts. Using LC-MS/MS, we analyzed proteins immunopurified from mitotic chromosome fractions and G0 nuclear fractions. We identified 296 putative sumoylated proteins, with 138 being modified specifically in mitosis. We identified proteins known to localize to the centromere and kinetochore and the chromosome protein scaffold, consistent with SUMO-2/3 localization. Furthermore, we demonstrate that utilizing cell synchronization and fractionation allowed for the discovery of low abundance sumoylated proteins that are not detected in large asynchronous analyses. Our results provide a foundation for further characterization of the roles of sumoylation in regulating diverse aspects of chromosome segregation in mitosis. Thermo .raw files were uploaded to the ProHits (Liu et al, 2010) analytical suite and converted to .mzXML format using ReAdW software. Data were searched using X!Tandem (Craig & Beavis, 2004) against human ORFs (RefSeq v45). Search parameters specified a parent MS tolerance of +/- 15ppm, and an MS/MS tolerance of 0.4 Da, with up to two missed cleavages for trypsin. Oxidation of methionine and tryptophan, ubiquitylation of lysine, and alkylation of cysteine (by NEM) were allowed as variable modifications. Statistical validation of the results was performed using Peptide Prophet and Protein Prophet (Keller et al, 2002; Nesvizhskii et al, 2003) as part of the trans-proteomic pipeline. For each search, the Protein Prophet probability at a 1% false discovery rate was used as a cutoff value to generate SAINT-compatible input files. SAINT parameters were as follows: 5000 iterations, low mode Off (0), minFold 1 and normalization (1) (Choi et al, 2011). SAINT cutoff values of 0.75 were used to generate a high-confidence list of protein identifications.

Project description:To identify low abundance autocrine growth factors in CHO cell conditioned media, we utilized a label-free shotgun proteomics approach. CHO cell conditioned media were harvested from fed batch bioreactors and concentrated using methanol/chlorofrom precipitation. Proteins in the samples were then subjected to proteolysis with trypsin, and then subjected to primary fractionation using a SCX column, followed by RP liquid chromatography MS (LC-MS) with a LTQ Orbitrap Velos instrument using the data dependent acquisition (DDA) method. The MS system was set up and run with a method which enabled fast acquisitions of high quality peptide precursor and fragment ion data, with the desired precursor mass accuracy of ±5 ppm. For the LTQ Orbitrap Velos MS, the data-dependent MS/MS analytical workflow in positive ion mode was used. Each precursor survey scan (m/z: 300 to 1800) by the Orbitrap mass analyzer (resolution = 60,000 FWHM) was linked to 10 MS/MS events using the 2D ion trap CID approach, with dynamic ion exclusion set at 60 s. This value was determined based on the observed mean peptide chromatographic peak width. All other instrument parameters were set up according to the manufacturer’s suggested values for complex peptide samples. The nano-ESI source was fitted with a 30-µm stainless steel nano-bore emitter (Thermo Fisher Scientific) with 1.7 kV applied near the tip. Raw data files from the LTQ Orbitrap Velos MS were processed using the Proteome Discoverer 1.3 software (Thermo Fisher Scientific). The LC-MS data were searched against the human (Homo sapiens; UniProtKb, updated in August 2012, 45 848 entries), mouse (Mus musculus; UniProtKb, updated in August 2012, 31 528 entries) and chinese hamster (Cricetulus griceus; UniprotKb, updated in August 2012, 24 609 entries) protein databases using the Sequest search engine for the LTQ Orbitrap Velos LC-MS data, assuming tryptic digestion with precursor ions to fall within 10 ppm of projected m/z values and a fragment ion mass tolerance of 0.5 m/z. The specified search parameters were carbamidomethylation of cysteine as fixed modification, oxidation of methionine as dynamic modification and a maximum of two missed cleavage events. Reverse database searching resulted in a specific false discovery rate (FDR) of 1% at the peptide and protein level.

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FLEXIQuant-LF: Robust Regression to quantify protein modification extent in label-free proteomics data

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