Automated protein turnover calculations from 15N partial metabolic labeling LC/MS shotgun proteomics data

David Lyon, Maria Angeles Castillejo, Christiana Staudinger, Wolfram Weckwerth, Stefanie Wienkoop, Volker Egelhofer

    Publications: Contribution to journalArticlePeer Reviewed

    Abstract

    Protein turnover is a well-controlled process in which polypeptides are constantly being degraded and subsequently replaced with newly synthesized copies. Extraction of composite spectral envelopes from complex LC/MS shotgun proteomics data can be a challenging task, due to the inherent complexity of biological samples. With partial metabolic labeling experiments this complexity increases as a result of the emergence of additional isotopic peaks. Automated spectral extraction and subsequent protein turnover calculations enable the analysis of gigabytes of data within minutes, a prerequisite for systems biology high throughput studies. Here we present a fully automated method for protein turnover calculations from shotgun proteomics data. The approach enables the analysis of complex shotgun LC/MS 15N partial metabolic labeling experiments. Spectral envelopes of 1419 peptides can be extracted within an hour. The method quantifies turnover by calculating the Relative Isotope Abundance (RIA), which is defined as the ratio between the intensity sum of all heavy (15N) to the intensity sum of all light (14N) and heavy peaks. To facilitate this process, we have developed a computer program based on our method, which is freely available to download at http://promex.pph.univie.ac.at/protover.

    Original languageEnglish
    Article numbere94692
    JournalPLoS ONE
    Volume9
    Issue number4
    DOIs
    Publication statusPublished - 15 Apr 2014

    Austrian Fields of Science 2012

    • 106005 Bioinformatics
    • 106037 Proteomics

    Fingerprint

    Dive into the research topics of 'Automated protein turnover calculations from 15N partial metabolic labeling LC/MS shotgun proteomics data'. Together they form a unique fingerprint.

    Cite this