Selective identification of newly synthesized proteins in mammalian cells using bioorthogonal noncanonical amino acid tagging (BONCAT)
Abstract
In both normal and pathological states, cells respond rapidly to environmental cues by synthesizing new proteins. The selective identification of a newly synthesized proteome has been hindered by the basic fact that all proteins, new and old, share the same pool of amino acids and thus are chemically indistinguishable. We describe here a technology, based on the cotranslational introduction of azide groups into proteins and the chemoselective tagging of azide-labeled proteins with an alkyne affinity tag, to separate and identify, specifically, the newly synthesized proteins in mammalian cells. Incorporation of the azide-bearing amino acid azidohomoalanine is unbiased, not toxic, and does not increase protein degradation. As a first demonstration of the method, we report the selective purification and identification of 195 metabolically labeled proteins with multidimensional liquid chromatography in-line with tandem MS. Furthermore, in combination with leucine-based mass tagging, candidates were immediately validated as newly synthesized proteins. The identified proteins, synthesized in a 2-h window, possess a broad range of biochemical properties and span most functional gene ontology categories. This technology makes it possible to address the temporal and spatial characteristics of newly synthesized proteomes in any cell type.
Additional Information
© 2006 by the National Academy of Sciences Edited by K. Barry Sharpless, The Scripps Research Institute, La Jolla, CA, and approved May 6, 2006 (received for review February 27, 2006) Published online before print June 12, 2006, 10.1073/pnas.0601637103 We thank Dr. Edoardo Marcora (Division of Biology, California Institute of Technology) for the gift of HA-HAP1A; Prof. R.J. Deshaies, Dr. T. Mayor, and all members of the Schuman laboratory for helpful discussions and comments; and Drs. C.-Y. Tai and Y.J. Yoon for critically reading the manuscript. D.C.D. especially thanks Dr. M. Landwehr for many fruitful and critical discussions and S. Materna for the introduction to Python. This work was supported by the Howard Hughes Medical Institute and the Beckman Institute at the California Institute of Technology. MS analysis was performed in the MS facility of the laboratory of R.J. Deshaies (Howard Hughes Medical Institute, California Institute of Technology), which is supported by the Beckman Institute at California Institute of Technology and a grant from the Department of Energy (to R.J. Deshaies) and Barbara J. Wold. D.C.D. is supported by the German Academy for Natural Scientists Leopoldina (Grant BMBF-LPD9901/8-95). J.G. is supported by R. J. Deshaies through Howard Hughes Medical Institute funds. A.J.L. was supported by a National Science Foundation Graduate Research Fellowship. Author contributions: D.C.D., D.A.T., and E.M.S. designed research; D.C.D., A.J.L., and J.G. performed research; D.C.D. and J.G. analyzed data; and D.C.D., D.A.T., and E.M.S. wrote the paper. Conflict of interest statement: No conflicts declared. This paper was submitted directly (Track II) to the PNAS office.Attached Files
Published - DIEpnas06.pdf
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Additional details
- PMCID
- PMC1480433
- Eprint ID
- 5957
- Resolver ID
- CaltechAUTHORS:DIEpnas06
- Howard Hughes Medical Institute (HHMI)
- Caltech Beckman Institute
- Department of Energy (DOE)
- MBF-LPD9901/8-95
- Deutsche Akademie der Naturforscher Leopoldina
- NSF Graduate Research Fellowship
- Created
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2006-12-11Created from EPrint's datestamp field
- Updated
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2023-06-01Created from EPrint's last_modified field