Yeast membrane proteomics using leucine metabolic labelling: Bioinformatic data processing and exemplary application to the ER-intramembrane protease Ypf1

Abstract

We describe in detail the usage of leucine metabolic labelling in yeast in order to monitor quantitative proteome alterations, e.g. upon removal of a protease. Since laboratory yeast strains are typically leucine auxotroph, metabolic labelling with trideuterated leucine (d3-leucine) is a straightforward, cost-effective, and ubiquitously applicable strategy for quantitative proteomic studies, similar to the widely used arginine/lysine metabolic labelling method for mammalian cells. We showcase the usage of advanced peptide quantification using the FeatureFinderMultiplex algorithm (part of the OpenMS software package) for robust and reliable quantification. Furthermore, we present an OpenMS bioinformatics data analysis workflow that combines accurate quantification with high proteome coverage. In order to enable visualization, peptide-mapping, and sharing of quantitative proteomic data, especially for membrane-spanning and cell-surface proteins, we further developed the web-application Proteator (http://proteator.appspot.com). Due to its simplicity and robustness, we expect metabolic leucine labelling in yeast to be of great interest to the research community. As an exemplary application, we show the identification of the copper transporter Ctr1 as a putative substrate of the ER-intramembrane protease Ypf1 by yeast membrane proteomics using d3-leucine isotopic labelling.

Quantitative proteomics screen identifies a substrate repertoire of rhomboid protease RHBDL2 in human cells and implicates it in epithelial homeostasis

“…The resulting MaxQuant 27 protein table was processed using Perseus 1.5.6.0 28 implementing Phobius 15 topology predictions (see Table S1 ). The quantified peptides were graphically mapped to the corresponding protein sequences using the Proteator tool ( http://proteator.appspot.com/ ) 38 with I/L nondifferentiation turned on; the html overview of the results can be found in the Supplementary Information. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium ( http://proteomecentral.proteomexchange.org ) via the PRIDE partner repository 39 with dataset identifier PXD006716.…”

The secreted inhibitor of invasive cell growth CREG1 is negatively regulated by cathepsin proteases

“…A reported minimum probability was chosen to achieve a 1% FDR at both peptide and protein levels. Peptide abundance was calculated using the FeatureFinderMultiplex tool from OpenMS (v.2.3) [43][44][45]. Peptide abundance features were mapped (IDMapper) to the identified peptides (iProphet) followed by IDConflictResolver and MultiplexResolver in OpenMS (v.2.3).…”

MANTI: Automated Annotation of Protein N-Termini for Rapid Interpretation of N-Terminome Data Sets

“…However, this classification is challenging, as many identified N-terminal peptide sequences match multiple, often distinct, protein sequences in the database. Several web-based software tools visualize peptide position in relation to protein sequences and annotated features: Protter 21 maps peptides to topological and domain features retrieved from UniProt, 22 while QARIP 23 and Proteator 24 additionally visualize peptide abundance data and thereby reveal differentially affected domains, as for example, membrane protein shedding, in quantitative proteome data sets. All of these tools can assist in mapping identified protein termini to sequence features but generate one graphical depiction per protein that requires labor-intensive manual assessment and interpretation.…”