A Correlation Algorithm for the Automated Quantitative Analysis of Shotgun Proteomics Data

TLDR

Shotgun proteomics uses chemical tags such as ICAT and stable‑isotope labeling to enable quantitative analysis. The study aims to develop software that automatically converts peptide mass‑spectrometry data into relative protein abundances for high‑throughput shotgun proteomics. RelEx applies least‑squares regression to ion chromatograms, discarding low‑quality data, and was validated on known mixtures and yeast osmotic‑stress samples. The correction improves accuracy by 32 ± 4 %, and the approach was validated on known mixtures and yeast osmotic‑stress samples.

Abstract

Quantitative shotgun proteomic analyses are facilitated using chemical tags such as ICAT and metabolic labeling strategies with stable isotopes. The rapid high-throughput production of quantitative "shotgun" proteomic data necessitates the development of software to automatically convert mass spectrometry-derived data of peptides into relative protein abundances. We describe a computer program called RelEx, which uses a least-squares regression for the calculation of the peptide ion current ratios from the mass spectrometry-derived ion chromatograms. RelEx is tolerant of poor signal-to-noise data and can automatically discard nonusable chromatograms and outlier ratios. We apply a simple correction for systematic errors that improves the accuracy of the quantitative measurement by 32 ± 4%. Our automated approach was validated using labeled mixtures composed of known molar ratios and demonstrated in a real sample by measuring the effect of osmotic stress on protein expression in Saccharomyces cerevisiae.