Open Access Method

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

Zia Khan123, Sasan Amini245, Joshua S Bloom24, Cristian Ruse6, Amy A Caudy27, Leonid Kruglyak289, Mona Singh12, David H Perlman1024 and Saeed Tavazoie1124*

Author affiliations

1 Department of Computer Science, Washington Rd, Princeton University, Princeton, NJ 08544, USA

2 Lewis-Sigler Institute for Integrative Genomics Washington Rd, Princeton University, Princeton, NJ 08544, USA

3 Current address: Department of Human Genetics, South Ellis Avenue, University of Chicago, Chicago, IL 60637, USA

4 Department of Molecular Biology, Washington Rd, Princeton University, Princeton, NJ 08544, USA

5 Current address: Illumina Inc., Towne Centre Drive, San Diego, CA 92121, USA

6 Cold Spring Harbor Laboratory, Bungtown Rd, Cold Spring Harbor, NY 11724, USA

7 Current address: Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, King's College Circle, Toronto, Ontario, M5S 1A1, Canada

8 Department of Ecology and Evolutionary Biology, Washington Rd, Princeton University, Princeton, NJ 08544, USA

9 Howard Hughes Medical Institute, Washington Rd, Princeton University, Princeton, NJ 08544, USA

10 Princeton Mass Spectrometry Center, Washington Rd, Princeton University, Princeton, NJ 08544, USA

11 Current address: Department of Biochemistry and Molecular Biophysics, 116th Street and Broadway, Columbia University, New York, NY 10027, USA

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Citation and License

Genome Biology 2011, 12:R122  doi:10.1186/gb-2011-12-12-r122

Published: 19 December 2011

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.