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Letters to Nature

Nature 425, 737-741 (16 October 2003) | doi:10.1038/nature02046; Received 28 July 2003; Accepted 28 August 2003

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Global analysis of protein expression in yeast

Sina Ghaemmaghami1,2, Won-Ki Huh1,3, Kiowa Bower1,2, Russell W. Howson1,3, Archana Belle1,3, Noah Dephoure1,3, Erin K. O'Shea1,3 & Jonathan S. Weissman1,2

  1. Howard Hughes Medical Institute, University of California–San Francisco, San Francisco, California 94143-2240, USA
  2. Departments of Cellular & Molecular Pharmacology, University of California–San Francisco, San Francisco, California 94143-2240, USA
  3. Biochemistry & Biophysics, University of California–San Francisco, San Francisco, California 94143-2240, USA

Correspondence to: Jonathan S. Weissman1,2 Email: jsw1@itsa.ucsf.edu

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The availability of complete genomic sequences and technologies that allow comprehensive analysis of global expression profiles of messenger RNA1, 2, 3 have greatly expanded our ability to monitor the internal state of a cell. Yet biological systems ultimately need to be explained in terms of the activity, regulation and modification of proteins—and the ubiquitous occurrence of post-transcriptional regulation makes mRNA an imperfect proxy for such information. To facilitate global protein analyses, we have created a Saccharomyces cerevisiae fusion library where each open reading frame is tagged with a high-affinity epitope and expressed from its natural chromosomal location. Through immunodetection of the common tag, we obtain a census of proteins expressed during log-phase growth and measurements of their absolute levels. We find that about 80% of the proteome is expressed during normal growth conditions, and, using additional sequence information, we systematically identify misannotated genes. The abundance of proteins ranges from fewer than 50 to more than 106 molecules per cell. Many of these molecules, including essential proteins and most transcription factors, are present at levels that are not readily detectable by other proteomic techniques nor predictable by mRNA levels or codon bias measurements.

  1. Howard Hughes Medical Institute, University of California–San Francisco, San Francisco, California 94143-2240, USA
  2. Departments of Cellular & Molecular Pharmacology, University of California–San Francisco, San Francisco, California 94143-2240, USA
  3. Biochemistry & Biophysics, University of California–San Francisco, San Francisco, California 94143-2240, USA

Correspondence to: Jonathan S. Weissman1,2 Email: jsw1@itsa.ucsf.edu