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Originally published as Genetics Published Articles Ahead of Print on March 23, 2009.
Genetics, Vol. 182, 33-39, May 2009, Copyright © 2009
doi:10.1534/genetics.109.101162
Detection of Protein–Protein Interactions Through Vesicle Targeting
Jacob H. Boysen*,
Saranna Fanning*,
,
Justin Newberg
,
Robert F. Murphy,
,**, and
Aaron P. Mitchell*,,1
* Department of Microbiology and Institute of Cancer Research, Columbia University, New York, New York 10032, Department of Microbiology, University College Cork, Cork, Ireland, Center for Bioimage Informatics and Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, Lane Center for Computational Biology and Department of Machine Learning, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, ** External Fellow, Freiburg Institute for Advanced Studies, University of Freiburg, 79104 Freiburg, Germany and Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
1 Corresponding author: Department of Biological Sciences, Carnegie Mellon University, 4400 Fifth Ave., MI 741 Pittsburgh, PA 15213.
E-mail: apm1{at}andrew.cmu.edu
The detection of protein–protein interactions through two-hybrid assays has revolutionized our understanding of biology. The remarkable impact of two-hybrid assay platforms derives from their speed, simplicity, and broad applicability. Yet for many organisms, the need to express test proteins in Saccharomyces cerevisiae or Escherichia coli presents a substantial barrier because variations in codon specificity or bias may result in aberrant protein expression. In particular, nonstandard genetic codes are characteristic of several eukaryotic pathogens, for which there are currently no genetically based systems for detection of protein–protein interactions. We have developed a protein–protein interaction assay that is carried out in native host cells by using GFP as the only foreign protein moiety, thus circumventing these problems. We show that interaction can be detected between two protein pairs in both the model yeast S. cerevisiae and the fungal pathogen Candida albicans. We use computational analysis of microscopic images to provide a quantitative and automated assessment of confidence.
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Genetics 2009 182: NP.
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