Accurate proteome-wide protein quantification from high-resolution 15N mass spectra

Zia Khan,Saeed Tavazoie,Joshua S Bloom,Cristian Ruse,Sasan Amini,Leonid Kruglyak,Amy A Caudy,David H Perlman,Mona Singh

doi:10.1186/gb-2011-12-12-r122

Abstract

In quantitative mass spectrometry-based proteomics, the metabolic incorporation of a single source of 15N-labeled nitrogen has many advantages over using stable isotope-labeled amino acids. However, the lack of a robust computational framework for analyzing the resulting spectra has impeded wide use of this approach. We have addressed this challenge by introducing a new computational methodology for analyzing 15N spectra in which quantification is integrated with identification. Application of this method to an Escherichia coli growth transition reveals significant improvement in quantification accuracy over previous methods.

Highlights

Experimental methods for proteome-wide quantification using liquid chromatography coupled mass spectrometry (LC-MS) rest heavily on computational methods that analyze mass spectra for peptide and protein quantification [1]
Computational analysis typically relies on two main components: (1) a peptide identification algorithm that assigns amino acid sequences to fragmentation spectra by searching a database of theoretical peptides obtained by an in silico digest of an organism’s proteome; and (2) a quantification algorithm that collects peaks across mass spectra in the form of extracted ion chromatograms (XICs) over the duration of chromatographic elution of peptides
In an experiment to compare the proteome of stationary phase E. coli with that of exponentially growing cells, we show that our method significantly improves the quantitative accuracy of protein abundance ratios obtained from 15N mass spectra

Summary

Introduction

Experimental methods for proteome-wide quantification using liquid chromatography coupled mass spectrometry (LC-MS) rest heavily on computational methods that analyze mass spectra for peptide and protein quantification [1]. Computational analysis typically relies on two main components: (1) a peptide identification algorithm that assigns amino acid sequences to fragmentation spectra by searching a database of theoretical peptides obtained by an in silico digest of an organism’s proteome; and (2) a quantification algorithm that collects peaks across mass spectra in the form of extracted ion chromatograms (XICs) over the duration of chromatographic elution of peptides. These algorithms have been shown to produce robust and accurate peptide quantification results across a wide range of quantification strategies. Labeled ammonium salts are much less expensive than labeled amino acids and are efficiently used as a nitrogen source by many microorganisms, including Escherichia coli and both budding and fission yeasts

Methods

Results

Conclusion