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A Highly Scalable Peptide-Based Assay System for Proteomics

Overview of attention for article published in PLOS ONE, June 2012
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Title
A Highly Scalable Peptide-Based Assay System for Proteomics
Published in
PLOS ONE, June 2012
DOI 10.1371/journal.pone.0037441
Pubmed ID
Authors

Igor A. Kozlov, Elliot R. Thomsen, Sarah E. Munchel, Patricia Villegas, Petr Capek, Austin J. Gower, Stephanie J. K. Pond, Eugene Chudin, Mark S. Chee

Abstract

We report a scalable and cost-effective technology for generating and screening high-complexity customizable peptide sets. The peptides are made as peptide-cDNA fusions by in vitro transcription/translation from pools of DNA templates generated by microarray-based synthesis. This approach enables large custom sets of peptides to be designed in silico, manufactured cost-effectively in parallel, and assayed efficiently in a multiplexed fashion. The utility of our peptide-cDNA fusion pools was demonstrated in two activity-based assays designed to discover protease and kinase substrates. In the protease assay, cleaved peptide substrates were separated from uncleaved and identified by digital sequencing of their cognate cDNAs. We screened the 3,011 amino acid HCV proteome for susceptibility to cleavage by the HCV NS3/4A protease and identified all 3 known trans cleavage sites with high specificity. In the kinase assay, peptide substrates phosphorylated by tyrosine kinases were captured and identified by sequencing of their cDNAs. We screened a pool of 3,243 peptides against Abl kinase and showed that phosphorylation events detected were specific and consistent with the known substrate preferences of Abl kinase. Our approach is scalable and adaptable to other protein-based assays.

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Geographical breakdown

Country Count As %
Germany 1 2%
Unknown 54 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 31%
Student > Ph. D. Student 12 22%
Student > Master 3 5%
Student > Doctoral Student 3 5%
Professor > Associate Professor 2 4%
Other 3 5%
Unknown 15 27%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 27%
Biochemistry, Genetics and Molecular Biology 6 11%
Chemistry 6 11%
Medicine and Dentistry 6 11%
Materials Science 3 5%
Other 4 7%
Unknown 15 27%