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Macro-to-Micro Structural Proteomics: Native Source Proteins for High-Throughput Crystallization

Overview of attention for article published in PLOS ONE, February 2012
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Title
Macro-to-Micro Structural Proteomics: Native Source Proteins for High-Throughput Crystallization
Published in
PLOS ONE, February 2012
DOI 10.1371/journal.pone.0032498
Pubmed ID
Authors

Monica Totir, Nathaniel Echols, Max Nanao, Christine L. Gee, Alisa Moskaleva, Scott Gradia, Anthony T. Iavarone, James M. Berger, Andrew P. May, Chloe Zubieta, Tom Alber

Abstract

Structural biology and structural genomics projects routinely rely on recombinantly expressed proteins, but many proteins and complexes are difficult to obtain by this approach. We investigated native source proteins for high-throughput protein crystallography applications. The Escherichia coli proteome was fractionated, purified, crystallized, and structurally characterized. Macro-scale fermentation and fractionation were used to subdivide the soluble proteome into 408 unique fractions of which 295 fractions yielded crystals in microfluidic crystallization chips. Of the 295 crystals, 152 were selected for optimization, diffraction screening, and data collection. Twenty-three structures were determined, four of which were novel. This study demonstrates the utility of native source proteins for high-throughput crystallography.

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Mendeley readers

The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Spain 1 2%
France 1 2%
Unknown 56 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 24%
Researcher 12 20%
Student > Bachelor 6 10%
Other 5 8%
Student > Master 4 7%
Other 8 14%
Unknown 10 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 18 31%
Agricultural and Biological Sciences 16 27%
Chemistry 6 10%
Chemical Engineering 2 3%
Engineering 2 3%
Other 4 7%
Unknown 11 19%